From 8f544b8af1c4d6c4a4e20bbecbe92c912e074ea6 Mon Sep 17 00:00:00 2001
From: Leif Denby <leif@denby.eu>
Date: Mon, 9 Dec 2024 15:30:37 +0100
Subject: [PATCH 01/28] add WIP notebook on technique from Tomas Landelius

---
 docs/domain-cropping.ipynb | 8238 ++++++++++++++++++++++++++++++++++++
 pdm.lock                   | 1512 ++++++-
 pyproject.toml             |    7 +
 3 files changed, 9680 insertions(+), 77 deletions(-)
 create mode 100644 docs/domain-cropping.ipynb

diff --git a/docs/domain-cropping.ipynb b/docs/domain-cropping.ipynb
new file mode 100644
index 0000000..e3f7546
--- /dev/null
+++ b/docs/domain-cropping.ipynb
@@ -0,0 +1,8238 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import cartopy.crs as ccrs\n",
+    "import spherical_geometry as sg\n",
+    "from spherical_geometry.polygon import SphericalPolygon"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import xarray as xr"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "url_lam = \"https://mllam-test-data.s3.eu-north-1.amazonaws.com/height_levels.zarr\"\n",
+    "url_boundary = \"gs://weatherbench2/datasets/era5/1959-2023_01_10-6h-64x32_equiangular_conservative.zarr\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 1GB\n",
+       "Dimensions:   (altitude: 1, y: 589, x: 789, time: 100)\n",
+       "Coordinates:\n",
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+       "Data variables:\n",
+       "    r         (altitude, time, y, x) float64 372MB dask.array&lt;chunksize=(1, 100, 256, 256), meta=np.ndarray&gt;\n",
+       "    t         (altitude, time, y, x) float64 372MB dask.array&lt;chunksize=(1, 100, 256, 256), meta=np.ndarray&gt;\n",
+       "    u         (altitude, time, y, x) float64 372MB dask.array&lt;chunksize=(1, 100, 256, 256), meta=np.ndarray&gt;\n",
+       "    v         (altitude, time, y, x) float64 372MB dask.array&lt;chunksize=(1, 100, 256, 256), meta=np.ndarray&gt;\n",
+       "Attributes:\n",
+       "    description:  All prognostic variables for 10-year period on reduced levels</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-b5e9ffe5-bc9f-42d1-a6e3-8b9042eb4793' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-b5e9ffe5-bc9f-42d1-a6e3-8b9042eb4793' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>altitude</span>: 1</li><li><span class='xr-has-index'>y</span>: 589</li><li><span class='xr-has-index'>x</span>: 789</li><li><span class='xr-has-index'>time</span>: 100</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-8d715141-a3f9-4db4-8b3d-dfa8ef9a06ec' class='xr-section-summary-in' type='checkbox'  checked><label for='section-8d715141-a3f9-4db4-8b3d-dfa8ef9a06ec' class='xr-section-summary' >Coordinates: <span>(6)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>altitude</span></div><div class='xr-var-dims'>(altitude)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>100</div><input id='attrs-cbfb8f8a-8ad2-4243-811b-9605123ce6d3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cbfb8f8a-8ad2-4243-811b-9605123ce6d3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c4c9319b-9706-4399-91a9-1b70a8e64839' class='xr-var-data-in' type='checkbox'><label for='data-c4c9319b-9706-4399-91a9-1b70a8e64839' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><pre>array([100])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lat</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 256), meta=np.ndarray&gt;</div><input id='attrs-127057bb-28eb-45bd-9885-c7154e043e90' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-127057bb-28eb-45bd-9885-c7154e043e90' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a4c21d07-f5a1-4040-aee0-12a91c9fcd96' class='xr-var-data-in' type='checkbox'><label for='data-a4c21d07-f5a1-4040-aee0-12a91c9fcd96' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 3.55 MiB </td>\n",
+       "                        <td> 512.00 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (589, 789) </td>\n",
+       "                        <td> (256, 256) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 12 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"139\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"38\" x2=\"120\" y2=\"38\" />\n",
+       "  <line x1=\"0\" y1=\"77\" x2=\"120\" y2=\"77\" />\n",
+       "  <line x1=\"0\" y1=\"89\" x2=\"120\" y2=\"89\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"89\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"38\" y1=\"0\" x2=\"38\" y2=\"89\" />\n",
+       "  <line x1=\"77\" y1=\"0\" x2=\"77\" y2=\"89\" />\n",
+       "  <line x1=\"116\" y1=\"0\" x2=\"116\" y2=\"89\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"89\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,89.58174904942965 0.0,89.58174904942965\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"109.581749\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >789</text>\n",
+       "  <text x=\"140.000000\" y=\"44.790875\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,44.790875)\">589</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 256), meta=np.ndarray&gt;</div><input id='attrs-3754004c-cb47-475f-9a3e-45add3306e82' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3754004c-cb47-475f-9a3e-45add3306e82' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-319b8b91-4db5-40f5-b13a-4bdd37903307' class='xr-var-data-in' type='checkbox'><label for='data-319b8b91-4db5-40f5-b13a-4bdd37903307' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 3.55 MiB </td>\n",
+       "                        <td> 512.00 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (589, 789) </td>\n",
+       "                        <td> (256, 256) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 12 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"139\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"38\" x2=\"120\" y2=\"38\" />\n",
+       "  <line x1=\"0\" y1=\"77\" x2=\"120\" y2=\"77\" />\n",
+       "  <line x1=\"0\" y1=\"89\" x2=\"120\" y2=\"89\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"89\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"38\" y1=\"0\" x2=\"38\" y2=\"89\" />\n",
+       "  <line x1=\"77\" y1=\"0\" x2=\"77\" y2=\"89\" />\n",
+       "  <line x1=\"116\" y1=\"0\" x2=\"116\" y2=\"89\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"89\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,89.58174904942965 0.0,89.58174904942965\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"109.581749\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >789</text>\n",
+       "  <text x=\"140.000000\" y=\"44.790875\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,44.790875)\">589</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1990-09-01 ... 1990-09-13T09:00:00</div><input id='attrs-cccbdd86-26bb-4c92-b4cd-45a615a463e9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-cccbdd86-26bb-4c92-b4cd-45a615a463e9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5515cb80-d0dc-476a-94f1-734fcae2717c' class='xr-var-data-in' type='checkbox'><label for='data-5515cb80-d0dc-476a-94f1-734fcae2717c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;1990-09-01T00:00:00.000000000&#x27;, &#x27;1990-09-01T03:00:00.000000000&#x27;,\n",
+       "       &#x27;1990-09-01T06:00:00.000000000&#x27;, &#x27;1990-09-01T09:00:00.000000000&#x27;,\n",
+       "       &#x27;1990-09-01T12:00:00.000000000&#x27;, &#x27;1990-09-01T15:00:00.000000000&#x27;,\n",
+       "       &#x27;1990-09-01T18:00:00.000000000&#x27;, &#x27;1990-09-01T21:00:00.000000000&#x27;,\n",
+       "       &#x27;1990-09-02T00:00:00.000000000&#x27;, &#x27;1990-09-02T03:00:00.000000000&#x27;,\n",
+       "       &#x27;1990-09-02T06:00:00.000000000&#x27;, &#x27;1990-09-02T09:00:00.000000000&#x27;,\n",
+       "       &#x27;1990-09-02T12:00:00.000000000&#x27;, &#x27;1990-09-02T15:00:00.000000000&#x27;,\n",
+       "       &#x27;1990-09-02T18:00:00.000000000&#x27;, &#x27;1990-09-02T21:00:00.000000000&#x27;,\n",
+       "       &#x27;1990-09-03T00:00:00.000000000&#x27;, &#x27;1990-09-03T03:00:00.000000000&#x27;,\n",
+       "       &#x27;1990-09-03T06:00:00.000000000&#x27;, &#x27;1990-09-03T09:00:00.000000000&#x27;,\n",
+       "       &#x27;1990-09-03T12:00:00.000000000&#x27;, &#x27;1990-09-03T15:00:00.000000000&#x27;,\n",
+       "       &#x27;1990-09-03T18:00:00.000000000&#x27;, &#x27;1990-09-03T21:00:00.000000000&#x27;,\n",
+       "       &#x27;1990-09-04T00:00:00.000000000&#x27;, &#x27;1990-09-04T03:00:00.000000000&#x27;,\n",
+       "       &#x27;1990-09-04T06:00:00.000000000&#x27;, &#x27;1990-09-04T09:00:00.000000000&#x27;,\n",
+       "       &#x27;1990-09-04T12:00:00.000000000&#x27;, &#x27;1990-09-04T15:00:00.000000000&#x27;,\n",
+       "       &#x27;1990-09-04T18:00:00.000000000&#x27;, &#x27;1990-09-04T21:00:00.000000000&#x27;,\n",
+       "       &#x27;1990-09-05T00:00:00.000000000&#x27;, &#x27;1990-09-05T03:00:00.000000000&#x27;,\n",
+       "       &#x27;1990-09-05T06:00:00.000000000&#x27;, &#x27;1990-09-05T09:00:00.000000000&#x27;,\n",
+       "       &#x27;1990-09-05T12:00:00.000000000&#x27;, &#x27;1990-09-05T15:00:00.000000000&#x27;,\n",
+       "       &#x27;1990-09-05T18:00:00.000000000&#x27;, &#x27;1990-09-05T21:00:00.000000000&#x27;,\n",
+       "       &#x27;1990-09-06T00:00:00.000000000&#x27;, &#x27;1990-09-06T03:00:00.000000000&#x27;,\n",
+       "       &#x27;1990-09-06T06:00:00.000000000&#x27;, &#x27;1990-09-06T09:00:00.000000000&#x27;,\n",
+       "       &#x27;1990-09-06T12:00:00.000000000&#x27;, &#x27;1990-09-06T15:00:00.000000000&#x27;,\n",
+       "       &#x27;1990-09-06T18:00:00.000000000&#x27;, &#x27;1990-09-06T21:00:00.000000000&#x27;,\n",
+       "       &#x27;1990-09-07T00:00:00.000000000&#x27;, &#x27;1990-09-07T03:00:00.000000000&#x27;,\n",
+       "       &#x27;1990-09-07T06:00:00.000000000&#x27;, &#x27;1990-09-07T09:00:00.000000000&#x27;,\n",
+       "       &#x27;1990-09-07T12:00:00.000000000&#x27;, &#x27;1990-09-07T15:00:00.000000000&#x27;,\n",
+       "       &#x27;1990-09-07T18:00:00.000000000&#x27;, &#x27;1990-09-07T21:00:00.000000000&#x27;,\n",
+       "       &#x27;1990-09-08T00:00:00.000000000&#x27;, &#x27;1990-09-08T03:00:00.000000000&#x27;,\n",
+       "       &#x27;1990-09-08T06:00:00.000000000&#x27;, &#x27;1990-09-08T09:00:00.000000000&#x27;,\n",
+       "       &#x27;1990-09-08T12:00:00.000000000&#x27;, &#x27;1990-09-08T15:00:00.000000000&#x27;,\n",
+       "       &#x27;1990-09-08T18:00:00.000000000&#x27;, &#x27;1990-09-08T21:00:00.000000000&#x27;,\n",
+       "       &#x27;1990-09-09T00:00:00.000000000&#x27;, &#x27;1990-09-09T03:00:00.000000000&#x27;,\n",
+       "       &#x27;1990-09-09T06:00:00.000000000&#x27;, &#x27;1990-09-09T09:00:00.000000000&#x27;,\n",
+       "       &#x27;1990-09-09T12:00:00.000000000&#x27;, &#x27;1990-09-09T15:00:00.000000000&#x27;,\n",
+       "       &#x27;1990-09-09T18:00:00.000000000&#x27;, &#x27;1990-09-09T21:00:00.000000000&#x27;,\n",
+       "       &#x27;1990-09-10T00:00:00.000000000&#x27;, &#x27;1990-09-10T03:00:00.000000000&#x27;,\n",
+       "       &#x27;1990-09-10T06:00:00.000000000&#x27;, &#x27;1990-09-10T09:00:00.000000000&#x27;,\n",
+       "       &#x27;1990-09-10T12:00:00.000000000&#x27;, &#x27;1990-09-10T15:00:00.000000000&#x27;,\n",
+       "       &#x27;1990-09-10T18:00:00.000000000&#x27;, &#x27;1990-09-10T21:00:00.000000000&#x27;,\n",
+       "       &#x27;1990-09-11T00:00:00.000000000&#x27;, &#x27;1990-09-11T03:00:00.000000000&#x27;,\n",
+       "       &#x27;1990-09-11T06:00:00.000000000&#x27;, &#x27;1990-09-11T09:00:00.000000000&#x27;,\n",
+       "       &#x27;1990-09-11T12:00:00.000000000&#x27;, &#x27;1990-09-11T15:00:00.000000000&#x27;,\n",
+       "       &#x27;1990-09-11T18:00:00.000000000&#x27;, &#x27;1990-09-11T21:00:00.000000000&#x27;,\n",
+       "       &#x27;1990-09-12T00:00:00.000000000&#x27;, &#x27;1990-09-12T03:00:00.000000000&#x27;,\n",
+       "       &#x27;1990-09-12T06:00:00.000000000&#x27;, &#x27;1990-09-12T09:00:00.000000000&#x27;,\n",
+       "       &#x27;1990-09-12T12:00:00.000000000&#x27;, &#x27;1990-09-12T15:00:00.000000000&#x27;,\n",
+       "       &#x27;1990-09-12T18:00:00.000000000&#x27;, &#x27;1990-09-12T21:00:00.000000000&#x27;,\n",
+       "       &#x27;1990-09-13T00:00:00.000000000&#x27;, &#x27;1990-09-13T03:00:00.000000000&#x27;,\n",
+       "       &#x27;1990-09-13T06:00:00.000000000&#x27;, &#x27;1990-09-13T09:00:00.000000000&#x27;],\n",
+       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-1.999e+06 ... -2.925e+04</div><input id='attrs-48e09d98-fd9a-4961-9176-4b98e53403ec' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-48e09d98-fd9a-4961-9176-4b98e53403ec' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3f02446e-c244-4d73-8686-4812fa688791' class='xr-var-data-in' type='checkbox'><label for='data-3f02446e-c244-4d73-8686-4812fa688791' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([-1999248.193226, -1996748.193226, -1994248.193226, ...,   -34248.193226,\n",
+       "         -31748.193226,   -29248.193226])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-6.095e+05 -6.07e+05 ... 8.605e+05</div><input id='attrs-7be88893-6aa7-44dc-babf-b178131c1b11' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7be88893-6aa7-44dc-babf-b178131c1b11' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b1909c6a-6fb5-4494-8647-cac66305542a' class='xr-var-data-in' type='checkbox'><label for='data-b1909c6a-6fb5-4494-8647-cac66305542a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([-609541.209133, -607041.209133, -604541.209133, ...,  855458.790867,\n",
+       "        857958.790867,  860458.790867])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-58922503-66dd-4311-926f-6e260a621375' class='xr-section-summary-in' type='checkbox'  checked><label for='section-58922503-66dd-4311-926f-6e260a621375' class='xr-section-summary' >Data variables: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>r</span></div><div class='xr-var-dims'>(altitude, time, y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 256, 256), meta=np.ndarray&gt;</div><input id='attrs-97d38130-c3cf-4837-8f78-0e0ae3ef5306' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-97d38130-c3cf-4837-8f78-0e0ae3ef5306' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a1f6186a-2329-4475-8ba4-947f86e6e259' class='xr-var-data-in' type='checkbox'><label for='data-a1f6186a-2329-4475-8ba4-947f86e6e259' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>level_type :</span></dt><dd>heightAboveGround</dd><dt><span>long_name :</span></dt><dd>Relative humidity</dd><dt><span>shortName :</span></dt><dd>r</dd><dt><span>stepType :</span></dt><dd>instant</dd><dt><span>stepUnits :</span></dt><dd>1</dd><dt><span>typeOfLevel :</span></dt><dd>heightAboveGround</dd><dt><span>units :</span></dt><dd>%</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 354.55 MiB </td>\n",
+       "                        <td> 50.00 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (1, 100, 589, 789) </td>\n",
+       "                        <td> (1, 100, 256, 256) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 12 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
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+       "\n",
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+       "  <line x1=\"238\" y1=\"23\" x2=\"238\" y2=\"113\" style=\"stroke-width:2\" />\n",
+       "\n",
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+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"178.450243\" y=\"133.031992\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >789</text>\n",
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+       "  <text x=\"96.725121\" y=\"121.306870\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,96.725121,121.306870)\">100</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>t</span></div><div class='xr-var-dims'>(altitude, time, y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 256, 256), meta=np.ndarray&gt;</div><input id='attrs-7a6631a0-0185-42e1-8341-fab0947a851e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7a6631a0-0185-42e1-8341-fab0947a851e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a71037cc-e674-4190-af23-35ebcef2c5b3' class='xr-var-data-in' type='checkbox'><label for='data-a71037cc-e674-4190-af23-35ebcef2c5b3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>level_type :</span></dt><dd>heightAboveGround</dd><dt><span>long_name :</span></dt><dd>Temperature</dd><dt><span>shortName :</span></dt><dd>t</dd><dt><span>stepType :</span></dt><dd>instant</dd><dt><span>stepUnits :</span></dt><dd>1</dd><dt><span>typeOfLevel :</span></dt><dd>heightAboveGround</dd><dt><span>units :</span></dt><dd>K</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 354.55 MiB </td>\n",
+       "                        <td> 50.00 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (1, 100, 589, 789) </td>\n",
+       "                        <td> (1, 100, 256, 256) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 12 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"383\" height=\"163\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"25\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"25\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
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+       "\n",
+       "  <!-- Colored Rectangle -->\n",
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+       "\n",
+       "  <!-- Text -->\n",
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+       "\n",
+       "  <!-- Horizontal lines -->\n",
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+       "\n",
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+       "\n",
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+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"178.450243\" y=\"133.031992\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >789</text>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>u</span></div><div class='xr-var-dims'>(altitude, time, y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 256, 256), meta=np.ndarray&gt;</div><input id='attrs-894b0388-c900-4c83-afe4-fb4c90695801' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-894b0388-c900-4c83-afe4-fb4c90695801' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4b7e992a-5c6f-4d1b-ab6f-8fccd608e8e2' class='xr-var-data-in' type='checkbox'><label for='data-4b7e992a-5c6f-4d1b-ab6f-8fccd608e8e2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>level_type :</span></dt><dd>heightAboveGround</dd><dt><span>long_name :</span></dt><dd>u-component of wind</dd><dt><span>shortName :</span></dt><dd>u</dd><dt><span>stepType :</span></dt><dd>instant</dd><dt><span>stepUnits :</span></dt><dd>1</dd><dt><span>typeOfLevel :</span></dt><dd>heightAboveGround</dd><dt><span>units :</span></dt><dd>m s**-1</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 354.55 MiB </td>\n",
+       "                        <td> 50.00 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (1, 100, 589, 789) </td>\n",
+       "                        <td> (1, 100, 256, 256) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 12 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"383\" height=\"163\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
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+       "  <line x1=\"0\" y1=\"25\" x2=\"25\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"25\" y1=\"0\" x2=\"25\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
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+       "\n",
+       "  <!-- Text -->\n",
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+       "\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"95\" y1=\"0\" x2=\"118\" y2=\"23\" style=\"stroke-width:2\" />\n",
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+       "  <line x1=\"95\" y1=\"89\" x2=\"118\" y2=\"113\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"95\" y1=\"0\" x2=\"95\" y2=\"89\" style=\"stroke-width:2\" />\n",
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+       "\n",
+       "  <!-- Colored Rectangle -->\n",
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+       "\n",
+       "  <!-- Horizontal lines -->\n",
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+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"95\" y1=\"0\" x2=\"118\" y2=\"23\" style=\"stroke-width:2\" />\n",
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+       "\n",
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+       "\n",
+       "  <!-- Horizontal lines -->\n",
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+       "  <line x1=\"118\" y1=\"101\" x2=\"238\" y2=\"101\" />\n",
+       "  <line x1=\"118\" y1=\"113\" x2=\"238\" y2=\"113\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"118\" y1=\"23\" x2=\"118\" y2=\"113\" style=\"stroke-width:2\" />\n",
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+       "  <line x1=\"196\" y1=\"23\" x2=\"196\" y2=\"113\" />\n",
+       "  <line x1=\"235\" y1=\"23\" x2=\"235\" y2=\"113\" />\n",
+       "  <line x1=\"238\" y1=\"23\" x2=\"238\" y2=\"113\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
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+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"178.450243\" y=\"133.031992\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >789</text>\n",
+       "  <text x=\"258.450243\" y=\"68.241117\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,258.450243,68.241117)\">589</text>\n",
+       "  <text x=\"96.725121\" y=\"121.306870\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,96.725121,121.306870)\">100</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>v</span></div><div class='xr-var-dims'>(altitude, time, y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 256, 256), meta=np.ndarray&gt;</div><input id='attrs-500c1538-83e0-4171-83af-558b3be2508e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-500c1538-83e0-4171-83af-558b3be2508e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6eb50b83-a60e-42f9-a85e-c36ea590b133' class='xr-var-data-in' type='checkbox'><label for='data-6eb50b83-a60e-42f9-a85e-c36ea590b133' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>level_type :</span></dt><dd>heightAboveGround</dd><dt><span>long_name :</span></dt><dd>v-component of wind</dd><dt><span>shortName :</span></dt><dd>v</dd><dt><span>stepType :</span></dt><dd>instant</dd><dt><span>stepUnits :</span></dt><dd>1</dd><dt><span>typeOfLevel :</span></dt><dd>heightAboveGround</dd><dt><span>units :</span></dt><dd>m s**-1</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 354.55 MiB </td>\n",
+       "                        <td> 50.00 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (1, 100, 589, 789) </td>\n",
+       "                        <td> (1, 100, 256, 256) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 12 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"383\" height=\"163\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
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+       "\n",
+       "  <!-- Vertical lines -->\n",
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+       "  <line x1=\"25\" y1=\"0\" x2=\"25\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
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+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"12.706308\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1</text>\n",
+       "  <text x=\"45.412617\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,45.412617,12.706308)\">1</text>\n",
+       "\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"95\" y1=\"0\" x2=\"118\" y2=\"23\" style=\"stroke-width:2\" />\n",
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+       "\n",
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+       "\n",
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+       "\n",
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+       "\n",
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+       "  <line x1=\"235\" y1=\"23\" x2=\"235\" y2=\"113\" />\n",
+       "  <line x1=\"238\" y1=\"23\" x2=\"238\" y2=\"113\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
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+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"178.450243\" y=\"133.031992\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >789</text>\n",
+       "  <text x=\"258.450243\" y=\"68.241117\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,258.450243,68.241117)\">589</text>\n",
+       "  <text x=\"96.725121\" y=\"121.306870\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,96.725121,121.306870)\">100</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-dd268975-9f27-407b-9173-0cec2cec49f7' class='xr-section-summary-in' type='checkbox'  ><label for='section-dd268975-9f27-407b-9173-0cec2cec49f7' class='xr-section-summary' >Indexes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>altitude</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-0477a632-4106-4daa-a6f9-cf4551bd8492' class='xr-index-data-in' type='checkbox'/><label for='index-0477a632-4106-4daa-a6f9-cf4551bd8492' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([100], dtype=&#x27;int64&#x27;, name=&#x27;altitude&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-820691c0-c176-4cc7-bfae-e4740569a4de' class='xr-index-data-in' type='checkbox'/><label for='index-820691c0-c176-4cc7-bfae-e4740569a4de' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;1990-09-01 00:00:00&#x27;, &#x27;1990-09-01 03:00:00&#x27;,\n",
+       "               &#x27;1990-09-01 06:00:00&#x27;, &#x27;1990-09-01 09:00:00&#x27;,\n",
+       "               &#x27;1990-09-01 12:00:00&#x27;, &#x27;1990-09-01 15:00:00&#x27;,\n",
+       "               &#x27;1990-09-01 18:00:00&#x27;, &#x27;1990-09-01 21:00:00&#x27;,\n",
+       "               &#x27;1990-09-02 00:00:00&#x27;, &#x27;1990-09-02 03:00:00&#x27;,\n",
+       "               &#x27;1990-09-02 06:00:00&#x27;, &#x27;1990-09-02 09:00:00&#x27;,\n",
+       "               &#x27;1990-09-02 12:00:00&#x27;, &#x27;1990-09-02 15:00:00&#x27;,\n",
+       "               &#x27;1990-09-02 18:00:00&#x27;, &#x27;1990-09-02 21:00:00&#x27;,\n",
+       "               &#x27;1990-09-03 00:00:00&#x27;, &#x27;1990-09-03 03:00:00&#x27;,\n",
+       "               &#x27;1990-09-03 06:00:00&#x27;, &#x27;1990-09-03 09:00:00&#x27;,\n",
+       "               &#x27;1990-09-03 12:00:00&#x27;, &#x27;1990-09-03 15:00:00&#x27;,\n",
+       "               &#x27;1990-09-03 18:00:00&#x27;, &#x27;1990-09-03 21:00:00&#x27;,\n",
+       "               &#x27;1990-09-04 00:00:00&#x27;, &#x27;1990-09-04 03:00:00&#x27;,\n",
+       "               &#x27;1990-09-04 06:00:00&#x27;, &#x27;1990-09-04 09:00:00&#x27;,\n",
+       "               &#x27;1990-09-04 12:00:00&#x27;, &#x27;1990-09-04 15:00:00&#x27;,\n",
+       "               &#x27;1990-09-04 18:00:00&#x27;, &#x27;1990-09-04 21:00:00&#x27;,\n",
+       "               &#x27;1990-09-05 00:00:00&#x27;, &#x27;1990-09-05 03:00:00&#x27;,\n",
+       "               &#x27;1990-09-05 06:00:00&#x27;, &#x27;1990-09-05 09:00:00&#x27;,\n",
+       "               &#x27;1990-09-05 12:00:00&#x27;, &#x27;1990-09-05 15:00:00&#x27;,\n",
+       "               &#x27;1990-09-05 18:00:00&#x27;, &#x27;1990-09-05 21:00:00&#x27;,\n",
+       "               &#x27;1990-09-06 00:00:00&#x27;, &#x27;1990-09-06 03:00:00&#x27;,\n",
+       "               &#x27;1990-09-06 06:00:00&#x27;, &#x27;1990-09-06 09:00:00&#x27;,\n",
+       "               &#x27;1990-09-06 12:00:00&#x27;, &#x27;1990-09-06 15:00:00&#x27;,\n",
+       "               &#x27;1990-09-06 18:00:00&#x27;, &#x27;1990-09-06 21:00:00&#x27;,\n",
+       "               &#x27;1990-09-07 00:00:00&#x27;, &#x27;1990-09-07 03:00:00&#x27;,\n",
+       "               &#x27;1990-09-07 06:00:00&#x27;, &#x27;1990-09-07 09:00:00&#x27;,\n",
+       "               &#x27;1990-09-07 12:00:00&#x27;, &#x27;1990-09-07 15:00:00&#x27;,\n",
+       "               &#x27;1990-09-07 18:00:00&#x27;, &#x27;1990-09-07 21:00:00&#x27;,\n",
+       "               &#x27;1990-09-08 00:00:00&#x27;, &#x27;1990-09-08 03:00:00&#x27;,\n",
+       "               &#x27;1990-09-08 06:00:00&#x27;, &#x27;1990-09-08 09:00:00&#x27;,\n",
+       "               &#x27;1990-09-08 12:00:00&#x27;, &#x27;1990-09-08 15:00:00&#x27;,\n",
+       "               &#x27;1990-09-08 18:00:00&#x27;, &#x27;1990-09-08 21:00:00&#x27;,\n",
+       "               &#x27;1990-09-09 00:00:00&#x27;, &#x27;1990-09-09 03:00:00&#x27;,\n",
+       "               &#x27;1990-09-09 06:00:00&#x27;, &#x27;1990-09-09 09:00:00&#x27;,\n",
+       "               &#x27;1990-09-09 12:00:00&#x27;, &#x27;1990-09-09 15:00:00&#x27;,\n",
+       "               &#x27;1990-09-09 18:00:00&#x27;, &#x27;1990-09-09 21:00:00&#x27;,\n",
+       "               &#x27;1990-09-10 00:00:00&#x27;, &#x27;1990-09-10 03:00:00&#x27;,\n",
+       "               &#x27;1990-09-10 06:00:00&#x27;, &#x27;1990-09-10 09:00:00&#x27;,\n",
+       "               &#x27;1990-09-10 12:00:00&#x27;, &#x27;1990-09-10 15:00:00&#x27;,\n",
+       "               &#x27;1990-09-10 18:00:00&#x27;, &#x27;1990-09-10 21:00:00&#x27;,\n",
+       "               &#x27;1990-09-11 00:00:00&#x27;, &#x27;1990-09-11 03:00:00&#x27;,\n",
+       "               &#x27;1990-09-11 06:00:00&#x27;, &#x27;1990-09-11 09:00:00&#x27;,\n",
+       "               &#x27;1990-09-11 12:00:00&#x27;, &#x27;1990-09-11 15:00:00&#x27;,\n",
+       "               &#x27;1990-09-11 18:00:00&#x27;, &#x27;1990-09-11 21:00:00&#x27;,\n",
+       "               &#x27;1990-09-12 00:00:00&#x27;, &#x27;1990-09-12 03:00:00&#x27;,\n",
+       "               &#x27;1990-09-12 06:00:00&#x27;, &#x27;1990-09-12 09:00:00&#x27;,\n",
+       "               &#x27;1990-09-12 12:00:00&#x27;, &#x27;1990-09-12 15:00:00&#x27;,\n",
+       "               &#x27;1990-09-12 18:00:00&#x27;, &#x27;1990-09-12 21:00:00&#x27;,\n",
+       "               &#x27;1990-09-13 00:00:00&#x27;, &#x27;1990-09-13 03:00:00&#x27;,\n",
+       "               &#x27;1990-09-13 06:00:00&#x27;, &#x27;1990-09-13 09:00:00&#x27;],\n",
+       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>x</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-3a58529f-768e-4ead-b545-336963c4fced' class='xr-index-data-in' type='checkbox'/><label for='index-3a58529f-768e-4ead-b545-336963c4fced' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([-1999248.1932260487, -1996748.1932260487, -1994248.1932260487,\n",
+       "       -1991748.1932260487, -1989248.1932260487, -1986748.1932260487,\n",
+       "       -1984248.1932260487, -1981748.1932260487, -1979248.1932260487,\n",
+       "       -1976748.1932260487,\n",
+       "       ...\n",
+       "        -51748.19322604872,  -49248.19322604872,  -46748.19322604872,\n",
+       "        -44248.19322604872,  -41748.19322604872,  -39248.19322604872,\n",
+       "        -36748.19322604872,  -34248.19322604872, -31748.193226048723,\n",
+       "       -29248.193226048723],\n",
+       "      dtype=&#x27;float64&#x27;, name=&#x27;x&#x27;, length=789))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>y</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-2c72a955-4ecc-4187-b4aa-d817696fa489' class='xr-index-data-in' type='checkbox'/><label for='index-2c72a955-4ecc-4187-b4aa-d817696fa489' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([-609541.2091329489, -607041.2091329489, -604541.2091329489,\n",
+       "       -602041.2091329489, -599541.2091329489, -597041.2091329489,\n",
+       "       -594541.2091329489, -592041.2091329489, -589541.2091329489,\n",
+       "       -587041.2091329489,\n",
+       "       ...\n",
+       "        837958.7908670511,  840458.7908670511,  842958.7908670511,\n",
+       "        845458.7908670511,  847958.7908670511,  850458.7908670511,\n",
+       "        852958.7908670511,  855458.7908670511,  857958.7908670511,\n",
+       "        860458.7908670511],\n",
+       "      dtype=&#x27;float64&#x27;, name=&#x27;y&#x27;, length=589))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-f7ee7580-1cba-48c3-a601-ed1c643a00c7' class='xr-section-summary-in' type='checkbox'  checked><label for='section-f7ee7580-1cba-48c3-a601-ed1c643a00c7' class='xr-section-summary' >Attributes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>description :</span></dt><dd>All prognostic variables for 10-year period on reduced levels</dd></dl></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.Dataset> Size: 1GB\n",
+       "Dimensions:   (altitude: 1, y: 589, x: 789, time: 100)\n",
+       "Coordinates:\n",
+       "  * altitude  (altitude) int64 8B 100\n",
+       "    lat       (y, x) float64 4MB dask.array<chunksize=(256, 256), meta=np.ndarray>\n",
+       "    lon       (y, x) float64 4MB dask.array<chunksize=(256, 256), meta=np.ndarray>\n",
+       "  * time      (time) datetime64[ns] 800B 1990-09-01 ... 1990-09-13T09:00:00\n",
+       "  * x         (x) float64 6kB -1.999e+06 -1.997e+06 ... -3.175e+04 -2.925e+04\n",
+       "  * y         (y) float64 5kB -6.095e+05 -6.07e+05 ... 8.58e+05 8.605e+05\n",
+       "Data variables:\n",
+       "    r         (altitude, time, y, x) float64 372MB dask.array<chunksize=(1, 100, 256, 256), meta=np.ndarray>\n",
+       "    t         (altitude, time, y, x) float64 372MB dask.array<chunksize=(1, 100, 256, 256), meta=np.ndarray>\n",
+       "    u         (altitude, time, y, x) float64 372MB dask.array<chunksize=(1, 100, 256, 256), meta=np.ndarray>\n",
+       "    v         (altitude, time, y, x) float64 372MB dask.array<chunksize=(1, 100, 256, 256), meta=np.ndarray>\n",
+       "Attributes:\n",
+       "    description:  All prognostic variables for 10-year period on reduced levels"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ds_lam = xr.open_zarr(url_lam)\n",
+    "ds_lam"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
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+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 175GB\n",
+       "Dimensions:                                           (time: 93544,\n",
+       "                                                       longitude: 64,\n",
+       "                                                       latitude: 32, level: 13)\n",
+       "Coordinates:\n",
+       "  * latitude                                          (latitude) float64 256B ...\n",
+       "  * level                                             (level) int64 104B 50 ....\n",
+       "  * longitude                                         (longitude) float64 512B ...\n",
+       "  * time                                              (time) datetime64[ns] 748kB ...\n",
+       "Data variables: (12/62)\n",
+       "    10m_u_component_of_wind                           (time, longitude, latitude) float32 766MB dask.array&lt;chunksize=(100, 64, 32), meta=np.ndarray&gt;\n",
+       "    10m_v_component_of_wind                           (time, longitude, latitude) float32 766MB dask.array&lt;chunksize=(100, 64, 32), meta=np.ndarray&gt;\n",
+       "    10m_wind_speed                                    (time, longitude, latitude) float32 766MB dask.array&lt;chunksize=(100, 64, 32), meta=np.ndarray&gt;\n",
+       "    2m_dewpoint_temperature                           (time, longitude, latitude) float32 766MB dask.array&lt;chunksize=(100, 64, 32), meta=np.ndarray&gt;\n",
+       "    2m_temperature                                    (time, longitude, latitude) float32 766MB dask.array&lt;chunksize=(100, 64, 32), meta=np.ndarray&gt;\n",
+       "    above_ground                                      (time, level, longitude, latitude) float32 10GB dask.array&lt;chunksize=(100, 13, 64, 32), meta=np.ndarray&gt;\n",
+       "    ...                                                ...\n",
+       "    volumetric_soil_water_layer_1                     (time, longitude, latitude) float32 766MB dask.array&lt;chunksize=(100, 64, 32), meta=np.ndarray&gt;\n",
+       "    volumetric_soil_water_layer_2                     (time, longitude, latitude) float32 766MB dask.array&lt;chunksize=(100, 64, 32), meta=np.ndarray&gt;\n",
+       "    volumetric_soil_water_layer_3                     (time, longitude, latitude) float32 766MB dask.array&lt;chunksize=(100, 64, 32), meta=np.ndarray&gt;\n",
+       "    volumetric_soil_water_layer_4                     (time, longitude, latitude) float32 766MB dask.array&lt;chunksize=(100, 64, 32), meta=np.ndarray&gt;\n",
+       "    vorticity                                         (time, level, longitude, latitude) float32 10GB dask.array&lt;chunksize=(100, 13, 64, 32), meta=np.ndarray&gt;\n",
+       "    wind_speed                                        (time, level, longitude, latitude) float32 10GB dask.array&lt;chunksize=(100, 13, 64, 32), meta=np.ndarray&gt;</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-5302ed2b-0db8-482b-87ce-460e9bff9b02' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-5302ed2b-0db8-482b-87ce-460e9bff9b02' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 93544</li><li><span class='xr-has-index'>longitude</span>: 64</li><li><span class='xr-has-index'>latitude</span>: 32</li><li><span class='xr-has-index'>level</span>: 13</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-440b9520-b38a-437e-b005-f89e893519e3' class='xr-section-summary-in' type='checkbox'  checked><label for='section-440b9520-b38a-437e-b005-f89e893519e3' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>latitude</span></div><div class='xr-var-dims'>(latitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-87.19 -81.56 ... 81.56 87.19</div><input id='attrs-65b0eda3-c303-4b91-8109-5cf878b979ce' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-65b0eda3-c303-4b91-8109-5cf878b979ce' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7c5ffd6f-73f2-485a-a7b9-908f931ddc53' class='xr-var-data-in' type='checkbox'><label for='data-7c5ffd6f-73f2-485a-a7b9-908f931ddc53' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([-87.1875, -81.5625, -75.9375, -70.3125, -64.6875, -59.0625, -53.4375,\n",
+       "       -47.8125, -42.1875, -36.5625, -30.9375, -25.3125, -19.6875, -14.0625,\n",
+       "        -8.4375,  -2.8125,   2.8125,   8.4375,  14.0625,  19.6875,  25.3125,\n",
+       "        30.9375,  36.5625,  42.1875,  47.8125,  53.4375,  59.0625,  64.6875,\n",
+       "        70.3125,  75.9375,  81.5625,  87.1875])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>level</span></div><div class='xr-var-dims'>(level)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>50 100 150 200 ... 700 850 925 1000</div><input id='attrs-b875f8b2-b2db-4345-a39b-bd82a45a3afc' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b875f8b2-b2db-4345-a39b-bd82a45a3afc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ef511226-f16e-4ac1-af6a-4465a7675f82' class='xr-var-data-in' type='checkbox'><label for='data-ef511226-f16e-4ac1-af6a-4465a7675f82' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  50,  100,  150,  200,  250,  300,  400,  500,  600,  700,  850,  925,\n",
+       "       1000])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>longitude</span></div><div class='xr-var-dims'>(longitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 5.625 11.25 ... 348.8 354.4</div><input id='attrs-5e8366d9-c922-404d-b466-63899a3a51de' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5e8366d9-c922-404d-b466-63899a3a51de' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-aedc6d56-bc6b-4b8b-981b-2051474eea48' class='xr-var-data-in' type='checkbox'><label for='data-aedc6d56-bc6b-4b8b-981b-2051474eea48' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  0.   ,   5.625,  11.25 ,  16.875,  22.5  ,  28.125,  33.75 ,  39.375,\n",
+       "        45.   ,  50.625,  56.25 ,  61.875,  67.5  ,  73.125,  78.75 ,  84.375,\n",
+       "        90.   ,  95.625, 101.25 , 106.875, 112.5  , 118.125, 123.75 , 129.375,\n",
+       "       135.   , 140.625, 146.25 , 151.875, 157.5  , 163.125, 168.75 , 174.375,\n",
+       "       180.   , 185.625, 191.25 , 196.875, 202.5  , 208.125, 213.75 , 219.375,\n",
+       "       225.   , 230.625, 236.25 , 241.875, 247.5  , 253.125, 258.75 , 264.375,\n",
+       "       270.   , 275.625, 281.25 , 286.875, 292.5  , 298.125, 303.75 , 309.375,\n",
+       "       315.   , 320.625, 326.25 , 331.875, 337.5  , 343.125, 348.75 , 354.375])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1959-01-01 ... 2023-01-10T18:00:00</div><input id='attrs-effe2c5a-aed9-4775-8993-871fa87984bf' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-effe2c5a-aed9-4775-8993-871fa87984bf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-097d1324-373c-4e10-8cf2-2e1fd81bc3db' class='xr-var-data-in' type='checkbox'><label for='data-097d1324-373c-4e10-8cf2-2e1fd81bc3db' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;1959-01-01T00:00:00.000000000&#x27;, &#x27;1959-01-01T06:00:00.000000000&#x27;,\n",
+       "       &#x27;1959-01-01T12:00:00.000000000&#x27;, ..., &#x27;2023-01-10T06:00:00.000000000&#x27;,\n",
+       "       &#x27;2023-01-10T12:00:00.000000000&#x27;, &#x27;2023-01-10T18:00:00.000000000&#x27;],\n",
+       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-f90709db-7c0d-4029-b42b-95177f7ca2d6' class='xr-section-summary-in' type='checkbox'  ><label for='section-f90709db-7c0d-4029-b42b-95177f7ca2d6' class='xr-section-summary' >Data variables: <span>(62)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>10m_u_component_of_wind</span></div><div class='xr-var-dims'>(time, longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(100, 64, 32), meta=np.ndarray&gt;</div><input id='attrs-7bfb6fc7-18b7-4c73-ad04-ddd7a2081526' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7bfb6fc7-18b7-4c73-ad04-ddd7a2081526' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ed650c15-8d1f-4157-ba6c-7861ef9967b6' class='xr-var-data-in' type='checkbox'><label for='data-ed650c15-8d1f-4157-ba6c-7861ef9967b6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>10 metre U wind component</dd><dt><span>short_name :</span></dt><dd>u10</dd><dt><span>units :</span></dt><dd>m s**-1</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 730.81 MiB </td>\n",
+       "                        <td> 800.00 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (93544, 64, 32) </td>\n",
+       "                        <td> (100, 64, 32) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 936 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>10m_v_component_of_wind</span></div><div class='xr-var-dims'>(time, longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(100, 64, 32), meta=np.ndarray&gt;</div><input id='attrs-90b028df-e120-470f-a65a-2c72ee19b875' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-90b028df-e120-470f-a65a-2c72ee19b875' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1d2d0db2-26d3-4d12-988f-b5796b105768' class='xr-var-data-in' type='checkbox'><label for='data-1d2d0db2-26d3-4d12-988f-b5796b105768' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>10 metre V wind component</dd><dt><span>short_name :</span></dt><dd>v10</dd><dt><span>units :</span></dt><dd>m s**-1</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 730.81 MiB </td>\n",
+       "                        <td> 800.00 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (93544, 64, 32) </td>\n",
+       "                        <td> (100, 64, 32) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 936 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
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+       "                        <td> 730.81 MiB </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>2m_dewpoint_temperature</span></div><div class='xr-var-dims'>(time, longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(100, 64, 32), meta=np.ndarray&gt;</div><input id='attrs-ceeb0fa3-c22d-47a2-a1d0-ae4653365f85' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ceeb0fa3-c22d-47a2-a1d0-ae4653365f85' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4b16ec7c-5465-42a4-8c6c-8902d1fdbb8b' class='xr-var-data-in' type='checkbox'><label for='data-4b16ec7c-5465-42a4-8c6c-8902d1fdbb8b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>2 metre dewpoint temperature</dd><dt><span>short_name :</span></dt><dd>d2m</dd><dt><span>units :</span></dt><dd>K</dd></dl></div><div class='xr-var-data'><table>\n",
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+       "        <td>\n",
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+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 730.81 MiB </td>\n",
+       "                        <td> 800.00 kiB </td>\n",
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+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
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+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>2m_temperature</span></div><div class='xr-var-dims'>(time, longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(100, 64, 32), meta=np.ndarray&gt;</div><input id='attrs-d1d7cec1-26f2-4b90-b376-c289248e4550' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d1d7cec1-26f2-4b90-b376-c289248e4550' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1ee4f3bf-2a65-4ea4-920d-c1acee951978' class='xr-var-data-in' type='checkbox'><label for='data-1ee4f3bf-2a65-4ea4-920d-c1acee951978' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>2 metre temperature</dd><dt><span>short_name :</span></dt><dd>t2m</dd><dt><span>units :</span></dt><dd>K</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
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+       "                <thead>\n",
+       "                    <tr>\n",
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+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 730.81 MiB </td>\n",
+       "                        <td> 800.00 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
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+       "                        <td> (100, 64, 32) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 936 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>above_ground</span></div><div class='xr-var-dims'>(time, level, longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(100, 13, 64, 32), meta=np.ndarray&gt;</div><input id='attrs-52bf3139-8df0-4dab-86f7-08b18ad04340' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-52bf3139-8df0-4dab-86f7-08b18ad04340' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0a5c4170-3baa-4851-a078-d292c1eb7069' class='xr-var-data-in' type='checkbox'><label for='data-0a5c4170-3baa-4851-a078-d292c1eb7069' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
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+       "                        <td> </td>\n",
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+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 9.28 GiB </td>\n",
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+       "                    <tr>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ageostrophic_wind_speed</span></div><div class='xr-var-dims'>(time, level, longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(100, 13, 64, 32), meta=np.ndarray&gt;</div><input id='attrs-136abed9-a596-4712-b4c0-e67b48c778d1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-136abed9-a596-4712-b4c0-e67b48c778d1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-46b47fcf-7db3-4812-818a-267c369d594a' class='xr-var-data-in' type='checkbox'><label for='data-46b47fcf-7db3-4812-818a-267c369d594a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
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+       "                </thead>\n",
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+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
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+       "                    <tr>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>angle_of_sub_gridscale_orography</span></div><div class='xr-var-dims'>(longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(64, 32), meta=np.ndarray&gt;</div><input id='attrs-88d658a4-57cb-483a-8aeb-ea2d5237eff6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-88d658a4-57cb-483a-8aeb-ea2d5237eff6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-aaf3e7f7-3e32-4de6-b421-aab64877f0de' class='xr-var-data-in' type='checkbox'><label for='data-aaf3e7f7-3e32-4de6-b421-aab64877f0de' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Angle of sub-gridscale orography</dd><dt><span>short_name :</span></dt><dd>anor</dd><dt><span>units :</span></dt><dd>radians</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 8.00 kiB </td>\n",
+       "                        <td> 8.00 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (64, 32) </td>\n",
+       "                        <td> (64, 32) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>anisotropy_of_sub_gridscale_orography</span></div><div class='xr-var-dims'>(longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(64, 32), meta=np.ndarray&gt;</div><input id='attrs-31ada38a-655c-4a31-97cf-d7e53ebdeadd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-31ada38a-655c-4a31-97cf-d7e53ebdeadd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-80e71f3d-787d-4c95-9580-747f4e1a278a' class='xr-var-data-in' type='checkbox'><label for='data-80e71f3d-787d-4c95-9580-747f4e1a278a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Anisotropy of sub-gridscale orography</dd><dt><span>short_name :</span></dt><dd>isor</dd><dt><span>units :</span></dt><dd>~</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 8.00 kiB </td>\n",
+       "                        <td> 8.00 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (64, 32) </td>\n",
+       "                        <td> (64, 32) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>boundary_layer_height</span></div><div class='xr-var-dims'>(time, longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(100, 64, 32), meta=np.ndarray&gt;</div><input id='attrs-7900e6f5-59f4-4fa8-8066-dda37e76b5e4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7900e6f5-59f4-4fa8-8066-dda37e76b5e4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3dc22554-a29b-46b4-a9c0-c75f67249a00' class='xr-var-data-in' type='checkbox'><label for='data-3dc22554-a29b-46b4-a9c0-c75f67249a00' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Boundary layer height</dd><dt><span>short_name :</span></dt><dd>blh</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
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+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
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+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 730.81 MiB </td>\n",
+       "                        <td> 800.00 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (93544, 64, 32) </td>\n",
+       "                        <td> (100, 64, 32) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 936 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
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+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>divergence</span></div><div class='xr-var-dims'>(time, level, longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(100, 13, 64, 32), meta=np.ndarray&gt;</div><input id='attrs-db2292a3-7bf5-43f8-9e1b-352fbf057924' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-db2292a3-7bf5-43f8-9e1b-352fbf057924' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8a0de5cb-3ab7-4563-b4cd-e68c76fdd01c' class='xr-var-data-in' type='checkbox'><label for='data-8a0de5cb-3ab7-4563-b4cd-e68c76fdd01c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 9.28 GiB </td>\n",
+       "                        <td> 10.16 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (93544, 13, 64, 32) </td>\n",
+       "                        <td> (100, 13, 64, 32) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 936 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>eddy_kinetic_energy</span></div><div class='xr-var-dims'>(time, longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(100, 64, 32), meta=np.ndarray&gt;</div><input id='attrs-4745a677-cd03-4990-9957-b737fb6363c9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4745a677-cd03-4990-9957-b737fb6363c9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c4f00c8b-3513-482f-a2f2-5131a608e5d7' class='xr-var-data-in' type='checkbox'><label for='data-c4f00c8b-3513-482f-a2f2-5131a608e5d7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
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+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 730.81 MiB </td>\n",
+       "                        <td> 800.00 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (93544, 64, 32) </td>\n",
+       "                        <td> (100, 64, 32) </td>\n",
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+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 936 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>geopotential</span></div><div class='xr-var-dims'>(time, level, longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(100, 13, 64, 32), meta=np.ndarray&gt;</div><input id='attrs-24ab2b33-a0d0-4286-99bb-69a2b71884b0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-24ab2b33-a0d0-4286-99bb-69a2b71884b0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-282c141a-cb3a-420a-a985-e3aeb7c2637e' class='xr-var-data-in' type='checkbox'><label for='data-282c141a-cb3a-420a-a985-e3aeb7c2637e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Geopotential</dd><dt><span>short_name :</span></dt><dd>z</dd><dt><span>standard_name :</span></dt><dd>geopotential</dd><dt><span>units :</span></dt><dd>m**2 s**-2</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
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+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 9.28 GiB </td>\n",
+       "                        <td> 10.16 MiB </td>\n",
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+       "                    \n",
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+       "                        <th> Shape </th>\n",
+       "                        <td> (93544, 13, 64, 32) </td>\n",
+       "                        <td> (100, 13, 64, 32) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 936 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
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+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>geopotential_at_surface</span></div><div class='xr-var-dims'>(longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(64, 32), meta=np.ndarray&gt;</div><input id='attrs-87c3657d-ac58-4703-afe1-49bb663a5503' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-87c3657d-ac58-4703-afe1-49bb663a5503' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6327023a-0343-4217-a844-f72d11ba2dde' class='xr-var-data-in' type='checkbox'><label for='data-6327023a-0343-4217-a844-f72d11ba2dde' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Geopotential</dd><dt><span>short_name :</span></dt><dd>z</dd><dt><span>standard_name :</span></dt><dd>geopotential</dd><dt><span>units :</span></dt><dd>m**2 s**-2</dd></dl></div><div class='xr-var-data'><table>\n",
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+       "        <td>\n",
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+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 8.00 kiB </td>\n",
+       "                        <td> 8.00 kiB </td>\n",
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+       "                        <td> (64, 32) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
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+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
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+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>geostrophic_wind_speed</span></div><div class='xr-var-dims'>(time, level, longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(100, 13, 64, 32), meta=np.ndarray&gt;</div><input id='attrs-c48b0e4a-05bb-43d0-92fb-3a35f80fa0d4' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c48b0e4a-05bb-43d0-92fb-3a35f80fa0d4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d6f529f5-c61a-4c23-b0bc-ca0307b7dd89' class='xr-var-data-in' type='checkbox'><label for='data-d6f529f5-c61a-4c23-b0bc-ca0307b7dd89' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
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+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 9.28 GiB </td>\n",
+       "                        <td> 10.16 MiB </td>\n",
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+       "                        <th> Shape </th>\n",
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+       "                        <td> (100, 13, 64, 32) </td>\n",
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+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>high_vegetation_cover</span></div><div class='xr-var-dims'>(longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(64, 32), meta=np.ndarray&gt;</div><input id='attrs-b681b21a-a063-4c18-8003-aa9de531c20a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b681b21a-a063-4c18-8003-aa9de531c20a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ea4c9546-5003-456c-b08a-292928e9e804' class='xr-var-data-in' type='checkbox'><label for='data-ea4c9546-5003-456c-b08a-292928e9e804' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>High vegetation cover</dd><dt><span>short_name :</span></dt><dd>cvh</dd><dt><span>units :</span></dt><dd>(0 - 1)</dd></dl></div><div class='xr-var-data'><table>\n",
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+       "        <td>\n",
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+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 8.00 kiB </td>\n",
+       "                        <td> 8.00 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (64, 32) </td>\n",
+       "                        <td> (64, 32) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
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+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>integrated_vapor_transport</span></div><div class='xr-var-dims'>(time, longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(100, 64, 32), meta=np.ndarray&gt;</div><input id='attrs-c48d5f9a-bb01-42c1-a5ba-18cda04a73f2' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c48d5f9a-bb01-42c1-a5ba-18cda04a73f2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d7bf6c9b-4b1e-4e84-b84f-1feee3d62268' class='xr-var-data-in' type='checkbox'><label for='data-d7bf6c9b-4b1e-4e84-b84f-1feee3d62268' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 730.81 MiB </td>\n",
+       "                        <td> 800.00 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (93544, 64, 32) </td>\n",
+       "                        <td> (100, 64, 32) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 936 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lake_cover</span></div><div class='xr-var-dims'>(longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(64, 32), meta=np.ndarray&gt;</div><input id='attrs-ba8b010f-8f7d-4c53-baca-ecf1e5313fd8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ba8b010f-8f7d-4c53-baca-ecf1e5313fd8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-aed430d3-533b-40dd-a257-1987140818f7' class='xr-var-data-in' type='checkbox'><label for='data-aed430d3-533b-40dd-a257-1987140818f7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Lake cover</dd><dt><span>short_name :</span></dt><dd>cl</dd><dt><span>units :</span></dt><dd>(0 - 1)</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 8.00 kiB </td>\n",
+       "                        <td> 8.00 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (64, 32) </td>\n",
+       "                        <td> (64, 32) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>land_sea_mask</span></div><div class='xr-var-dims'>(longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(64, 32), meta=np.ndarray&gt;</div><input id='attrs-b5878ba9-d41a-471e-be42-5a797359e8ec' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b5878ba9-d41a-471e-be42-5a797359e8ec' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-694505f6-88c7-4a79-9876-77ffefd499a5' class='xr-var-data-in' type='checkbox'><label for='data-694505f6-88c7-4a79-9876-77ffefd499a5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Land-sea mask</dd><dt><span>short_name :</span></dt><dd>lsm</dd><dt><span>standard_name :</span></dt><dd>land_binary_mask</dd><dt><span>units :</span></dt><dd>(0 - 1)</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 8.00 kiB </td>\n",
+       "                        <td> 8.00 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (64, 32) </td>\n",
+       "                        <td> (64, 32) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "        </td>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lapse_rate</span></div><div class='xr-var-dims'>(time, level, longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(100, 13, 64, 32), meta=np.ndarray&gt;</div><input id='attrs-4e4f34f8-e55a-49ca-bed4-5c9858315e66' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4e4f34f8-e55a-49ca-bed4-5c9858315e66' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9c95e16a-2e91-4e07-af08-edadc95a6a92' class='xr-var-data-in' type='checkbox'><label for='data-9c95e16a-2e91-4e07-af08-edadc95a6a92' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 9.28 GiB </td>\n",
+       "                        <td> 10.16 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (93544, 13, 64, 32) </td>\n",
+       "                        <td> (100, 13, 64, 32) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 936 chunks in 2 graph layers </td>\n",
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+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>leaf_area_index_high_vegetation</span></div><div class='xr-var-dims'>(time, longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(100, 64, 32), meta=np.ndarray&gt;</div><input id='attrs-4cfca378-254b-4539-b557-f75078eb16c1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4cfca378-254b-4539-b557-f75078eb16c1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-25952fa0-081b-4134-a539-e866e30e1885' class='xr-var-data-in' type='checkbox'><label for='data-25952fa0-081b-4134-a539-e866e30e1885' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Leaf area index, high vegetation</dd><dt><span>short_name :</span></dt><dd>lai_hv</dd><dt><span>units :</span></dt><dd>m**2 m**-2</dd></dl></div><div class='xr-var-data'><table>\n",
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+       "                </thead>\n",
+       "                <tbody>\n",
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+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 730.81 MiB </td>\n",
+       "                        <td> 800.00 kiB </td>\n",
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+       "                    \n",
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+       "                        <th> Shape </th>\n",
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+       "                        <td> (100, 64, 32) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 936 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>leaf_area_index_low_vegetation</span></div><div class='xr-var-dims'>(time, longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(100, 64, 32), meta=np.ndarray&gt;</div><input id='attrs-44f79726-e29b-49cb-967d-b0945e0b5380' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-44f79726-e29b-49cb-967d-b0945e0b5380' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-97fc10a2-b63c-4545-979b-cffea155592d' class='xr-var-data-in' type='checkbox'><label for='data-97fc10a2-b63c-4545-979b-cffea155592d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Leaf area index, low vegetation</dd><dt><span>short_name :</span></dt><dd>lai_lv</dd><dt><span>units :</span></dt><dd>m**2 m**-2</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 730.81 MiB </td>\n",
+       "                        <td> 800.00 kiB </td>\n",
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+       "                        <th> Shape </th>\n",
+       "                        <td> (93544, 64, 32) </td>\n",
+       "                        <td> (100, 64, 32) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 936 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>low_vegetation_cover</span></div><div class='xr-var-dims'>(longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(64, 32), meta=np.ndarray&gt;</div><input id='attrs-94d63713-d755-4f21-9410-ec1cedaa62e0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-94d63713-d755-4f21-9410-ec1cedaa62e0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8ba845c9-9b84-4bb5-9172-5d5159fc1f22' class='xr-var-data-in' type='checkbox'><label for='data-8ba845c9-9b84-4bb5-9172-5d5159fc1f22' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Low vegetation cover</dd><dt><span>short_name :</span></dt><dd>cvl</dd><dt><span>units :</span></dt><dd>(0 - 1)</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
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+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 8.00 kiB </td>\n",
+       "                        <td> 8.00 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (64, 32) </td>\n",
+       "                        <td> (64, 32) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mean_sea_level_pressure</span></div><div class='xr-var-dims'>(time, longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(100, 64, 32), meta=np.ndarray&gt;</div><input id='attrs-2526be45-39fe-4cb1-beb1-dd8756ce2a9c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2526be45-39fe-4cb1-beb1-dd8756ce2a9c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-047b7b9f-263d-4133-835d-c49dab68a2b4' class='xr-var-data-in' type='checkbox'><label for='data-047b7b9f-263d-4133-835d-c49dab68a2b4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Mean sea level pressure</dd><dt><span>short_name :</span></dt><dd>msl</dd><dt><span>standard_name :</span></dt><dd>air_pressure_at_mean_sea_level</dd><dt><span>units :</span></dt><dd>Pa</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
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+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 730.81 MiB </td>\n",
+       "                        <td> 800.00 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
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+       "                        <th> Shape </th>\n",
+       "                        <td> (93544, 64, 32) </td>\n",
+       "                        <td> (100, 64, 32) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 936 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mean_surface_latent_heat_flux</span></div><div class='xr-var-dims'>(time, longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(100, 64, 32), meta=np.ndarray&gt;</div><input id='attrs-411911e6-9a6e-4ae2-9933-2059138bfff2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-411911e6-9a6e-4ae2-9933-2059138bfff2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-89cb31df-b04c-4941-9143-398d3efd8b2c' class='xr-var-data-in' type='checkbox'><label for='data-89cb31df-b04c-4941-9143-398d3efd8b2c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Mean surface latent heat flux</dd><dt><span>short_name :</span></dt><dd>mslhf</dd><dt><span>units :</span></dt><dd>W m**-2</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
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+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 730.81 MiB </td>\n",
+       "                        <td> 800.00 kiB </td>\n",
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+       "                    \n",
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+       "                        <th> Shape </th>\n",
+       "                        <td> (93544, 64, 32) </td>\n",
+       "                        <td> (100, 64, 32) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 936 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
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+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 730.81 MiB </td>\n",
+       "                        <td> 800.00 kiB </td>\n",
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+       "                        <td> (100, 64, 32) </td>\n",
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+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
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+       "                    </tr>\n",
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+       "                        <th> Data type </th>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mean_surface_net_short_wave_radiation_flux</span></div><div class='xr-var-dims'>(time, longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(100, 64, 32), meta=np.ndarray&gt;</div><input id='attrs-a2c199db-cbe2-48f5-89a6-d8c89908d947' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a2c199db-cbe2-48f5-89a6-d8c89908d947' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-93ce6ada-0608-45e0-b718-c97f803bc479' class='xr-var-data-in' type='checkbox'><label for='data-93ce6ada-0608-45e0-b718-c97f803bc479' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Mean surface net short-wave radiation flux</dd><dt><span>short_name :</span></dt><dd>msnswrf</dd><dt><span>units :</span></dt><dd>W m**-2</dd></dl></div><div class='xr-var-data'><table>\n",
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+       "                        <td> 730.81 MiB </td>\n",
+       "                        <td> 800.00 kiB </td>\n",
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+       "                        <td> (100, 64, 32) </td>\n",
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+       "                    <tr>\n",
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+       "                        <th> Data type </th>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mean_surface_sensible_heat_flux</span></div><div class='xr-var-dims'>(time, longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(100, 64, 32), meta=np.ndarray&gt;</div><input id='attrs-7d3719d1-5798-4f28-86da-a61b76ee8481' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7d3719d1-5798-4f28-86da-a61b76ee8481' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b85e193c-acc6-4006-b8c7-6c85de862ff2' class='xr-var-data-in' type='checkbox'><label for='data-b85e193c-acc6-4006-b8c7-6c85de862ff2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Mean surface sensible heat flux</dd><dt><span>short_name :</span></dt><dd>msshf</dd><dt><span>units :</span></dt><dd>W m**-2</dd></dl></div><div class='xr-var-data'><table>\n",
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+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
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+       "                <tbody>\n",
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+       "                        <th> Bytes </th>\n",
+       "                        <td> 730.81 MiB </td>\n",
+       "                        <td> 800.00 kiB </td>\n",
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+       "                    <tr>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mean_top_downward_short_wave_radiation_flux</span></div><div class='xr-var-dims'>(time, longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(100, 64, 32), meta=np.ndarray&gt;</div><input id='attrs-e857e01f-4782-4878-9621-7b6132d8ada5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e857e01f-4782-4878-9621-7b6132d8ada5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-820524ba-8a8b-418b-a492-a4c0821e905a' class='xr-var-data-in' type='checkbox'><label for='data-820524ba-8a8b-418b-a492-a4c0821e905a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Mean top downward short-wave radiation flux</dd><dt><span>short_name :</span></dt><dd>mtdwswrf</dd><dt><span>units :</span></dt><dd>W m**-2</dd></dl></div><div class='xr-var-data'><table>\n",
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+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 730.81 MiB </td>\n",
+       "                        <td> 800.00 kiB </td>\n",
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+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 936 chunks in 2 graph layers </td>\n",
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+       "                        <th> Data type </th>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mean_top_net_long_wave_radiation_flux</span></div><div class='xr-var-dims'>(time, longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(100, 64, 32), meta=np.ndarray&gt;</div><input id='attrs-86703bed-2638-451d-91a3-b58427f2f682' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-86703bed-2638-451d-91a3-b58427f2f682' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c43fbb67-dbe4-4d64-8145-3027f0178361' class='xr-var-data-in' type='checkbox'><label for='data-c43fbb67-dbe4-4d64-8145-3027f0178361' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Mean top net long-wave radiation flux</dd><dt><span>short_name :</span></dt><dd>mtnlwrf</dd><dt><span>units :</span></dt><dd>W m**-2</dd></dl></div><div class='xr-var-data'><table>\n",
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+       "                        <td> 730.81 MiB </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mean_top_net_short_wave_radiation_flux</span></div><div class='xr-var-dims'>(time, longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(100, 64, 32), meta=np.ndarray&gt;</div><input id='attrs-1263100b-751c-4fb3-b0e4-2a94de42f2e4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1263100b-751c-4fb3-b0e4-2a94de42f2e4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-033daee6-902b-44eb-9af5-d99c503678d0' class='xr-var-data-in' type='checkbox'><label for='data-033daee6-902b-44eb-9af5-d99c503678d0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Mean top net short-wave radiation flux</dd><dt><span>short_name :</span></dt><dd>mtnswrf</dd><dt><span>units :</span></dt><dd>W m**-2</dd></dl></div><div class='xr-var-data'><table>\n",
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+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 730.81 MiB </td>\n",
+       "                        <td> 800.00 kiB </td>\n",
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+       "                        <th> Shape </th>\n",
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+       "                        <td> (100, 64, 32) </td>\n",
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+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
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+       "                    </tr>\n",
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+       "                        <th> Data type </th>\n",
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+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mean_vertically_integrated_moisture_divergence</span></div><div class='xr-var-dims'>(time, longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(100, 64, 32), meta=np.ndarray&gt;</div><input id='attrs-067f18d0-087a-4d26-949c-0016ff2a64ef' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-067f18d0-087a-4d26-949c-0016ff2a64ef' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cc12a7c0-369a-4ed0-a8e0-0f0de1227d7f' class='xr-var-data-in' type='checkbox'><label for='data-cc12a7c0-369a-4ed0-a8e0-0f0de1227d7f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Mean vertically integrated moisture divergence</dd><dt><span>short_name :</span></dt><dd>mvimd</dd><dt><span>units :</span></dt><dd>kg m**-2 s**-1</dd></dl></div><div class='xr-var-data'><table>\n",
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+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
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+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 730.81 MiB </td>\n",
+       "                        <td> 800.00 kiB </td>\n",
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+       "                        <th> Shape </th>\n",
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+       "                        <td> (100, 64, 32) </td>\n",
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+       "                    <tr>\n",
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+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>potential_vorticity</span></div><div class='xr-var-dims'>(time, level, longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(100, 13, 64, 32), meta=np.ndarray&gt;</div><input id='attrs-933f2b93-4555-4d78-a22a-bc19e347296c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-933f2b93-4555-4d78-a22a-bc19e347296c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-50ea2655-582f-4b60-86b0-c1d15b7602d8' class='xr-var-data-in' type='checkbox'><label for='data-50ea2655-582f-4b60-86b0-c1d15b7602d8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Potential vorticity</dd><dt><span>short_name :</span></dt><dd>pv</dd><dt><span>units :</span></dt><dd>K m**2 kg**-1 s**-1</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 9.28 GiB </td>\n",
+       "                        <td> 10.16 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (93544, 13, 64, 32) </td>\n",
+       "                        <td> (100, 13, 64, 32) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 936 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>relative_humidity</span></div><div class='xr-var-dims'>(time, level, longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(100, 13, 64, 32), meta=np.ndarray&gt;</div><input id='attrs-f0cf23d0-2a93-465a-b074-37ea1d5dec48' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f0cf23d0-2a93-465a-b074-37ea1d5dec48' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dff25af8-57d5-44bb-b3e2-a66ca7493d31' class='xr-var-data-in' type='checkbox'><label for='data-dff25af8-57d5-44bb-b3e2-a66ca7493d31' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
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+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 9.28 GiB </td>\n",
+       "                        <td> 10.16 MiB </td>\n",
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+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
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+       "                        <td> (100, 13, 64, 32) </td>\n",
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+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 936 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
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+       "                        <th> Data type </th>\n",
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+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sea_ice_cover</span></div><div class='xr-var-dims'>(time, longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(100, 64, 32), meta=np.ndarray&gt;</div><input id='attrs-3779050e-3a84-42e8-a843-8d5da40417ff' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3779050e-3a84-42e8-a843-8d5da40417ff' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-171262d3-3dd9-4089-9255-29d876d58d08' class='xr-var-data-in' type='checkbox'><label for='data-171262d3-3dd9-4089-9255-29d876d58d08' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Sea ice area fraction</dd><dt><span>short_name :</span></dt><dd>siconc</dd><dt><span>standard_name :</span></dt><dd>sea_ice_area_fraction</dd><dt><span>units :</span></dt><dd>(0 - 1)</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
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+       "                    <tr>\n",
+       "                        <td> </td>\n",
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+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 730.81 MiB </td>\n",
+       "                        <td> 800.00 kiB </td>\n",
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+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (93544, 64, 32) </td>\n",
+       "                        <td> (100, 64, 32) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 936 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sea_surface_temperature</span></div><div class='xr-var-dims'>(time, longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(100, 64, 32), meta=np.ndarray&gt;</div><input id='attrs-025b4c2f-b8f4-4773-b24a-014cd7b9e716' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-025b4c2f-b8f4-4773-b24a-014cd7b9e716' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-50dd482d-b7c5-4293-91e9-d1440fe01b20' class='xr-var-data-in' type='checkbox'><label for='data-50dd482d-b7c5-4293-91e9-d1440fe01b20' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Sea surface temperature</dd><dt><span>short_name :</span></dt><dd>sst</dd><dt><span>units :</span></dt><dd>K</dd></dl></div><div class='xr-var-data'><table>\n",
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+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 730.81 MiB </td>\n",
+       "                        <td> 800.00 kiB </td>\n",
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+       "                        <td> (100, 64, 32) </td>\n",
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+       "                    <tr>\n",
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+       "                        <th> Data type </th>\n",
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+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>slope_of_sub_gridscale_orography</span></div><div class='xr-var-dims'>(longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(64, 32), meta=np.ndarray&gt;</div><input id='attrs-540ce475-deb4-40b4-9dbd-be9d96c84b59' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-540ce475-deb4-40b4-9dbd-be9d96c84b59' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2a2780f9-a95d-43ba-9d9c-0c6eb201ef0f' class='xr-var-data-in' type='checkbox'><label for='data-2a2780f9-a95d-43ba-9d9c-0c6eb201ef0f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Slope of sub-gridscale orography</dd><dt><span>short_name :</span></dt><dd>slor</dd><dt><span>units :</span></dt><dd>~</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
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+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 8.00 kiB </td>\n",
+       "                        <td> 8.00 kiB </td>\n",
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+       "                        <th> Shape </th>\n",
+       "                        <td> (64, 32) </td>\n",
+       "                        <td> (64, 32) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>snow_depth</span></div><div class='xr-var-dims'>(time, longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(100, 64, 32), meta=np.ndarray&gt;</div><input id='attrs-1715a96c-08a6-4d39-9050-19b5cef678da' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1715a96c-08a6-4d39-9050-19b5cef678da' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2386c35c-6c83-4213-ada1-a9a49bc30d6d' class='xr-var-data-in' type='checkbox'><label for='data-2386c35c-6c83-4213-ada1-a9a49bc30d6d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Snow depth</dd><dt><span>short_name :</span></dt><dd>sd</dd><dt><span>standard_name :</span></dt><dd>lwe_thickness_of_surface_snow_amount</dd><dt><span>units :</span></dt><dd>m of water equivalent</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
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+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 730.81 MiB </td>\n",
+       "                        <td> 800.00 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
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+       "                        <th> Shape </th>\n",
+       "                        <td> (93544, 64, 32) </td>\n",
+       "                        <td> (100, 64, 32) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 936 chunks in 2 graph layers </td>\n",
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+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>soil_type</span></div><div class='xr-var-dims'>(longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(64, 32), meta=np.ndarray&gt;</div><input id='attrs-4ade55ee-11ba-41ed-920a-05cb59494012' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4ade55ee-11ba-41ed-920a-05cb59494012' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-88ff3b51-cc9b-4282-b073-b94241860859' class='xr-var-data-in' type='checkbox'><label for='data-88ff3b51-cc9b-4282-b073-b94241860859' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Soil type</dd><dt><span>short_name :</span></dt><dd>slt</dd><dt><span>units :</span></dt><dd>~</dd></dl></div><div class='xr-var-data'><table>\n",
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+       "                <tbody>\n",
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+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 8.00 kiB </td>\n",
+       "                        <td> 8.00 kiB </td>\n",
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+       "                        <td> (64, 32) </td>\n",
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+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
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+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
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+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>specific_humidity</span></div><div class='xr-var-dims'>(time, level, longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(100, 13, 64, 32), meta=np.ndarray&gt;</div><input id='attrs-0ed5013e-85ee-4b4e-8127-e2c4146d90a1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0ed5013e-85ee-4b4e-8127-e2c4146d90a1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-59bd4fb9-b5cc-4594-8939-159b45ae946d' class='xr-var-data-in' type='checkbox'><label for='data-59bd4fb9-b5cc-4594-8939-159b45ae946d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Specific humidity</dd><dt><span>short_name :</span></dt><dd>q</dd><dt><span>standard_name :</span></dt><dd>specific_humidity</dd><dt><span>units :</span></dt><dd>kg kg**-1</dd></dl></div><div class='xr-var-data'><table>\n",
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+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 9.28 GiB </td>\n",
+       "                        <td> 10.16 MiB </td>\n",
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+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (93544, 13, 64, 32) </td>\n",
+       "                        <td> (100, 13, 64, 32) </td>\n",
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+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 936 chunks in 2 graph layers </td>\n",
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+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>standard_deviation_of_filtered_subgrid_orography</span></div><div class='xr-var-dims'>(longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(64, 32), meta=np.ndarray&gt;</div><input id='attrs-5977c0e4-c927-4f80-bd28-d80c925052be' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5977c0e4-c927-4f80-bd28-d80c925052be' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-69d6c01a-f409-4f3d-968e-8108df1018a7' class='xr-var-data-in' type='checkbox'><label for='data-69d6c01a-f409-4f3d-968e-8108df1018a7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Standard deviation of filtered subgrid orography</dd><dt><span>short_name :</span></dt><dd>sdfor</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
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+       "                    <tr>\n",
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+       "                    </tr>\n",
+       "                </thead>\n",
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+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 8.00 kiB </td>\n",
+       "                        <td> 8.00 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
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+       "                        <td> (64, 32) </td>\n",
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+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
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+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>standard_deviation_of_orography</span></div><div class='xr-var-dims'>(longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(64, 32), meta=np.ndarray&gt;</div><input id='attrs-13cf41d5-6099-440d-9bef-96657f88c59a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-13cf41d5-6099-440d-9bef-96657f88c59a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5072e0e7-3e3c-4159-a121-e38ddfa3db4f' class='xr-var-data-in' type='checkbox'><label for='data-5072e0e7-3e3c-4159-a121-e38ddfa3db4f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Standard deviation of orography</dd><dt><span>short_name :</span></dt><dd>sdor</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><table>\n",
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+       "        <td>\n",
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+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 8.00 kiB </td>\n",
+       "                        <td> 8.00 kiB </td>\n",
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+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
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+       "                        <td> (64, 32) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>surface_pressure</span></div><div class='xr-var-dims'>(time, longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(100, 64, 32), meta=np.ndarray&gt;</div><input id='attrs-00840fb0-b9ca-4f32-963d-d37cd72639ff' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-00840fb0-b9ca-4f32-963d-d37cd72639ff' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c10b1df4-1aa2-43cf-ae60-0eddca2e7137' class='xr-var-data-in' type='checkbox'><label for='data-c10b1df4-1aa2-43cf-ae60-0eddca2e7137' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Surface pressure</dd><dt><span>short_name :</span></dt><dd>sp</dd><dt><span>standard_name :</span></dt><dd>surface_air_pressure</dd><dt><span>units :</span></dt><dd>Pa</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
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+       "                <tbody>\n",
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+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 730.81 MiB </td>\n",
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+       "                        <th> Shape </th>\n",
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+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
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+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>temperature</span></div><div class='xr-var-dims'>(time, level, longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(100, 13, 64, 32), meta=np.ndarray&gt;</div><input id='attrs-f4d48550-226a-4e2c-aa65-1cbae0235996' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f4d48550-226a-4e2c-aa65-1cbae0235996' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-700e9344-4105-407b-a6e9-77714664581f' class='xr-var-data-in' type='checkbox'><label for='data-700e9344-4105-407b-a6e9-77714664581f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Temperature</dd><dt><span>short_name :</span></dt><dd>t</dd><dt><span>standard_name :</span></dt><dd>air_temperature</dd><dt><span>units :</span></dt><dd>K</dd></dl></div><div class='xr-var-data'><table>\n",
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+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 9.28 GiB </td>\n",
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+       "                    <tr>\n",
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+       "                        <th> Data type </th>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>total_cloud_cover</span></div><div class='xr-var-dims'>(time, longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(100, 64, 32), meta=np.ndarray&gt;</div><input id='attrs-b280954f-8fb0-4f9c-b520-eebf2922ab67' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b280954f-8fb0-4f9c-b520-eebf2922ab67' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1a7a2572-90d3-4d07-ada5-bbee2d1ad17d' class='xr-var-data-in' type='checkbox'><label for='data-1a7a2572-90d3-4d07-ada5-bbee2d1ad17d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Total cloud cover</dd><dt><span>short_name :</span></dt><dd>tcc</dd><dt><span>standard_name :</span></dt><dd>cloud_area_fraction</dd><dt><span>units :</span></dt><dd>(0 - 1)</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 730.81 MiB </td>\n",
+       "                        <td> 800.00 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (93544, 64, 32) </td>\n",
+       "                        <td> (100, 64, 32) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 936 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>total_column_vapor</span></div><div class='xr-var-dims'>(time, longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(100, 64, 32), meta=np.ndarray&gt;</div><input id='attrs-2f5c4eae-6e05-40df-9dad-38dc95e5888e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2f5c4eae-6e05-40df-9dad-38dc95e5888e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b42006f7-dc63-4935-8400-fc20bc74a871' class='xr-var-data-in' type='checkbox'><label for='data-b42006f7-dc63-4935-8400-fc20bc74a871' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 730.81 MiB </td>\n",
+       "                        <td> 800.00 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (93544, 64, 32) </td>\n",
+       "                        <td> (100, 64, 32) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 936 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>total_column_water</span></div><div class='xr-var-dims'>(time, longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(100, 64, 32), meta=np.ndarray&gt;</div><input id='attrs-dadfe20c-13e0-471e-801f-8060986ce151' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-dadfe20c-13e0-471e-801f-8060986ce151' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-077109eb-4fd2-4b76-9901-1e105eccdf4f' class='xr-var-data-in' type='checkbox'><label for='data-077109eb-4fd2-4b76-9901-1e105eccdf4f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Total column water</dd><dt><span>short_name :</span></dt><dd>tcw</dd><dt><span>units :</span></dt><dd>kg m**-2</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 730.81 MiB </td>\n",
+       "                        <td> 800.00 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (93544, 64, 32) </td>\n",
+       "                        <td> (100, 64, 32) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 936 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
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+       "                        <th> Data type </th>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>total_column_water_vapour</span></div><div class='xr-var-dims'>(time, longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(100, 64, 32), meta=np.ndarray&gt;</div><input id='attrs-53b24baf-7197-474b-bddb-2acb73c3f5cd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-53b24baf-7197-474b-bddb-2acb73c3f5cd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dfef7db6-bee7-4c6e-8300-47d38b06f9bb' class='xr-var-data-in' type='checkbox'><label for='data-dfef7db6-bee7-4c6e-8300-47d38b06f9bb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Total column vertically-integrated water vapour</dd><dt><span>short_name :</span></dt><dd>tcwv</dd><dt><span>standard_name :</span></dt><dd>lwe_thickness_of_atmosphere_mass_content_of_water_vapor</dd><dt><span>units :</span></dt><dd>kg m**-2</dd></dl></div><div class='xr-var-data'><table>\n",
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+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 730.81 MiB </td>\n",
+       "                        <td> 800.00 kiB </td>\n",
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+       "                        <td> (100, 64, 32) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 936 chunks in 2 graph layers </td>\n",
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+       "                        <th> Data type </th>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>total_precipitation_12hr</span></div><div class='xr-var-dims'>(time, longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(100, 64, 32), meta=np.ndarray&gt;</div><input id='attrs-8e027e00-a620-42ec-9f96-6b0ad0b2c5ff' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8e027e00-a620-42ec-9f96-6b0ad0b2c5ff' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-86d622ff-1453-442c-8a99-33e15f077a29' class='xr-var-data-in' type='checkbox'><label for='data-86d622ff-1453-442c-8a99-33e15f077a29' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Total precipitation</dd><dt><span>short_name :</span></dt><dd>tp</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
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+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 730.81 MiB </td>\n",
+       "                        <td> 800.00 kiB </td>\n",
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+       "                        <td> (100, 64, 32) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 936 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>total_precipitation_24hr</span></div><div class='xr-var-dims'>(time, longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(100, 64, 32), meta=np.ndarray&gt;</div><input id='attrs-25c476d9-48b3-43ef-b3eb-d9e3b8323f63' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-25c476d9-48b3-43ef-b3eb-d9e3b8323f63' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-601fa678-c1d9-43b3-91c2-753f230ff5f6' class='xr-var-data-in' type='checkbox'><label for='data-601fa678-c1d9-43b3-91c2-753f230ff5f6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Total precipitation</dd><dt><span>short_name :</span></dt><dd>tp</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 730.81 MiB </td>\n",
+       "                        <td> 800.00 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (93544, 64, 32) </td>\n",
+       "                        <td> (100, 64, 32) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 936 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>total_precipitation_6hr</span></div><div class='xr-var-dims'>(time, longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(100, 64, 32), meta=np.ndarray&gt;</div><input id='attrs-3c78ad4c-21e9-4eed-b2fe-d1d4e4a24de2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3c78ad4c-21e9-4eed-b2fe-d1d4e4a24de2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-573b0cf6-5cc2-4bd1-9703-cd942876b361' class='xr-var-data-in' type='checkbox'><label for='data-573b0cf6-5cc2-4bd1-9703-cd942876b361' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Total precipitation</dd><dt><span>short_name :</span></dt><dd>tp</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 730.81 MiB </td>\n",
+       "                        <td> 800.00 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (93544, 64, 32) </td>\n",
+       "                        <td> (100, 64, 32) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 936 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>type_of_high_vegetation</span></div><div class='xr-var-dims'>(longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(64, 32), meta=np.ndarray&gt;</div><input id='attrs-cb245654-ee00-4afc-8f13-0837c86479e6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cb245654-ee00-4afc-8f13-0837c86479e6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7ba22be9-390c-4b2c-aecf-f5ccaf04f08a' class='xr-var-data-in' type='checkbox'><label for='data-7ba22be9-390c-4b2c-aecf-f5ccaf04f08a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Type of high vegetation</dd><dt><span>short_name :</span></dt><dd>tvh</dd><dt><span>units :</span></dt><dd>~</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 8.00 kiB </td>\n",
+       "                        <td> 8.00 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (64, 32) </td>\n",
+       "                        <td> (64, 32) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>type_of_low_vegetation</span></div><div class='xr-var-dims'>(longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(64, 32), meta=np.ndarray&gt;</div><input id='attrs-ddbd3d48-34d5-463b-be0a-6f64784ffbb5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ddbd3d48-34d5-463b-be0a-6f64784ffbb5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7ecab63d-39ff-4345-b4dc-35086e2a1042' class='xr-var-data-in' type='checkbox'><label for='data-7ecab63d-39ff-4345-b4dc-35086e2a1042' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Type of low vegetation</dd><dt><span>short_name :</span></dt><dd>tvl</dd><dt><span>units :</span></dt><dd>~</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 8.00 kiB </td>\n",
+       "                        <td> 8.00 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (64, 32) </td>\n",
+       "                        <td> (64, 32) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>u_component_of_wind</span></div><div class='xr-var-dims'>(time, level, longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(100, 13, 64, 32), meta=np.ndarray&gt;</div><input id='attrs-5a2ca166-c21e-4dc9-ac36-93ae6f7118d6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5a2ca166-c21e-4dc9-ac36-93ae6f7118d6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-281755f8-9cf6-47d5-b108-ff3677c39910' class='xr-var-data-in' type='checkbox'><label for='data-281755f8-9cf6-47d5-b108-ff3677c39910' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>U component of wind</dd><dt><span>short_name :</span></dt><dd>u</dd><dt><span>standard_name :</span></dt><dd>eastward_wind</dd><dt><span>units :</span></dt><dd>m s**-1</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 9.28 GiB </td>\n",
+       "                        <td> 10.16 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (93544, 13, 64, 32) </td>\n",
+       "                        <td> (100, 13, 64, 32) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 936 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>v_component_of_wind</span></div><div class='xr-var-dims'>(time, level, longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(100, 13, 64, 32), meta=np.ndarray&gt;</div><input id='attrs-6d633111-8814-459d-ac26-ab106d462455' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6d633111-8814-459d-ac26-ab106d462455' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a69fb79a-e601-485f-86e0-e77d65e9b4d8' class='xr-var-data-in' type='checkbox'><label for='data-a69fb79a-e601-485f-86e0-e77d65e9b4d8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>V component of wind</dd><dt><span>short_name :</span></dt><dd>v</dd><dt><span>standard_name :</span></dt><dd>northward_wind</dd><dt><span>units :</span></dt><dd>m s**-1</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 9.28 GiB </td>\n",
+       "                        <td> 10.16 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (93544, 13, 64, 32) </td>\n",
+       "                        <td> (100, 13, 64, 32) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 936 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "            </table>\n",
+       "        </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vertical_velocity</span></div><div class='xr-var-dims'>(time, level, longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(100, 13, 64, 32), meta=np.ndarray&gt;</div><input id='attrs-666e39c5-e387-4301-b0c2-1f75577bb42e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-666e39c5-e387-4301-b0c2-1f75577bb42e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9df96f42-bbdc-402e-8950-c1ad1f44b8e7' class='xr-var-data-in' type='checkbox'><label for='data-9df96f42-bbdc-402e-8950-c1ad1f44b8e7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Vertical velocity</dd><dt><span>short_name :</span></dt><dd>w</dd><dt><span>standard_name :</span></dt><dd>lagrangian_tendency_of_air_pressure</dd><dt><span>units :</span></dt><dd>Pa s**-1</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 9.28 GiB </td>\n",
+       "                        <td> 10.16 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (93544, 13, 64, 32) </td>\n",
+       "                        <td> (100, 13, 64, 32) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 936 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>volumetric_soil_water_layer_1</span></div><div class='xr-var-dims'>(time, longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(100, 64, 32), meta=np.ndarray&gt;</div><input id='attrs-68f80a67-8ef6-428d-ae99-dc22dfcbb630' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-68f80a67-8ef6-428d-ae99-dc22dfcbb630' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e8b21339-d7ae-4bb3-9208-3f3622bc936a' class='xr-var-data-in' type='checkbox'><label for='data-e8b21339-d7ae-4bb3-9208-3f3622bc936a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Volumetric soil water layer 1</dd><dt><span>short_name :</span></dt><dd>swvl1</dd><dt><span>units :</span></dt><dd>m**3 m**-3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
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+       "                    <tr>\n",
+       "                        <td> </td>\n",
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+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 730.81 MiB </td>\n",
+       "                        <td> 800.00 kiB </td>\n",
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+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (93544, 64, 32) </td>\n",
+       "                        <td> (100, 64, 32) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 936 chunks in 2 graph layers </td>\n",
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+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
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+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>volumetric_soil_water_layer_2</span></div><div class='xr-var-dims'>(time, longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(100, 64, 32), meta=np.ndarray&gt;</div><input id='attrs-d7024c37-3eae-45eb-a334-7ea6bb8d4650' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d7024c37-3eae-45eb-a334-7ea6bb8d4650' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e524157f-56b2-4557-92b6-7a56d369e8f7' class='xr-var-data-in' type='checkbox'><label for='data-e524157f-56b2-4557-92b6-7a56d369e8f7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Volumetric soil water layer 2</dd><dt><span>short_name :</span></dt><dd>swvl2</dd><dt><span>units :</span></dt><dd>m**3 m**-3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
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+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 730.81 MiB </td>\n",
+       "                        <td> 800.00 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
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+       "                        <td> (100, 64, 32) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 936 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>volumetric_soil_water_layer_3</span></div><div class='xr-var-dims'>(time, longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(100, 64, 32), meta=np.ndarray&gt;</div><input id='attrs-a775efc8-b3a5-4cd5-904a-6eb5fabe06d0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a775efc8-b3a5-4cd5-904a-6eb5fabe06d0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7b921dcd-ead4-4cad-9998-76e6925e28cd' class='xr-var-data-in' type='checkbox'><label for='data-7b921dcd-ead4-4cad-9998-76e6925e28cd' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Volumetric soil water layer 3</dd><dt><span>short_name :</span></dt><dd>swvl3</dd><dt><span>units :</span></dt><dd>m**3 m**-3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
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+       "                    <tr>\n",
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+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 730.81 MiB </td>\n",
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+       "                        <th> Shape </th>\n",
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+       "                        <td> (100, 64, 32) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 936 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>volumetric_soil_water_layer_4</span></div><div class='xr-var-dims'>(time, longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(100, 64, 32), meta=np.ndarray&gt;</div><input id='attrs-dd12e885-f244-4890-8a93-835a82acf16f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-dd12e885-f244-4890-8a93-835a82acf16f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-211b4b32-e116-4e22-b2f2-70ddf86b66e3' class='xr-var-data-in' type='checkbox'><label for='data-211b4b32-e116-4e22-b2f2-70ddf86b66e3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Volumetric soil water layer 4</dd><dt><span>short_name :</span></dt><dd>swvl4</dd><dt><span>units :</span></dt><dd>m**3 m**-3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
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+       "                </thead>\n",
+       "                <tbody>\n",
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+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 730.81 MiB </td>\n",
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+       "                        <td> (100, 64, 32) </td>\n",
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+       "                    <tr>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vorticity</span></div><div class='xr-var-dims'>(time, level, longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(100, 13, 64, 32), meta=np.ndarray&gt;</div><input id='attrs-8f591d59-42a0-48ab-a957-66f8c549ed36' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8f591d59-42a0-48ab-a957-66f8c549ed36' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9734042d-b332-476a-ae6f-2b997cb9d20b' class='xr-var-data-in' type='checkbox'><label for='data-9734042d-b332-476a-ae6f-2b997cb9d20b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 9.28 GiB </td>\n",
+       "                        <td> 10.16 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (93544, 13, 64, 32) </td>\n",
+       "                        <td> (100, 13, 64, 32) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 936 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>wind_speed</span></div><div class='xr-var-dims'>(time, level, longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(100, 13, 64, 32), meta=np.ndarray&gt;</div><input id='attrs-9d13ed3d-01f0-4d65-bbe1-0ee337e5ce15' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9d13ed3d-01f0-4d65-bbe1-0ee337e5ce15' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c3243064-849b-4fa8-9378-9d716c570da3' class='xr-var-data-in' type='checkbox'><label for='data-c3243064-849b-4fa8-9378-9d716c570da3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 9.28 GiB </td>\n",
+       "                        <td> 10.16 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (93544, 13, 64, 32) </td>\n",
+       "                        <td> (100, 13, 64, 32) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 936 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "                </tbody>\n",
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+       "  <polygon points=\"190.0,0.0 204.9485979497544,14.948597949754403 204.9485979497544,40.36121446433689 190.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"190\" y1=\"0\" x2=\"215\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"204\" y1=\"14\" x2=\"230\" y2=\"14\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"190\" y1=\"0\" x2=\"204\" y2=\"14\" style=\"stroke-width:2\" />\n",
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+       "\n",
+       "  <!-- Colored Rectangle -->\n",
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+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"204\" y1=\"14\" x2=\"230\" y2=\"14\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"204\" y1=\"40\" x2=\"230\" y2=\"40\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"204\" y1=\"14\" x2=\"204\" y2=\"40\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"230\" y1=\"14\" x2=\"230\" y2=\"40\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
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+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"217.654906\" y=\"60.361214\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >32</text>\n",
+       "  <text x=\"250.361214\" y=\"27.654906\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,250.361214,27.654906)\">64</text>\n",
+       "  <text x=\"187.474299\" y=\"52.886915\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,187.474299,52.886915)\">13</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-0c123ec2-16d4-4dc0-b135-2b08ea076cbe' class='xr-section-summary-in' type='checkbox'  ><label for='section-0c123ec2-16d4-4dc0-b135-2b08ea076cbe' class='xr-section-summary' >Indexes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>latitude</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-3c4a142c-c9e2-4064-9d05-a6aad6c866bc' class='xr-index-data-in' type='checkbox'/><label for='index-3c4a142c-c9e2-4064-9d05-a6aad6c866bc' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ -87.18750000000003,  -81.56250000000001,            -75.9375,\n",
+       "        -70.31249999999999,  -64.68750000000001,            -59.0625,\n",
+       "                  -53.4375,            -47.8125,            -42.1875,\n",
+       "                  -36.5625, -30.937499999999996, -25.312500000000004,\n",
+       "       -19.687499999999996, -14.062499999999991,  -8.437499999999996,\n",
+       "        -2.812500000000003,   2.812500000000003,   8.437500000000009,\n",
+       "        14.062500000000004,  19.687499999999996,  25.312500000000004,\n",
+       "         30.93750000000001,  36.562499999999986,             42.1875,\n",
+       "                   47.8125,             53.4375,  59.062500000000014,\n",
+       "         64.68750000000001,             70.3125,             75.9375,\n",
+       "         81.56249999999997,   87.18750000000003],\n",
+       "      dtype=&#x27;float64&#x27;, name=&#x27;latitude&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>level</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-bc41b355-d509-4fd0-8e73-9796df3fa430' class='xr-index-data-in' type='checkbox'/><label for='index-bc41b355-d509-4fd0-8e73-9796df3fa430' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([50, 100, 150, 200, 250, 300, 400, 500, 600, 700, 850, 925, 1000], dtype=&#x27;int64&#x27;, name=&#x27;level&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>longitude</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-90e8b5f6-4fcd-4abf-a239-256b9cd24b08' class='xr-index-data-in' type='checkbox'/><label for='index-90e8b5f6-4fcd-4abf-a239-256b9cd24b08' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([               0.0,              5.625,              11.25,\n",
+       "                   16.875,               22.5,             28.125,\n",
+       "                    33.75,             39.375,               45.0,\n",
+       "                   50.625,              56.25,  61.87499999999999,\n",
+       "                     67.5,             73.125,              78.75,\n",
+       "                   84.375,               90.0,             95.625,\n",
+       "                   101.25,            106.875,              112.5,\n",
+       "                  118.125, 123.74999999999999,            129.375,\n",
+       "                    135.0,            140.625,             146.25,\n",
+       "                  151.875,              157.5,            163.125,\n",
+       "                   168.75,            174.375,              180.0,\n",
+       "                  185.625,             191.25,            196.875,\n",
+       "                    202.5,            208.125,             213.75,\n",
+       "                  219.375,              225.0, 230.62499999999997,\n",
+       "                   236.25,            241.875, 247.49999999999997,\n",
+       "                  253.125,             258.75,            264.375,\n",
+       "                    270.0,            275.625,             281.25,\n",
+       "                  286.875,              292.5,            298.125,\n",
+       "                   303.75,            309.375,              315.0,\n",
+       "                  320.625,             326.25,            331.875,\n",
+       "                    337.5,            343.125,             348.75,\n",
+       "                  354.375],\n",
+       "      dtype=&#x27;float64&#x27;, name=&#x27;longitude&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-a84bf387-7e62-4903-8b38-4288e1547ca1' class='xr-index-data-in' type='checkbox'/><label for='index-a84bf387-7e62-4903-8b38-4288e1547ca1' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;1959-01-01 00:00:00&#x27;, &#x27;1959-01-01 06:00:00&#x27;,\n",
+       "               &#x27;1959-01-01 12:00:00&#x27;, &#x27;1959-01-01 18:00:00&#x27;,\n",
+       "               &#x27;1959-01-02 00:00:00&#x27;, &#x27;1959-01-02 06:00:00&#x27;,\n",
+       "               &#x27;1959-01-02 12:00:00&#x27;, &#x27;1959-01-02 18:00:00&#x27;,\n",
+       "               &#x27;1959-01-03 00:00:00&#x27;, &#x27;1959-01-03 06:00:00&#x27;,\n",
+       "               ...\n",
+       "               &#x27;2023-01-08 12:00:00&#x27;, &#x27;2023-01-08 18:00:00&#x27;,\n",
+       "               &#x27;2023-01-09 00:00:00&#x27;, &#x27;2023-01-09 06:00:00&#x27;,\n",
+       "               &#x27;2023-01-09 12:00:00&#x27;, &#x27;2023-01-09 18:00:00&#x27;,\n",
+       "               &#x27;2023-01-10 00:00:00&#x27;, &#x27;2023-01-10 06:00:00&#x27;,\n",
+       "               &#x27;2023-01-10 12:00:00&#x27;, &#x27;2023-01-10 18:00:00&#x27;],\n",
+       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=93544, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-c9bab06f-89aa-4f9e-9dc3-b83fec8a222b' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-c9bab06f-89aa-4f9e-9dc3-b83fec8a222b' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.Dataset> Size: 175GB\n",
+       "Dimensions:                                           (time: 93544,\n",
+       "                                                       longitude: 64,\n",
+       "                                                       latitude: 32, level: 13)\n",
+       "Coordinates:\n",
+       "  * latitude                                          (latitude) float64 256B ...\n",
+       "  * level                                             (level) int64 104B 50 ....\n",
+       "  * longitude                                         (longitude) float64 512B ...\n",
+       "  * time                                              (time) datetime64[ns] 748kB ...\n",
+       "Data variables: (12/62)\n",
+       "    10m_u_component_of_wind                           (time, longitude, latitude) float32 766MB dask.array<chunksize=(100, 64, 32), meta=np.ndarray>\n",
+       "    10m_v_component_of_wind                           (time, longitude, latitude) float32 766MB dask.array<chunksize=(100, 64, 32), meta=np.ndarray>\n",
+       "    10m_wind_speed                                    (time, longitude, latitude) float32 766MB dask.array<chunksize=(100, 64, 32), meta=np.ndarray>\n",
+       "    2m_dewpoint_temperature                           (time, longitude, latitude) float32 766MB dask.array<chunksize=(100, 64, 32), meta=np.ndarray>\n",
+       "    2m_temperature                                    (time, longitude, latitude) float32 766MB dask.array<chunksize=(100, 64, 32), meta=np.ndarray>\n",
+       "    above_ground                                      (time, level, longitude, latitude) float32 10GB dask.array<chunksize=(100, 13, 64, 32), meta=np.ndarray>\n",
+       "    ...                                                ...\n",
+       "    volumetric_soil_water_layer_1                     (time, longitude, latitude) float32 766MB dask.array<chunksize=(100, 64, 32), meta=np.ndarray>\n",
+       "    volumetric_soil_water_layer_2                     (time, longitude, latitude) float32 766MB dask.array<chunksize=(100, 64, 32), meta=np.ndarray>\n",
+       "    volumetric_soil_water_layer_3                     (time, longitude, latitude) float32 766MB dask.array<chunksize=(100, 64, 32), meta=np.ndarray>\n",
+       "    volumetric_soil_water_layer_4                     (time, longitude, latitude) float32 766MB dask.array<chunksize=(100, 64, 32), meta=np.ndarray>\n",
+       "    vorticity                                         (time, level, longitude, latitude) float32 10GB dask.array<chunksize=(100, 13, 64, 32), meta=np.ndarray>\n",
+       "    wind_speed                                        (time, level, longitude, latitude) float32 10GB dask.array<chunksize=(100, 13, 64, 32), meta=np.ndarray>"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ds_boundary = xr.open_zarr(url_boundary)\n",
+    "ds_boundary"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_latlon_arrays(ds, lon='longitude', lat='latitude'):\n",
+    "    da_lon = ds[lon]\n",
+    "    da_lat = ds[lat]\n",
+    "\n",
+    "    # check if lat and lon dataarrays share dimensions, otherwise we need to\n",
+    "    # broadcast\n",
+    "    if da_lon.dims != da_lat.dims:\n",
+    "        lons, lats = np.meshgrid(da_lon.values, da_lat.values)\n",
+    "        lons = lons.flatten()\n",
+    "        lats = lats.flatten()\n",
+    "    else:\n",
+    "        lons = da_lon.values.flatten()\n",
+    "        lats = da_lat.values.flatten()\n",
+    "        \n",
+    "    return lons, lats\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "((2048, 2), (4661, 2))"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "k = 10\n",
+    "ds_lam_subsampled = ds_lam.isel(x=slice(None, None, k), y=slice(None, None, k))\n",
+    "points_bnd = np.array(get_latlon_arrays(ds_boundary)).T\n",
+    "points_lam = np.array(get_latlon_arrays(ds_lam_subsampled, lon='lon', lat='lat')).T\n",
+    "\n",
+    "points_bnd.shape, points_lam.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.PathCollection at 0x13ecca270>"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(subplot_kw={'projection': ccrs.PlateCarree()})\n",
+    "ax.coastlines()\n",
+    "ax.scatter(points_bnd[:, 0], points_bnd[:, 1], color='red', s=1)\n",
+    "ax.scatter(points_lam[:, 0], points_lam[:, 1], color='blue', s=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x13eee94f0>]"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x1000 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# latlon to cartesian coords\n",
+    "pts_bnd_x = np.array(sg.vector.lonlat_to_vector(points_bnd[:,0], points_bnd[:,1])).T\n",
+    "pts_lam_x = np.array(sg.vector.lonlat_to_vector(points_lam[:,0], points_lam[:,1])).T\n",
+    "\n",
+    "chull_lam = SphericalPolygon.convex_hull(pts_lam_x)\n",
+    "\n",
+    "# Finding the boundary points inside the convec hull is what takes time. Possibly speed up by restricting to a subset of points_bnd that are within some distance from area center or similar?\n",
+    "msk_in = np.array([chull_lam.contains_lonlat(x[1], x[0]) for x in points_bnd])\n",
+    "\n",
+    "# Distances from all boundary points outside the region to the closest point inside\n",
+    "d = np.array([np.min(np.arccos(np.dot(pts_lam_x, x))) for x in pts_bnd_x[~msk_in]])\n",
+    "\n",
+    "# Define boundary distance and mask boundary points outside that are closer\n",
+    "# This is now in radians but should be defined as meters based on Earth radius\n",
+    "ii = d < 0.2\n",
+    "\n",
+    "# Plot boundary valid boundary points outside and the LAM interior points (subset)\n",
+    "fig, ax = plt.subplots(subplot_kw={'projection': ccrs.PlateCarree()}, figsize=(10, 10))\n",
+    "ax.coastlines()\n",
+    "# xr.plot.scatter(ds_boundary, x='longitude', y='latitude', color='red', s=1, ax=ax, transform=ccrs.Geodetic())\n",
+    "xr.plot.scatter(ds_lam_subsampled, x='lon', y='lat', color='blue', s=1, ax=ax, transform=ccrs.Geodetic())\n",
+    "ax.plot(points_bnd[~msk_in,0][ii], points_bnd[~msk_in,1][ii], '.', color='green', markersize=1, transform=ccrs.Geodetic())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([False, False, False, ..., False, False, False])"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ii"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": ".venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/pdm.lock b/pdm.lock
index 30a0e20..e89b9b0 100644
--- a/pdm.lock
+++ b/pdm.lock
@@ -2,10 +2,10 @@
 # It is not intended for manual editing.
 
 [metadata]
-groups = ["default", "dev"]
+groups = ["default", "dev", "latlon-domain-crop"]
 strategy = ["inherit_metadata"]
 lock_version = "4.5.0"
-content_hash = "sha256:ed345b0df8664a5ab1aadb77d5c4218ef15f135ddacd674f0d029e5a39f9654d"
+content_hash = "sha256:5433ccdea7b0ccc8b1c0e5c0d3b0b14ade0af67f891744fc473ee9c72e779963"
 
 [[metadata.targets]]
 requires_python = ">=3.9"
@@ -15,7 +15,7 @@ name = "aiohttp"
 version = "3.9.5"
 requires_python = ">=3.8"
 summary = "Async http client/server framework (asyncio)"
-groups = ["default"]
+groups = ["default", "latlon-domain-crop"]
 dependencies = [
     "aiosignal>=1.1.2",
     "async-timeout<5.0,>=4.0; python_version < \"3.11\"",
@@ -78,7 +78,7 @@ name = "aiosignal"
 version = "1.3.1"
 requires_python = ">=3.7"
 summary = "aiosignal: a list of registered asynchronous callbacks"
-groups = ["default"]
+groups = ["default", "latlon-domain-crop"]
 dependencies = [
     "frozenlist>=1.1.0",
 ]
@@ -87,6 +87,18 @@ files = [
     {file = "aiosignal-1.3.1.tar.gz", hash = "sha256:54cd96e15e1649b75d6c87526a6ff0b6c1b0dd3459f43d9ca11d48c339b68cfc"},
 ]
 
+[[package]]
+name = "appnope"
+version = "0.1.4"
+requires_python = ">=3.6"
+summary = "Disable App Nap on macOS >= 10.9"
+groups = ["dev"]
+marker = "platform_system == \"Darwin\""
+files = [
+    {file = "appnope-0.1.4-py2.py3-none-any.whl", hash = "sha256:502575ee11cd7a28c0205f379b525beefebab9d161b7c964670864014ed7213c"},
+    {file = "appnope-0.1.4.tar.gz", hash = "sha256:1de3860566df9caf38f01f86f65e0e13e379af54f9e4bee1e66b48f2efffd1ee"},
+]
+
 [[package]]
 name = "asciitree"
 version = "0.3.3"
@@ -96,12 +108,67 @@ files = [
     {file = "asciitree-0.3.3.tar.gz", hash = "sha256:4aa4b9b649f85e3fcb343363d97564aa1fb62e249677f2e18a96765145cc0f6e"},
 ]
 
+[[package]]
+name = "astropy"
+version = "6.0.1"
+requires_python = ">=3.9"
+summary = "Astronomy and astrophysics core library"
+groups = ["latlon-domain-crop"]
+dependencies = [
+    "PyYAML>=3.13",
+    "astropy-iers-data>=0.2024.2.26.0.28.55",
+    "numpy<2,>=1.22",
+    "packaging>=19.0",
+    "pyerfa>=2.0.1.1",
+]
+files = [
+    {file = "astropy-6.0.1-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:2b5ff962b0e586953f95b63ec047e1d7a3b6a12a13d11c6e909e0bcd3e05b445"},
+    {file = "astropy-6.0.1-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:129ed1fb1d23e6fbf8b8e697c2e7340d99bc6271b8c59f9572f3f47063a42e6a"},
+    {file = "astropy-6.0.1-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:6e998ee0ffa58342b4d44f2843b036015e3a6326b53185c5361fea4430658466"},
+    {file = "astropy-6.0.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:c33e3d746c3e7a324dbd76b236fe1e44304d5b6d941a1f724f419d01666d6d88"},
+    {file = "astropy-6.0.1-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:2f53caf9efebcc9040a92c977dcdae78dd0ff4de218fd316e4fcaffd9ace8dc1"},
+    {file = "astropy-6.0.1-cp310-cp310-win32.whl", hash = "sha256:242b8f101301ab303366109d0dfe3cf0db745bf778f7b859fb486105197577d1"},
+    {file = "astropy-6.0.1-cp310-cp310-win_amd64.whl", hash = "sha256:1db9e95438472f6ed53fa2f4e2811c2d84f4085eeacc3cb8820d770d1ea61d1c"},
+    {file = "astropy-6.0.1-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:c682967736228cc4477e63db0e8854375dd31d755de55b30256de98f1f7b7c23"},
+    {file = "astropy-6.0.1-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:5208b6f10956ca92efb73375364c81a7df365b441b07f4941a24ee0f1bd9e292"},
+    {file = "astropy-6.0.1-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:6f28facb5800c0617f233c1db0e622da83de1f74ca28d0ff8646e360d4fda74e"},
+    {file = "astropy-6.0.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:2c00922548a666b026e2630a563090341d74c8222066e9c84c9673395bca7363"},
+    {file = "astropy-6.0.1-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:9b3bf27c51fb46bba993695eebd0c39a4e2a792b707e65b28ac4e8ae703f93d4"},
+    {file = "astropy-6.0.1-cp311-cp311-win32.whl", hash = "sha256:1f183ab42655ad09b064a4e8eb9cd1eaa138b90ca2f0cd82a200afda062063a5"},
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+summary = "Common protobufs used in Google APIs"
+groups = ["latlon-domain-crop"]
+dependencies = [
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@@ -444,7 +982,7 @@ name = "idna"
 version = "3.7"
 requires_python = ">=3.5"
 summary = "Internationalized Domain Names in Applications (IDNA)"
-groups = ["default"]
+groups = ["default", "latlon-domain-crop"]
 files = [
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@@ -455,7 +993,7 @@ name = "importlib-metadata"
 version = "7.2.1"
 requires_python = ">=3.8"
 summary = "Read metadata from Python packages"
-groups = ["default"]
+groups = ["default", "dev"]
 marker = "python_version < \"3.12\""
 dependencies = [
     "typing-extensions>=3.6.4; python_version < \"3.8\"",
@@ -466,6 +1004,21 @@ files = [
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+[[package]]
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+requires_python = ">=3.8"
+summary = "Read resources from Python packages"
+groups = ["latlon-domain-crop"]
+marker = "python_version < \"3.10\""
+dependencies = [
+    "zipp>=3.1.0; python_version < \"3.10\"",
+]
+files = [
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+]
+
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 version = "2.0.0"
@@ -508,13 +1061,38 @@ files = [
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+groups = ["dev"]
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+    "comm>=0.1.1",
+    "debugpy>=1.6.5",
+    "ipython>=7.23.1",
+    "jupyter-client>=6.1.12",
+    "jupyter-core!=5.0.*,>=4.12",
+    "matplotlib-inline>=0.1",
+    "nest-asyncio",
+    "packaging",
+    "psutil",
+    "pyzmq>=24",
+    "tornado>=6.1",
+    "traitlets>=5.4.0",
+]
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 requires_python = ">=3.9"
 summary = "IPython: Productive Interactive Computing"
 groups = ["dev"]
-marker = "python_version > \"3.6\""
 dependencies = [
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@@ -552,7 +1130,6 @@ version = "0.19.1"
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 summary = "An autocompletion tool for Python that can be used for text editors."
 groups = ["dev"]
-marker = "python_version > \"3.6\""
 dependencies = [
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+summary = "A very fast and expressive template engine."
+groups = ["latlon-domain-crop"]
+dependencies = [
+    "MarkupSafe>=2.0",
+]
+files = [
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+]
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+requires_python = ">=3.8"
+summary = "Jupyter protocol implementation and client libraries"
+groups = ["dev"]
+dependencies = [
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+    "jupyter-core!=5.0.*,>=4.12",
+    "python-dateutil>=2.8.2",
+    "pyzmq>=23.0",
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+summary = "Jupyter core package. A base package on which Jupyter projects rely."
+groups = ["dev"]
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 summary = "Run a subprocess in a pseudo terminal"
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-marker = "sys_platform != \"win32\" and python_version > \"3.6\""
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+    {file = "shapely-2.0.6-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:b02154b3e9d076a29a8513dffcb80f047a5ea63c897c0cd3d3679f29363cf7e5"},
+    {file = "shapely-2.0.6-cp39-cp39-win32.whl", hash = "sha256:44246d30124a4f1a638a7d5419149959532b99dfa25b54393512e6acc9c211ac"},
+    {file = "shapely-2.0.6-cp39-cp39-win_amd64.whl", hash = "sha256:2b542d7f1dbb89192d3512c52b679c822ba916f93479fa5d4fc2fe4fa0b3c9e8"},
+    {file = "shapely-2.0.6.tar.gz", hash = "sha256:997f6159b1484059ec239cacaa53467fd8b5564dabe186cd84ac2944663b0bf6"},
+]
+
 [[package]]
 name = "six"
 version = "1.16.0"
 requires_python = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*"
 summary = "Python 2 and 3 compatibility utilities"
-groups = ["default", "dev"]
+groups = ["default", "dev", "latlon-domain-crop"]
 files = [
     {file = "six-1.16.0-py2.py3-none-any.whl", hash = "sha256:8abb2f1d86890a2dfb989f9a77cfcfd3e47c2a354b01111771326f8aa26e0254"},
     {file = "six-1.16.0.tar.gz", hash = "sha256:1e61c37477a1626458e36f7b1d82aa5c9b094fa4802892072e49de9c60c4c926"},
 ]
 
+[[package]]
+name = "spherical-geometry"
+version = "1.3.2"
+requires_python = ">=3.9"
+summary = "Python based tools for spherical geometry"
+groups = ["latlon-domain-crop"]
+dependencies = [
+    "astropy>=5.0.4",
+    "numpy>=1.23",
+]
+files = [
+    {file = "spherical_geometry-1.3.2-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:06cb0965aed16f4247dee04df5a177cff24076b40bf752fc3c6885d2f0006185"},
+    {file = "spherical_geometry-1.3.2-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:b87668f88f0a16cff4061f8d4d6099b800316888e27824db3d1892fb4d0b97fb"},
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+    {file = "spherical_geometry-1.3.2-cp310-cp310-musllinux_1_2_x86_64.whl", hash = "sha256:0c09f8caba44873398dbe1a5dfbefd9d70e17140125593ec9ec4eef753f2f111"},
+    {file = "spherical_geometry-1.3.2-cp310-cp310-win32.whl", hash = "sha256:06cd418fb475fb6a51b2b62dbc35a5e828ae00fd6e7f1c08dc902f8a97faa52a"},
+    {file = "spherical_geometry-1.3.2-cp310-cp310-win_amd64.whl", hash = "sha256:1513841201510a071168cb6bbaaf03a5054878d373a3f8fe9a3aa38c4edf4b63"},
+    {file = "spherical_geometry-1.3.2-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:03e010efaa470d708201262cf8f1ac94b26775e8e039f6ea77559c798a3cf697"},
+    {file = "spherical_geometry-1.3.2-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:0e27786b7509aa16f15809a1ed2d2fcca7570a65e7376c5b867bc0c726557322"},
+    {file = "spherical_geometry-1.3.2-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:6ebefd216e64bfdbe150036c117593b5cb22f75ecf552598e35aa3cd1b822310"},
+    {file = "spherical_geometry-1.3.2-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:6b125ac4dc9ed8cab0325a84b6d2c676fc3a32c9f8c1d5055a053c30413f67fb"},
+    {file = "spherical_geometry-1.3.2-cp311-cp311-win32.whl", hash = "sha256:e3c01f2074c2b36f803311180d1a78b914bb30283f6eea1644c93928e7782c20"},
+    {file = "spherical_geometry-1.3.2-cp311-cp311-win_amd64.whl", hash = "sha256:54d178c3cddc271ae5e0f1e4c49cd99e22358f8047a12804fcf201979172de9c"},
+    {file = "spherical_geometry-1.3.2-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:ce5ceef9902e2564d44b5c22c11a7e4590918acf149c06f99611e10b6a76a3f7"},
+    {file = "spherical_geometry-1.3.2-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:5173f56001b8721181adc12b377898c58afca76159937a3391aafcfc756d2a05"},
+    {file = "spherical_geometry-1.3.2-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:728e1cda8ad1ebebf60cbb48d763946813169133f12640bc1c11b49ea838b8db"},
+    {file = "spherical_geometry-1.3.2-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:8fd68dffe3fc33ea6e1c50730ed59ffcd433d609a51daf599fd4ec1848652901"},
+    {file = "spherical_geometry-1.3.2-cp312-cp312-win32.whl", hash = "sha256:0c59cae46ede5a0c1352edebffef8651620c6ab2e863f1dd35043b74ac298b77"},
+    {file = "spherical_geometry-1.3.2-cp312-cp312-win_amd64.whl", hash = "sha256:a3090d10f67ad0cb9497fe2517028c7241de32e97e5d6cf5042d7cd85a2db4c2"},
+    {file = "spherical_geometry-1.3.2-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:accab32117fdaf7aa94b3ffe7568b9f5fe9cb0d519a59d15f44ddab6030a277e"},
+    {file = "spherical_geometry-1.3.2-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:b0a1ff01e0c7f2f749c0c038ba91fa0096069ae243febda2c980f455fb99e886"},
+    {file = "spherical_geometry-1.3.2-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:8c6e34806b25f965e4564ef11e4c487df6afc71e25605772e2d56244cc57cb62"},
+    {file = "spherical_geometry-1.3.2-cp39-cp39-musllinux_1_2_x86_64.whl", hash = "sha256:bc9e125bd75014f8ad6c677ca2913e70b039c14125680811336ba0247956a54e"},
+    {file = "spherical_geometry-1.3.2-cp39-cp39-win32.whl", hash = "sha256:c910d4767b73a2c9f695135aefb7ff5eca6263c1b4cfba2a2d366e5ab30d9e15"},
+    {file = "spherical_geometry-1.3.2-cp39-cp39-win_amd64.whl", hash = "sha256:caf55117b9fbb130cb1ca090d395bb89bd032bd449534a74f095e9490dbcec50"},
+    {file = "spherical_geometry-1.3.2.tar.gz", hash = "sha256:bed481a8b080748bf93434c3b113eae53609a61d0c0694db83c07685b52c3d23"},
+]
+
 [[package]]
 name = "stack-data"
 version = "0.6.3"
 summary = "Extract data from python stack frames and tracebacks for informative displays"
 groups = ["dev"]
-marker = "python_version > \"3.6\""
 dependencies = [
     "asttokens>=2.1.0",
     "executing>=1.2.0",
@@ -1130,13 +2470,32 @@ files = [
     {file = "toolz-0.12.1.tar.gz", hash = "sha256:ecca342664893f177a13dac0e6b41cbd8ac25a358e5f215316d43e2100224f4d"},
 ]
 
+[[package]]
+name = "tornado"
+version = "6.4.2"
+requires_python = ">=3.8"
+summary = "Tornado is a Python web framework and asynchronous networking library, originally developed at FriendFeed."
+groups = ["dev"]
+files = [
+    {file = "tornado-6.4.2-cp38-abi3-macosx_10_9_universal2.whl", hash = "sha256:e828cce1123e9e44ae2a50a9de3055497ab1d0aeb440c5ac23064d9e44880da1"},
+    {file = "tornado-6.4.2-cp38-abi3-macosx_10_9_x86_64.whl", hash = "sha256:072ce12ada169c5b00b7d92a99ba089447ccc993ea2143c9ede887e0937aa803"},
+    {file = "tornado-6.4.2-cp38-abi3-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:1a017d239bd1bb0919f72af256a970624241f070496635784d9bf0db640d3fec"},
+    {file = "tornado-6.4.2-cp38-abi3-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:c36e62ce8f63409301537222faffcef7dfc5284f27eec227389f2ad11b09d946"},
+    {file = "tornado-6.4.2-cp38-abi3-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:bca9eb02196e789c9cb5c3c7c0f04fb447dc2adffd95265b2c7223a8a615ccbf"},
+    {file = "tornado-6.4.2-cp38-abi3-musllinux_1_2_aarch64.whl", hash = "sha256:304463bd0772442ff4d0f5149c6f1c2135a1fae045adf070821c6cdc76980634"},
+    {file = "tornado-6.4.2-cp38-abi3-musllinux_1_2_i686.whl", hash = "sha256:c82c46813ba483a385ab2a99caeaedf92585a1f90defb5693351fa7e4ea0bf73"},
+    {file = "tornado-6.4.2-cp38-abi3-musllinux_1_2_x86_64.whl", hash = "sha256:932d195ca9015956fa502c6b56af9eb06106140d844a335590c1ec7f5277d10c"},
+    {file = "tornado-6.4.2-cp38-abi3-win32.whl", hash = "sha256:2876cef82e6c5978fde1e0d5b1f919d756968d5b4282418f3146b79b58556482"},
+    {file = "tornado-6.4.2-cp38-abi3-win_amd64.whl", hash = "sha256:908b71bf3ff37d81073356a5fadcc660eb10c1476ee6e2725588626ce7e5ca38"},
+    {file = "tornado-6.4.2.tar.gz", hash = "sha256:92bad5b4746e9879fd7bf1eb21dce4e3fc5128d71601f80005afa39237ad620b"},
+]
+
 [[package]]
 name = "traitlets"
 version = "5.14.3"
 requires_python = ">=3.8"
 summary = "Traitlets Python configuration system"
 groups = ["dev"]
-marker = "python_version > \"3.6\""
 files = [
     {file = "traitlets-5.14.3-py3-none-any.whl", hash = "sha256:b74e89e397b1ed28cc831db7aea759ba6640cb3de13090ca145426688ff1ac4f"},
     {file = "traitlets-5.14.3.tar.gz", hash = "sha256:9ed0579d3502c94b4b3732ac120375cda96f923114522847de4b3bb98b96b6b7"},
@@ -1170,7 +2529,7 @@ name = "urllib3"
 version = "2.2.2"
 requires_python = ">=3.8"
 summary = "HTTP library with thread-safe connection pooling, file post, and more."
-groups = ["default"]
+groups = ["default", "latlon-domain-crop"]
 files = [
     {file = "urllib3-2.2.2-py3-none-any.whl", hash = "sha256:a448b2f64d686155468037e1ace9f2d2199776e17f0a46610480d311f73e3472"},
     {file = "urllib3-2.2.2.tar.gz", hash = "sha256:dd505485549a7a552833da5e6063639d0d177c04f23bc3864e41e5dc5f612168"},
@@ -1198,7 +2557,6 @@ name = "wcwidth"
 version = "0.2.13"
 summary = "Measures the displayed width of unicode strings in a terminal"
 groups = ["dev"]
-marker = "python_version > \"3.6\""
 dependencies = [
     "backports-functools-lru-cache>=1.2.1; python_version < \"3.2\"",
 ]
@@ -1240,7 +2598,7 @@ name = "yarl"
 version = "1.9.4"
 requires_python = ">=3.7"
 summary = "Yet another URL library"
-groups = ["default"]
+groups = ["default", "latlon-domain-crop"]
 dependencies = [
     "idna>=2.0",
     "multidict>=4.0",
@@ -1318,7 +2676,7 @@ name = "zipp"
 version = "3.19.2"
 requires_python = ">=3.8"
 summary = "Backport of pathlib-compatible object wrapper for zip files"
-groups = ["default"]
+groups = ["default", "dev", "latlon-domain-crop"]
 marker = "python_version < \"3.12\""
 files = [
     {file = "zipp-3.19.2-py3-none-any.whl", hash = "sha256:f091755f667055f2d02b32c53771a7a6c8b47e1fdbc4b72a8b9072b3eef8015c"},
diff --git a/pyproject.toml b/pyproject.toml
index 9c65473..4891fa5 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -32,6 +32,12 @@ dask-distributed = [
     "dask[distributed]>=2024.7.1",
     "bokeh!=3.0.*,>=2.4.2",
 ]
+latlon-domain-crop = [
+    "spherical-geometry>=1.3.2",
+    "cartopy>=0.23.0",
+    "gcsfs>=2024.10.0",
+    "jinja2>=3.1.4",
+]
 [build-system]
 requires = ["pdm-backend"]
 build-backend = "pdm.backend"
@@ -47,4 +53,5 @@ dev = [
     "pytest>=8.0.2",
     "ipdb>=0.13.13",
     "pre-commit>=3.7.1",
+    "ipykernel>=6.29.5",
 ]

From 9a3b5ad0196c292f57e9a0270f5f8be092d56743 Mon Sep 17 00:00:00 2001
From: Leif Denby <leif@denby.eu>
Date: Tue, 10 Dec 2024 17:04:35 +0100
Subject: [PATCH 02/28] expose xlim/ylim and add config creation for testdata

---
 tests/data.py | 214 +++++++++++++++++++++++++++++++++++++++++++++-----
 1 file changed, 193 insertions(+), 21 deletions(-)

diff --git a/tests/data.py b/tests/data.py
index 78739ee..fdd2c77 100644
--- a/tests/data.py
+++ b/tests/data.py
@@ -1,10 +1,14 @@
 import uuid
+from pathlib import Path
+from typing import List
 
 import isodate
 import numpy as np
 import pandas as pd
 import xarray as xr
 
+import mllam_data_prep as mdp
+
 SCHEMA_VERSION = "v0.5.0"
 
 NX, NY = 10, 8
@@ -29,9 +33,17 @@
     "static",
 ]
 
+DEFAULT_XLIM = DEFAULT_YLIM = DEFAULT_ZLIM = (0.0, 1.0)
+
 
 def create_surface_forecast_dataset(
-    nt_analysis, nt_forecast, nx, ny, var_names=DEFAULT_FORECAST_VARS
+    nt_analysis,
+    nt_forecast,
+    nx,
+    ny,
+    var_names=DEFAULT_FORECAST_VARS,
+    xlim=DEFAULT_XLIM,
+    ylim=DEFAULT_YLIM,
 ):
     """
     Create a fake forecast dataset with `nt_analysis` analysis times, `nt_forecast`
@@ -44,8 +56,8 @@ def create_surface_forecast_dataset(
         T_START, periods=nt_forecast, freq=DT_FORECAST
     ).tz_localize(None)
 
-    x = np.arange(nx)
-    y = np.arange(ny)
+    x = np.linspace(*xlim, nx)
+    y = np.linspace(*ylim, ny)
 
     dataarrays = {}
     for var_name in var_names:
@@ -66,7 +78,12 @@ def create_surface_forecast_dataset(
 
 
 def create_surface_analysis_dataset(
-    nt_analysis, nx, ny, var_names=DEFAULT_SURFACE_ANALYSIS_VARS
+    nt_analysis,
+    nx,
+    ny,
+    var_names=DEFAULT_SURFACE_ANALYSIS_VARS,
+    xlim=DEFAULT_XLIM,
+    ylim=DEFAULT_YLIM,
 ):
     """
     Create a fake analysis dataset with `nt_analysis` analysis times, `nx` grid points
@@ -76,8 +93,8 @@ def create_surface_analysis_dataset(
         T_START, periods=nt_analysis, freq=DT_ANALYSIS
     ).tz_localize(None)
 
-    x = np.arange(nx)
-    y = np.arange(ny)
+    x = np.linspace(*xlim, nx)
+    y = np.linspace(*ylim, ny)
 
     dataarrays = {}
     for var_name in var_names:
@@ -103,6 +120,9 @@ def create_analysis_dataset_on_levels(
     nz,
     level_dim="altitude",
     var_names=DEFAULT_ATMOSPHERIC_ANALYSIS_VARS,
+    xlim=DEFAULT_XLIM,
+    ylim=DEFAULT_YLIM,
+    zlim=DEFAULT_ZLIM,
 ):
     """
     Create a fake analysis dataset with `nt_analysis` analysis times, `nx` grid points in x-direction,
@@ -120,14 +140,25 @@ def create_analysis_dataset_on_levels(
         Number of levels
     level_dim : str, optional
         Name of the level dimension, by default "altitude"
+    xlim : tuple, optional
+        Tuple of the form (xmin, xmax) defining the x-limits, by default DEFAULT_XLIM
+    ylim : tuple, optional
+        Tuple of the form (ymin, ymax) defining the y-limits, by default DEFAULT_YLIM
+    zlim : tuple, optional
+        Tuple of the form (zmin, zmax) defining the z-limits, by default DEFAULT_ZLIM
+
+    Returns
+    -------
+    xarray.Dataset
+        The created dataset
     """
     ts_analysis = pd.date_range(
         T_START, periods=nt_analysis, freq=DT_ANALYSIS
     ).tz_localize(None)
 
-    x = np.arange(nx)
-    y = np.arange(ny)
-    z = np.arange(nz)
+    x = np.linspace(*xlim, nx)
+    y = np.linspace(*ylim, ny)
+    z = np.linspace(*zlim, nz)
 
     dataarrays = {}
     for var_name in var_names:
@@ -155,6 +186,9 @@ def create_forecast_dataset_on_levels(
     nz,
     level_dim="altitude",
     var_names=DEFAULT_FORECAST_VARS,
+    xlim=DEFAULT_XLIM,
+    ylim=DEFAULT_YLIM,
+    zlim=DEFAULT_ZLIM,
 ):
     """
     Create a fake forecast dataset with `nt_analysis` analysis times, `nt_forecast`
@@ -175,6 +209,17 @@ def create_forecast_dataset_on_levels(
         Number of levels
     level_dim : str, optional
         Name of the level dimension, by default "altitude"
+    xlim : tuple, optional
+        Tuple of the form (xmin, xmax) defining the x-limits, by default DEFAULT_XLIM
+    ylim : tuple, optional
+        Tuple of the form (ymin, ymax) defining the y-limits, by default DEFAULT_YLIM
+    zlim : tuple, optional
+        Tuple of the form (zmin, zmax) defining the z-limits, by default DEFAULT_ZLIM
+
+    Returns
+    -------
+    xarray.Dataset
+        The created dataset
     """
 
     ts_analysis = pd.date_range(
@@ -184,9 +229,9 @@ def create_forecast_dataset_on_levels(
         T_START, periods=nt_forecast, freq=DT_FORECAST
     ).tz_localize(None)
 
-    x = np.arange(nx)
-    y = np.arange(ny)
-    z = np.arange(nz)
+    x = np.linspace(*xlim, nx)
+    y = np.linspace(*ylim, ny)
+    z = np.linspace(*zlim, nz)
 
     dataarrays = {}
     for var_name in var_names:
@@ -207,12 +252,32 @@ def create_forecast_dataset_on_levels(
     return ds
 
 
-def create_static_dataset(nx, ny, var_names=DEFAULT_STATIC_VARS):
+def create_static_dataset(
+    nx, ny, var_names=DEFAULT_STATIC_VARS, xlim=DEFAULT_XLIM, ylim=DEFAULT_YLIM
+):
     """
     Create a fake static dataset with `nx` grid points in x-direction and `ny` grid points in y-direction.
+
+    Parameters
+    ----------
+    nx : int
+        Number of grid points in x-direction
+    ny : int
+        Number of grid points in y-direction
+    var_names : list, optional
+        List of variable names to create, by default DEFAULT_STATIC_VARS
+    xlim : tuple, optional
+        Tuple of the form (xmin, xmax) defining the x-limits, by default DEFAULT_XLIM
+    ylim : tuple, optional
+        Tuple of the form (ymin, ymax) defining the y-limits, by default DEFAULT_YLIM
+
+    Returns
+    -------
+    xarray.Dataset
+        The created dataset
     """
-    x = np.arange(nx)
-    y = np.arange(ny)
+    x = np.linspace(*xlim, nx)
+    y = np.linspace(*ylim, ny)
 
     dataarrays = {}
     for var_name in var_names:
@@ -230,7 +295,7 @@ def create_static_dataset(nx, ny, var_names=DEFAULT_STATIC_VARS):
     return ds
 
 
-def create_data_collection(data_kinds, fp_root):
+def create_data_collection(data_kinds, fp_root, xlim=DEFAULT_XLIM, ylim=DEFAULT_YLIM):
     """
     Create a fake data collection with the given `data_kinds` and save it to `fp_root`, with
     each dataset having the `data_kind` name with a unique suffix and saved in `.zarr` format.
@@ -259,15 +324,23 @@ def create_data_collection(data_kinds, fp_root):
 
     for data_kind in data_kinds:
         if data_kind == "surface_forecast":
-            ds = create_surface_forecast_dataset(NT_ANALYSIS, NT_FORECAST, NX, NY)
+            ds = create_surface_forecast_dataset(
+                NT_ANALYSIS, NT_FORECAST, NX, NY, xlim=xlim, ylim=ylim
+            )
         elif data_kind == "surface_analysis":
-            ds = create_surface_analysis_dataset(NT_ANALYSIS, NX, NY)
+            ds = create_surface_analysis_dataset(
+                NT_ANALYSIS, NX, NY, xlim=xlim, ylim=ylim
+            )
         elif data_kind == "analysis_on_levels":
-            ds = create_analysis_dataset_on_levels(NT_ANALYSIS, NX, NY, NZ)
+            ds = create_analysis_dataset_on_levels(
+                NT_ANALYSIS, NX, NY, NZ, xlim=xlim, ylim=ylim
+            )
         elif data_kind == "forecast_on_levels":
-            ds = create_forecast_dataset_on_levels(NT_ANALYSIS, NT_FORECAST, NX, NY, NZ)
+            ds = create_forecast_dataset_on_levels(
+                NT_ANALYSIS, NT_FORECAST, NX, NY, NZ, xlim=xlim, ylim=ylim
+            )
         elif data_kind == "static":
-            ds = create_static_dataset(NX, NY)
+            ds = create_static_dataset(NX, NY, xlim=xlim, ylim=ylim)
         else:
             raise ValueError(f"Unknown data kind: {data_kind}")
 
@@ -279,3 +352,102 @@ def create_data_collection(data_kinds, fp_root):
         datasets[data_kind] = fp
 
     return datasets
+
+
+def create_input_datasets_and_config(
+    identifier: str,
+    tmpdir: Path,
+    data_categories: List[str],
+    xlim: List[float] = DEFAULT_XLIM,
+    ylim: List[float] = DEFAULT_YLIM,
+):
+    """
+    Create a config and input datasets with test data for it with a given set
+    of data categories.
+
+    Parameters
+    ----------
+    identifier : str
+        Named identifier for the data collection
+    tmpdir : Path
+        Temporary directory to save the data collection
+    data_catagories : List[str]
+        List of categories of data to create, from state/forcing/static.
+    xlim : List[float], optional
+        List of the form [xmin, xmax] defining the x-limits, by default DEFAULT_XLIM
+    ylim : List[float], optional
+        List of the form [ymin, ymax] defining the y-limits, by default DEFAULT_YLIM
+
+    Returns
+    -------
+    mdp.Config
+        The created config
+    """
+
+    output_variables = {}
+    inputs = {}
+
+    for data_category in data_categories:
+        input_dims = []
+        output_dims = []
+        if data_category in ["state", "forcing"]:
+            data_kinds = ["surface_analysis"]
+            variable_names = DEFAULT_SURFACE_ANALYSIS_VARS
+            input_dims.append("analysis_time")
+            output_dims.append("time")
+        elif data_category == "static":
+            data_kinds = ["static"]
+            variable_names = DEFAULT_STATIC_VARS
+        else:
+            raise NotImplementedError(f"Unknown data category: {data_category}")
+        input_dims.extend(["x", "y"])
+        output_dims += ["grid_index", f"{data_category}_feature"]
+
+        datasets = create_data_collection(
+            data_kinds=data_kinds,
+            fp_root=Path(tmpdir.name) / identifier,
+            xlim=xlim,
+            ylim=ylim,
+        )
+
+        assert len(datasets) == 1
+        dataset_kind, dataset_path = datasets.popitem()
+
+        output_variables[data_category] = output_dims
+        dim_mapping = {}
+
+        for d in output_dims:
+            if d == "time":
+                dim_mapping[d] = mdp.config.DimMapping(
+                    method="rename",
+                    dim="analysis_time",
+                )
+            elif d == "grid_index":
+                dim_mapping[d] = mdp.config.DimMapping(
+                    method="stack",
+                    dims=["x", "y"],
+                )
+            else:
+                dim_mapping[d] = mdp.config.DimMapping(
+                    method="stack_variables_by_var_name",
+                    name_format="{var_name}",
+                )
+
+        inputs[f"{identifier}_{dataset_kind}"] = mdp.config.InputDataset(
+            path=dataset_path,
+            dims=input_dims,
+            variables=variable_names,
+            dim_mapping=dim_mapping,
+            target_output_variable=data_category,
+        )
+
+    config = mdp.Config(
+        schema_version=SCHEMA_VERSION,
+        dataset_version="v0.1.0",
+        output=mdp.config.Output(
+            variables=output_variables,
+        ),
+        inputs=inputs,
+    )
+
+    return config

From 4cda6428dd6bca005e7ca992d00bb5215cd9ad30 Mon Sep 17 00:00:00 2001
From: Leif Denby <leif@denby.eu>
Date: Tue, 10 Dec 2024 20:47:01 +0100
Subject: [PATCH 03/28] convex hull mask creation as DataArray complete

---
 docs/domain-cropping-mllam.ipynb          | 1731 +++++++++++++++++++++
 docs/domain-cropping.ipynb                | 1285 ++++++++-------
 mllam_data_prep/ops/cropping.py           |  113 ++
 tests/test_latlon_convex_hull_cropping.py |   31 +
 4 files changed, 2561 insertions(+), 599 deletions(-)
 create mode 100644 docs/domain-cropping-mllam.ipynb
 create mode 100644 mllam_data_prep/ops/cropping.py
 create mode 100644 tests/test_latlon_convex_hull_cropping.py

diff --git a/docs/domain-cropping-mllam.ipynb b/docs/domain-cropping-mllam.ipynb
new file mode 100644
index 0000000..95def46
--- /dev/null
+++ b/docs/domain-cropping-mllam.ipynb
@@ -0,0 +1,1731 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import tests.data as testdata\n",
+    "import tempfile\n",
+    "\n",
+    "import xarray as xr\n",
+    "import matplotlib.pyplot as plt\n",
+    "import cartopy.crs as ccrs\n",
+    "\n",
+    "import mllam_data_prep as mdp\n",
+    "import tests.data as testdata\n",
+    "from mllam_data_prep.ops import cropping    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tmpdir = tempfile.TemporaryDirectory()\n",
+    "l = 500 * 1.0e3  # length and width of domain in meters\n",
+    "config_lam = testdata.create_input_datasets_and_config(\n",
+    "    identifier=\"lam\",\n",
+    "    data_categories=[\"state\"],\n",
+    "    tmpdir=tmpdir,\n",
+    "    xlim=[-l/2.0, l/2.0],\n",
+    "    ylim=[-l/2.0, l/2.0],\n",
+    ")\n",
+    "# make the global domain twice as large as the LAM domain so that the lam\n",
+    "# domain is contained within the global domain\n",
+    "config_global = testdata.create_input_datasets_and_config(\n",
+    "    identifier=\"global\",\n",
+    "    data_categories=[\"state\"],\n",
+    "    tmpdir=tmpdir,\n",
+    "    xlim=[-l, l],\n",
+    "    ylim=[-l, l]\n",
+    ")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\u001b[32m2024-12-10 17:02:17.477\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmllam_data_prep.create_dataset\u001b[0m:\u001b[36mcreate_dataset\u001b[0m:\u001b[36m135\u001b[0m - \u001b[1mLoading dataset lam_surface_analysis from /var/folders/n8/kcprfkd918967drydbc_bphw0000gr/T/tmp6h_c0_ze/lam/surface_analysis_ca45dbbf-5bbd-42f3-980f-55f6abe03466.zarr\u001b[0m\n",
+      "\u001b[32m2024-12-10 17:02:17.481\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmllam_data_prep.create_dataset\u001b[0m:\u001b[36mcreate_dataset\u001b[0m:\u001b[36m160\u001b[0m - \u001b[1mMapping dimensions and variables for dataset lam_surface_analysis to state\u001b[0m\n",
+      "\u001b[32m2024-12-10 17:02:17.489\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmllam_data_prep.create_dataset\u001b[0m:\u001b[36m_merge_dataarrays_by_target\u001b[0m:\u001b[36m48\u001b[0m - \u001b[1mMerging dataarrays for target variable `state`\u001b[0m\n",
+      "\u001b[32m2024-12-10 17:02:17.499\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmllam_data_prep.create_dataset\u001b[0m:\u001b[36mcreate_dataset\u001b[0m:\u001b[36m195\u001b[0m - \u001b[1mChunking dataset with {}\u001b[0m\n",
+      "\u001b[32m2024-12-10 17:02:17.500\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmllam_data_prep.create_dataset\u001b[0m:\u001b[36mcreate_dataset\u001b[0m:\u001b[36m135\u001b[0m - \u001b[1mLoading dataset global_surface_analysis from /var/folders/n8/kcprfkd918967drydbc_bphw0000gr/T/tmp6h_c0_ze/global/surface_analysis_c93d0634-bd93-4b36-9708-d180d7f57fe0.zarr\u001b[0m\n",
+      "\u001b[32m2024-12-10 17:02:17.510\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmllam_data_prep.create_dataset\u001b[0m:\u001b[36mcreate_dataset\u001b[0m:\u001b[36m160\u001b[0m - \u001b[1mMapping dimensions and variables for dataset global_surface_analysis to state\u001b[0m\n",
+      "\u001b[32m2024-12-10 17:02:17.513\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmllam_data_prep.create_dataset\u001b[0m:\u001b[36m_merge_dataarrays_by_target\u001b[0m:\u001b[36m48\u001b[0m - \u001b[1mMerging dataarrays for target variable `state`\u001b[0m\n",
+      "\u001b[32m2024-12-10 17:02:17.516\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmllam_data_prep.create_dataset\u001b[0m:\u001b[36mcreate_dataset\u001b[0m:\u001b[36m195\u001b[0m - \u001b[1mChunking dataset with {}\u001b[0m\n"
+     ]
+    }
+   ],
+   "source": [
+    "ds_lam = mdp.create_dataset(config=config_lam)\n",
+    "ds_global = mdp.create_dataset(config=config_global)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
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+       "  grid-column: 3;\n",
+       "  text-align: right;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-preview {\n",
+       "  grid-column: 4;\n",
+       "}\n",
+       "\n",
+       ".xr-index-preview {\n",
+       "  grid-column: 2 / 5;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name,\n",
+       ".xr-var-dims,\n",
+       ".xr-var-dtype,\n",
+       ".xr-preview,\n",
+       ".xr-attrs dt {\n",
+       "  white-space: nowrap;\n",
+       "  overflow: hidden;\n",
+       "  text-overflow: ellipsis;\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name:hover,\n",
+       ".xr-var-dims:hover,\n",
+       ".xr-var-dtype:hover,\n",
+       ".xr-attrs dt:hover {\n",
+       "  overflow: visible;\n",
+       "  width: auto;\n",
+       "  z-index: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  display: none;\n",
+       "  background-color: var(--xr-background-color) !important;\n",
+       "  padding-bottom: 5px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+       ".xr-var-data-in:checked ~ .xr-var-data,\n",
+       ".xr-index-data-in:checked ~ .xr-index-data {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > table {\n",
+       "  float: right;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-index-name div,\n",
+       ".xr-index-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 125px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt,\n",
+       ".xr-attrs dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2,\n",
+       ".xr-no-icon {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 5kB\n",
+       "Dimensions:                       (time: 5, grid_index: 80, state_feature: 1)\n",
+       "Coordinates:\n",
+       "  * time                          (time) datetime64[ns] 40B 2000-01-01 ... 20...\n",
+       "    x                             (grid_index) float64 640B dask.array&lt;chunksize=(80,), meta=np.ndarray&gt;\n",
+       "    y                             (grid_index) float64 640B dask.array&lt;chunksize=(80,), meta=np.ndarray&gt;\n",
+       "  * state_feature                 (state_feature) &lt;U15 60B &#x27;pres_seasurface&#x27;\n",
+       "    state_feature_units           (state_feature) &lt;U1 4B dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;\n",
+       "    state_feature_long_name       (state_feature) &lt;U1 4B dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;\n",
+       "    state_feature_source_dataset  (state_feature) &lt;U20 80B dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;\n",
+       "Dimensions without coordinates: grid_index\n",
+       "Data variables:\n",
+       "    state                         (state_feature, time, grid_index) float64 3kB dask.array&lt;chunksize=(1, 5, 80), meta=np.ndarray&gt;\n",
+       "Attributes:\n",
+       "    schema_version:   v0.5.0\n",
+       "    dataset_version:  v0.1.0\n",
+       "    created_on:       2024-12-10T17:02:17\n",
+       "    created_with:     mllam-data-prep (https://github.com/mllam/mllam-data-prep)\n",
+       "    mdp_version:      v0.5.0</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-bd4acf93-d08d-472f-bd32-502780773664' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-bd4acf93-d08d-472f-bd32-502780773664' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 5</li><li><span>grid_index</span>: 80</li><li><span class='xr-has-index'>state_feature</span>: 1</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-36da9a63-86e0-4c8b-b364-56f5d772c314' class='xr-section-summary-in' type='checkbox'  checked><label for='section-36da9a63-86e0-4c8b-b364-56f5d772c314' class='xr-section-summary' >Coordinates: <span>(7)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2000-01-01 ... 2000-01-02</div><input id='attrs-ac004efc-31d3-4c55-9bc1-e1bd22155cdd' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ac004efc-31d3-4c55-9bc1-e1bd22155cdd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-639ef3ee-26bf-4b1e-9788-ab7d1b6bb590' class='xr-var-data-in' type='checkbox'><label for='data-639ef3ee-26bf-4b1e-9788-ab7d1b6bb590' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;2000-01-01T00:00:00.000000000&#x27;, &#x27;2000-01-01T06:00:00.000000000&#x27;,\n",
+       "       &#x27;2000-01-01T12:00:00.000000000&#x27;, &#x27;2000-01-01T18:00:00.000000000&#x27;,\n",
+       "       &#x27;2000-01-02T00:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>x</span></div><div class='xr-var-dims'>(grid_index)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(80,), meta=np.ndarray&gt;</div><input id='attrs-888c2688-34fc-44a7-a225-8e44e95449d8' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-888c2688-34fc-44a7-a225-8e44e95449d8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-258f52a6-89ee-42c5-983c-2c35d355e5d9' class='xr-var-data-in' type='checkbox'><label for='data-258f52a6-89ee-42c5-983c-2c35d355e5d9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 640 B </td>\n",
+       "                        <td> 640 B </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (80,) </td>\n",
+       "                        <td> (80,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 1 graph layer </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"76\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"26\" x2=\"120\" y2=\"26\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"26\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"26\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,26.512733859679475 0.0,26.512733859679475\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"46.512734\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >80</text>\n",
+       "  <text x=\"140.000000\" y=\"13.256367\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,13.256367)\">1</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>y</span></div><div class='xr-var-dims'>(grid_index)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(80,), meta=np.ndarray&gt;</div><input id='attrs-a2fc4064-f71e-49d6-b7ac-10d147b661ba' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a2fc4064-f71e-49d6-b7ac-10d147b661ba' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a5babf03-3391-42ff-983a-6ee54a4fc4f6' class='xr-var-data-in' type='checkbox'><label for='data-a5babf03-3391-42ff-983a-6ee54a4fc4f6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 640 B </td>\n",
+       "                        <td> 640 B </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (80,) </td>\n",
+       "                        <td> (80,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 1 graph layer </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"76\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"26\" x2=\"120\" y2=\"26\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"26\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"26\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,26.512733859679475 0.0,26.512733859679475\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"46.512734\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >80</text>\n",
+       "  <text x=\"140.000000\" y=\"13.256367\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,13.256367)\">1</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>state_feature</span></div><div class='xr-var-dims'>(state_feature)</div><div class='xr-var-dtype'>&lt;U15</div><div class='xr-var-preview xr-preview'>&#x27;pres_seasurface&#x27;</div><input id='attrs-76d039e4-886b-4fdb-80dd-b65084f63dde' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-76d039e4-886b-4fdb-80dd-b65084f63dde' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0a32e3c6-fd46-46b6-912c-4e74a0a8929e' class='xr-var-data-in' type='checkbox'><label for='data-0a32e3c6-fd46-46b6-912c-4e74a0a8929e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;pres_seasurface&#x27;], dtype=&#x27;&lt;U15&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>state_feature_units</span></div><div class='xr-var-dims'>(state_feature)</div><div class='xr-var-dtype'>&lt;U1</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;</div><input id='attrs-95043526-328b-4695-b4cc-b65fd419a93c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-95043526-328b-4695-b4cc-b65fd419a93c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-50ba9af0-9724-4804-b0a6-9e3e0d10530a' class='xr-var-data-in' type='checkbox'><label for='data-50ba9af0-9724-4804-b0a6-9e3e0d10530a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 4 B </td>\n",
+       "                        <td> 4 B </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (1,) </td>\n",
+       "                        <td> (1,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 1 graph layer </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> <U1 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"120\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1</text>\n",
+       "  <text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,60.000000)\">1</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>state_feature_long_name</span></div><div class='xr-var-dims'>(state_feature)</div><div class='xr-var-dtype'>&lt;U1</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;</div><input id='attrs-c82e228a-8da6-44a6-850f-e4bdae3dd371' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c82e228a-8da6-44a6-850f-e4bdae3dd371' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a6098df1-6903-499c-8627-b7a78342a7a2' class='xr-var-data-in' type='checkbox'><label for='data-a6098df1-6903-499c-8627-b7a78342a7a2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 4 B </td>\n",
+       "                        <td> 4 B </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (1,) </td>\n",
+       "                        <td> (1,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 1 graph layer </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> <U1 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"120\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1</text>\n",
+       "  <text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,60.000000)\">1</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>state_feature_source_dataset</span></div><div class='xr-var-dims'>(state_feature)</div><div class='xr-var-dtype'>&lt;U20</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;</div><input id='attrs-56cd4af8-a4c0-4f1b-a8c1-fb192655f82c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-56cd4af8-a4c0-4f1b-a8c1-fb192655f82c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6e58fca1-1d5d-4169-a439-b976c2002aab' class='xr-var-data-in' type='checkbox'><label for='data-6e58fca1-1d5d-4169-a439-b976c2002aab' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 80 B </td>\n",
+       "                        <td> 80 B </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (1,) </td>\n",
+       "                        <td> (1,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 1 graph layer </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> <U20 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
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+       "  <line x1=\"0\" y1=\"120\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1</text>\n",
+       "  <text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,60.000000)\">1</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-b958e553-8c03-49e9-a1ab-c6b38c23123b' class='xr-section-summary-in' type='checkbox'  checked><label for='section-b958e553-8c03-49e9-a1ab-c6b38c23123b' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>state</span></div><div class='xr-var-dims'>(state_feature, time, grid_index)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 5, 80), meta=np.ndarray&gt;</div><input id='attrs-5b3a306e-b2f7-4c08-ae70-23303a86ec49' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5b3a306e-b2f7-4c08-ae70-23303a86ec49' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-de259f88-7136-4207-9ee0-9cfd8cdb28b2' class='xr-var-data-in' type='checkbox'><label for='data-de259f88-7136-4207-9ee0-9cfd8cdb28b2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>variables_mapping_dim :</span></dt><dd>state_feature</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 3.12 kiB </td>\n",
+       "                        <td> 3.12 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (1, 5, 80) </td>\n",
+       "                        <td> (1, 5, 80) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 4 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"195\" height=\"101\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
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+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"25\" y2=\"15\" style=\"stroke-width:2\" />\n",
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+       "\n",
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+       "  <line x1=\"25\" y1=\"15\" x2=\"25\" y2=\"51\" style=\"stroke-width:2\" />\n",
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+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"85.595726\" y=\"71.455273\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >80</text>\n",
+       "  <text x=\"165.595726\" y=\"33.525499\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,165.595726,33.525499)\">5</text>\n",
+       "  <text x=\"7.797863\" y=\"63.657410\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,7.797863,63.657410)\">1</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-f34d7a0e-3e8d-4267-ae41-a363e8e9f0cb' class='xr-section-summary-in' type='checkbox'  ><label for='section-f34d7a0e-3e8d-4267-ae41-a363e8e9f0cb' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-1fce4a56-d2cb-486d-81c9-e3cbae3e66f6' class='xr-index-data-in' type='checkbox'/><label for='index-1fce4a56-d2cb-486d-81c9-e3cbae3e66f6' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2000-01-01 00:00:00&#x27;, &#x27;2000-01-01 06:00:00&#x27;,\n",
+       "               &#x27;2000-01-01 12:00:00&#x27;, &#x27;2000-01-01 18:00:00&#x27;,\n",
+       "               &#x27;2000-01-02 00:00:00&#x27;],\n",
+       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>state_feature</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-2f80c03b-504c-4517-a3dc-d09fa3b66250' class='xr-index-data-in' type='checkbox'/><label for='index-2f80c03b-504c-4517-a3dc-d09fa3b66250' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;pres_seasurface&#x27;], dtype=&#x27;object&#x27;, name=&#x27;state_feature&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-dde30d88-c881-4a5a-8166-f96574213c31' class='xr-section-summary-in' type='checkbox'  checked><label for='section-dde30d88-c881-4a5a-8166-f96574213c31' class='xr-section-summary' >Attributes: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>schema_version :</span></dt><dd>v0.5.0</dd><dt><span>dataset_version :</span></dt><dd>v0.1.0</dd><dt><span>created_on :</span></dt><dd>2024-12-10T17:02:17</dd><dt><span>created_with :</span></dt><dd>mllam-data-prep (https://github.com/mllam/mllam-data-prep)</dd><dt><span>mdp_version :</span></dt><dd>v0.5.0</dd></dl></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.Dataset> Size: 5kB\n",
+       "Dimensions:                       (time: 5, grid_index: 80, state_feature: 1)\n",
+       "Coordinates:\n",
+       "  * time                          (time) datetime64[ns] 40B 2000-01-01 ... 20...\n",
+       "    x                             (grid_index) float64 640B dask.array<chunksize=(80,), meta=np.ndarray>\n",
+       "    y                             (grid_index) float64 640B dask.array<chunksize=(80,), meta=np.ndarray>\n",
+       "  * state_feature                 (state_feature) <U15 60B 'pres_seasurface'\n",
+       "    state_feature_units           (state_feature) <U1 4B dask.array<chunksize=(1,), meta=np.ndarray>\n",
+       "    state_feature_long_name       (state_feature) <U1 4B dask.array<chunksize=(1,), meta=np.ndarray>\n",
+       "    state_feature_source_dataset  (state_feature) <U20 80B dask.array<chunksize=(1,), meta=np.ndarray>\n",
+       "Dimensions without coordinates: grid_index\n",
+       "Data variables:\n",
+       "    state                         (state_feature, time, grid_index) float64 3kB dask.array<chunksize=(1, 5, 80), meta=np.ndarray>\n",
+       "Attributes:\n",
+       "    schema_version:   v0.5.0\n",
+       "    dataset_version:  v0.1.0\n",
+       "    created_on:       2024-12-10T17:02:17\n",
+       "    created_with:     mllam-data-prep (https://github.com/mllam/mllam-data-prep)\n",
+       "    mdp_version:      v0.5.0"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ds_lam"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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+       "  --xr-background-color-row-even: #111111;\n",
+       "  --xr-background-color-row-odd: #313131;\n",
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+       "\n",
+       ".xr-section-item input + label {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label {\n",
+       "  cursor: pointer;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label:hover {\n",
+       "  color: var(--xr-font-color0);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary {\n",
+       "  grid-column: 1;\n",
+       "  color: var(--xr-font-color2);\n",
+       "  font-weight: 500;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary > span {\n",
+       "  display: inline-block;\n",
+       "  padding-left: 0.5em;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in + label:before {\n",
+       "  display: inline-block;\n",
+       "  content: '►';\n",
+       "  font-size: 11px;\n",
+       "  width: 15px;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label:before {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label:before {\n",
+       "  content: '▼';\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label > span {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary,\n",
+       ".xr-section-inline-details {\n",
+       "  padding-top: 4px;\n",
+       "  padding-bottom: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-inline-details {\n",
+       "  grid-column: 2 / -1;\n",
+       "}\n",
+       "\n",
+       ".xr-section-details {\n",
+       "  display: none;\n",
+       "  grid-column: 1 / -1;\n",
+       "  margin-bottom: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap {\n",
+       "  grid-column: 1 / -1;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 20px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap > label {\n",
+       "  grid-column: 1;\n",
+       "  vertical-align: top;\n",
+       "}\n",
+       "\n",
+       ".xr-preview {\n",
+       "  color: var(--xr-font-color3);\n",
+       "}\n",
+       "\n",
+       ".xr-array-preview,\n",
+       ".xr-array-data {\n",
+       "  padding: 0 5px !important;\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-array-data,\n",
+       ".xr-array-in:checked ~ .xr-array-preview {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-array-in:checked ~ .xr-array-data,\n",
+       ".xr-array-preview {\n",
+       "  display: inline-block;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list {\n",
+       "  display: inline-block !important;\n",
+       "  list-style: none;\n",
+       "  padding: 0 !important;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li {\n",
+       "  display: inline-block;\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:before {\n",
+       "  content: '(';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:after {\n",
+       "  content: ')';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li:not(:last-child):after {\n",
+       "  content: ',';\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-has-index {\n",
+       "  font-weight: bold;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list,\n",
+       ".xr-var-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > div,\n",
+       ".xr-var-item label,\n",
+       ".xr-var-item > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-even);\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > .xr-var-name:hover span {\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list > li:nth-child(odd) > div,\n",
+       ".xr-var-list > li:nth-child(odd) > label,\n",
+       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-odd);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name {\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dims {\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dtype {\n",
+       "  grid-column: 3;\n",
+       "  text-align: right;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-preview {\n",
+       "  grid-column: 4;\n",
+       "}\n",
+       "\n",
+       ".xr-index-preview {\n",
+       "  grid-column: 2 / 5;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name,\n",
+       ".xr-var-dims,\n",
+       ".xr-var-dtype,\n",
+       ".xr-preview,\n",
+       ".xr-attrs dt {\n",
+       "  white-space: nowrap;\n",
+       "  overflow: hidden;\n",
+       "  text-overflow: ellipsis;\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name:hover,\n",
+       ".xr-var-dims:hover,\n",
+       ".xr-var-dtype:hover,\n",
+       ".xr-attrs dt:hover {\n",
+       "  overflow: visible;\n",
+       "  width: auto;\n",
+       "  z-index: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  display: none;\n",
+       "  background-color: var(--xr-background-color) !important;\n",
+       "  padding-bottom: 5px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+       ".xr-var-data-in:checked ~ .xr-var-data,\n",
+       ".xr-index-data-in:checked ~ .xr-index-data {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > table {\n",
+       "  float: right;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-index-name div,\n",
+       ".xr-index-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 125px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt,\n",
+       ".xr-attrs dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2,\n",
+       ".xr-no-icon {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 5kB\n",
+       "Dimensions:                       (time: 5, grid_index: 80, state_feature: 1)\n",
+       "Coordinates:\n",
+       "  * time                          (time) datetime64[ns] 40B 2000-01-01 ... 20...\n",
+       "    x                             (grid_index) float64 640B dask.array&lt;chunksize=(80,), meta=np.ndarray&gt;\n",
+       "    y                             (grid_index) float64 640B dask.array&lt;chunksize=(80,), meta=np.ndarray&gt;\n",
+       "  * state_feature                 (state_feature) &lt;U15 60B &#x27;pres_seasurface&#x27;\n",
+       "    state_feature_units           (state_feature) &lt;U1 4B dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;\n",
+       "    state_feature_long_name       (state_feature) &lt;U1 4B dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;\n",
+       "    state_feature_source_dataset  (state_feature) &lt;U23 92B dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;\n",
+       "Dimensions without coordinates: grid_index\n",
+       "Data variables:\n",
+       "    state                         (state_feature, time, grid_index) float64 3kB dask.array&lt;chunksize=(1, 5, 80), meta=np.ndarray&gt;\n",
+       "Attributes:\n",
+       "    schema_version:   v0.5.0\n",
+       "    dataset_version:  v0.1.0\n",
+       "    created_on:       2024-12-10T17:02:17\n",
+       "    created_with:     mllam-data-prep (https://github.com/mllam/mllam-data-prep)\n",
+       "    mdp_version:      v0.5.0</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-75ac721c-8fb2-476c-8b90-99a03fede3d9' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-75ac721c-8fb2-476c-8b90-99a03fede3d9' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 5</li><li><span>grid_index</span>: 80</li><li><span class='xr-has-index'>state_feature</span>: 1</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-3ebbe816-f2ac-4457-9397-e8f9fce98b67' class='xr-section-summary-in' type='checkbox'  checked><label for='section-3ebbe816-f2ac-4457-9397-e8f9fce98b67' class='xr-section-summary' >Coordinates: <span>(7)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2000-01-01 ... 2000-01-02</div><input id='attrs-9b8417fe-6a2a-402b-afd1-0e9db36da452' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9b8417fe-6a2a-402b-afd1-0e9db36da452' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5b36a42b-3572-467c-9fbd-a546ce7e7e27' class='xr-var-data-in' type='checkbox'><label for='data-5b36a42b-3572-467c-9fbd-a546ce7e7e27' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;2000-01-01T00:00:00.000000000&#x27;, &#x27;2000-01-01T06:00:00.000000000&#x27;,\n",
+       "       &#x27;2000-01-01T12:00:00.000000000&#x27;, &#x27;2000-01-01T18:00:00.000000000&#x27;,\n",
+       "       &#x27;2000-01-02T00:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>x</span></div><div class='xr-var-dims'>(grid_index)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(80,), meta=np.ndarray&gt;</div><input id='attrs-8e50702e-f810-4721-90c4-372f33e09fd4' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8e50702e-f810-4721-90c4-372f33e09fd4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-22fcf2aa-f6a6-49c9-9da2-8755bca10c83' class='xr-var-data-in' type='checkbox'><label for='data-22fcf2aa-f6a6-49c9-9da2-8755bca10c83' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 640 B </td>\n",
+       "                        <td> 640 B </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (80,) </td>\n",
+       "                        <td> (80,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 1 graph layer </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"76\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"26\" x2=\"120\" y2=\"26\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"26\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"26\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,26.512733859679475 0.0,26.512733859679475\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"46.512734\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >80</text>\n",
+       "  <text x=\"140.000000\" y=\"13.256367\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,13.256367)\">1</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>y</span></div><div class='xr-var-dims'>(grid_index)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(80,), meta=np.ndarray&gt;</div><input id='attrs-418540bf-c945-48e2-9b2a-f0e3c2926c8d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-418540bf-c945-48e2-9b2a-f0e3c2926c8d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5555329d-c4b6-4aed-9905-750491de1295' class='xr-var-data-in' type='checkbox'><label for='data-5555329d-c4b6-4aed-9905-750491de1295' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 640 B </td>\n",
+       "                        <td> 640 B </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (80,) </td>\n",
+       "                        <td> (80,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 1 graph layer </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"76\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"26\" x2=\"120\" y2=\"26\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"26\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"26\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,26.512733859679475 0.0,26.512733859679475\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"46.512734\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >80</text>\n",
+       "  <text x=\"140.000000\" y=\"13.256367\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,13.256367)\">1</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>state_feature</span></div><div class='xr-var-dims'>(state_feature)</div><div class='xr-var-dtype'>&lt;U15</div><div class='xr-var-preview xr-preview'>&#x27;pres_seasurface&#x27;</div><input id='attrs-8751b2fc-6c00-498b-8a93-eb9ac851c26a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8751b2fc-6c00-498b-8a93-eb9ac851c26a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8da1cb7b-caf8-4996-8d1a-084281f94795' class='xr-var-data-in' type='checkbox'><label for='data-8da1cb7b-caf8-4996-8d1a-084281f94795' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;pres_seasurface&#x27;], dtype=&#x27;&lt;U15&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>state_feature_units</span></div><div class='xr-var-dims'>(state_feature)</div><div class='xr-var-dtype'>&lt;U1</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;</div><input id='attrs-78a8becd-a073-4a22-9e07-a7403f3a8b5f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-78a8becd-a073-4a22-9e07-a7403f3a8b5f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-10c51f6e-bca6-48a0-ae62-ef4da4f564bc' class='xr-var-data-in' type='checkbox'><label for='data-10c51f6e-bca6-48a0-ae62-ef4da4f564bc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 4 B </td>\n",
+       "                        <td> 4 B </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (1,) </td>\n",
+       "                        <td> (1,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 1 graph layer </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> <U1 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"120\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1</text>\n",
+       "  <text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,60.000000)\">1</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>state_feature_long_name</span></div><div class='xr-var-dims'>(state_feature)</div><div class='xr-var-dtype'>&lt;U1</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;</div><input id='attrs-cb7e35ad-81dd-4cab-a370-1be912b9fa33' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-cb7e35ad-81dd-4cab-a370-1be912b9fa33' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-73f27cf7-0088-4587-8b6c-9752e578a618' class='xr-var-data-in' type='checkbox'><label for='data-73f27cf7-0088-4587-8b6c-9752e578a618' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 4 B </td>\n",
+       "                        <td> 4 B </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (1,) </td>\n",
+       "                        <td> (1,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 1 graph layer </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> <U1 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"120\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1</text>\n",
+       "  <text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,60.000000)\">1</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>state_feature_source_dataset</span></div><div class='xr-var-dims'>(state_feature)</div><div class='xr-var-dtype'>&lt;U23</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;</div><input id='attrs-8745f29a-c36e-45cf-af58-03c2c6ea2a93' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8745f29a-c36e-45cf-af58-03c2c6ea2a93' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f5c8c75a-b82a-4561-9062-4dff840499f3' class='xr-var-data-in' type='checkbox'><label for='data-f5c8c75a-b82a-4561-9062-4dff840499f3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 92 B </td>\n",
+       "                        <td> 92 B </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (1,) </td>\n",
+       "                        <td> (1,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 1 graph layer </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> <U23 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"120\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1</text>\n",
+       "  <text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,60.000000)\">1</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-00106609-43ed-441a-9b8e-bb96fffc6276' class='xr-section-summary-in' type='checkbox'  checked><label for='section-00106609-43ed-441a-9b8e-bb96fffc6276' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>state</span></div><div class='xr-var-dims'>(state_feature, time, grid_index)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 5, 80), meta=np.ndarray&gt;</div><input id='attrs-4a13f8c6-e4f7-4d79-bfde-4a949f53ef17' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4a13f8c6-e4f7-4d79-bfde-4a949f53ef17' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dcae873f-05cd-480b-8e85-95c59259bd11' class='xr-var-data-in' type='checkbox'><label for='data-dcae873f-05cd-480b-8e85-95c59259bd11' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>variables_mapping_dim :</span></dt><dd>state_feature</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 3.12 kiB </td>\n",
+       "                        <td> 3.12 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (1, 5, 80) </td>\n",
+       "                        <td> (1, 5, 80) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 4 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"195\" height=\"101\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"25\" y2=\"15\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"10\" y1=\"35\" x2=\"25\" y2=\"51\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"35\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"25\" y1=\"15\" x2=\"25\" y2=\"51\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"10.0,0.0 25.595725799811454,15.595725799811456 25.595725799811454,51.45527314902021 10.0,35.85954734920875\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"25\" y1=\"15\" x2=\"145\" y2=\"15\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"25\" y2=\"15\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"130\" y1=\"0\" x2=\"145\" y2=\"15\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"10.0,0.0 130.0,0.0 145.59572579981145,15.595725799811456 25.595725799811454,15.595725799811456\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"25\" y1=\"15\" x2=\"145\" y2=\"15\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"25\" y1=\"51\" x2=\"145\" y2=\"51\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"25\" y1=\"15\" x2=\"25\" y2=\"51\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"145\" y1=\"15\" x2=\"145\" y2=\"51\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"25.595725799811454,15.595725799811456 145.59572579981145,15.595725799811456 145.59572579981145,51.45527314902021 25.595725799811454,51.45527314902021\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"85.595726\" y=\"71.455273\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >80</text>\n",
+       "  <text x=\"165.595726\" y=\"33.525499\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,165.595726,33.525499)\">5</text>\n",
+       "  <text x=\"7.797863\" y=\"63.657410\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,7.797863,63.657410)\">1</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-576631bc-7cfe-41f7-a835-27fa382bfdd1' class='xr-section-summary-in' type='checkbox'  ><label for='section-576631bc-7cfe-41f7-a835-27fa382bfdd1' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-e66009e2-450e-441f-86b7-2e415b9c9d96' class='xr-index-data-in' type='checkbox'/><label for='index-e66009e2-450e-441f-86b7-2e415b9c9d96' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2000-01-01 00:00:00&#x27;, &#x27;2000-01-01 06:00:00&#x27;,\n",
+       "               &#x27;2000-01-01 12:00:00&#x27;, &#x27;2000-01-01 18:00:00&#x27;,\n",
+       "               &#x27;2000-01-02 00:00:00&#x27;],\n",
+       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>state_feature</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-3ad22165-b896-40f4-b501-d53f43f2b75b' class='xr-index-data-in' type='checkbox'/><label for='index-3ad22165-b896-40f4-b501-d53f43f2b75b' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;pres_seasurface&#x27;], dtype=&#x27;object&#x27;, name=&#x27;state_feature&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-acc2ff92-4a8b-4521-9c9a-0521390223ae' class='xr-section-summary-in' type='checkbox'  checked><label for='section-acc2ff92-4a8b-4521-9c9a-0521390223ae' class='xr-section-summary' >Attributes: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>schema_version :</span></dt><dd>v0.5.0</dd><dt><span>dataset_version :</span></dt><dd>v0.1.0</dd><dt><span>created_on :</span></dt><dd>2024-12-10T17:02:17</dd><dt><span>created_with :</span></dt><dd>mllam-data-prep (https://github.com/mllam/mllam-data-prep)</dd><dt><span>mdp_version :</span></dt><dd>v0.5.0</dd></dl></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.Dataset> Size: 5kB\n",
+       "Dimensions:                       (time: 5, grid_index: 80, state_feature: 1)\n",
+       "Coordinates:\n",
+       "  * time                          (time) datetime64[ns] 40B 2000-01-01 ... 20...\n",
+       "    x                             (grid_index) float64 640B dask.array<chunksize=(80,), meta=np.ndarray>\n",
+       "    y                             (grid_index) float64 640B dask.array<chunksize=(80,), meta=np.ndarray>\n",
+       "  * state_feature                 (state_feature) <U15 60B 'pres_seasurface'\n",
+       "    state_feature_units           (state_feature) <U1 4B dask.array<chunksize=(1,), meta=np.ndarray>\n",
+       "    state_feature_long_name       (state_feature) <U1 4B dask.array<chunksize=(1,), meta=np.ndarray>\n",
+       "    state_feature_source_dataset  (state_feature) <U23 92B dask.array<chunksize=(1,), meta=np.ndarray>\n",
+       "Dimensions without coordinates: grid_index\n",
+       "Data variables:\n",
+       "    state                         (state_feature, time, grid_index) float64 3kB dask.array<chunksize=(1, 5, 80), meta=np.ndarray>\n",
+       "Attributes:\n",
+       "    schema_version:   v0.5.0\n",
+       "    dataset_version:  v0.1.0\n",
+       "    created_on:       2024-12-10T17:02:17\n",
+       "    created_with:     mllam-data-prep (https://github.com/mllam/mllam-data-prep)\n",
+       "    mdp_version:      v0.5.0"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ds_global"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\u001b[32m2024-12-10 17:02:17.553\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mmllam_data_prep.ops.cropping\u001b[0m:\u001b[36m_add_latlon\u001b[0m:\u001b[36m30\u001b[0m - \u001b[33m\u001b[1mCould not find lat/lon coordinates. Assuming equal area projection centered on (lat, lon) = (0, 0) for now. This should be replaced!\u001b[0m\n",
+      "\u001b[32m2024-12-10 17:02:17.557\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mmllam_data_prep.ops.cropping\u001b[0m:\u001b[36m_add_latlon\u001b[0m:\u001b[36m30\u001b[0m - \u001b[33m\u001b[1mCould not find lat/lon coordinates. Assuming equal area projection centered on (lat, lon) = (0, 0) for now. This should be replaced!\u001b[0m\n"
+     ]
+    }
+   ],
+   "source": [
+    "da_interior_mask = cropping.create_convex_hull_mask(ds=ds_global, ds_reference=ds_lam)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "da_interior_mask = da_interior_mask.where(da_interior_mask, drop=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ds_reference = ds_lam\n",
+    "ds = ds_global\n",
+    "\n",
+    "fig, ax = plt.subplots(subplot_kw=dict(projection=ccrs.PlateCarree()))\n",
+    "xr.plot.scatter(ax=ax, ds=ds, x=\"lon\", y=\"lat\", label=\"ds\", transform=ccrs.PlateCarree())\n",
+    "xr.plot.scatter(ax=ax, ds=ds_reference, x=\"lon\", y=\"lat\", label=\"ds_ref\", transform=ccrs.PlateCarree())\n",
+    "xr.plot.scatter(ax=ax, c=da_interior_mask, ds=da_interior_mask.to_dataset(name=\"mask\"), x=\"lon\", y=\"lat\", marker=\"x\", label=\"ds (interior)\")\n",
+    "ax.coastlines()\n",
+    "ax.gridlines(draw_labels=True)\n",
+    "ax.legend()\n",
+    "plt.show()\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "    \n",
+    "    import matplotlib.pyplot as plt\n",
+    "    import cartopy.crs as ccrs\n",
+    "\n",
+    "    # fig, ax = plt.subplots()\n",
+    "    # xr.plot.scatter(ax=ax, ds=ds, x=\"x\", y=\"y\", label=\"ds\")\n",
+    "    # xr.plot.scatter(ax=ax, ds=ds_reference, x=\"x\", y=\"y\", label=\"ds_ref\")\n",
+    "    # xr.plot.scatter(ax=ax, ds=da_interior_mask.to_dataset(name=\"mask\"), x=\"x\", y=\"y\", color=\"blue\", label=\"mask\")\n",
+    "    # ax.legend()\n",
+    "    # plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": ".venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/docs/domain-cropping.ipynb b/docs/domain-cropping.ipynb
index e3f7546..b12e6f1 100644
--- a/docs/domain-cropping.ipynb
+++ b/docs/domain-cropping.ipynb
@@ -418,7 +418,7 @@
        "    u         (altitude, time, y, x) float64 372MB dask.array&lt;chunksize=(1, 100, 256, 256), meta=np.ndarray&gt;\n",
        "    v         (altitude, time, y, x) float64 372MB dask.array&lt;chunksize=(1, 100, 256, 256), meta=np.ndarray&gt;\n",
        "Attributes:\n",
-       "    description:  All prognostic variables for 10-year period on reduced levels</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-b5e9ffe5-bc9f-42d1-a6e3-8b9042eb4793' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-b5e9ffe5-bc9f-42d1-a6e3-8b9042eb4793' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>altitude</span>: 1</li><li><span class='xr-has-index'>y</span>: 589</li><li><span class='xr-has-index'>x</span>: 789</li><li><span class='xr-has-index'>time</span>: 100</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-8d715141-a3f9-4db4-8b3d-dfa8ef9a06ec' class='xr-section-summary-in' type='checkbox'  checked><label for='section-8d715141-a3f9-4db4-8b3d-dfa8ef9a06ec' class='xr-section-summary' >Coordinates: <span>(6)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>altitude</span></div><div class='xr-var-dims'>(altitude)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>100</div><input id='attrs-cbfb8f8a-8ad2-4243-811b-9605123ce6d3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cbfb8f8a-8ad2-4243-811b-9605123ce6d3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c4c9319b-9706-4399-91a9-1b70a8e64839' class='xr-var-data-in' type='checkbox'><label for='data-c4c9319b-9706-4399-91a9-1b70a8e64839' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><pre>array([100])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lat</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 256), meta=np.ndarray&gt;</div><input id='attrs-127057bb-28eb-45bd-9885-c7154e043e90' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-127057bb-28eb-45bd-9885-c7154e043e90' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a4c21d07-f5a1-4040-aee0-12a91c9fcd96' class='xr-var-data-in' type='checkbox'><label for='data-a4c21d07-f5a1-4040-aee0-12a91c9fcd96' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    description:  All prognostic variables for 10-year period on reduced levels</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-9f257df3-6cae-4853-9da5-b638ba9b8322' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-9f257df3-6cae-4853-9da5-b638ba9b8322' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>altitude</span>: 1</li><li><span class='xr-has-index'>y</span>: 589</li><li><span class='xr-has-index'>x</span>: 789</li><li><span class='xr-has-index'>time</span>: 100</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-f86ee731-6581-4ce5-ad26-fab85ffb9d3d' class='xr-section-summary-in' type='checkbox'  checked><label for='section-f86ee731-6581-4ce5-ad26-fab85ffb9d3d' class='xr-section-summary' >Coordinates: <span>(6)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>altitude</span></div><div class='xr-var-dims'>(altitude)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>100</div><input id='attrs-ccc0c7c5-fd9f-4560-a8f2-f6244ee6a32b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ccc0c7c5-fd9f-4560-a8f2-f6244ee6a32b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e2b9a677-9011-4017-9d8e-e409e60e6010' class='xr-var-data-in' type='checkbox'><label for='data-e2b9a677-9011-4017-9d8e-e409e60e6010' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><pre>array([100])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lat</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 256), meta=np.ndarray&gt;</div><input id='attrs-2bdf6b28-6f3a-4ded-bbef-3472e131b097' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2bdf6b28-6f3a-4ded-bbef-3472e131b097' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-253c5792-1529-4812-a72a-bf9c876e3791' class='xr-var-data-in' type='checkbox'><label for='data-253c5792-1529-4812-a72a-bf9c876e3791' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -478,7 +478,7 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 256), meta=np.ndarray&gt;</div><input id='attrs-3754004c-cb47-475f-9a3e-45add3306e82' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3754004c-cb47-475f-9a3e-45add3306e82' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-319b8b91-4db5-40f5-b13a-4bdd37903307' class='xr-var-data-in' type='checkbox'><label for='data-319b8b91-4db5-40f5-b13a-4bdd37903307' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 256), meta=np.ndarray&gt;</div><input id='attrs-8a70168e-ede7-40ae-be11-156970c32541' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8a70168e-ede7-40ae-be11-156970c32541' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4f3b8236-0e20-4d56-9f0c-92402aa6a25c' class='xr-var-data-in' type='checkbox'><label for='data-4f3b8236-0e20-4d56-9f0c-92402aa6a25c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -538,7 +538,7 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1990-09-01 ... 1990-09-13T09:00:00</div><input id='attrs-cccbdd86-26bb-4c92-b4cd-45a615a463e9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-cccbdd86-26bb-4c92-b4cd-45a615a463e9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5515cb80-d0dc-476a-94f1-734fcae2717c' class='xr-var-data-in' type='checkbox'><label for='data-5515cb80-d0dc-476a-94f1-734fcae2717c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;1990-09-01T00:00:00.000000000&#x27;, &#x27;1990-09-01T03:00:00.000000000&#x27;,\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1990-09-01 ... 1990-09-13T09:00:00</div><input id='attrs-13408428-09a3-4052-9c30-1d2f6ea36976' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-13408428-09a3-4052-9c30-1d2f6ea36976' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ee053363-c038-4251-9c21-5b7ee108ff6d' class='xr-var-data-in' type='checkbox'><label for='data-ee053363-c038-4251-9c21-5b7ee108ff6d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;1990-09-01T00:00:00.000000000&#x27;, &#x27;1990-09-01T03:00:00.000000000&#x27;,\n",
        "       &#x27;1990-09-01T06:00:00.000000000&#x27;, &#x27;1990-09-01T09:00:00.000000000&#x27;,\n",
        "       &#x27;1990-09-01T12:00:00.000000000&#x27;, &#x27;1990-09-01T15:00:00.000000000&#x27;,\n",
        "       &#x27;1990-09-01T18:00:00.000000000&#x27;, &#x27;1990-09-01T21:00:00.000000000&#x27;,\n",
@@ -588,9 +588,9 @@
        "       &#x27;1990-09-12T18:00:00.000000000&#x27;, &#x27;1990-09-12T21:00:00.000000000&#x27;,\n",
        "       &#x27;1990-09-13T00:00:00.000000000&#x27;, &#x27;1990-09-13T03:00:00.000000000&#x27;,\n",
        "       &#x27;1990-09-13T06:00:00.000000000&#x27;, &#x27;1990-09-13T09:00:00.000000000&#x27;],\n",
-       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-1.999e+06 ... -2.925e+04</div><input id='attrs-48e09d98-fd9a-4961-9176-4b98e53403ec' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-48e09d98-fd9a-4961-9176-4b98e53403ec' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3f02446e-c244-4d73-8686-4812fa688791' class='xr-var-data-in' type='checkbox'><label for='data-3f02446e-c244-4d73-8686-4812fa688791' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([-1999248.193226, -1996748.193226, -1994248.193226, ...,   -34248.193226,\n",
-       "         -31748.193226,   -29248.193226])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-6.095e+05 -6.07e+05 ... 8.605e+05</div><input id='attrs-7be88893-6aa7-44dc-babf-b178131c1b11' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7be88893-6aa7-44dc-babf-b178131c1b11' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b1909c6a-6fb5-4494-8647-cac66305542a' class='xr-var-data-in' type='checkbox'><label for='data-b1909c6a-6fb5-4494-8647-cac66305542a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([-609541.209133, -607041.209133, -604541.209133, ...,  855458.790867,\n",
-       "        857958.790867,  860458.790867])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-58922503-66dd-4311-926f-6e260a621375' class='xr-section-summary-in' type='checkbox'  checked><label for='section-58922503-66dd-4311-926f-6e260a621375' class='xr-section-summary' >Data variables: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>r</span></div><div class='xr-var-dims'>(altitude, time, y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 256, 256), meta=np.ndarray&gt;</div><input id='attrs-97d38130-c3cf-4837-8f78-0e0ae3ef5306' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-97d38130-c3cf-4837-8f78-0e0ae3ef5306' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a1f6186a-2329-4475-8ba4-947f86e6e259' class='xr-var-data-in' type='checkbox'><label for='data-a1f6186a-2329-4475-8ba4-947f86e6e259' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>level_type :</span></dt><dd>heightAboveGround</dd><dt><span>long_name :</span></dt><dd>Relative humidity</dd><dt><span>shortName :</span></dt><dd>r</dd><dt><span>stepType :</span></dt><dd>instant</dd><dt><span>stepUnits :</span></dt><dd>1</dd><dt><span>typeOfLevel :</span></dt><dd>heightAboveGround</dd><dt><span>units :</span></dt><dd>%</dd></dl></div><div class='xr-var-data'><table>\n",
+       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-1.999e+06 ... -2.925e+04</div><input id='attrs-a6c7d444-9427-4ae8-a067-829124b04464' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a6c7d444-9427-4ae8-a067-829124b04464' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fc319e59-664b-4d60-9acf-dc308c7f5531' class='xr-var-data-in' type='checkbox'><label for='data-fc319e59-664b-4d60-9acf-dc308c7f5531' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([-1999248.193226, -1996748.193226, -1994248.193226, ...,   -34248.193226,\n",
+       "         -31748.193226,   -29248.193226])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-6.095e+05 -6.07e+05 ... 8.605e+05</div><input id='attrs-13eb6525-069c-44dd-bada-d5332bfb81fc' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-13eb6525-069c-44dd-bada-d5332bfb81fc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f86831ed-3636-403a-ba5a-6794149c1b6b' class='xr-var-data-in' type='checkbox'><label for='data-f86831ed-3636-403a-ba5a-6794149c1b6b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([-609541.209133, -607041.209133, -604541.209133, ...,  855458.790867,\n",
+       "        857958.790867,  860458.790867])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-5224701a-3b18-4f04-abb8-e7e30330716f' class='xr-section-summary-in' type='checkbox'  checked><label for='section-5224701a-3b18-4f04-abb8-e7e30330716f' class='xr-section-summary' >Data variables: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>r</span></div><div class='xr-var-dims'>(altitude, time, y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 256, 256), meta=np.ndarray&gt;</div><input id='attrs-5c6afe49-690d-4796-b16c-9dfc815a9836' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5c6afe49-690d-4796-b16c-9dfc815a9836' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a8474546-490c-481c-a98a-8d57a0342894' class='xr-var-data-in' type='checkbox'><label for='data-a8474546-490c-481c-a98a-8d57a0342894' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>level_type :</span></dt><dd>heightAboveGround</dd><dt><span>long_name :</span></dt><dd>Relative humidity</dd><dt><span>shortName :</span></dt><dd>r</dd><dt><span>stepType :</span></dt><dd>instant</dd><dt><span>stepUnits :</span></dt><dd>1</dd><dt><span>typeOfLevel :</span></dt><dd>heightAboveGround</dd><dt><span>units :</span></dt><dd>%</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -694,7 +694,7 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>t</span></div><div class='xr-var-dims'>(altitude, time, y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 256, 256), meta=np.ndarray&gt;</div><input id='attrs-7a6631a0-0185-42e1-8341-fab0947a851e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7a6631a0-0185-42e1-8341-fab0947a851e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a71037cc-e674-4190-af23-35ebcef2c5b3' class='xr-var-data-in' type='checkbox'><label for='data-a71037cc-e674-4190-af23-35ebcef2c5b3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>level_type :</span></dt><dd>heightAboveGround</dd><dt><span>long_name :</span></dt><dd>Temperature</dd><dt><span>shortName :</span></dt><dd>t</dd><dt><span>stepType :</span></dt><dd>instant</dd><dt><span>stepUnits :</span></dt><dd>1</dd><dt><span>typeOfLevel :</span></dt><dd>heightAboveGround</dd><dt><span>units :</span></dt><dd>K</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>t</span></div><div class='xr-var-dims'>(altitude, time, y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 256, 256), meta=np.ndarray&gt;</div><input id='attrs-0f9c07cf-637c-4fc1-8289-9488b698d208' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0f9c07cf-637c-4fc1-8289-9488b698d208' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-135a2950-9c46-4b28-88c8-a77d7513354d' class='xr-var-data-in' type='checkbox'><label for='data-135a2950-9c46-4b28-88c8-a77d7513354d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>level_type :</span></dt><dd>heightAboveGround</dd><dt><span>long_name :</span></dt><dd>Temperature</dd><dt><span>shortName :</span></dt><dd>t</dd><dt><span>stepType :</span></dt><dd>instant</dd><dt><span>stepUnits :</span></dt><dd>1</dd><dt><span>typeOfLevel :</span></dt><dd>heightAboveGround</dd><dt><span>units :</span></dt><dd>K</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -798,7 +798,7 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>u</span></div><div class='xr-var-dims'>(altitude, time, y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 256, 256), meta=np.ndarray&gt;</div><input id='attrs-894b0388-c900-4c83-afe4-fb4c90695801' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-894b0388-c900-4c83-afe4-fb4c90695801' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4b7e992a-5c6f-4d1b-ab6f-8fccd608e8e2' class='xr-var-data-in' type='checkbox'><label for='data-4b7e992a-5c6f-4d1b-ab6f-8fccd608e8e2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>level_type :</span></dt><dd>heightAboveGround</dd><dt><span>long_name :</span></dt><dd>u-component of wind</dd><dt><span>shortName :</span></dt><dd>u</dd><dt><span>stepType :</span></dt><dd>instant</dd><dt><span>stepUnits :</span></dt><dd>1</dd><dt><span>typeOfLevel :</span></dt><dd>heightAboveGround</dd><dt><span>units :</span></dt><dd>m s**-1</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>u</span></div><div class='xr-var-dims'>(altitude, time, y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 256, 256), meta=np.ndarray&gt;</div><input id='attrs-0c105a73-758a-4360-b3b5-a7f5bc832383' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0c105a73-758a-4360-b3b5-a7f5bc832383' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2900e441-f08d-4847-9b39-201e18af2506' class='xr-var-data-in' type='checkbox'><label for='data-2900e441-f08d-4847-9b39-201e18af2506' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>level_type :</span></dt><dd>heightAboveGround</dd><dt><span>long_name :</span></dt><dd>u-component of wind</dd><dt><span>shortName :</span></dt><dd>u</dd><dt><span>stepType :</span></dt><dd>instant</dd><dt><span>stepUnits :</span></dt><dd>1</dd><dt><span>typeOfLevel :</span></dt><dd>heightAboveGround</dd><dt><span>units :</span></dt><dd>m s**-1</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -902,7 +902,7 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>v</span></div><div class='xr-var-dims'>(altitude, time, y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 256, 256), meta=np.ndarray&gt;</div><input id='attrs-500c1538-83e0-4171-83af-558b3be2508e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-500c1538-83e0-4171-83af-558b3be2508e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6eb50b83-a60e-42f9-a85e-c36ea590b133' class='xr-var-data-in' type='checkbox'><label for='data-6eb50b83-a60e-42f9-a85e-c36ea590b133' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>level_type :</span></dt><dd>heightAboveGround</dd><dt><span>long_name :</span></dt><dd>v-component of wind</dd><dt><span>shortName :</span></dt><dd>v</dd><dt><span>stepType :</span></dt><dd>instant</dd><dt><span>stepUnits :</span></dt><dd>1</dd><dt><span>typeOfLevel :</span></dt><dd>heightAboveGround</dd><dt><span>units :</span></dt><dd>m s**-1</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>v</span></div><div class='xr-var-dims'>(altitude, time, y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 256, 256), meta=np.ndarray&gt;</div><input id='attrs-f7c55f69-8c64-492c-a65b-07c1f3cd2a68' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f7c55f69-8c64-492c-a65b-07c1f3cd2a68' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1dcf886f-c4e9-4e04-bbd3-64bce5ce1cf3' class='xr-var-data-in' type='checkbox'><label for='data-1dcf886f-c4e9-4e04-bbd3-64bce5ce1cf3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>level_type :</span></dt><dd>heightAboveGround</dd><dt><span>long_name :</span></dt><dd>v-component of wind</dd><dt><span>shortName :</span></dt><dd>v</dd><dt><span>stepType :</span></dt><dd>instant</dd><dt><span>stepUnits :</span></dt><dd>1</dd><dt><span>typeOfLevel :</span></dt><dd>heightAboveGround</dd><dt><span>units :</span></dt><dd>m s**-1</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -1006,7 +1006,7 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-dd268975-9f27-407b-9173-0cec2cec49f7' class='xr-section-summary-in' type='checkbox'  ><label for='section-dd268975-9f27-407b-9173-0cec2cec49f7' class='xr-section-summary' >Indexes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>altitude</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-0477a632-4106-4daa-a6f9-cf4551bd8492' class='xr-index-data-in' type='checkbox'/><label for='index-0477a632-4106-4daa-a6f9-cf4551bd8492' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([100], dtype=&#x27;int64&#x27;, name=&#x27;altitude&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-820691c0-c176-4cc7-bfae-e4740569a4de' class='xr-index-data-in' type='checkbox'/><label for='index-820691c0-c176-4cc7-bfae-e4740569a4de' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;1990-09-01 00:00:00&#x27;, &#x27;1990-09-01 03:00:00&#x27;,\n",
+       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-1881b840-3d1f-41d1-ba2a-0d30211d1af4' class='xr-section-summary-in' type='checkbox'  ><label for='section-1881b840-3d1f-41d1-ba2a-0d30211d1af4' class='xr-section-summary' >Indexes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>altitude</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-c25c56be-e28e-4de6-9358-1808744c777f' class='xr-index-data-in' type='checkbox'/><label for='index-c25c56be-e28e-4de6-9358-1808744c777f' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([100], dtype=&#x27;int64&#x27;, name=&#x27;altitude&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-9fcd8317-5384-46e9-b153-85e942d48957' class='xr-index-data-in' type='checkbox'/><label for='index-9fcd8317-5384-46e9-b153-85e942d48957' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;1990-09-01 00:00:00&#x27;, &#x27;1990-09-01 03:00:00&#x27;,\n",
        "               &#x27;1990-09-01 06:00:00&#x27;, &#x27;1990-09-01 09:00:00&#x27;,\n",
        "               &#x27;1990-09-01 12:00:00&#x27;, &#x27;1990-09-01 15:00:00&#x27;,\n",
        "               &#x27;1990-09-01 18:00:00&#x27;, &#x27;1990-09-01 21:00:00&#x27;,\n",
@@ -1056,7 +1056,7 @@
        "               &#x27;1990-09-12 18:00:00&#x27;, &#x27;1990-09-12 21:00:00&#x27;,\n",
        "               &#x27;1990-09-13 00:00:00&#x27;, &#x27;1990-09-13 03:00:00&#x27;,\n",
        "               &#x27;1990-09-13 06:00:00&#x27;, &#x27;1990-09-13 09:00:00&#x27;],\n",
-       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>x</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-3a58529f-768e-4ead-b545-336963c4fced' class='xr-index-data-in' type='checkbox'/><label for='index-3a58529f-768e-4ead-b545-336963c4fced' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([-1999248.1932260487, -1996748.1932260487, -1994248.1932260487,\n",
+       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>x</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-ca3ade16-ecca-4c51-a57b-e674418d705e' class='xr-index-data-in' type='checkbox'/><label for='index-ca3ade16-ecca-4c51-a57b-e674418d705e' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([-1999248.1932260487, -1996748.1932260487, -1994248.1932260487,\n",
        "       -1991748.1932260487, -1989248.1932260487, -1986748.1932260487,\n",
        "       -1984248.1932260487, -1981748.1932260487, -1979248.1932260487,\n",
        "       -1976748.1932260487,\n",
@@ -1065,7 +1065,7 @@
        "        -44248.19322604872,  -41748.19322604872,  -39248.19322604872,\n",
        "        -36748.19322604872,  -34248.19322604872, -31748.193226048723,\n",
        "       -29248.193226048723],\n",
-       "      dtype=&#x27;float64&#x27;, name=&#x27;x&#x27;, length=789))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>y</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-2c72a955-4ecc-4187-b4aa-d817696fa489' class='xr-index-data-in' type='checkbox'/><label for='index-2c72a955-4ecc-4187-b4aa-d817696fa489' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([-609541.2091329489, -607041.2091329489, -604541.2091329489,\n",
+       "      dtype=&#x27;float64&#x27;, name=&#x27;x&#x27;, length=789))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>y</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-3ddbec6c-f148-464e-b423-b64064a8444f' class='xr-index-data-in' type='checkbox'/><label for='index-3ddbec6c-f148-464e-b423-b64064a8444f' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([-609541.2091329489, -607041.2091329489, -604541.2091329489,\n",
        "       -602041.2091329489, -599541.2091329489, -597041.2091329489,\n",
        "       -594541.2091329489, -592041.2091329489, -589541.2091329489,\n",
        "       -587041.2091329489,\n",
@@ -1074,7 +1074,7 @@
        "        845458.7908670511,  847958.7908670511,  850458.7908670511,\n",
        "        852958.7908670511,  855458.7908670511,  857958.7908670511,\n",
        "        860458.7908670511],\n",
-       "      dtype=&#x27;float64&#x27;, name=&#x27;y&#x27;, length=589))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-f7ee7580-1cba-48c3-a601-ed1c643a00c7' class='xr-section-summary-in' type='checkbox'  checked><label for='section-f7ee7580-1cba-48c3-a601-ed1c643a00c7' class='xr-section-summary' >Attributes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>description :</span></dt><dd>All prognostic variables for 10-year period on reduced levels</dd></dl></div></li></ul></div></div>"
+       "      dtype=&#x27;float64&#x27;, name=&#x27;y&#x27;, length=589))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-3ab224de-ed1b-4450-ac27-4c4e744cfd61' class='xr-section-summary-in' type='checkbox'  checked><label for='section-3ab224de-ed1b-4450-ac27-4c4e744cfd61' class='xr-section-summary' >Attributes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>description :</span></dt><dd>All prognostic variables for 10-year period on reduced levels</dd></dl></div></li></ul></div></div>"
       ],
       "text/plain": [
        "<xarray.Dataset> Size: 1GB\n",
@@ -1110,6 +1110,14 @@
    "execution_count": 5,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/B280936/git-repos/mllam/mllam-data-prep/.venv/lib/python3.12/site-packages/xarray/core/dataset.py:274: UserWarning: The specified chunks separate the stored chunks along dimension \"time\" starting at index 48. This could degrade performance. Instead, consider rechunking after loading.\n",
+      "  warnings.warn(\n"
+     ]
+    },
     {
      "data": {
       "text/html": [
@@ -1486,34 +1494,34 @@
        "  * longitude                                         (longitude) float64 512B ...\n",
        "  * time                                              (time) datetime64[ns] 748kB ...\n",
        "Data variables: (12/62)\n",
-       "    10m_u_component_of_wind                           (time, longitude, latitude) float32 766MB dask.array&lt;chunksize=(100, 64, 32), meta=np.ndarray&gt;\n",
-       "    10m_v_component_of_wind                           (time, longitude, latitude) float32 766MB dask.array&lt;chunksize=(100, 64, 32), meta=np.ndarray&gt;\n",
-       "    10m_wind_speed                                    (time, longitude, latitude) float32 766MB dask.array&lt;chunksize=(100, 64, 32), meta=np.ndarray&gt;\n",
-       "    2m_dewpoint_temperature                           (time, longitude, latitude) float32 766MB dask.array&lt;chunksize=(100, 64, 32), meta=np.ndarray&gt;\n",
-       "    2m_temperature                                    (time, longitude, latitude) float32 766MB dask.array&lt;chunksize=(100, 64, 32), meta=np.ndarray&gt;\n",
-       "    above_ground                                      (time, level, longitude, latitude) float32 10GB dask.array&lt;chunksize=(100, 13, 64, 32), meta=np.ndarray&gt;\n",
+       "    10m_u_component_of_wind                           (time, longitude, latitude) float32 766MB dask.array&lt;chunksize=(48, 64, 32), meta=np.ndarray&gt;\n",
+       "    10m_v_component_of_wind                           (time, longitude, latitude) float32 766MB dask.array&lt;chunksize=(48, 64, 32), meta=np.ndarray&gt;\n",
+       "    10m_wind_speed                                    (time, longitude, latitude) float32 766MB dask.array&lt;chunksize=(48, 64, 32), meta=np.ndarray&gt;\n",
+       "    2m_dewpoint_temperature                           (time, longitude, latitude) float32 766MB dask.array&lt;chunksize=(48, 64, 32), meta=np.ndarray&gt;\n",
+       "    2m_temperature                                    (time, longitude, latitude) float32 766MB dask.array&lt;chunksize=(48, 64, 32), meta=np.ndarray&gt;\n",
+       "    above_ground                                      (time, level, longitude, latitude) float32 10GB dask.array&lt;chunksize=(48, 13, 64, 32), meta=np.ndarray&gt;\n",
        "    ...                                                ...\n",
-       "    volumetric_soil_water_layer_1                     (time, longitude, latitude) float32 766MB dask.array&lt;chunksize=(100, 64, 32), meta=np.ndarray&gt;\n",
-       "    volumetric_soil_water_layer_2                     (time, longitude, latitude) float32 766MB dask.array&lt;chunksize=(100, 64, 32), meta=np.ndarray&gt;\n",
-       "    volumetric_soil_water_layer_3                     (time, longitude, latitude) float32 766MB dask.array&lt;chunksize=(100, 64, 32), meta=np.ndarray&gt;\n",
-       "    volumetric_soil_water_layer_4                     (time, longitude, latitude) float32 766MB dask.array&lt;chunksize=(100, 64, 32), meta=np.ndarray&gt;\n",
-       "    vorticity                                         (time, level, longitude, latitude) float32 10GB dask.array&lt;chunksize=(100, 13, 64, 32), meta=np.ndarray&gt;\n",
-       "    wind_speed                                        (time, level, longitude, latitude) float32 10GB dask.array&lt;chunksize=(100, 13, 64, 32), meta=np.ndarray&gt;</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-5302ed2b-0db8-482b-87ce-460e9bff9b02' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-5302ed2b-0db8-482b-87ce-460e9bff9b02' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 93544</li><li><span class='xr-has-index'>longitude</span>: 64</li><li><span class='xr-has-index'>latitude</span>: 32</li><li><span class='xr-has-index'>level</span>: 13</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-440b9520-b38a-437e-b005-f89e893519e3' class='xr-section-summary-in' type='checkbox'  checked><label for='section-440b9520-b38a-437e-b005-f89e893519e3' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>latitude</span></div><div class='xr-var-dims'>(latitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-87.19 -81.56 ... 81.56 87.19</div><input id='attrs-65b0eda3-c303-4b91-8109-5cf878b979ce' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-65b0eda3-c303-4b91-8109-5cf878b979ce' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7c5ffd6f-73f2-485a-a7b9-908f931ddc53' class='xr-var-data-in' type='checkbox'><label for='data-7c5ffd6f-73f2-485a-a7b9-908f931ddc53' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([-87.1875, -81.5625, -75.9375, -70.3125, -64.6875, -59.0625, -53.4375,\n",
+       "    volumetric_soil_water_layer_1                     (time, longitude, latitude) float32 766MB dask.array&lt;chunksize=(48, 64, 32), meta=np.ndarray&gt;\n",
+       "    volumetric_soil_water_layer_2                     (time, longitude, latitude) float32 766MB dask.array&lt;chunksize=(48, 64, 32), meta=np.ndarray&gt;\n",
+       "    volumetric_soil_water_layer_3                     (time, longitude, latitude) float32 766MB dask.array&lt;chunksize=(48, 64, 32), meta=np.ndarray&gt;\n",
+       "    volumetric_soil_water_layer_4                     (time, longitude, latitude) float32 766MB dask.array&lt;chunksize=(48, 64, 32), meta=np.ndarray&gt;\n",
+       "    vorticity                                         (time, level, longitude, latitude) float32 10GB dask.array&lt;chunksize=(48, 13, 64, 32), meta=np.ndarray&gt;\n",
+       "    wind_speed                                        (time, level, longitude, latitude) float32 10GB dask.array&lt;chunksize=(48, 13, 64, 32), meta=np.ndarray&gt;</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-af5ca88f-347c-40e3-b01d-6a7688ec9d21' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-af5ca88f-347c-40e3-b01d-6a7688ec9d21' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 93544</li><li><span class='xr-has-index'>longitude</span>: 64</li><li><span class='xr-has-index'>latitude</span>: 32</li><li><span class='xr-has-index'>level</span>: 13</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-8c5fa463-0a2b-4503-9922-54d5003d98fc' class='xr-section-summary-in' type='checkbox'  checked><label for='section-8c5fa463-0a2b-4503-9922-54d5003d98fc' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>latitude</span></div><div class='xr-var-dims'>(latitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-87.19 -81.56 ... 81.56 87.19</div><input id='attrs-bfbf8daa-7071-46be-ae33-5205f82c8da7' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-bfbf8daa-7071-46be-ae33-5205f82c8da7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a2194b3e-ce3d-4e8d-990a-fd7f567669fc' class='xr-var-data-in' type='checkbox'><label for='data-a2194b3e-ce3d-4e8d-990a-fd7f567669fc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([-87.1875, -81.5625, -75.9375, -70.3125, -64.6875, -59.0625, -53.4375,\n",
        "       -47.8125, -42.1875, -36.5625, -30.9375, -25.3125, -19.6875, -14.0625,\n",
        "        -8.4375,  -2.8125,   2.8125,   8.4375,  14.0625,  19.6875,  25.3125,\n",
        "        30.9375,  36.5625,  42.1875,  47.8125,  53.4375,  59.0625,  64.6875,\n",
-       "        70.3125,  75.9375,  81.5625,  87.1875])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>level</span></div><div class='xr-var-dims'>(level)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>50 100 150 200 ... 700 850 925 1000</div><input id='attrs-b875f8b2-b2db-4345-a39b-bd82a45a3afc' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b875f8b2-b2db-4345-a39b-bd82a45a3afc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ef511226-f16e-4ac1-af6a-4465a7675f82' class='xr-var-data-in' type='checkbox'><label for='data-ef511226-f16e-4ac1-af6a-4465a7675f82' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  50,  100,  150,  200,  250,  300,  400,  500,  600,  700,  850,  925,\n",
-       "       1000])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>longitude</span></div><div class='xr-var-dims'>(longitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 5.625 11.25 ... 348.8 354.4</div><input id='attrs-5e8366d9-c922-404d-b466-63899a3a51de' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5e8366d9-c922-404d-b466-63899a3a51de' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-aedc6d56-bc6b-4b8b-981b-2051474eea48' class='xr-var-data-in' type='checkbox'><label for='data-aedc6d56-bc6b-4b8b-981b-2051474eea48' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  0.   ,   5.625,  11.25 ,  16.875,  22.5  ,  28.125,  33.75 ,  39.375,\n",
+       "        70.3125,  75.9375,  81.5625,  87.1875])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>level</span></div><div class='xr-var-dims'>(level)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>50 100 150 200 ... 700 850 925 1000</div><input id='attrs-76cc0fbf-631e-45b5-8b40-e5ce5c19e53e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-76cc0fbf-631e-45b5-8b40-e5ce5c19e53e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-eb82b27a-5c5a-491c-b36f-a18d2d87b752' class='xr-var-data-in' type='checkbox'><label for='data-eb82b27a-5c5a-491c-b36f-a18d2d87b752' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  50,  100,  150,  200,  250,  300,  400,  500,  600,  700,  850,  925,\n",
+       "       1000])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>longitude</span></div><div class='xr-var-dims'>(longitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 5.625 11.25 ... 348.8 354.4</div><input id='attrs-2224ad9d-4634-4eeb-b4ed-30ce6a0a614a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2224ad9d-4634-4eeb-b4ed-30ce6a0a614a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-78e25346-ec58-46cb-96c0-f1043730114b' class='xr-var-data-in' type='checkbox'><label for='data-78e25346-ec58-46cb-96c0-f1043730114b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  0.   ,   5.625,  11.25 ,  16.875,  22.5  ,  28.125,  33.75 ,  39.375,\n",
        "        45.   ,  50.625,  56.25 ,  61.875,  67.5  ,  73.125,  78.75 ,  84.375,\n",
        "        90.   ,  95.625, 101.25 , 106.875, 112.5  , 118.125, 123.75 , 129.375,\n",
        "       135.   , 140.625, 146.25 , 151.875, 157.5  , 163.125, 168.75 , 174.375,\n",
        "       180.   , 185.625, 191.25 , 196.875, 202.5  , 208.125, 213.75 , 219.375,\n",
        "       225.   , 230.625, 236.25 , 241.875, 247.5  , 253.125, 258.75 , 264.375,\n",
        "       270.   , 275.625, 281.25 , 286.875, 292.5  , 298.125, 303.75 , 309.375,\n",
-       "       315.   , 320.625, 326.25 , 331.875, 337.5  , 343.125, 348.75 , 354.375])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1959-01-01 ... 2023-01-10T18:00:00</div><input id='attrs-effe2c5a-aed9-4775-8993-871fa87984bf' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-effe2c5a-aed9-4775-8993-871fa87984bf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-097d1324-373c-4e10-8cf2-2e1fd81bc3db' class='xr-var-data-in' type='checkbox'><label for='data-097d1324-373c-4e10-8cf2-2e1fd81bc3db' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;1959-01-01T00:00:00.000000000&#x27;, &#x27;1959-01-01T06:00:00.000000000&#x27;,\n",
+       "       315.   , 320.625, 326.25 , 331.875, 337.5  , 343.125, 348.75 , 354.375])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1959-01-01 ... 2023-01-10T18:00:00</div><input id='attrs-85b442db-2df3-466b-b800-8ea724843565' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-85b442db-2df3-466b-b800-8ea724843565' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e9666272-6009-4e90-bf05-c7d31a5351c6' class='xr-var-data-in' type='checkbox'><label for='data-e9666272-6009-4e90-bf05-c7d31a5351c6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;1959-01-01T00:00:00.000000000&#x27;, &#x27;1959-01-01T06:00:00.000000000&#x27;,\n",
        "       &#x27;1959-01-01T12:00:00.000000000&#x27;, ..., &#x27;2023-01-10T06:00:00.000000000&#x27;,\n",
        "       &#x27;2023-01-10T12:00:00.000000000&#x27;, &#x27;2023-01-10T18:00:00.000000000&#x27;],\n",
-       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-f90709db-7c0d-4029-b42b-95177f7ca2d6' class='xr-section-summary-in' type='checkbox'  ><label for='section-f90709db-7c0d-4029-b42b-95177f7ca2d6' class='xr-section-summary' >Data variables: <span>(62)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>10m_u_component_of_wind</span></div><div class='xr-var-dims'>(time, longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(100, 64, 32), meta=np.ndarray&gt;</div><input id='attrs-7bfb6fc7-18b7-4c73-ad04-ddd7a2081526' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7bfb6fc7-18b7-4c73-ad04-ddd7a2081526' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ed650c15-8d1f-4157-ba6c-7861ef9967b6' class='xr-var-data-in' type='checkbox'><label for='data-ed650c15-8d1f-4157-ba6c-7861ef9967b6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>10 metre U wind component</dd><dt><span>short_name :</span></dt><dd>u10</dd><dt><span>units :</span></dt><dd>m s**-1</dd></dl></div><div class='xr-var-data'><table>\n",
+       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-00c57a2e-0612-4f56-8352-b46ceb3becfa' class='xr-section-summary-in' type='checkbox'  ><label for='section-00c57a2e-0612-4f56-8352-b46ceb3becfa' class='xr-section-summary' >Data variables: <span>(62)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>10m_u_component_of_wind</span></div><div class='xr-var-dims'>(time, longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(48, 64, 32), meta=np.ndarray&gt;</div><input id='attrs-3da1798e-163d-4ee1-a7f7-cce96f26c16f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3da1798e-163d-4ee1-a7f7-cce96f26c16f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bc2b5e06-7713-4e4a-8690-b5140f4d40c1' class='xr-var-data-in' type='checkbox'><label for='data-bc2b5e06-7713-4e4a-8690-b5140f4d40c1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>10 metre U wind component</dd><dt><span>short_name :</span></dt><dd>u10</dd><dt><span>units :</span></dt><dd>m s**-1</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -1529,17 +1537,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 730.81 MiB </td>\n",
-       "                        <td> 800.00 kiB </td>\n",
+       "                        <td> 384.00 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (93544, 64, 32) </td>\n",
-       "                        <td> (100, 64, 32) </td>\n",
+       "                        <td> (48, 64, 32) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 936 chunks in 2 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 1949 chunks in 2 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -1563,14 +1571,14 @@
        "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"40\" />\n",
        "  <line x1=\"28\" y1=\"18\" x2=\"28\" y2=\"43\" />\n",
        "  <line x1=\"32\" y1=\"22\" x2=\"32\" y2=\"47\" />\n",
-       "  <line x1=\"35\" y1=\"25\" x2=\"35\" y2=\"51\" />\n",
+       "  <line x1=\"36\" y1=\"26\" x2=\"36\" y2=\"51\" />\n",
        "  <line x1=\"39\" y1=\"29\" x2=\"39\" y2=\"55\" />\n",
        "  <line x1=\"43\" y1=\"33\" x2=\"43\" y2=\"58\" />\n",
        "  <line x1=\"47\" y1=\"37\" x2=\"47\" y2=\"62\" />\n",
        "  <line x1=\"50\" y1=\"40\" x2=\"50\" y2=\"66\" />\n",
-       "  <line x1=\"54\" y1=\"44\" x2=\"54\" y2=\"70\" />\n",
+       "  <line x1=\"54\" y1=\"44\" x2=\"54\" y2=\"69\" />\n",
        "  <line x1=\"58\" y1=\"48\" x2=\"58\" y2=\"73\" />\n",
-       "  <line x1=\"61\" y1=\"51\" x2=\"61\" y2=\"77\" />\n",
+       "  <line x1=\"62\" y1=\"52\" x2=\"62\" y2=\"77\" />\n",
        "  <line x1=\"65\" y1=\"55\" x2=\"65\" y2=\"81\" />\n",
        "  <line x1=\"69\" y1=\"59\" x2=\"69\" y2=\"84\" />\n",
        "  <line x1=\"73\" y1=\"63\" x2=\"73\" y2=\"88\" />\n",
@@ -1588,14 +1596,14 @@
        "  <line x1=\"24\" y1=\"14\" x2=\"50\" y2=\"14\" />\n",
        "  <line x1=\"28\" y1=\"18\" x2=\"53\" y2=\"18\" />\n",
        "  <line x1=\"32\" y1=\"22\" x2=\"57\" y2=\"22\" />\n",
-       "  <line x1=\"35\" y1=\"25\" x2=\"61\" y2=\"25\" />\n",
+       "  <line x1=\"36\" y1=\"26\" x2=\"61\" y2=\"26\" />\n",
        "  <line x1=\"39\" y1=\"29\" x2=\"65\" y2=\"29\" />\n",
        "  <line x1=\"43\" y1=\"33\" x2=\"68\" y2=\"33\" />\n",
        "  <line x1=\"47\" y1=\"37\" x2=\"72\" y2=\"37\" />\n",
        "  <line x1=\"50\" y1=\"40\" x2=\"76\" y2=\"40\" />\n",
-       "  <line x1=\"54\" y1=\"44\" x2=\"80\" y2=\"44\" />\n",
+       "  <line x1=\"54\" y1=\"44\" x2=\"79\" y2=\"44\" />\n",
        "  <line x1=\"58\" y1=\"48\" x2=\"83\" y2=\"48\" />\n",
-       "  <line x1=\"61\" y1=\"51\" x2=\"87\" y2=\"51\" />\n",
+       "  <line x1=\"62\" y1=\"52\" x2=\"87\" y2=\"52\" />\n",
        "  <line x1=\"65\" y1=\"55\" x2=\"91\" y2=\"55\" />\n",
        "  <line x1=\"69\" y1=\"59\" x2=\"94\" y2=\"59\" />\n",
        "  <line x1=\"73\" y1=\"63\" x2=\"98\" y2=\"63\" />\n",
@@ -1627,7 +1635,7 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>10m_v_component_of_wind</span></div><div class='xr-var-dims'>(time, longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(100, 64, 32), meta=np.ndarray&gt;</div><input id='attrs-90b028df-e120-470f-a65a-2c72ee19b875' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-90b028df-e120-470f-a65a-2c72ee19b875' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1d2d0db2-26d3-4d12-988f-b5796b105768' class='xr-var-data-in' type='checkbox'><label for='data-1d2d0db2-26d3-4d12-988f-b5796b105768' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>10 metre V wind component</dd><dt><span>short_name :</span></dt><dd>v10</dd><dt><span>units :</span></dt><dd>m s**-1</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>10m_v_component_of_wind</span></div><div class='xr-var-dims'>(time, longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(48, 64, 32), meta=np.ndarray&gt;</div><input id='attrs-72158da6-044c-4d27-9ecf-c77a51cfd916' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-72158da6-044c-4d27-9ecf-c77a51cfd916' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0c91fa1f-4cb2-4a5f-8cb0-098a07129ea4' class='xr-var-data-in' type='checkbox'><label for='data-0c91fa1f-4cb2-4a5f-8cb0-098a07129ea4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>10 metre V wind component</dd><dt><span>short_name :</span></dt><dd>v10</dd><dt><span>units :</span></dt><dd>m s**-1</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -1643,17 +1651,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 730.81 MiB </td>\n",
-       "                        <td> 800.00 kiB </td>\n",
+       "                        <td> 384.00 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (93544, 64, 32) </td>\n",
-       "                        <td> (100, 64, 32) </td>\n",
+       "                        <td> (48, 64, 32) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 936 chunks in 2 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 1949 chunks in 2 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -1677,14 +1685,14 @@
        "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"40\" />\n",
        "  <line x1=\"28\" y1=\"18\" x2=\"28\" y2=\"43\" />\n",
        "  <line x1=\"32\" y1=\"22\" x2=\"32\" y2=\"47\" />\n",
-       "  <line x1=\"35\" y1=\"25\" x2=\"35\" y2=\"51\" />\n",
+       "  <line x1=\"36\" y1=\"26\" x2=\"36\" y2=\"51\" />\n",
        "  <line x1=\"39\" y1=\"29\" x2=\"39\" y2=\"55\" />\n",
        "  <line x1=\"43\" y1=\"33\" x2=\"43\" y2=\"58\" />\n",
        "  <line x1=\"47\" y1=\"37\" x2=\"47\" y2=\"62\" />\n",
        "  <line x1=\"50\" y1=\"40\" x2=\"50\" y2=\"66\" />\n",
-       "  <line x1=\"54\" y1=\"44\" x2=\"54\" y2=\"70\" />\n",
+       "  <line x1=\"54\" y1=\"44\" x2=\"54\" y2=\"69\" />\n",
        "  <line x1=\"58\" y1=\"48\" x2=\"58\" y2=\"73\" />\n",
-       "  <line x1=\"61\" y1=\"51\" x2=\"61\" y2=\"77\" />\n",
+       "  <line x1=\"62\" y1=\"52\" x2=\"62\" y2=\"77\" />\n",
        "  <line x1=\"65\" y1=\"55\" x2=\"65\" y2=\"81\" />\n",
        "  <line x1=\"69\" y1=\"59\" x2=\"69\" y2=\"84\" />\n",
        "  <line x1=\"73\" y1=\"63\" x2=\"73\" y2=\"88\" />\n",
@@ -1702,14 +1710,14 @@
        "  <line x1=\"24\" y1=\"14\" x2=\"50\" y2=\"14\" />\n",
        "  <line x1=\"28\" y1=\"18\" x2=\"53\" y2=\"18\" />\n",
        "  <line x1=\"32\" y1=\"22\" x2=\"57\" y2=\"22\" />\n",
-       "  <line x1=\"35\" y1=\"25\" x2=\"61\" y2=\"25\" />\n",
+       "  <line x1=\"36\" y1=\"26\" x2=\"61\" y2=\"26\" />\n",
        "  <line x1=\"39\" y1=\"29\" x2=\"65\" y2=\"29\" />\n",
        "  <line x1=\"43\" y1=\"33\" x2=\"68\" y2=\"33\" />\n",
        "  <line x1=\"47\" y1=\"37\" x2=\"72\" y2=\"37\" />\n",
        "  <line x1=\"50\" y1=\"40\" x2=\"76\" y2=\"40\" />\n",
-       "  <line x1=\"54\" y1=\"44\" x2=\"80\" y2=\"44\" />\n",
+       "  <line x1=\"54\" y1=\"44\" x2=\"79\" y2=\"44\" />\n",
        "  <line x1=\"58\" y1=\"48\" x2=\"83\" y2=\"48\" />\n",
-       "  <line x1=\"61\" y1=\"51\" x2=\"87\" y2=\"51\" />\n",
+       "  <line x1=\"62\" y1=\"52\" x2=\"87\" y2=\"52\" />\n",
        "  <line x1=\"65\" y1=\"55\" x2=\"91\" y2=\"55\" />\n",
        "  <line x1=\"69\" y1=\"59\" x2=\"94\" y2=\"59\" />\n",
        "  <line x1=\"73\" y1=\"63\" x2=\"98\" y2=\"63\" />\n",
@@ -1741,7 +1749,7 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>10m_wind_speed</span></div><div class='xr-var-dims'>(time, longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(100, 64, 32), meta=np.ndarray&gt;</div><input id='attrs-a38bad85-8dfb-4296-a53e-08ba27900eb4' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a38bad85-8dfb-4296-a53e-08ba27900eb4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-12c4f12d-c9ab-4bcf-bfc0-9e97c0fe07b5' class='xr-var-data-in' type='checkbox'><label for='data-12c4f12d-c9ab-4bcf-bfc0-9e97c0fe07b5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>10m_wind_speed</span></div><div class='xr-var-dims'>(time, longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(48, 64, 32), meta=np.ndarray&gt;</div><input id='attrs-f2e85888-ca43-4b33-b0d6-cb2bf04ea616' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f2e85888-ca43-4b33-b0d6-cb2bf04ea616' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a51e75c5-bc15-47ad-89e5-c7c731cca42c' class='xr-var-data-in' type='checkbox'><label for='data-a51e75c5-bc15-47ad-89e5-c7c731cca42c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -1757,17 +1765,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 730.81 MiB </td>\n",
-       "                        <td> 800.00 kiB </td>\n",
+       "                        <td> 384.00 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (93544, 64, 32) </td>\n",
-       "                        <td> (100, 64, 32) </td>\n",
+       "                        <td> (48, 64, 32) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 936 chunks in 2 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 1949 chunks in 2 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -1791,14 +1799,14 @@
        "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"40\" />\n",
        "  <line x1=\"28\" y1=\"18\" x2=\"28\" y2=\"43\" />\n",
        "  <line x1=\"32\" y1=\"22\" x2=\"32\" y2=\"47\" />\n",
-       "  <line x1=\"35\" y1=\"25\" x2=\"35\" y2=\"51\" />\n",
+       "  <line x1=\"36\" y1=\"26\" x2=\"36\" y2=\"51\" />\n",
        "  <line x1=\"39\" y1=\"29\" x2=\"39\" y2=\"55\" />\n",
        "  <line x1=\"43\" y1=\"33\" x2=\"43\" y2=\"58\" />\n",
        "  <line x1=\"47\" y1=\"37\" x2=\"47\" y2=\"62\" />\n",
        "  <line x1=\"50\" y1=\"40\" x2=\"50\" y2=\"66\" />\n",
-       "  <line x1=\"54\" y1=\"44\" x2=\"54\" y2=\"70\" />\n",
+       "  <line x1=\"54\" y1=\"44\" x2=\"54\" y2=\"69\" />\n",
        "  <line x1=\"58\" y1=\"48\" x2=\"58\" y2=\"73\" />\n",
-       "  <line x1=\"61\" y1=\"51\" x2=\"61\" y2=\"77\" />\n",
+       "  <line x1=\"62\" y1=\"52\" x2=\"62\" y2=\"77\" />\n",
        "  <line x1=\"65\" y1=\"55\" x2=\"65\" y2=\"81\" />\n",
        "  <line x1=\"69\" y1=\"59\" x2=\"69\" y2=\"84\" />\n",
        "  <line x1=\"73\" y1=\"63\" x2=\"73\" y2=\"88\" />\n",
@@ -1816,14 +1824,14 @@
        "  <line x1=\"24\" y1=\"14\" x2=\"50\" y2=\"14\" />\n",
        "  <line x1=\"28\" y1=\"18\" x2=\"53\" y2=\"18\" />\n",
        "  <line x1=\"32\" y1=\"22\" x2=\"57\" y2=\"22\" />\n",
-       "  <line x1=\"35\" y1=\"25\" x2=\"61\" y2=\"25\" />\n",
+       "  <line x1=\"36\" y1=\"26\" x2=\"61\" y2=\"26\" />\n",
        "  <line x1=\"39\" y1=\"29\" x2=\"65\" y2=\"29\" />\n",
        "  <line x1=\"43\" y1=\"33\" x2=\"68\" y2=\"33\" />\n",
        "  <line x1=\"47\" y1=\"37\" x2=\"72\" y2=\"37\" />\n",
        "  <line x1=\"50\" y1=\"40\" x2=\"76\" y2=\"40\" />\n",
-       "  <line x1=\"54\" y1=\"44\" x2=\"80\" y2=\"44\" />\n",
+       "  <line x1=\"54\" y1=\"44\" x2=\"79\" y2=\"44\" />\n",
        "  <line x1=\"58\" y1=\"48\" x2=\"83\" y2=\"48\" />\n",
-       "  <line x1=\"61\" y1=\"51\" x2=\"87\" y2=\"51\" />\n",
+       "  <line x1=\"62\" y1=\"52\" x2=\"87\" y2=\"52\" />\n",
        "  <line x1=\"65\" y1=\"55\" x2=\"91\" y2=\"55\" />\n",
        "  <line x1=\"69\" y1=\"59\" x2=\"94\" y2=\"59\" />\n",
        "  <line x1=\"73\" y1=\"63\" x2=\"98\" y2=\"63\" />\n",
@@ -1855,7 +1863,7 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>2m_dewpoint_temperature</span></div><div class='xr-var-dims'>(time, longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(100, 64, 32), meta=np.ndarray&gt;</div><input id='attrs-ceeb0fa3-c22d-47a2-a1d0-ae4653365f85' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ceeb0fa3-c22d-47a2-a1d0-ae4653365f85' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4b16ec7c-5465-42a4-8c6c-8902d1fdbb8b' class='xr-var-data-in' type='checkbox'><label for='data-4b16ec7c-5465-42a4-8c6c-8902d1fdbb8b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>2 metre dewpoint temperature</dd><dt><span>short_name :</span></dt><dd>d2m</dd><dt><span>units :</span></dt><dd>K</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>2m_dewpoint_temperature</span></div><div class='xr-var-dims'>(time, longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(48, 64, 32), meta=np.ndarray&gt;</div><input id='attrs-9e70e01c-6967-4ebe-a166-11fdfe9d3b64' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9e70e01c-6967-4ebe-a166-11fdfe9d3b64' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bb754b0f-3883-4b19-ab83-877a1c4038d7' class='xr-var-data-in' type='checkbox'><label for='data-bb754b0f-3883-4b19-ab83-877a1c4038d7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>2 metre dewpoint temperature</dd><dt><span>short_name :</span></dt><dd>d2m</dd><dt><span>units :</span></dt><dd>K</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -1871,17 +1879,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 730.81 MiB </td>\n",
-       "                        <td> 800.00 kiB </td>\n",
+       "                        <td> 384.00 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (93544, 64, 32) </td>\n",
-       "                        <td> (100, 64, 32) </td>\n",
+       "                        <td> (48, 64, 32) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 936 chunks in 2 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 1949 chunks in 2 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -1905,14 +1913,14 @@
        "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"40\" />\n",
        "  <line x1=\"28\" y1=\"18\" x2=\"28\" y2=\"43\" />\n",
        "  <line x1=\"32\" y1=\"22\" x2=\"32\" y2=\"47\" />\n",
-       "  <line x1=\"35\" y1=\"25\" x2=\"35\" y2=\"51\" />\n",
+       "  <line x1=\"36\" y1=\"26\" x2=\"36\" y2=\"51\" />\n",
        "  <line x1=\"39\" y1=\"29\" x2=\"39\" y2=\"55\" />\n",
        "  <line x1=\"43\" y1=\"33\" x2=\"43\" y2=\"58\" />\n",
        "  <line x1=\"47\" y1=\"37\" x2=\"47\" y2=\"62\" />\n",
        "  <line x1=\"50\" y1=\"40\" x2=\"50\" y2=\"66\" />\n",
-       "  <line x1=\"54\" y1=\"44\" x2=\"54\" y2=\"70\" />\n",
+       "  <line x1=\"54\" y1=\"44\" x2=\"54\" y2=\"69\" />\n",
        "  <line x1=\"58\" y1=\"48\" x2=\"58\" y2=\"73\" />\n",
-       "  <line x1=\"61\" y1=\"51\" x2=\"61\" y2=\"77\" />\n",
+       "  <line x1=\"62\" y1=\"52\" x2=\"62\" y2=\"77\" />\n",
        "  <line x1=\"65\" y1=\"55\" x2=\"65\" y2=\"81\" />\n",
        "  <line x1=\"69\" y1=\"59\" x2=\"69\" y2=\"84\" />\n",
        "  <line x1=\"73\" y1=\"63\" x2=\"73\" y2=\"88\" />\n",
@@ -1930,14 +1938,14 @@
        "  <line x1=\"24\" y1=\"14\" x2=\"50\" y2=\"14\" />\n",
        "  <line x1=\"28\" y1=\"18\" x2=\"53\" y2=\"18\" />\n",
        "  <line x1=\"32\" y1=\"22\" x2=\"57\" y2=\"22\" />\n",
-       "  <line x1=\"35\" y1=\"25\" x2=\"61\" y2=\"25\" />\n",
+       "  <line x1=\"36\" y1=\"26\" x2=\"61\" y2=\"26\" />\n",
        "  <line x1=\"39\" y1=\"29\" x2=\"65\" y2=\"29\" />\n",
        "  <line x1=\"43\" y1=\"33\" x2=\"68\" y2=\"33\" />\n",
        "  <line x1=\"47\" y1=\"37\" x2=\"72\" y2=\"37\" />\n",
        "  <line x1=\"50\" y1=\"40\" x2=\"76\" y2=\"40\" />\n",
-       "  <line x1=\"54\" y1=\"44\" x2=\"80\" y2=\"44\" />\n",
+       "  <line x1=\"54\" y1=\"44\" x2=\"79\" y2=\"44\" />\n",
        "  <line x1=\"58\" y1=\"48\" x2=\"83\" y2=\"48\" />\n",
-       "  <line x1=\"61\" y1=\"51\" x2=\"87\" y2=\"51\" />\n",
+       "  <line x1=\"62\" y1=\"52\" x2=\"87\" y2=\"52\" />\n",
        "  <line x1=\"65\" y1=\"55\" x2=\"91\" y2=\"55\" />\n",
        "  <line x1=\"69\" y1=\"59\" x2=\"94\" y2=\"59\" />\n",
        "  <line x1=\"73\" y1=\"63\" x2=\"98\" y2=\"63\" />\n",
@@ -1969,7 +1977,7 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>2m_temperature</span></div><div class='xr-var-dims'>(time, longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(100, 64, 32), meta=np.ndarray&gt;</div><input id='attrs-d1d7cec1-26f2-4b90-b376-c289248e4550' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d1d7cec1-26f2-4b90-b376-c289248e4550' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1ee4f3bf-2a65-4ea4-920d-c1acee951978' class='xr-var-data-in' type='checkbox'><label for='data-1ee4f3bf-2a65-4ea4-920d-c1acee951978' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>2 metre temperature</dd><dt><span>short_name :</span></dt><dd>t2m</dd><dt><span>units :</span></dt><dd>K</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>2m_temperature</span></div><div class='xr-var-dims'>(time, longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(48, 64, 32), meta=np.ndarray&gt;</div><input id='attrs-865263af-5a4f-4ac0-9048-535f3cf21cb5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-865263af-5a4f-4ac0-9048-535f3cf21cb5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a5d4ed7c-e28e-4cf1-8f0e-a9a7f09c118a' class='xr-var-data-in' type='checkbox'><label for='data-a5d4ed7c-e28e-4cf1-8f0e-a9a7f09c118a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>2 metre temperature</dd><dt><span>short_name :</span></dt><dd>t2m</dd><dt><span>units :</span></dt><dd>K</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -1985,17 +1993,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 730.81 MiB </td>\n",
-       "                        <td> 800.00 kiB </td>\n",
+       "                        <td> 384.00 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (93544, 64, 32) </td>\n",
-       "                        <td> (100, 64, 32) </td>\n",
+       "                        <td> (48, 64, 32) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 936 chunks in 2 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 1949 chunks in 2 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -2019,14 +2027,14 @@
        "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"40\" />\n",
        "  <line x1=\"28\" y1=\"18\" x2=\"28\" y2=\"43\" />\n",
        "  <line x1=\"32\" y1=\"22\" x2=\"32\" y2=\"47\" />\n",
-       "  <line x1=\"35\" y1=\"25\" x2=\"35\" y2=\"51\" />\n",
+       "  <line x1=\"36\" y1=\"26\" x2=\"36\" y2=\"51\" />\n",
        "  <line x1=\"39\" y1=\"29\" x2=\"39\" y2=\"55\" />\n",
        "  <line x1=\"43\" y1=\"33\" x2=\"43\" y2=\"58\" />\n",
        "  <line x1=\"47\" y1=\"37\" x2=\"47\" y2=\"62\" />\n",
        "  <line x1=\"50\" y1=\"40\" x2=\"50\" y2=\"66\" />\n",
-       "  <line x1=\"54\" y1=\"44\" x2=\"54\" y2=\"70\" />\n",
+       "  <line x1=\"54\" y1=\"44\" x2=\"54\" y2=\"69\" />\n",
        "  <line x1=\"58\" y1=\"48\" x2=\"58\" y2=\"73\" />\n",
-       "  <line x1=\"61\" y1=\"51\" x2=\"61\" y2=\"77\" />\n",
+       "  <line x1=\"62\" y1=\"52\" x2=\"62\" y2=\"77\" />\n",
        "  <line x1=\"65\" y1=\"55\" x2=\"65\" y2=\"81\" />\n",
        "  <line x1=\"69\" y1=\"59\" x2=\"69\" y2=\"84\" />\n",
        "  <line x1=\"73\" y1=\"63\" x2=\"73\" y2=\"88\" />\n",
@@ -2044,14 +2052,14 @@
        "  <line x1=\"24\" y1=\"14\" x2=\"50\" y2=\"14\" />\n",
        "  <line x1=\"28\" y1=\"18\" x2=\"53\" y2=\"18\" />\n",
        "  <line x1=\"32\" y1=\"22\" x2=\"57\" y2=\"22\" />\n",
-       "  <line x1=\"35\" y1=\"25\" x2=\"61\" y2=\"25\" />\n",
+       "  <line x1=\"36\" y1=\"26\" x2=\"61\" y2=\"26\" />\n",
        "  <line x1=\"39\" y1=\"29\" x2=\"65\" y2=\"29\" />\n",
        "  <line x1=\"43\" y1=\"33\" x2=\"68\" y2=\"33\" />\n",
        "  <line x1=\"47\" y1=\"37\" x2=\"72\" y2=\"37\" />\n",
        "  <line x1=\"50\" y1=\"40\" x2=\"76\" y2=\"40\" />\n",
-       "  <line x1=\"54\" y1=\"44\" x2=\"80\" y2=\"44\" />\n",
+       "  <line x1=\"54\" y1=\"44\" x2=\"79\" y2=\"44\" />\n",
        "  <line x1=\"58\" y1=\"48\" x2=\"83\" y2=\"48\" />\n",
-       "  <line x1=\"61\" y1=\"51\" x2=\"87\" y2=\"51\" />\n",
+       "  <line x1=\"62\" y1=\"52\" x2=\"87\" y2=\"52\" />\n",
        "  <line x1=\"65\" y1=\"55\" x2=\"91\" y2=\"55\" />\n",
        "  <line x1=\"69\" y1=\"59\" x2=\"94\" y2=\"59\" />\n",
        "  <line x1=\"73\" y1=\"63\" x2=\"98\" y2=\"63\" />\n",
@@ -2083,7 +2091,7 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>above_ground</span></div><div class='xr-var-dims'>(time, level, longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(100, 13, 64, 32), meta=np.ndarray&gt;</div><input id='attrs-52bf3139-8df0-4dab-86f7-08b18ad04340' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-52bf3139-8df0-4dab-86f7-08b18ad04340' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0a5c4170-3baa-4851-a078-d292c1eb7069' class='xr-var-data-in' type='checkbox'><label for='data-0a5c4170-3baa-4851-a078-d292c1eb7069' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>above_ground</span></div><div class='xr-var-dims'>(time, level, longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(48, 13, 64, 32), meta=np.ndarray&gt;</div><input id='attrs-d24b9c17-d8ed-49e6-864e-574da0aa00ad' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d24b9c17-d8ed-49e6-864e-574da0aa00ad' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5680b8de-3ede-41db-8547-3d47377674ca' class='xr-var-data-in' type='checkbox'><label for='data-5680b8de-3ede-41db-8547-3d47377674ca' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -2099,17 +2107,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 9.28 GiB </td>\n",
-       "                        <td> 10.16 MiB </td>\n",
+       "                        <td> 4.88 MiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (93544, 13, 64, 32) </td>\n",
-       "                        <td> (100, 13, 64, 32) </td>\n",
+       "                        <td> (48, 13, 64, 32) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 936 chunks in 2 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 1949 chunks in 2 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -2130,31 +2138,31 @@
        "  <line x1=\"3\" y1=\"0\" x2=\"3\" y2=\"25\" />\n",
        "  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n",
        "  <line x1=\"11\" y1=\"0\" x2=\"11\" y2=\"25\" />\n",
-       "  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n",
+       "  <line x1=\"14\" y1=\"0\" x2=\"14\" y2=\"25\" />\n",
        "  <line x1=\"18\" y1=\"0\" x2=\"18\" y2=\"25\" />\n",
        "  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n",
        "  <line x1=\"26\" y1=\"0\" x2=\"26\" y2=\"25\" />\n",
-       "  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n",
+       "  <line x1=\"29\" y1=\"0\" x2=\"29\" y2=\"25\" />\n",
        "  <line x1=\"33\" y1=\"0\" x2=\"33\" y2=\"25\" />\n",
        "  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n",
        "  <line x1=\"41\" y1=\"0\" x2=\"41\" y2=\"25\" />\n",
-       "  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n",
+       "  <line x1=\"44\" y1=\"0\" x2=\"44\" y2=\"25\" />\n",
        "  <line x1=\"48\" y1=\"0\" x2=\"48\" y2=\"25\" />\n",
        "  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n",
        "  <line x1=\"56\" y1=\"0\" x2=\"56\" y2=\"25\" />\n",
-       "  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n",
+       "  <line x1=\"59\" y1=\"0\" x2=\"59\" y2=\"25\" />\n",
        "  <line x1=\"63\" y1=\"0\" x2=\"63\" y2=\"25\" />\n",
        "  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n",
        "  <line x1=\"71\" y1=\"0\" x2=\"71\" y2=\"25\" />\n",
-       "  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n",
+       "  <line x1=\"74\" y1=\"0\" x2=\"74\" y2=\"25\" />\n",
        "  <line x1=\"78\" y1=\"0\" x2=\"78\" y2=\"25\" />\n",
        "  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n",
        "  <line x1=\"86\" y1=\"0\" x2=\"86\" y2=\"25\" />\n",
-       "  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n",
+       "  <line x1=\"89\" y1=\"0\" x2=\"89\" y2=\"25\" />\n",
        "  <line x1=\"93\" y1=\"0\" x2=\"93\" y2=\"25\" />\n",
        "  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n",
        "  <line x1=\"101\" y1=\"0\" x2=\"101\" y2=\"25\" />\n",
-       "  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n",
+       "  <line x1=\"104\" y1=\"0\" x2=\"104\" y2=\"25\" />\n",
        "  <line x1=\"108\" y1=\"0\" x2=\"108\" y2=\"25\" />\n",
        "  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n",
        "  <line x1=\"116\" y1=\"0\" x2=\"116\" y2=\"25\" />\n",
@@ -2208,7 +2216,7 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ageostrophic_wind_speed</span></div><div class='xr-var-dims'>(time, level, longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(100, 13, 64, 32), meta=np.ndarray&gt;</div><input id='attrs-136abed9-a596-4712-b4c0-e67b48c778d1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-136abed9-a596-4712-b4c0-e67b48c778d1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-46b47fcf-7db3-4812-818a-267c369d594a' class='xr-var-data-in' type='checkbox'><label for='data-46b47fcf-7db3-4812-818a-267c369d594a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ageostrophic_wind_speed</span></div><div class='xr-var-dims'>(time, level, longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(48, 13, 64, 32), meta=np.ndarray&gt;</div><input id='attrs-2878a584-1128-4b76-af3b-68487945eaed' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2878a584-1128-4b76-af3b-68487945eaed' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2483d4c1-d064-4b52-9a18-c85f23eff902' class='xr-var-data-in' type='checkbox'><label for='data-2483d4c1-d064-4b52-9a18-c85f23eff902' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -2224,17 +2232,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 9.28 GiB </td>\n",
-       "                        <td> 10.16 MiB </td>\n",
+       "                        <td> 4.88 MiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (93544, 13, 64, 32) </td>\n",
-       "                        <td> (100, 13, 64, 32) </td>\n",
+       "                        <td> (48, 13, 64, 32) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 936 chunks in 2 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 1949 chunks in 2 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -2255,31 +2263,31 @@
        "  <line x1=\"3\" y1=\"0\" x2=\"3\" y2=\"25\" />\n",
        "  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n",
        "  <line x1=\"11\" y1=\"0\" x2=\"11\" y2=\"25\" />\n",
-       "  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n",
+       "  <line x1=\"14\" y1=\"0\" x2=\"14\" y2=\"25\" />\n",
        "  <line x1=\"18\" y1=\"0\" x2=\"18\" y2=\"25\" />\n",
        "  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n",
        "  <line x1=\"26\" y1=\"0\" x2=\"26\" y2=\"25\" />\n",
-       "  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n",
+       "  <line x1=\"29\" y1=\"0\" x2=\"29\" y2=\"25\" />\n",
        "  <line x1=\"33\" y1=\"0\" x2=\"33\" y2=\"25\" />\n",
        "  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n",
        "  <line x1=\"41\" y1=\"0\" x2=\"41\" y2=\"25\" />\n",
-       "  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n",
+       "  <line x1=\"44\" y1=\"0\" x2=\"44\" y2=\"25\" />\n",
        "  <line x1=\"48\" y1=\"0\" x2=\"48\" y2=\"25\" />\n",
        "  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n",
        "  <line x1=\"56\" y1=\"0\" x2=\"56\" y2=\"25\" />\n",
-       "  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n",
+       "  <line x1=\"59\" y1=\"0\" x2=\"59\" y2=\"25\" />\n",
        "  <line x1=\"63\" y1=\"0\" x2=\"63\" y2=\"25\" />\n",
        "  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n",
        "  <line x1=\"71\" y1=\"0\" x2=\"71\" y2=\"25\" />\n",
-       "  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n",
+       "  <line x1=\"74\" y1=\"0\" x2=\"74\" y2=\"25\" />\n",
        "  <line x1=\"78\" y1=\"0\" x2=\"78\" y2=\"25\" />\n",
        "  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n",
        "  <line x1=\"86\" y1=\"0\" x2=\"86\" y2=\"25\" />\n",
-       "  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n",
+       "  <line x1=\"89\" y1=\"0\" x2=\"89\" y2=\"25\" />\n",
        "  <line x1=\"93\" y1=\"0\" x2=\"93\" y2=\"25\" />\n",
        "  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n",
        "  <line x1=\"101\" y1=\"0\" x2=\"101\" y2=\"25\" />\n",
-       "  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n",
+       "  <line x1=\"104\" y1=\"0\" x2=\"104\" y2=\"25\" />\n",
        "  <line x1=\"108\" y1=\"0\" x2=\"108\" y2=\"25\" />\n",
        "  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n",
        "  <line x1=\"116\" y1=\"0\" x2=\"116\" y2=\"25\" />\n",
@@ -2333,7 +2341,7 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>angle_of_sub_gridscale_orography</span></div><div class='xr-var-dims'>(longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(64, 32), meta=np.ndarray&gt;</div><input id='attrs-88d658a4-57cb-483a-8aeb-ea2d5237eff6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-88d658a4-57cb-483a-8aeb-ea2d5237eff6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-aaf3e7f7-3e32-4de6-b421-aab64877f0de' class='xr-var-data-in' type='checkbox'><label for='data-aaf3e7f7-3e32-4de6-b421-aab64877f0de' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Angle of sub-gridscale orography</dd><dt><span>short_name :</span></dt><dd>anor</dd><dt><span>units :</span></dt><dd>radians</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>angle_of_sub_gridscale_orography</span></div><div class='xr-var-dims'>(longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(64, 32), meta=np.ndarray&gt;</div><input id='attrs-bd0ce40a-aa2a-4d07-8ef2-205ae291999c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bd0ce40a-aa2a-4d07-8ef2-205ae291999c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bfed1924-df0a-4160-868c-0b9f58685a6b' class='xr-var-data-in' type='checkbox'><label for='data-bfed1924-df0a-4160-868c-0b9f58685a6b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Angle of sub-gridscale orography</dd><dt><span>short_name :</span></dt><dd>anor</dd><dt><span>units :</span></dt><dd>radians</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -2388,7 +2396,7 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>anisotropy_of_sub_gridscale_orography</span></div><div class='xr-var-dims'>(longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(64, 32), meta=np.ndarray&gt;</div><input id='attrs-31ada38a-655c-4a31-97cf-d7e53ebdeadd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-31ada38a-655c-4a31-97cf-d7e53ebdeadd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-80e71f3d-787d-4c95-9580-747f4e1a278a' class='xr-var-data-in' type='checkbox'><label for='data-80e71f3d-787d-4c95-9580-747f4e1a278a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Anisotropy of sub-gridscale orography</dd><dt><span>short_name :</span></dt><dd>isor</dd><dt><span>units :</span></dt><dd>~</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>anisotropy_of_sub_gridscale_orography</span></div><div class='xr-var-dims'>(longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(64, 32), meta=np.ndarray&gt;</div><input id='attrs-e28512b5-8df2-4338-b97f-4726624657b2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e28512b5-8df2-4338-b97f-4726624657b2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b5fe0375-0aca-4ddc-bf4d-08d2c9080fbe' class='xr-var-data-in' type='checkbox'><label for='data-b5fe0375-0aca-4ddc-bf4d-08d2c9080fbe' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Anisotropy of sub-gridscale orography</dd><dt><span>short_name :</span></dt><dd>isor</dd><dt><span>units :</span></dt><dd>~</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -2443,7 +2451,7 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>boundary_layer_height</span></div><div class='xr-var-dims'>(time, longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(100, 64, 32), meta=np.ndarray&gt;</div><input id='attrs-7900e6f5-59f4-4fa8-8066-dda37e76b5e4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7900e6f5-59f4-4fa8-8066-dda37e76b5e4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3dc22554-a29b-46b4-a9c0-c75f67249a00' class='xr-var-data-in' type='checkbox'><label for='data-3dc22554-a29b-46b4-a9c0-c75f67249a00' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Boundary layer height</dd><dt><span>short_name :</span></dt><dd>blh</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>boundary_layer_height</span></div><div class='xr-var-dims'>(time, longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(48, 64, 32), meta=np.ndarray&gt;</div><input id='attrs-b0ebedeb-09b3-4dfe-8ec7-d00e2f338d2c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b0ebedeb-09b3-4dfe-8ec7-d00e2f338d2c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c190ee70-7ff4-4e94-8747-397ed0fd34a8' class='xr-var-data-in' type='checkbox'><label for='data-c190ee70-7ff4-4e94-8747-397ed0fd34a8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Boundary layer height</dd><dt><span>short_name :</span></dt><dd>blh</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -2459,17 +2467,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 730.81 MiB </td>\n",
-       "                        <td> 800.00 kiB </td>\n",
+       "                        <td> 384.00 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (93544, 64, 32) </td>\n",
-       "                        <td> (100, 64, 32) </td>\n",
+       "                        <td> (48, 64, 32) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 936 chunks in 2 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 1949 chunks in 2 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -2493,14 +2501,14 @@
        "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"40\" />\n",
        "  <line x1=\"28\" y1=\"18\" x2=\"28\" y2=\"43\" />\n",
        "  <line x1=\"32\" y1=\"22\" x2=\"32\" y2=\"47\" />\n",
-       "  <line x1=\"35\" y1=\"25\" x2=\"35\" y2=\"51\" />\n",
+       "  <line x1=\"36\" y1=\"26\" x2=\"36\" y2=\"51\" />\n",
        "  <line x1=\"39\" y1=\"29\" x2=\"39\" y2=\"55\" />\n",
        "  <line x1=\"43\" y1=\"33\" x2=\"43\" y2=\"58\" />\n",
        "  <line x1=\"47\" y1=\"37\" x2=\"47\" y2=\"62\" />\n",
        "  <line x1=\"50\" y1=\"40\" x2=\"50\" y2=\"66\" />\n",
-       "  <line x1=\"54\" y1=\"44\" x2=\"54\" y2=\"70\" />\n",
+       "  <line x1=\"54\" y1=\"44\" x2=\"54\" y2=\"69\" />\n",
        "  <line x1=\"58\" y1=\"48\" x2=\"58\" y2=\"73\" />\n",
-       "  <line x1=\"61\" y1=\"51\" x2=\"61\" y2=\"77\" />\n",
+       "  <line x1=\"62\" y1=\"52\" x2=\"62\" y2=\"77\" />\n",
        "  <line x1=\"65\" y1=\"55\" x2=\"65\" y2=\"81\" />\n",
        "  <line x1=\"69\" y1=\"59\" x2=\"69\" y2=\"84\" />\n",
        "  <line x1=\"73\" y1=\"63\" x2=\"73\" y2=\"88\" />\n",
@@ -2518,14 +2526,14 @@
        "  <line x1=\"24\" y1=\"14\" x2=\"50\" y2=\"14\" />\n",
        "  <line x1=\"28\" y1=\"18\" x2=\"53\" y2=\"18\" />\n",
        "  <line x1=\"32\" y1=\"22\" x2=\"57\" y2=\"22\" />\n",
-       "  <line x1=\"35\" y1=\"25\" x2=\"61\" y2=\"25\" />\n",
+       "  <line x1=\"36\" y1=\"26\" x2=\"61\" y2=\"26\" />\n",
        "  <line x1=\"39\" y1=\"29\" x2=\"65\" y2=\"29\" />\n",
        "  <line x1=\"43\" y1=\"33\" x2=\"68\" y2=\"33\" />\n",
        "  <line x1=\"47\" y1=\"37\" x2=\"72\" y2=\"37\" />\n",
        "  <line x1=\"50\" y1=\"40\" x2=\"76\" y2=\"40\" />\n",
-       "  <line x1=\"54\" y1=\"44\" x2=\"80\" y2=\"44\" />\n",
+       "  <line x1=\"54\" y1=\"44\" x2=\"79\" y2=\"44\" />\n",
        "  <line x1=\"58\" y1=\"48\" x2=\"83\" y2=\"48\" />\n",
-       "  <line x1=\"61\" y1=\"51\" x2=\"87\" y2=\"51\" />\n",
+       "  <line x1=\"62\" y1=\"52\" x2=\"87\" y2=\"52\" />\n",
        "  <line x1=\"65\" y1=\"55\" x2=\"91\" y2=\"55\" />\n",
        "  <line x1=\"69\" y1=\"59\" x2=\"94\" y2=\"59\" />\n",
        "  <line x1=\"73\" y1=\"63\" x2=\"98\" y2=\"63\" />\n",
@@ -2557,7 +2565,7 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>divergence</span></div><div class='xr-var-dims'>(time, level, longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(100, 13, 64, 32), meta=np.ndarray&gt;</div><input id='attrs-db2292a3-7bf5-43f8-9e1b-352fbf057924' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-db2292a3-7bf5-43f8-9e1b-352fbf057924' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8a0de5cb-3ab7-4563-b4cd-e68c76fdd01c' class='xr-var-data-in' type='checkbox'><label for='data-8a0de5cb-3ab7-4563-b4cd-e68c76fdd01c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>divergence</span></div><div class='xr-var-dims'>(time, level, longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(48, 13, 64, 32), meta=np.ndarray&gt;</div><input id='attrs-433ab6ec-a276-478a-8b8e-7b633f1a3b5d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-433ab6ec-a276-478a-8b8e-7b633f1a3b5d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6c998a0e-380c-4dd9-8fbb-642d70df59b7' class='xr-var-data-in' type='checkbox'><label for='data-6c998a0e-380c-4dd9-8fbb-642d70df59b7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -2573,17 +2581,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 9.28 GiB </td>\n",
-       "                        <td> 10.16 MiB </td>\n",
+       "                        <td> 4.88 MiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (93544, 13, 64, 32) </td>\n",
-       "                        <td> (100, 13, 64, 32) </td>\n",
+       "                        <td> (48, 13, 64, 32) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 936 chunks in 2 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 1949 chunks in 2 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -2604,31 +2612,31 @@
        "  <line x1=\"3\" y1=\"0\" x2=\"3\" y2=\"25\" />\n",
        "  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n",
        "  <line x1=\"11\" y1=\"0\" x2=\"11\" y2=\"25\" />\n",
-       "  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n",
+       "  <line x1=\"14\" y1=\"0\" x2=\"14\" y2=\"25\" />\n",
        "  <line x1=\"18\" y1=\"0\" x2=\"18\" y2=\"25\" />\n",
        "  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n",
        "  <line x1=\"26\" y1=\"0\" x2=\"26\" y2=\"25\" />\n",
-       "  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n",
+       "  <line x1=\"29\" y1=\"0\" x2=\"29\" y2=\"25\" />\n",
        "  <line x1=\"33\" y1=\"0\" x2=\"33\" y2=\"25\" />\n",
        "  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n",
        "  <line x1=\"41\" y1=\"0\" x2=\"41\" y2=\"25\" />\n",
-       "  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n",
+       "  <line x1=\"44\" y1=\"0\" x2=\"44\" y2=\"25\" />\n",
        "  <line x1=\"48\" y1=\"0\" x2=\"48\" y2=\"25\" />\n",
        "  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n",
        "  <line x1=\"56\" y1=\"0\" x2=\"56\" y2=\"25\" />\n",
-       "  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n",
+       "  <line x1=\"59\" y1=\"0\" x2=\"59\" y2=\"25\" />\n",
        "  <line x1=\"63\" y1=\"0\" x2=\"63\" y2=\"25\" />\n",
        "  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n",
        "  <line x1=\"71\" y1=\"0\" x2=\"71\" y2=\"25\" />\n",
-       "  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n",
+       "  <line x1=\"74\" y1=\"0\" x2=\"74\" y2=\"25\" />\n",
        "  <line x1=\"78\" y1=\"0\" x2=\"78\" y2=\"25\" />\n",
        "  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n",
        "  <line x1=\"86\" y1=\"0\" x2=\"86\" y2=\"25\" />\n",
-       "  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n",
+       "  <line x1=\"89\" y1=\"0\" x2=\"89\" y2=\"25\" />\n",
        "  <line x1=\"93\" y1=\"0\" x2=\"93\" y2=\"25\" />\n",
        "  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n",
        "  <line x1=\"101\" y1=\"0\" x2=\"101\" y2=\"25\" />\n",
-       "  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n",
+       "  <line x1=\"104\" y1=\"0\" x2=\"104\" y2=\"25\" />\n",
        "  <line x1=\"108\" y1=\"0\" x2=\"108\" y2=\"25\" />\n",
        "  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n",
        "  <line x1=\"116\" y1=\"0\" x2=\"116\" y2=\"25\" />\n",
@@ -2682,7 +2690,7 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>eddy_kinetic_energy</span></div><div class='xr-var-dims'>(time, longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(100, 64, 32), meta=np.ndarray&gt;</div><input id='attrs-4745a677-cd03-4990-9957-b737fb6363c9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4745a677-cd03-4990-9957-b737fb6363c9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c4f00c8b-3513-482f-a2f2-5131a608e5d7' class='xr-var-data-in' type='checkbox'><label for='data-c4f00c8b-3513-482f-a2f2-5131a608e5d7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>eddy_kinetic_energy</span></div><div class='xr-var-dims'>(time, longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(48, 64, 32), meta=np.ndarray&gt;</div><input id='attrs-a3d65ed5-d379-44d7-89e8-a802b63ddb30' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a3d65ed5-d379-44d7-89e8-a802b63ddb30' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-de08693c-c237-4f62-886c-27b9e3980394' class='xr-var-data-in' type='checkbox'><label for='data-de08693c-c237-4f62-886c-27b9e3980394' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -2698,17 +2706,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 730.81 MiB </td>\n",
-       "                        <td> 800.00 kiB </td>\n",
+       "                        <td> 384.00 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (93544, 64, 32) </td>\n",
-       "                        <td> (100, 64, 32) </td>\n",
+       "                        <td> (48, 64, 32) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 936 chunks in 2 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 1949 chunks in 2 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -2732,14 +2740,14 @@
        "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"40\" />\n",
        "  <line x1=\"28\" y1=\"18\" x2=\"28\" y2=\"43\" />\n",
        "  <line x1=\"32\" y1=\"22\" x2=\"32\" y2=\"47\" />\n",
-       "  <line x1=\"35\" y1=\"25\" x2=\"35\" y2=\"51\" />\n",
+       "  <line x1=\"36\" y1=\"26\" x2=\"36\" y2=\"51\" />\n",
        "  <line x1=\"39\" y1=\"29\" x2=\"39\" y2=\"55\" />\n",
        "  <line x1=\"43\" y1=\"33\" x2=\"43\" y2=\"58\" />\n",
        "  <line x1=\"47\" y1=\"37\" x2=\"47\" y2=\"62\" />\n",
        "  <line x1=\"50\" y1=\"40\" x2=\"50\" y2=\"66\" />\n",
-       "  <line x1=\"54\" y1=\"44\" x2=\"54\" y2=\"70\" />\n",
+       "  <line x1=\"54\" y1=\"44\" x2=\"54\" y2=\"69\" />\n",
        "  <line x1=\"58\" y1=\"48\" x2=\"58\" y2=\"73\" />\n",
-       "  <line x1=\"61\" y1=\"51\" x2=\"61\" y2=\"77\" />\n",
+       "  <line x1=\"62\" y1=\"52\" x2=\"62\" y2=\"77\" />\n",
        "  <line x1=\"65\" y1=\"55\" x2=\"65\" y2=\"81\" />\n",
        "  <line x1=\"69\" y1=\"59\" x2=\"69\" y2=\"84\" />\n",
        "  <line x1=\"73\" y1=\"63\" x2=\"73\" y2=\"88\" />\n",
@@ -2757,14 +2765,14 @@
        "  <line x1=\"24\" y1=\"14\" x2=\"50\" y2=\"14\" />\n",
        "  <line x1=\"28\" y1=\"18\" x2=\"53\" y2=\"18\" />\n",
        "  <line x1=\"32\" y1=\"22\" x2=\"57\" y2=\"22\" />\n",
-       "  <line x1=\"35\" y1=\"25\" x2=\"61\" y2=\"25\" />\n",
+       "  <line x1=\"36\" y1=\"26\" x2=\"61\" y2=\"26\" />\n",
        "  <line x1=\"39\" y1=\"29\" x2=\"65\" y2=\"29\" />\n",
        "  <line x1=\"43\" y1=\"33\" x2=\"68\" y2=\"33\" />\n",
        "  <line x1=\"47\" y1=\"37\" x2=\"72\" y2=\"37\" />\n",
        "  <line x1=\"50\" y1=\"40\" x2=\"76\" y2=\"40\" />\n",
-       "  <line x1=\"54\" y1=\"44\" x2=\"80\" y2=\"44\" />\n",
+       "  <line x1=\"54\" y1=\"44\" x2=\"79\" y2=\"44\" />\n",
        "  <line x1=\"58\" y1=\"48\" x2=\"83\" y2=\"48\" />\n",
-       "  <line x1=\"61\" y1=\"51\" x2=\"87\" y2=\"51\" />\n",
+       "  <line x1=\"62\" y1=\"52\" x2=\"87\" y2=\"52\" />\n",
        "  <line x1=\"65\" y1=\"55\" x2=\"91\" y2=\"55\" />\n",
        "  <line x1=\"69\" y1=\"59\" x2=\"94\" y2=\"59\" />\n",
        "  <line x1=\"73\" y1=\"63\" x2=\"98\" y2=\"63\" />\n",
@@ -2796,7 +2804,7 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>geopotential</span></div><div class='xr-var-dims'>(time, level, longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(100, 13, 64, 32), meta=np.ndarray&gt;</div><input id='attrs-24ab2b33-a0d0-4286-99bb-69a2b71884b0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-24ab2b33-a0d0-4286-99bb-69a2b71884b0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-282c141a-cb3a-420a-a985-e3aeb7c2637e' class='xr-var-data-in' type='checkbox'><label for='data-282c141a-cb3a-420a-a985-e3aeb7c2637e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Geopotential</dd><dt><span>short_name :</span></dt><dd>z</dd><dt><span>standard_name :</span></dt><dd>geopotential</dd><dt><span>units :</span></dt><dd>m**2 s**-2</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>geopotential</span></div><div class='xr-var-dims'>(time, level, longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(48, 13, 64, 32), meta=np.ndarray&gt;</div><input id='attrs-35365651-24d5-4966-8fa8-0839006811ff' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-35365651-24d5-4966-8fa8-0839006811ff' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3f385c44-b079-4561-b109-1a7ea001bfd0' class='xr-var-data-in' type='checkbox'><label for='data-3f385c44-b079-4561-b109-1a7ea001bfd0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Geopotential</dd><dt><span>short_name :</span></dt><dd>z</dd><dt><span>standard_name :</span></dt><dd>geopotential</dd><dt><span>units :</span></dt><dd>m**2 s**-2</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -2812,17 +2820,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 9.28 GiB </td>\n",
-       "                        <td> 10.16 MiB </td>\n",
+       "                        <td> 4.88 MiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (93544, 13, 64, 32) </td>\n",
-       "                        <td> (100, 13, 64, 32) </td>\n",
+       "                        <td> (48, 13, 64, 32) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 936 chunks in 2 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 1949 chunks in 2 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -2843,31 +2851,31 @@
        "  <line x1=\"3\" y1=\"0\" x2=\"3\" y2=\"25\" />\n",
        "  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n",
        "  <line x1=\"11\" y1=\"0\" x2=\"11\" y2=\"25\" />\n",
-       "  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n",
+       "  <line x1=\"14\" y1=\"0\" x2=\"14\" y2=\"25\" />\n",
        "  <line x1=\"18\" y1=\"0\" x2=\"18\" y2=\"25\" />\n",
        "  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n",
        "  <line x1=\"26\" y1=\"0\" x2=\"26\" y2=\"25\" />\n",
-       "  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n",
+       "  <line x1=\"29\" y1=\"0\" x2=\"29\" y2=\"25\" />\n",
        "  <line x1=\"33\" y1=\"0\" x2=\"33\" y2=\"25\" />\n",
        "  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n",
        "  <line x1=\"41\" y1=\"0\" x2=\"41\" y2=\"25\" />\n",
-       "  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n",
+       "  <line x1=\"44\" y1=\"0\" x2=\"44\" y2=\"25\" />\n",
        "  <line x1=\"48\" y1=\"0\" x2=\"48\" y2=\"25\" />\n",
        "  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n",
        "  <line x1=\"56\" y1=\"0\" x2=\"56\" y2=\"25\" />\n",
-       "  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n",
+       "  <line x1=\"59\" y1=\"0\" x2=\"59\" y2=\"25\" />\n",
        "  <line x1=\"63\" y1=\"0\" x2=\"63\" y2=\"25\" />\n",
        "  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n",
        "  <line x1=\"71\" y1=\"0\" x2=\"71\" y2=\"25\" />\n",
-       "  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n",
+       "  <line x1=\"74\" y1=\"0\" x2=\"74\" y2=\"25\" />\n",
        "  <line x1=\"78\" y1=\"0\" x2=\"78\" y2=\"25\" />\n",
        "  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n",
        "  <line x1=\"86\" y1=\"0\" x2=\"86\" y2=\"25\" />\n",
-       "  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n",
+       "  <line x1=\"89\" y1=\"0\" x2=\"89\" y2=\"25\" />\n",
        "  <line x1=\"93\" y1=\"0\" x2=\"93\" y2=\"25\" />\n",
        "  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n",
        "  <line x1=\"101\" y1=\"0\" x2=\"101\" y2=\"25\" />\n",
-       "  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n",
+       "  <line x1=\"104\" y1=\"0\" x2=\"104\" y2=\"25\" />\n",
        "  <line x1=\"108\" y1=\"0\" x2=\"108\" y2=\"25\" />\n",
        "  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n",
        "  <line x1=\"116\" y1=\"0\" x2=\"116\" y2=\"25\" />\n",
@@ -2921,7 +2929,7 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>geopotential_at_surface</span></div><div class='xr-var-dims'>(longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(64, 32), meta=np.ndarray&gt;</div><input id='attrs-87c3657d-ac58-4703-afe1-49bb663a5503' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-87c3657d-ac58-4703-afe1-49bb663a5503' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6327023a-0343-4217-a844-f72d11ba2dde' class='xr-var-data-in' type='checkbox'><label for='data-6327023a-0343-4217-a844-f72d11ba2dde' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Geopotential</dd><dt><span>short_name :</span></dt><dd>z</dd><dt><span>standard_name :</span></dt><dd>geopotential</dd><dt><span>units :</span></dt><dd>m**2 s**-2</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>geopotential_at_surface</span></div><div class='xr-var-dims'>(longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(64, 32), meta=np.ndarray&gt;</div><input id='attrs-a01e4e9c-0570-4127-8ead-d1ee6aeeaeda' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a01e4e9c-0570-4127-8ead-d1ee6aeeaeda' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4f62d9d9-204c-40a8-a5e7-4290e0f5d1b5' class='xr-var-data-in' type='checkbox'><label for='data-4f62d9d9-204c-40a8-a5e7-4290e0f5d1b5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Geopotential</dd><dt><span>short_name :</span></dt><dd>z</dd><dt><span>standard_name :</span></dt><dd>geopotential</dd><dt><span>units :</span></dt><dd>m**2 s**-2</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -2976,7 +2984,7 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>geostrophic_wind_speed</span></div><div class='xr-var-dims'>(time, level, longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(100, 13, 64, 32), meta=np.ndarray&gt;</div><input id='attrs-c48b0e4a-05bb-43d0-92fb-3a35f80fa0d4' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c48b0e4a-05bb-43d0-92fb-3a35f80fa0d4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d6f529f5-c61a-4c23-b0bc-ca0307b7dd89' class='xr-var-data-in' type='checkbox'><label for='data-d6f529f5-c61a-4c23-b0bc-ca0307b7dd89' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>geostrophic_wind_speed</span></div><div class='xr-var-dims'>(time, level, longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(48, 13, 64, 32), meta=np.ndarray&gt;</div><input id='attrs-08b1f146-290d-4bbc-b456-caef08fc956e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-08b1f146-290d-4bbc-b456-caef08fc956e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-440eb057-2a5e-49a0-8820-7aecc096c239' class='xr-var-data-in' type='checkbox'><label for='data-440eb057-2a5e-49a0-8820-7aecc096c239' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -2992,17 +3000,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 9.28 GiB </td>\n",
-       "                        <td> 10.16 MiB </td>\n",
+       "                        <td> 4.88 MiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (93544, 13, 64, 32) </td>\n",
-       "                        <td> (100, 13, 64, 32) </td>\n",
+       "                        <td> (48, 13, 64, 32) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 936 chunks in 2 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 1949 chunks in 2 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -3023,31 +3031,31 @@
        "  <line x1=\"3\" y1=\"0\" x2=\"3\" y2=\"25\" />\n",
        "  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n",
        "  <line x1=\"11\" y1=\"0\" x2=\"11\" y2=\"25\" />\n",
-       "  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n",
+       "  <line x1=\"14\" y1=\"0\" x2=\"14\" y2=\"25\" />\n",
        "  <line x1=\"18\" y1=\"0\" x2=\"18\" y2=\"25\" />\n",
        "  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n",
        "  <line x1=\"26\" y1=\"0\" x2=\"26\" y2=\"25\" />\n",
-       "  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n",
+       "  <line x1=\"29\" y1=\"0\" x2=\"29\" y2=\"25\" />\n",
        "  <line x1=\"33\" y1=\"0\" x2=\"33\" y2=\"25\" />\n",
        "  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n",
        "  <line x1=\"41\" y1=\"0\" x2=\"41\" y2=\"25\" />\n",
-       "  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n",
+       "  <line x1=\"44\" y1=\"0\" x2=\"44\" y2=\"25\" />\n",
        "  <line x1=\"48\" y1=\"0\" x2=\"48\" y2=\"25\" />\n",
        "  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n",
        "  <line x1=\"56\" y1=\"0\" x2=\"56\" y2=\"25\" />\n",
-       "  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n",
+       "  <line x1=\"59\" y1=\"0\" x2=\"59\" y2=\"25\" />\n",
        "  <line x1=\"63\" y1=\"0\" x2=\"63\" y2=\"25\" />\n",
        "  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n",
        "  <line x1=\"71\" y1=\"0\" x2=\"71\" y2=\"25\" />\n",
-       "  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n",
+       "  <line x1=\"74\" y1=\"0\" x2=\"74\" y2=\"25\" />\n",
        "  <line x1=\"78\" y1=\"0\" x2=\"78\" y2=\"25\" />\n",
        "  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n",
        "  <line x1=\"86\" y1=\"0\" x2=\"86\" y2=\"25\" />\n",
-       "  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n",
+       "  <line x1=\"89\" y1=\"0\" x2=\"89\" y2=\"25\" />\n",
        "  <line x1=\"93\" y1=\"0\" x2=\"93\" y2=\"25\" />\n",
        "  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n",
        "  <line x1=\"101\" y1=\"0\" x2=\"101\" y2=\"25\" />\n",
-       "  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n",
+       "  <line x1=\"104\" y1=\"0\" x2=\"104\" y2=\"25\" />\n",
        "  <line x1=\"108\" y1=\"0\" x2=\"108\" y2=\"25\" />\n",
        "  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n",
        "  <line x1=\"116\" y1=\"0\" x2=\"116\" y2=\"25\" />\n",
@@ -3101,7 +3109,7 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>high_vegetation_cover</span></div><div class='xr-var-dims'>(longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(64, 32), meta=np.ndarray&gt;</div><input id='attrs-b681b21a-a063-4c18-8003-aa9de531c20a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b681b21a-a063-4c18-8003-aa9de531c20a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ea4c9546-5003-456c-b08a-292928e9e804' class='xr-var-data-in' type='checkbox'><label for='data-ea4c9546-5003-456c-b08a-292928e9e804' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>High vegetation cover</dd><dt><span>short_name :</span></dt><dd>cvh</dd><dt><span>units :</span></dt><dd>(0 - 1)</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>high_vegetation_cover</span></div><div class='xr-var-dims'>(longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(64, 32), meta=np.ndarray&gt;</div><input id='attrs-9d26d2ae-73e0-4c72-9722-2d47178ce5f9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9d26d2ae-73e0-4c72-9722-2d47178ce5f9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-82c3176f-5d3c-4c9e-885d-00adb82817fe' class='xr-var-data-in' type='checkbox'><label for='data-82c3176f-5d3c-4c9e-885d-00adb82817fe' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>High vegetation cover</dd><dt><span>short_name :</span></dt><dd>cvh</dd><dt><span>units :</span></dt><dd>(0 - 1)</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -3156,7 +3164,7 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>integrated_vapor_transport</span></div><div class='xr-var-dims'>(time, longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(100, 64, 32), meta=np.ndarray&gt;</div><input id='attrs-c48d5f9a-bb01-42c1-a5ba-18cda04a73f2' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c48d5f9a-bb01-42c1-a5ba-18cda04a73f2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d7bf6c9b-4b1e-4e84-b84f-1feee3d62268' class='xr-var-data-in' type='checkbox'><label for='data-d7bf6c9b-4b1e-4e84-b84f-1feee3d62268' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>integrated_vapor_transport</span></div><div class='xr-var-dims'>(time, longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(48, 64, 32), meta=np.ndarray&gt;</div><input id='attrs-87aedd6f-8959-4929-beee-41cb72e0bf33' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-87aedd6f-8959-4929-beee-41cb72e0bf33' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-05df653b-1489-4ab4-a89d-ebe418b0f353' class='xr-var-data-in' type='checkbox'><label for='data-05df653b-1489-4ab4-a89d-ebe418b0f353' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -3172,17 +3180,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 730.81 MiB </td>\n",
-       "                        <td> 800.00 kiB </td>\n",
+       "                        <td> 384.00 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (93544, 64, 32) </td>\n",
-       "                        <td> (100, 64, 32) </td>\n",
+       "                        <td> (48, 64, 32) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 936 chunks in 2 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 1949 chunks in 2 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -3206,14 +3214,14 @@
        "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"40\" />\n",
        "  <line x1=\"28\" y1=\"18\" x2=\"28\" y2=\"43\" />\n",
        "  <line x1=\"32\" y1=\"22\" x2=\"32\" y2=\"47\" />\n",
-       "  <line x1=\"35\" y1=\"25\" x2=\"35\" y2=\"51\" />\n",
+       "  <line x1=\"36\" y1=\"26\" x2=\"36\" y2=\"51\" />\n",
        "  <line x1=\"39\" y1=\"29\" x2=\"39\" y2=\"55\" />\n",
        "  <line x1=\"43\" y1=\"33\" x2=\"43\" y2=\"58\" />\n",
        "  <line x1=\"47\" y1=\"37\" x2=\"47\" y2=\"62\" />\n",
        "  <line x1=\"50\" y1=\"40\" x2=\"50\" y2=\"66\" />\n",
-       "  <line x1=\"54\" y1=\"44\" x2=\"54\" y2=\"70\" />\n",
+       "  <line x1=\"54\" y1=\"44\" x2=\"54\" y2=\"69\" />\n",
        "  <line x1=\"58\" y1=\"48\" x2=\"58\" y2=\"73\" />\n",
-       "  <line x1=\"61\" y1=\"51\" x2=\"61\" y2=\"77\" />\n",
+       "  <line x1=\"62\" y1=\"52\" x2=\"62\" y2=\"77\" />\n",
        "  <line x1=\"65\" y1=\"55\" x2=\"65\" y2=\"81\" />\n",
        "  <line x1=\"69\" y1=\"59\" x2=\"69\" y2=\"84\" />\n",
        "  <line x1=\"73\" y1=\"63\" x2=\"73\" y2=\"88\" />\n",
@@ -3231,14 +3239,14 @@
        "  <line x1=\"24\" y1=\"14\" x2=\"50\" y2=\"14\" />\n",
        "  <line x1=\"28\" y1=\"18\" x2=\"53\" y2=\"18\" />\n",
        "  <line x1=\"32\" y1=\"22\" x2=\"57\" y2=\"22\" />\n",
-       "  <line x1=\"35\" y1=\"25\" x2=\"61\" y2=\"25\" />\n",
+       "  <line x1=\"36\" y1=\"26\" x2=\"61\" y2=\"26\" />\n",
        "  <line x1=\"39\" y1=\"29\" x2=\"65\" y2=\"29\" />\n",
        "  <line x1=\"43\" y1=\"33\" x2=\"68\" y2=\"33\" />\n",
        "  <line x1=\"47\" y1=\"37\" x2=\"72\" y2=\"37\" />\n",
        "  <line x1=\"50\" y1=\"40\" x2=\"76\" y2=\"40\" />\n",
-       "  <line x1=\"54\" y1=\"44\" x2=\"80\" y2=\"44\" />\n",
+       "  <line x1=\"54\" y1=\"44\" x2=\"79\" y2=\"44\" />\n",
        "  <line x1=\"58\" y1=\"48\" x2=\"83\" y2=\"48\" />\n",
-       "  <line x1=\"61\" y1=\"51\" x2=\"87\" y2=\"51\" />\n",
+       "  <line x1=\"62\" y1=\"52\" x2=\"87\" y2=\"52\" />\n",
        "  <line x1=\"65\" y1=\"55\" x2=\"91\" y2=\"55\" />\n",
        "  <line x1=\"69\" y1=\"59\" x2=\"94\" y2=\"59\" />\n",
        "  <line x1=\"73\" y1=\"63\" x2=\"98\" y2=\"63\" />\n",
@@ -3270,7 +3278,7 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lake_cover</span></div><div class='xr-var-dims'>(longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(64, 32), meta=np.ndarray&gt;</div><input id='attrs-ba8b010f-8f7d-4c53-baca-ecf1e5313fd8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ba8b010f-8f7d-4c53-baca-ecf1e5313fd8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-aed430d3-533b-40dd-a257-1987140818f7' class='xr-var-data-in' type='checkbox'><label for='data-aed430d3-533b-40dd-a257-1987140818f7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Lake cover</dd><dt><span>short_name :</span></dt><dd>cl</dd><dt><span>units :</span></dt><dd>(0 - 1)</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lake_cover</span></div><div class='xr-var-dims'>(longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(64, 32), meta=np.ndarray&gt;</div><input id='attrs-1944b985-db29-4dc0-96c6-10c6d83491bd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1944b985-db29-4dc0-96c6-10c6d83491bd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-41f5f5bf-7163-4175-9984-043e63bac143' class='xr-var-data-in' type='checkbox'><label for='data-41f5f5bf-7163-4175-9984-043e63bac143' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Lake cover</dd><dt><span>short_name :</span></dt><dd>cl</dd><dt><span>units :</span></dt><dd>(0 - 1)</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -3325,7 +3333,7 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>land_sea_mask</span></div><div class='xr-var-dims'>(longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(64, 32), meta=np.ndarray&gt;</div><input id='attrs-b5878ba9-d41a-471e-be42-5a797359e8ec' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b5878ba9-d41a-471e-be42-5a797359e8ec' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-694505f6-88c7-4a79-9876-77ffefd499a5' class='xr-var-data-in' type='checkbox'><label for='data-694505f6-88c7-4a79-9876-77ffefd499a5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Land-sea mask</dd><dt><span>short_name :</span></dt><dd>lsm</dd><dt><span>standard_name :</span></dt><dd>land_binary_mask</dd><dt><span>units :</span></dt><dd>(0 - 1)</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>land_sea_mask</span></div><div class='xr-var-dims'>(longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(64, 32), meta=np.ndarray&gt;</div><input id='attrs-cc98ef21-a954-41b1-aa42-d19adc938a2c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cc98ef21-a954-41b1-aa42-d19adc938a2c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fe50e744-0edb-4c45-9d7a-6b75ea49e327' class='xr-var-data-in' type='checkbox'><label for='data-fe50e744-0edb-4c45-9d7a-6b75ea49e327' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Land-sea mask</dd><dt><span>short_name :</span></dt><dd>lsm</dd><dt><span>standard_name :</span></dt><dd>land_binary_mask</dd><dt><span>units :</span></dt><dd>(0 - 1)</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -3380,7 +3388,7 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lapse_rate</span></div><div class='xr-var-dims'>(time, level, longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(100, 13, 64, 32), meta=np.ndarray&gt;</div><input id='attrs-4e4f34f8-e55a-49ca-bed4-5c9858315e66' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4e4f34f8-e55a-49ca-bed4-5c9858315e66' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9c95e16a-2e91-4e07-af08-edadc95a6a92' class='xr-var-data-in' type='checkbox'><label for='data-9c95e16a-2e91-4e07-af08-edadc95a6a92' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lapse_rate</span></div><div class='xr-var-dims'>(time, level, longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(48, 13, 64, 32), meta=np.ndarray&gt;</div><input id='attrs-78403719-16db-49d8-85bc-68fb6eec4bc6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-78403719-16db-49d8-85bc-68fb6eec4bc6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7685adf6-cab3-4a82-a2ac-06dec37bdd81' class='xr-var-data-in' type='checkbox'><label for='data-7685adf6-cab3-4a82-a2ac-06dec37bdd81' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -3396,17 +3404,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 9.28 GiB </td>\n",
-       "                        <td> 10.16 MiB </td>\n",
+       "                        <td> 4.88 MiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (93544, 13, 64, 32) </td>\n",
-       "                        <td> (100, 13, 64, 32) </td>\n",
+       "                        <td> (48, 13, 64, 32) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 936 chunks in 2 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 1949 chunks in 2 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -3427,31 +3435,31 @@
        "  <line x1=\"3\" y1=\"0\" x2=\"3\" y2=\"25\" />\n",
        "  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n",
        "  <line x1=\"11\" y1=\"0\" x2=\"11\" y2=\"25\" />\n",
-       "  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n",
+       "  <line x1=\"14\" y1=\"0\" x2=\"14\" y2=\"25\" />\n",
        "  <line x1=\"18\" y1=\"0\" x2=\"18\" y2=\"25\" />\n",
        "  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n",
        "  <line x1=\"26\" y1=\"0\" x2=\"26\" y2=\"25\" />\n",
-       "  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n",
+       "  <line x1=\"29\" y1=\"0\" x2=\"29\" y2=\"25\" />\n",
        "  <line x1=\"33\" y1=\"0\" x2=\"33\" y2=\"25\" />\n",
        "  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n",
        "  <line x1=\"41\" y1=\"0\" x2=\"41\" y2=\"25\" />\n",
-       "  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n",
+       "  <line x1=\"44\" y1=\"0\" x2=\"44\" y2=\"25\" />\n",
        "  <line x1=\"48\" y1=\"0\" x2=\"48\" y2=\"25\" />\n",
        "  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n",
        "  <line x1=\"56\" y1=\"0\" x2=\"56\" y2=\"25\" />\n",
-       "  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n",
+       "  <line x1=\"59\" y1=\"0\" x2=\"59\" y2=\"25\" />\n",
        "  <line x1=\"63\" y1=\"0\" x2=\"63\" y2=\"25\" />\n",
        "  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n",
        "  <line x1=\"71\" y1=\"0\" x2=\"71\" y2=\"25\" />\n",
-       "  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n",
+       "  <line x1=\"74\" y1=\"0\" x2=\"74\" y2=\"25\" />\n",
        "  <line x1=\"78\" y1=\"0\" x2=\"78\" y2=\"25\" />\n",
        "  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n",
        "  <line x1=\"86\" y1=\"0\" x2=\"86\" y2=\"25\" />\n",
-       "  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n",
+       "  <line x1=\"89\" y1=\"0\" x2=\"89\" y2=\"25\" />\n",
        "  <line x1=\"93\" y1=\"0\" x2=\"93\" y2=\"25\" />\n",
        "  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n",
        "  <line x1=\"101\" y1=\"0\" x2=\"101\" y2=\"25\" />\n",
-       "  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n",
+       "  <line x1=\"104\" y1=\"0\" x2=\"104\" y2=\"25\" />\n",
        "  <line x1=\"108\" y1=\"0\" x2=\"108\" y2=\"25\" />\n",
        "  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n",
        "  <line x1=\"116\" y1=\"0\" x2=\"116\" y2=\"25\" />\n",
@@ -3505,7 +3513,7 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>leaf_area_index_high_vegetation</span></div><div class='xr-var-dims'>(time, longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(100, 64, 32), meta=np.ndarray&gt;</div><input id='attrs-4cfca378-254b-4539-b557-f75078eb16c1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4cfca378-254b-4539-b557-f75078eb16c1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-25952fa0-081b-4134-a539-e866e30e1885' class='xr-var-data-in' type='checkbox'><label for='data-25952fa0-081b-4134-a539-e866e30e1885' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Leaf area index, high vegetation</dd><dt><span>short_name :</span></dt><dd>lai_hv</dd><dt><span>units :</span></dt><dd>m**2 m**-2</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>leaf_area_index_high_vegetation</span></div><div class='xr-var-dims'>(time, longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(48, 64, 32), meta=np.ndarray&gt;</div><input id='attrs-1186f3f4-9d91-4972-a9cf-af3b2cb3a7eb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1186f3f4-9d91-4972-a9cf-af3b2cb3a7eb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cb97dac7-366b-4518-8b0d-a728c75ca6a5' class='xr-var-data-in' type='checkbox'><label for='data-cb97dac7-366b-4518-8b0d-a728c75ca6a5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Leaf area index, high vegetation</dd><dt><span>short_name :</span></dt><dd>lai_hv</dd><dt><span>units :</span></dt><dd>m**2 m**-2</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -3521,17 +3529,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 730.81 MiB </td>\n",
-       "                        <td> 800.00 kiB </td>\n",
+       "                        <td> 384.00 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (93544, 64, 32) </td>\n",
-       "                        <td> (100, 64, 32) </td>\n",
+       "                        <td> (48, 64, 32) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 936 chunks in 2 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 1949 chunks in 2 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -3555,14 +3563,14 @@
        "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"40\" />\n",
        "  <line x1=\"28\" y1=\"18\" x2=\"28\" y2=\"43\" />\n",
        "  <line x1=\"32\" y1=\"22\" x2=\"32\" y2=\"47\" />\n",
-       "  <line x1=\"35\" y1=\"25\" x2=\"35\" y2=\"51\" />\n",
+       "  <line x1=\"36\" y1=\"26\" x2=\"36\" y2=\"51\" />\n",
        "  <line x1=\"39\" y1=\"29\" x2=\"39\" y2=\"55\" />\n",
        "  <line x1=\"43\" y1=\"33\" x2=\"43\" y2=\"58\" />\n",
        "  <line x1=\"47\" y1=\"37\" x2=\"47\" y2=\"62\" />\n",
        "  <line x1=\"50\" y1=\"40\" x2=\"50\" y2=\"66\" />\n",
-       "  <line x1=\"54\" y1=\"44\" x2=\"54\" y2=\"70\" />\n",
+       "  <line x1=\"54\" y1=\"44\" x2=\"54\" y2=\"69\" />\n",
        "  <line x1=\"58\" y1=\"48\" x2=\"58\" y2=\"73\" />\n",
-       "  <line x1=\"61\" y1=\"51\" x2=\"61\" y2=\"77\" />\n",
+       "  <line x1=\"62\" y1=\"52\" x2=\"62\" y2=\"77\" />\n",
        "  <line x1=\"65\" y1=\"55\" x2=\"65\" y2=\"81\" />\n",
        "  <line x1=\"69\" y1=\"59\" x2=\"69\" y2=\"84\" />\n",
        "  <line x1=\"73\" y1=\"63\" x2=\"73\" y2=\"88\" />\n",
@@ -3580,14 +3588,14 @@
        "  <line x1=\"24\" y1=\"14\" x2=\"50\" y2=\"14\" />\n",
        "  <line x1=\"28\" y1=\"18\" x2=\"53\" y2=\"18\" />\n",
        "  <line x1=\"32\" y1=\"22\" x2=\"57\" y2=\"22\" />\n",
-       "  <line x1=\"35\" y1=\"25\" x2=\"61\" y2=\"25\" />\n",
+       "  <line x1=\"36\" y1=\"26\" x2=\"61\" y2=\"26\" />\n",
        "  <line x1=\"39\" y1=\"29\" x2=\"65\" y2=\"29\" />\n",
        "  <line x1=\"43\" y1=\"33\" x2=\"68\" y2=\"33\" />\n",
        "  <line x1=\"47\" y1=\"37\" x2=\"72\" y2=\"37\" />\n",
        "  <line x1=\"50\" y1=\"40\" x2=\"76\" y2=\"40\" />\n",
-       "  <line x1=\"54\" y1=\"44\" x2=\"80\" y2=\"44\" />\n",
+       "  <line x1=\"54\" y1=\"44\" x2=\"79\" y2=\"44\" />\n",
        "  <line x1=\"58\" y1=\"48\" x2=\"83\" y2=\"48\" />\n",
-       "  <line x1=\"61\" y1=\"51\" x2=\"87\" y2=\"51\" />\n",
+       "  <line x1=\"62\" y1=\"52\" x2=\"87\" y2=\"52\" />\n",
        "  <line x1=\"65\" y1=\"55\" x2=\"91\" y2=\"55\" />\n",
        "  <line x1=\"69\" y1=\"59\" x2=\"94\" y2=\"59\" />\n",
        "  <line x1=\"73\" y1=\"63\" x2=\"98\" y2=\"63\" />\n",
@@ -3619,7 +3627,7 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>leaf_area_index_low_vegetation</span></div><div class='xr-var-dims'>(time, longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(100, 64, 32), meta=np.ndarray&gt;</div><input id='attrs-44f79726-e29b-49cb-967d-b0945e0b5380' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-44f79726-e29b-49cb-967d-b0945e0b5380' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-97fc10a2-b63c-4545-979b-cffea155592d' class='xr-var-data-in' type='checkbox'><label for='data-97fc10a2-b63c-4545-979b-cffea155592d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Leaf area index, low vegetation</dd><dt><span>short_name :</span></dt><dd>lai_lv</dd><dt><span>units :</span></dt><dd>m**2 m**-2</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>leaf_area_index_low_vegetation</span></div><div class='xr-var-dims'>(time, longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(48, 64, 32), meta=np.ndarray&gt;</div><input id='attrs-96f2c0fa-750b-4b31-be1c-083e4512f94e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-96f2c0fa-750b-4b31-be1c-083e4512f94e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5c2bc5dc-c705-4400-aa05-0e0a6ad3cb1e' class='xr-var-data-in' type='checkbox'><label for='data-5c2bc5dc-c705-4400-aa05-0e0a6ad3cb1e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Leaf area index, low vegetation</dd><dt><span>short_name :</span></dt><dd>lai_lv</dd><dt><span>units :</span></dt><dd>m**2 m**-2</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -3635,17 +3643,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 730.81 MiB </td>\n",
-       "                        <td> 800.00 kiB </td>\n",
+       "                        <td> 384.00 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (93544, 64, 32) </td>\n",
-       "                        <td> (100, 64, 32) </td>\n",
+       "                        <td> (48, 64, 32) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 936 chunks in 2 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 1949 chunks in 2 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -3669,14 +3677,14 @@
        "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"40\" />\n",
        "  <line x1=\"28\" y1=\"18\" x2=\"28\" y2=\"43\" />\n",
        "  <line x1=\"32\" y1=\"22\" x2=\"32\" y2=\"47\" />\n",
-       "  <line x1=\"35\" y1=\"25\" x2=\"35\" y2=\"51\" />\n",
+       "  <line x1=\"36\" y1=\"26\" x2=\"36\" y2=\"51\" />\n",
        "  <line x1=\"39\" y1=\"29\" x2=\"39\" y2=\"55\" />\n",
        "  <line x1=\"43\" y1=\"33\" x2=\"43\" y2=\"58\" />\n",
        "  <line x1=\"47\" y1=\"37\" x2=\"47\" y2=\"62\" />\n",
        "  <line x1=\"50\" y1=\"40\" x2=\"50\" y2=\"66\" />\n",
-       "  <line x1=\"54\" y1=\"44\" x2=\"54\" y2=\"70\" />\n",
+       "  <line x1=\"54\" y1=\"44\" x2=\"54\" y2=\"69\" />\n",
        "  <line x1=\"58\" y1=\"48\" x2=\"58\" y2=\"73\" />\n",
-       "  <line x1=\"61\" y1=\"51\" x2=\"61\" y2=\"77\" />\n",
+       "  <line x1=\"62\" y1=\"52\" x2=\"62\" y2=\"77\" />\n",
        "  <line x1=\"65\" y1=\"55\" x2=\"65\" y2=\"81\" />\n",
        "  <line x1=\"69\" y1=\"59\" x2=\"69\" y2=\"84\" />\n",
        "  <line x1=\"73\" y1=\"63\" x2=\"73\" y2=\"88\" />\n",
@@ -3694,14 +3702,14 @@
        "  <line x1=\"24\" y1=\"14\" x2=\"50\" y2=\"14\" />\n",
        "  <line x1=\"28\" y1=\"18\" x2=\"53\" y2=\"18\" />\n",
        "  <line x1=\"32\" y1=\"22\" x2=\"57\" y2=\"22\" />\n",
-       "  <line x1=\"35\" y1=\"25\" x2=\"61\" y2=\"25\" />\n",
+       "  <line x1=\"36\" y1=\"26\" x2=\"61\" y2=\"26\" />\n",
        "  <line x1=\"39\" y1=\"29\" x2=\"65\" y2=\"29\" />\n",
        "  <line x1=\"43\" y1=\"33\" x2=\"68\" y2=\"33\" />\n",
        "  <line x1=\"47\" y1=\"37\" x2=\"72\" y2=\"37\" />\n",
        "  <line x1=\"50\" y1=\"40\" x2=\"76\" y2=\"40\" />\n",
-       "  <line x1=\"54\" y1=\"44\" x2=\"80\" y2=\"44\" />\n",
+       "  <line x1=\"54\" y1=\"44\" x2=\"79\" y2=\"44\" />\n",
        "  <line x1=\"58\" y1=\"48\" x2=\"83\" y2=\"48\" />\n",
-       "  <line x1=\"61\" y1=\"51\" x2=\"87\" y2=\"51\" />\n",
+       "  <line x1=\"62\" y1=\"52\" x2=\"87\" y2=\"52\" />\n",
        "  <line x1=\"65\" y1=\"55\" x2=\"91\" y2=\"55\" />\n",
        "  <line x1=\"69\" y1=\"59\" x2=\"94\" y2=\"59\" />\n",
        "  <line x1=\"73\" y1=\"63\" x2=\"98\" y2=\"63\" />\n",
@@ -3733,7 +3741,7 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>low_vegetation_cover</span></div><div class='xr-var-dims'>(longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(64, 32), meta=np.ndarray&gt;</div><input id='attrs-94d63713-d755-4f21-9410-ec1cedaa62e0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-94d63713-d755-4f21-9410-ec1cedaa62e0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8ba845c9-9b84-4bb5-9172-5d5159fc1f22' class='xr-var-data-in' type='checkbox'><label for='data-8ba845c9-9b84-4bb5-9172-5d5159fc1f22' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Low vegetation cover</dd><dt><span>short_name :</span></dt><dd>cvl</dd><dt><span>units :</span></dt><dd>(0 - 1)</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>low_vegetation_cover</span></div><div class='xr-var-dims'>(longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(64, 32), meta=np.ndarray&gt;</div><input id='attrs-754dd1f9-0a2e-4efa-97f4-8f43c90d7939' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-754dd1f9-0a2e-4efa-97f4-8f43c90d7939' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7120d69d-9d42-4f66-bb75-06f7939c5223' class='xr-var-data-in' type='checkbox'><label for='data-7120d69d-9d42-4f66-bb75-06f7939c5223' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Low vegetation cover</dd><dt><span>short_name :</span></dt><dd>cvl</dd><dt><span>units :</span></dt><dd>(0 - 1)</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -3788,7 +3796,7 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mean_sea_level_pressure</span></div><div class='xr-var-dims'>(time, longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(100, 64, 32), meta=np.ndarray&gt;</div><input id='attrs-2526be45-39fe-4cb1-beb1-dd8756ce2a9c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2526be45-39fe-4cb1-beb1-dd8756ce2a9c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-047b7b9f-263d-4133-835d-c49dab68a2b4' class='xr-var-data-in' type='checkbox'><label for='data-047b7b9f-263d-4133-835d-c49dab68a2b4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Mean sea level pressure</dd><dt><span>short_name :</span></dt><dd>msl</dd><dt><span>standard_name :</span></dt><dd>air_pressure_at_mean_sea_level</dd><dt><span>units :</span></dt><dd>Pa</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mean_sea_level_pressure</span></div><div class='xr-var-dims'>(time, longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(48, 64, 32), meta=np.ndarray&gt;</div><input id='attrs-bdfc89d5-3f6c-4060-bd20-88859a8d7567' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bdfc89d5-3f6c-4060-bd20-88859a8d7567' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-420327e6-c872-4670-819c-5bdc85305bc3' class='xr-var-data-in' type='checkbox'><label for='data-420327e6-c872-4670-819c-5bdc85305bc3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Mean sea level pressure</dd><dt><span>short_name :</span></dt><dd>msl</dd><dt><span>standard_name :</span></dt><dd>air_pressure_at_mean_sea_level</dd><dt><span>units :</span></dt><dd>Pa</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -3804,17 +3812,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 730.81 MiB </td>\n",
-       "                        <td> 800.00 kiB </td>\n",
+       "                        <td> 384.00 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (93544, 64, 32) </td>\n",
-       "                        <td> (100, 64, 32) </td>\n",
+       "                        <td> (48, 64, 32) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 936 chunks in 2 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 1949 chunks in 2 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -3838,14 +3846,14 @@
        "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"40\" />\n",
        "  <line x1=\"28\" y1=\"18\" x2=\"28\" y2=\"43\" />\n",
        "  <line x1=\"32\" y1=\"22\" x2=\"32\" y2=\"47\" />\n",
-       "  <line x1=\"35\" y1=\"25\" x2=\"35\" y2=\"51\" />\n",
+       "  <line x1=\"36\" y1=\"26\" x2=\"36\" y2=\"51\" />\n",
        "  <line x1=\"39\" y1=\"29\" x2=\"39\" y2=\"55\" />\n",
        "  <line x1=\"43\" y1=\"33\" x2=\"43\" y2=\"58\" />\n",
        "  <line x1=\"47\" y1=\"37\" x2=\"47\" y2=\"62\" />\n",
        "  <line x1=\"50\" y1=\"40\" x2=\"50\" y2=\"66\" />\n",
-       "  <line x1=\"54\" y1=\"44\" x2=\"54\" y2=\"70\" />\n",
+       "  <line x1=\"54\" y1=\"44\" x2=\"54\" y2=\"69\" />\n",
        "  <line x1=\"58\" y1=\"48\" x2=\"58\" y2=\"73\" />\n",
-       "  <line x1=\"61\" y1=\"51\" x2=\"61\" y2=\"77\" />\n",
+       "  <line x1=\"62\" y1=\"52\" x2=\"62\" y2=\"77\" />\n",
        "  <line x1=\"65\" y1=\"55\" x2=\"65\" y2=\"81\" />\n",
        "  <line x1=\"69\" y1=\"59\" x2=\"69\" y2=\"84\" />\n",
        "  <line x1=\"73\" y1=\"63\" x2=\"73\" y2=\"88\" />\n",
@@ -3863,14 +3871,14 @@
        "  <line x1=\"24\" y1=\"14\" x2=\"50\" y2=\"14\" />\n",
        "  <line x1=\"28\" y1=\"18\" x2=\"53\" y2=\"18\" />\n",
        "  <line x1=\"32\" y1=\"22\" x2=\"57\" y2=\"22\" />\n",
-       "  <line x1=\"35\" y1=\"25\" x2=\"61\" y2=\"25\" />\n",
+       "  <line x1=\"36\" y1=\"26\" x2=\"61\" y2=\"26\" />\n",
        "  <line x1=\"39\" y1=\"29\" x2=\"65\" y2=\"29\" />\n",
        "  <line x1=\"43\" y1=\"33\" x2=\"68\" y2=\"33\" />\n",
        "  <line x1=\"47\" y1=\"37\" x2=\"72\" y2=\"37\" />\n",
        "  <line x1=\"50\" y1=\"40\" x2=\"76\" y2=\"40\" />\n",
-       "  <line x1=\"54\" y1=\"44\" x2=\"80\" y2=\"44\" />\n",
+       "  <line x1=\"54\" y1=\"44\" x2=\"79\" y2=\"44\" />\n",
        "  <line x1=\"58\" y1=\"48\" x2=\"83\" y2=\"48\" />\n",
-       "  <line x1=\"61\" y1=\"51\" x2=\"87\" y2=\"51\" />\n",
+       "  <line x1=\"62\" y1=\"52\" x2=\"87\" y2=\"52\" />\n",
        "  <line x1=\"65\" y1=\"55\" x2=\"91\" y2=\"55\" />\n",
        "  <line x1=\"69\" y1=\"59\" x2=\"94\" y2=\"59\" />\n",
        "  <line x1=\"73\" y1=\"63\" x2=\"98\" y2=\"63\" />\n",
@@ -3902,7 +3910,7 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mean_surface_latent_heat_flux</span></div><div class='xr-var-dims'>(time, longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(100, 64, 32), meta=np.ndarray&gt;</div><input id='attrs-411911e6-9a6e-4ae2-9933-2059138bfff2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-411911e6-9a6e-4ae2-9933-2059138bfff2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-89cb31df-b04c-4941-9143-398d3efd8b2c' class='xr-var-data-in' type='checkbox'><label for='data-89cb31df-b04c-4941-9143-398d3efd8b2c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Mean surface latent heat flux</dd><dt><span>short_name :</span></dt><dd>mslhf</dd><dt><span>units :</span></dt><dd>W m**-2</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mean_surface_latent_heat_flux</span></div><div class='xr-var-dims'>(time, longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(48, 64, 32), meta=np.ndarray&gt;</div><input id='attrs-02eacea9-289f-439e-9f05-5e3c45507d5e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-02eacea9-289f-439e-9f05-5e3c45507d5e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5b860610-ddc8-4a0e-a792-e73945251f47' class='xr-var-data-in' type='checkbox'><label for='data-5b860610-ddc8-4a0e-a792-e73945251f47' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Mean surface latent heat flux</dd><dt><span>short_name :</span></dt><dd>mslhf</dd><dt><span>units :</span></dt><dd>W m**-2</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -3918,17 +3926,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 730.81 MiB </td>\n",
-       "                        <td> 800.00 kiB </td>\n",
+       "                        <td> 384.00 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (93544, 64, 32) </td>\n",
-       "                        <td> (100, 64, 32) </td>\n",
+       "                        <td> (48, 64, 32) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 936 chunks in 2 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 1949 chunks in 2 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -3952,14 +3960,14 @@
        "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"40\" />\n",
        "  <line x1=\"28\" y1=\"18\" x2=\"28\" y2=\"43\" />\n",
        "  <line x1=\"32\" y1=\"22\" x2=\"32\" y2=\"47\" />\n",
-       "  <line x1=\"35\" y1=\"25\" x2=\"35\" y2=\"51\" />\n",
+       "  <line x1=\"36\" y1=\"26\" x2=\"36\" y2=\"51\" />\n",
        "  <line x1=\"39\" y1=\"29\" x2=\"39\" y2=\"55\" />\n",
        "  <line x1=\"43\" y1=\"33\" x2=\"43\" y2=\"58\" />\n",
        "  <line x1=\"47\" y1=\"37\" x2=\"47\" y2=\"62\" />\n",
        "  <line x1=\"50\" y1=\"40\" x2=\"50\" y2=\"66\" />\n",
-       "  <line x1=\"54\" y1=\"44\" x2=\"54\" y2=\"70\" />\n",
+       "  <line x1=\"54\" y1=\"44\" x2=\"54\" y2=\"69\" />\n",
        "  <line x1=\"58\" y1=\"48\" x2=\"58\" y2=\"73\" />\n",
-       "  <line x1=\"61\" y1=\"51\" x2=\"61\" y2=\"77\" />\n",
+       "  <line x1=\"62\" y1=\"52\" x2=\"62\" y2=\"77\" />\n",
        "  <line x1=\"65\" y1=\"55\" x2=\"65\" y2=\"81\" />\n",
        "  <line x1=\"69\" y1=\"59\" x2=\"69\" y2=\"84\" />\n",
        "  <line x1=\"73\" y1=\"63\" x2=\"73\" y2=\"88\" />\n",
@@ -3977,14 +3985,14 @@
        "  <line x1=\"24\" y1=\"14\" x2=\"50\" y2=\"14\" />\n",
        "  <line x1=\"28\" y1=\"18\" x2=\"53\" y2=\"18\" />\n",
        "  <line x1=\"32\" y1=\"22\" x2=\"57\" y2=\"22\" />\n",
-       "  <line x1=\"35\" y1=\"25\" x2=\"61\" y2=\"25\" />\n",
+       "  <line x1=\"36\" y1=\"26\" x2=\"61\" y2=\"26\" />\n",
        "  <line x1=\"39\" y1=\"29\" x2=\"65\" y2=\"29\" />\n",
        "  <line x1=\"43\" y1=\"33\" x2=\"68\" y2=\"33\" />\n",
        "  <line x1=\"47\" y1=\"37\" x2=\"72\" y2=\"37\" />\n",
        "  <line x1=\"50\" y1=\"40\" x2=\"76\" y2=\"40\" />\n",
-       "  <line x1=\"54\" y1=\"44\" x2=\"80\" y2=\"44\" />\n",
+       "  <line x1=\"54\" y1=\"44\" x2=\"79\" y2=\"44\" />\n",
        "  <line x1=\"58\" y1=\"48\" x2=\"83\" y2=\"48\" />\n",
-       "  <line x1=\"61\" y1=\"51\" x2=\"87\" y2=\"51\" />\n",
+       "  <line x1=\"62\" y1=\"52\" x2=\"87\" y2=\"52\" />\n",
        "  <line x1=\"65\" y1=\"55\" x2=\"91\" y2=\"55\" />\n",
        "  <line x1=\"69\" y1=\"59\" x2=\"94\" y2=\"59\" />\n",
        "  <line x1=\"73\" y1=\"63\" x2=\"98\" y2=\"63\" />\n",
@@ -4016,7 +4024,7 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mean_surface_net_long_wave_radiation_flux</span></div><div class='xr-var-dims'>(time, longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(100, 64, 32), meta=np.ndarray&gt;</div><input id='attrs-02653cfc-4cb1-4f4e-ad7e-32af60c5606b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-02653cfc-4cb1-4f4e-ad7e-32af60c5606b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-690e81a7-64e5-412b-9c86-2baa9b77f87b' class='xr-var-data-in' type='checkbox'><label for='data-690e81a7-64e5-412b-9c86-2baa9b77f87b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Mean surface net long-wave radiation flux</dd><dt><span>short_name :</span></dt><dd>msnlwrf</dd><dt><span>units :</span></dt><dd>W m**-2</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mean_surface_net_long_wave_radiation_flux</span></div><div class='xr-var-dims'>(time, longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(48, 64, 32), meta=np.ndarray&gt;</div><input id='attrs-5b584e5d-0ef7-4dd7-892e-b74b9f659e7f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5b584e5d-0ef7-4dd7-892e-b74b9f659e7f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0593fd9f-0fb9-4678-ac49-b1e4e9d56258' class='xr-var-data-in' type='checkbox'><label for='data-0593fd9f-0fb9-4678-ac49-b1e4e9d56258' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Mean surface net long-wave radiation flux</dd><dt><span>short_name :</span></dt><dd>msnlwrf</dd><dt><span>units :</span></dt><dd>W m**-2</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -4032,17 +4040,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 730.81 MiB </td>\n",
-       "                        <td> 800.00 kiB </td>\n",
+       "                        <td> 384.00 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (93544, 64, 32) </td>\n",
-       "                        <td> (100, 64, 32) </td>\n",
+       "                        <td> (48, 64, 32) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 936 chunks in 2 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 1949 chunks in 2 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -4066,14 +4074,14 @@
        "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"40\" />\n",
        "  <line x1=\"28\" y1=\"18\" x2=\"28\" y2=\"43\" />\n",
        "  <line x1=\"32\" y1=\"22\" x2=\"32\" y2=\"47\" />\n",
-       "  <line x1=\"35\" y1=\"25\" x2=\"35\" y2=\"51\" />\n",
+       "  <line x1=\"36\" y1=\"26\" x2=\"36\" y2=\"51\" />\n",
        "  <line x1=\"39\" y1=\"29\" x2=\"39\" y2=\"55\" />\n",
        "  <line x1=\"43\" y1=\"33\" x2=\"43\" y2=\"58\" />\n",
        "  <line x1=\"47\" y1=\"37\" x2=\"47\" y2=\"62\" />\n",
        "  <line x1=\"50\" y1=\"40\" x2=\"50\" y2=\"66\" />\n",
-       "  <line x1=\"54\" y1=\"44\" x2=\"54\" y2=\"70\" />\n",
+       "  <line x1=\"54\" y1=\"44\" x2=\"54\" y2=\"69\" />\n",
        "  <line x1=\"58\" y1=\"48\" x2=\"58\" y2=\"73\" />\n",
-       "  <line x1=\"61\" y1=\"51\" x2=\"61\" y2=\"77\" />\n",
+       "  <line x1=\"62\" y1=\"52\" x2=\"62\" y2=\"77\" />\n",
        "  <line x1=\"65\" y1=\"55\" x2=\"65\" y2=\"81\" />\n",
        "  <line x1=\"69\" y1=\"59\" x2=\"69\" y2=\"84\" />\n",
        "  <line x1=\"73\" y1=\"63\" x2=\"73\" y2=\"88\" />\n",
@@ -4091,14 +4099,14 @@
        "  <line x1=\"24\" y1=\"14\" x2=\"50\" y2=\"14\" />\n",
        "  <line x1=\"28\" y1=\"18\" x2=\"53\" y2=\"18\" />\n",
        "  <line x1=\"32\" y1=\"22\" x2=\"57\" y2=\"22\" />\n",
-       "  <line x1=\"35\" y1=\"25\" x2=\"61\" y2=\"25\" />\n",
+       "  <line x1=\"36\" y1=\"26\" x2=\"61\" y2=\"26\" />\n",
        "  <line x1=\"39\" y1=\"29\" x2=\"65\" y2=\"29\" />\n",
        "  <line x1=\"43\" y1=\"33\" x2=\"68\" y2=\"33\" />\n",
        "  <line x1=\"47\" y1=\"37\" x2=\"72\" y2=\"37\" />\n",
        "  <line x1=\"50\" y1=\"40\" x2=\"76\" y2=\"40\" />\n",
-       "  <line x1=\"54\" y1=\"44\" x2=\"80\" y2=\"44\" />\n",
+       "  <line x1=\"54\" y1=\"44\" x2=\"79\" y2=\"44\" />\n",
        "  <line x1=\"58\" y1=\"48\" x2=\"83\" y2=\"48\" />\n",
-       "  <line x1=\"61\" y1=\"51\" x2=\"87\" y2=\"51\" />\n",
+       "  <line x1=\"62\" y1=\"52\" x2=\"87\" y2=\"52\" />\n",
        "  <line x1=\"65\" y1=\"55\" x2=\"91\" y2=\"55\" />\n",
        "  <line x1=\"69\" y1=\"59\" x2=\"94\" y2=\"59\" />\n",
        "  <line x1=\"73\" y1=\"63\" x2=\"98\" y2=\"63\" />\n",
@@ -4130,7 +4138,7 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mean_surface_net_short_wave_radiation_flux</span></div><div class='xr-var-dims'>(time, longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(100, 64, 32), meta=np.ndarray&gt;</div><input id='attrs-a2c199db-cbe2-48f5-89a6-d8c89908d947' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a2c199db-cbe2-48f5-89a6-d8c89908d947' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-93ce6ada-0608-45e0-b718-c97f803bc479' class='xr-var-data-in' type='checkbox'><label for='data-93ce6ada-0608-45e0-b718-c97f803bc479' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Mean surface net short-wave radiation flux</dd><dt><span>short_name :</span></dt><dd>msnswrf</dd><dt><span>units :</span></dt><dd>W m**-2</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mean_surface_net_short_wave_radiation_flux</span></div><div class='xr-var-dims'>(time, longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(48, 64, 32), meta=np.ndarray&gt;</div><input id='attrs-703c84a9-6dce-45e3-810b-0d3388ae0cd3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-703c84a9-6dce-45e3-810b-0d3388ae0cd3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-441f998f-3e21-435b-b2bd-277963147b3e' class='xr-var-data-in' type='checkbox'><label for='data-441f998f-3e21-435b-b2bd-277963147b3e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Mean surface net short-wave radiation flux</dd><dt><span>short_name :</span></dt><dd>msnswrf</dd><dt><span>units :</span></dt><dd>W m**-2</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -4146,17 +4154,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 730.81 MiB </td>\n",
-       "                        <td> 800.00 kiB </td>\n",
+       "                        <td> 384.00 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (93544, 64, 32) </td>\n",
-       "                        <td> (100, 64, 32) </td>\n",
+       "                        <td> (48, 64, 32) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 936 chunks in 2 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 1949 chunks in 2 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -4180,14 +4188,14 @@
        "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"40\" />\n",
        "  <line x1=\"28\" y1=\"18\" x2=\"28\" y2=\"43\" />\n",
        "  <line x1=\"32\" y1=\"22\" x2=\"32\" y2=\"47\" />\n",
-       "  <line x1=\"35\" y1=\"25\" x2=\"35\" y2=\"51\" />\n",
+       "  <line x1=\"36\" y1=\"26\" x2=\"36\" y2=\"51\" />\n",
        "  <line x1=\"39\" y1=\"29\" x2=\"39\" y2=\"55\" />\n",
        "  <line x1=\"43\" y1=\"33\" x2=\"43\" y2=\"58\" />\n",
        "  <line x1=\"47\" y1=\"37\" x2=\"47\" y2=\"62\" />\n",
        "  <line x1=\"50\" y1=\"40\" x2=\"50\" y2=\"66\" />\n",
-       "  <line x1=\"54\" y1=\"44\" x2=\"54\" y2=\"70\" />\n",
+       "  <line x1=\"54\" y1=\"44\" x2=\"54\" y2=\"69\" />\n",
        "  <line x1=\"58\" y1=\"48\" x2=\"58\" y2=\"73\" />\n",
-       "  <line x1=\"61\" y1=\"51\" x2=\"61\" y2=\"77\" />\n",
+       "  <line x1=\"62\" y1=\"52\" x2=\"62\" y2=\"77\" />\n",
        "  <line x1=\"65\" y1=\"55\" x2=\"65\" y2=\"81\" />\n",
        "  <line x1=\"69\" y1=\"59\" x2=\"69\" y2=\"84\" />\n",
        "  <line x1=\"73\" y1=\"63\" x2=\"73\" y2=\"88\" />\n",
@@ -4205,14 +4213,14 @@
        "  <line x1=\"24\" y1=\"14\" x2=\"50\" y2=\"14\" />\n",
        "  <line x1=\"28\" y1=\"18\" x2=\"53\" y2=\"18\" />\n",
        "  <line x1=\"32\" y1=\"22\" x2=\"57\" y2=\"22\" />\n",
-       "  <line x1=\"35\" y1=\"25\" x2=\"61\" y2=\"25\" />\n",
+       "  <line x1=\"36\" y1=\"26\" x2=\"61\" y2=\"26\" />\n",
        "  <line x1=\"39\" y1=\"29\" x2=\"65\" y2=\"29\" />\n",
        "  <line x1=\"43\" y1=\"33\" x2=\"68\" y2=\"33\" />\n",
        "  <line x1=\"47\" y1=\"37\" x2=\"72\" y2=\"37\" />\n",
        "  <line x1=\"50\" y1=\"40\" x2=\"76\" y2=\"40\" />\n",
-       "  <line x1=\"54\" y1=\"44\" x2=\"80\" y2=\"44\" />\n",
+       "  <line x1=\"54\" y1=\"44\" x2=\"79\" y2=\"44\" />\n",
        "  <line x1=\"58\" y1=\"48\" x2=\"83\" y2=\"48\" />\n",
-       "  <line x1=\"61\" y1=\"51\" x2=\"87\" y2=\"51\" />\n",
+       "  <line x1=\"62\" y1=\"52\" x2=\"87\" y2=\"52\" />\n",
        "  <line x1=\"65\" y1=\"55\" x2=\"91\" y2=\"55\" />\n",
        "  <line x1=\"69\" y1=\"59\" x2=\"94\" y2=\"59\" />\n",
        "  <line x1=\"73\" y1=\"63\" x2=\"98\" y2=\"63\" />\n",
@@ -4244,7 +4252,7 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mean_surface_sensible_heat_flux</span></div><div class='xr-var-dims'>(time, longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(100, 64, 32), meta=np.ndarray&gt;</div><input id='attrs-7d3719d1-5798-4f28-86da-a61b76ee8481' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7d3719d1-5798-4f28-86da-a61b76ee8481' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b85e193c-acc6-4006-b8c7-6c85de862ff2' class='xr-var-data-in' type='checkbox'><label for='data-b85e193c-acc6-4006-b8c7-6c85de862ff2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Mean surface sensible heat flux</dd><dt><span>short_name :</span></dt><dd>msshf</dd><dt><span>units :</span></dt><dd>W m**-2</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mean_surface_sensible_heat_flux</span></div><div class='xr-var-dims'>(time, longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(48, 64, 32), meta=np.ndarray&gt;</div><input id='attrs-86de8c1c-4937-4dc3-820e-088a65680e34' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-86de8c1c-4937-4dc3-820e-088a65680e34' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-07f6a7d2-e124-410f-a59e-2676bdc1c712' class='xr-var-data-in' type='checkbox'><label for='data-07f6a7d2-e124-410f-a59e-2676bdc1c712' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Mean surface sensible heat flux</dd><dt><span>short_name :</span></dt><dd>msshf</dd><dt><span>units :</span></dt><dd>W m**-2</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -4260,17 +4268,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 730.81 MiB </td>\n",
-       "                        <td> 800.00 kiB </td>\n",
+       "                        <td> 384.00 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (93544, 64, 32) </td>\n",
-       "                        <td> (100, 64, 32) </td>\n",
+       "                        <td> (48, 64, 32) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 936 chunks in 2 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 1949 chunks in 2 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -4294,14 +4302,14 @@
        "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"40\" />\n",
        "  <line x1=\"28\" y1=\"18\" x2=\"28\" y2=\"43\" />\n",
        "  <line x1=\"32\" y1=\"22\" x2=\"32\" y2=\"47\" />\n",
-       "  <line x1=\"35\" y1=\"25\" x2=\"35\" y2=\"51\" />\n",
+       "  <line x1=\"36\" y1=\"26\" x2=\"36\" y2=\"51\" />\n",
        "  <line x1=\"39\" y1=\"29\" x2=\"39\" y2=\"55\" />\n",
        "  <line x1=\"43\" y1=\"33\" x2=\"43\" y2=\"58\" />\n",
        "  <line x1=\"47\" y1=\"37\" x2=\"47\" y2=\"62\" />\n",
        "  <line x1=\"50\" y1=\"40\" x2=\"50\" y2=\"66\" />\n",
-       "  <line x1=\"54\" y1=\"44\" x2=\"54\" y2=\"70\" />\n",
+       "  <line x1=\"54\" y1=\"44\" x2=\"54\" y2=\"69\" />\n",
        "  <line x1=\"58\" y1=\"48\" x2=\"58\" y2=\"73\" />\n",
-       "  <line x1=\"61\" y1=\"51\" x2=\"61\" y2=\"77\" />\n",
+       "  <line x1=\"62\" y1=\"52\" x2=\"62\" y2=\"77\" />\n",
        "  <line x1=\"65\" y1=\"55\" x2=\"65\" y2=\"81\" />\n",
        "  <line x1=\"69\" y1=\"59\" x2=\"69\" y2=\"84\" />\n",
        "  <line x1=\"73\" y1=\"63\" x2=\"73\" y2=\"88\" />\n",
@@ -4319,14 +4327,14 @@
        "  <line x1=\"24\" y1=\"14\" x2=\"50\" y2=\"14\" />\n",
        "  <line x1=\"28\" y1=\"18\" x2=\"53\" y2=\"18\" />\n",
        "  <line x1=\"32\" y1=\"22\" x2=\"57\" y2=\"22\" />\n",
-       "  <line x1=\"35\" y1=\"25\" x2=\"61\" y2=\"25\" />\n",
+       "  <line x1=\"36\" y1=\"26\" x2=\"61\" y2=\"26\" />\n",
        "  <line x1=\"39\" y1=\"29\" x2=\"65\" y2=\"29\" />\n",
        "  <line x1=\"43\" y1=\"33\" x2=\"68\" y2=\"33\" />\n",
        "  <line x1=\"47\" y1=\"37\" x2=\"72\" y2=\"37\" />\n",
        "  <line x1=\"50\" y1=\"40\" x2=\"76\" y2=\"40\" />\n",
-       "  <line x1=\"54\" y1=\"44\" x2=\"80\" y2=\"44\" />\n",
+       "  <line x1=\"54\" y1=\"44\" x2=\"79\" y2=\"44\" />\n",
        "  <line x1=\"58\" y1=\"48\" x2=\"83\" y2=\"48\" />\n",
-       "  <line x1=\"61\" y1=\"51\" x2=\"87\" y2=\"51\" />\n",
+       "  <line x1=\"62\" y1=\"52\" x2=\"87\" y2=\"52\" />\n",
        "  <line x1=\"65\" y1=\"55\" x2=\"91\" y2=\"55\" />\n",
        "  <line x1=\"69\" y1=\"59\" x2=\"94\" y2=\"59\" />\n",
        "  <line x1=\"73\" y1=\"63\" x2=\"98\" y2=\"63\" />\n",
@@ -4358,7 +4366,7 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mean_top_downward_short_wave_radiation_flux</span></div><div class='xr-var-dims'>(time, longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(100, 64, 32), meta=np.ndarray&gt;</div><input id='attrs-e857e01f-4782-4878-9621-7b6132d8ada5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e857e01f-4782-4878-9621-7b6132d8ada5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-820524ba-8a8b-418b-a492-a4c0821e905a' class='xr-var-data-in' type='checkbox'><label for='data-820524ba-8a8b-418b-a492-a4c0821e905a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Mean top downward short-wave radiation flux</dd><dt><span>short_name :</span></dt><dd>mtdwswrf</dd><dt><span>units :</span></dt><dd>W m**-2</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mean_top_downward_short_wave_radiation_flux</span></div><div class='xr-var-dims'>(time, longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(48, 64, 32), meta=np.ndarray&gt;</div><input id='attrs-031e3f21-f967-423f-8488-341de2a06bbb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-031e3f21-f967-423f-8488-341de2a06bbb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-30e16c7b-88ce-4a5f-9b46-d5297fce57d6' class='xr-var-data-in' type='checkbox'><label for='data-30e16c7b-88ce-4a5f-9b46-d5297fce57d6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Mean top downward short-wave radiation flux</dd><dt><span>short_name :</span></dt><dd>mtdwswrf</dd><dt><span>units :</span></dt><dd>W m**-2</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -4374,17 +4382,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 730.81 MiB </td>\n",
-       "                        <td> 800.00 kiB </td>\n",
+       "                        <td> 384.00 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (93544, 64, 32) </td>\n",
-       "                        <td> (100, 64, 32) </td>\n",
+       "                        <td> (48, 64, 32) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 936 chunks in 2 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 1949 chunks in 2 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -4408,14 +4416,14 @@
        "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"40\" />\n",
        "  <line x1=\"28\" y1=\"18\" x2=\"28\" y2=\"43\" />\n",
        "  <line x1=\"32\" y1=\"22\" x2=\"32\" y2=\"47\" />\n",
-       "  <line x1=\"35\" y1=\"25\" x2=\"35\" y2=\"51\" />\n",
+       "  <line x1=\"36\" y1=\"26\" x2=\"36\" y2=\"51\" />\n",
        "  <line x1=\"39\" y1=\"29\" x2=\"39\" y2=\"55\" />\n",
        "  <line x1=\"43\" y1=\"33\" x2=\"43\" y2=\"58\" />\n",
        "  <line x1=\"47\" y1=\"37\" x2=\"47\" y2=\"62\" />\n",
        "  <line x1=\"50\" y1=\"40\" x2=\"50\" y2=\"66\" />\n",
-       "  <line x1=\"54\" y1=\"44\" x2=\"54\" y2=\"70\" />\n",
+       "  <line x1=\"54\" y1=\"44\" x2=\"54\" y2=\"69\" />\n",
        "  <line x1=\"58\" y1=\"48\" x2=\"58\" y2=\"73\" />\n",
-       "  <line x1=\"61\" y1=\"51\" x2=\"61\" y2=\"77\" />\n",
+       "  <line x1=\"62\" y1=\"52\" x2=\"62\" y2=\"77\" />\n",
        "  <line x1=\"65\" y1=\"55\" x2=\"65\" y2=\"81\" />\n",
        "  <line x1=\"69\" y1=\"59\" x2=\"69\" y2=\"84\" />\n",
        "  <line x1=\"73\" y1=\"63\" x2=\"73\" y2=\"88\" />\n",
@@ -4433,14 +4441,14 @@
        "  <line x1=\"24\" y1=\"14\" x2=\"50\" y2=\"14\" />\n",
        "  <line x1=\"28\" y1=\"18\" x2=\"53\" y2=\"18\" />\n",
        "  <line x1=\"32\" y1=\"22\" x2=\"57\" y2=\"22\" />\n",
-       "  <line x1=\"35\" y1=\"25\" x2=\"61\" y2=\"25\" />\n",
+       "  <line x1=\"36\" y1=\"26\" x2=\"61\" y2=\"26\" />\n",
        "  <line x1=\"39\" y1=\"29\" x2=\"65\" y2=\"29\" />\n",
        "  <line x1=\"43\" y1=\"33\" x2=\"68\" y2=\"33\" />\n",
        "  <line x1=\"47\" y1=\"37\" x2=\"72\" y2=\"37\" />\n",
        "  <line x1=\"50\" y1=\"40\" x2=\"76\" y2=\"40\" />\n",
-       "  <line x1=\"54\" y1=\"44\" x2=\"80\" y2=\"44\" />\n",
+       "  <line x1=\"54\" y1=\"44\" x2=\"79\" y2=\"44\" />\n",
        "  <line x1=\"58\" y1=\"48\" x2=\"83\" y2=\"48\" />\n",
-       "  <line x1=\"61\" y1=\"51\" x2=\"87\" y2=\"51\" />\n",
+       "  <line x1=\"62\" y1=\"52\" x2=\"87\" y2=\"52\" />\n",
        "  <line x1=\"65\" y1=\"55\" x2=\"91\" y2=\"55\" />\n",
        "  <line x1=\"69\" y1=\"59\" x2=\"94\" y2=\"59\" />\n",
        "  <line x1=\"73\" y1=\"63\" x2=\"98\" y2=\"63\" />\n",
@@ -4472,7 +4480,7 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mean_top_net_long_wave_radiation_flux</span></div><div class='xr-var-dims'>(time, longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(100, 64, 32), meta=np.ndarray&gt;</div><input id='attrs-86703bed-2638-451d-91a3-b58427f2f682' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-86703bed-2638-451d-91a3-b58427f2f682' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c43fbb67-dbe4-4d64-8145-3027f0178361' class='xr-var-data-in' type='checkbox'><label for='data-c43fbb67-dbe4-4d64-8145-3027f0178361' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Mean top net long-wave radiation flux</dd><dt><span>short_name :</span></dt><dd>mtnlwrf</dd><dt><span>units :</span></dt><dd>W m**-2</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mean_top_net_long_wave_radiation_flux</span></div><div class='xr-var-dims'>(time, longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(48, 64, 32), meta=np.ndarray&gt;</div><input id='attrs-9810bab0-ab50-45e0-8eaa-2aed56d8cb1a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9810bab0-ab50-45e0-8eaa-2aed56d8cb1a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9f775944-9507-467b-900f-97f5cbd33109' class='xr-var-data-in' type='checkbox'><label for='data-9f775944-9507-467b-900f-97f5cbd33109' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Mean top net long-wave radiation flux</dd><dt><span>short_name :</span></dt><dd>mtnlwrf</dd><dt><span>units :</span></dt><dd>W m**-2</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -4488,17 +4496,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 730.81 MiB </td>\n",
-       "                        <td> 800.00 kiB </td>\n",
+       "                        <td> 384.00 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (93544, 64, 32) </td>\n",
-       "                        <td> (100, 64, 32) </td>\n",
+       "                        <td> (48, 64, 32) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 936 chunks in 2 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 1949 chunks in 2 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -4522,14 +4530,14 @@
        "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"40\" />\n",
        "  <line x1=\"28\" y1=\"18\" x2=\"28\" y2=\"43\" />\n",
        "  <line x1=\"32\" y1=\"22\" x2=\"32\" y2=\"47\" />\n",
-       "  <line x1=\"35\" y1=\"25\" x2=\"35\" y2=\"51\" />\n",
+       "  <line x1=\"36\" y1=\"26\" x2=\"36\" y2=\"51\" />\n",
        "  <line x1=\"39\" y1=\"29\" x2=\"39\" y2=\"55\" />\n",
        "  <line x1=\"43\" y1=\"33\" x2=\"43\" y2=\"58\" />\n",
        "  <line x1=\"47\" y1=\"37\" x2=\"47\" y2=\"62\" />\n",
        "  <line x1=\"50\" y1=\"40\" x2=\"50\" y2=\"66\" />\n",
-       "  <line x1=\"54\" y1=\"44\" x2=\"54\" y2=\"70\" />\n",
+       "  <line x1=\"54\" y1=\"44\" x2=\"54\" y2=\"69\" />\n",
        "  <line x1=\"58\" y1=\"48\" x2=\"58\" y2=\"73\" />\n",
-       "  <line x1=\"61\" y1=\"51\" x2=\"61\" y2=\"77\" />\n",
+       "  <line x1=\"62\" y1=\"52\" x2=\"62\" y2=\"77\" />\n",
        "  <line x1=\"65\" y1=\"55\" x2=\"65\" y2=\"81\" />\n",
        "  <line x1=\"69\" y1=\"59\" x2=\"69\" y2=\"84\" />\n",
        "  <line x1=\"73\" y1=\"63\" x2=\"73\" y2=\"88\" />\n",
@@ -4547,14 +4555,14 @@
        "  <line x1=\"24\" y1=\"14\" x2=\"50\" y2=\"14\" />\n",
        "  <line x1=\"28\" y1=\"18\" x2=\"53\" y2=\"18\" />\n",
        "  <line x1=\"32\" y1=\"22\" x2=\"57\" y2=\"22\" />\n",
-       "  <line x1=\"35\" y1=\"25\" x2=\"61\" y2=\"25\" />\n",
+       "  <line x1=\"36\" y1=\"26\" x2=\"61\" y2=\"26\" />\n",
        "  <line x1=\"39\" y1=\"29\" x2=\"65\" y2=\"29\" />\n",
        "  <line x1=\"43\" y1=\"33\" x2=\"68\" y2=\"33\" />\n",
        "  <line x1=\"47\" y1=\"37\" x2=\"72\" y2=\"37\" />\n",
        "  <line x1=\"50\" y1=\"40\" x2=\"76\" y2=\"40\" />\n",
-       "  <line x1=\"54\" y1=\"44\" x2=\"80\" y2=\"44\" />\n",
+       "  <line x1=\"54\" y1=\"44\" x2=\"79\" y2=\"44\" />\n",
        "  <line x1=\"58\" y1=\"48\" x2=\"83\" y2=\"48\" />\n",
-       "  <line x1=\"61\" y1=\"51\" x2=\"87\" y2=\"51\" />\n",
+       "  <line x1=\"62\" y1=\"52\" x2=\"87\" y2=\"52\" />\n",
        "  <line x1=\"65\" y1=\"55\" x2=\"91\" y2=\"55\" />\n",
        "  <line x1=\"69\" y1=\"59\" x2=\"94\" y2=\"59\" />\n",
        "  <line x1=\"73\" y1=\"63\" x2=\"98\" y2=\"63\" />\n",
@@ -4586,7 +4594,7 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mean_top_net_short_wave_radiation_flux</span></div><div class='xr-var-dims'>(time, longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(100, 64, 32), meta=np.ndarray&gt;</div><input id='attrs-1263100b-751c-4fb3-b0e4-2a94de42f2e4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1263100b-751c-4fb3-b0e4-2a94de42f2e4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-033daee6-902b-44eb-9af5-d99c503678d0' class='xr-var-data-in' type='checkbox'><label for='data-033daee6-902b-44eb-9af5-d99c503678d0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Mean top net short-wave radiation flux</dd><dt><span>short_name :</span></dt><dd>mtnswrf</dd><dt><span>units :</span></dt><dd>W m**-2</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mean_top_net_short_wave_radiation_flux</span></div><div class='xr-var-dims'>(time, longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(48, 64, 32), meta=np.ndarray&gt;</div><input id='attrs-8d1dac1a-cacb-47cd-8bb6-1e9c868bc68f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8d1dac1a-cacb-47cd-8bb6-1e9c868bc68f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-788f30cf-d823-4552-a1cf-a85f8e8d4f66' class='xr-var-data-in' type='checkbox'><label for='data-788f30cf-d823-4552-a1cf-a85f8e8d4f66' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Mean top net short-wave radiation flux</dd><dt><span>short_name :</span></dt><dd>mtnswrf</dd><dt><span>units :</span></dt><dd>W m**-2</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -4602,17 +4610,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 730.81 MiB </td>\n",
-       "                        <td> 800.00 kiB </td>\n",
+       "                        <td> 384.00 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (93544, 64, 32) </td>\n",
-       "                        <td> (100, 64, 32) </td>\n",
+       "                        <td> (48, 64, 32) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 936 chunks in 2 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 1949 chunks in 2 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -4636,14 +4644,14 @@
        "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"40\" />\n",
        "  <line x1=\"28\" y1=\"18\" x2=\"28\" y2=\"43\" />\n",
        "  <line x1=\"32\" y1=\"22\" x2=\"32\" y2=\"47\" />\n",
-       "  <line x1=\"35\" y1=\"25\" x2=\"35\" y2=\"51\" />\n",
+       "  <line x1=\"36\" y1=\"26\" x2=\"36\" y2=\"51\" />\n",
        "  <line x1=\"39\" y1=\"29\" x2=\"39\" y2=\"55\" />\n",
        "  <line x1=\"43\" y1=\"33\" x2=\"43\" y2=\"58\" />\n",
        "  <line x1=\"47\" y1=\"37\" x2=\"47\" y2=\"62\" />\n",
        "  <line x1=\"50\" y1=\"40\" x2=\"50\" y2=\"66\" />\n",
-       "  <line x1=\"54\" y1=\"44\" x2=\"54\" y2=\"70\" />\n",
+       "  <line x1=\"54\" y1=\"44\" x2=\"54\" y2=\"69\" />\n",
        "  <line x1=\"58\" y1=\"48\" x2=\"58\" y2=\"73\" />\n",
-       "  <line x1=\"61\" y1=\"51\" x2=\"61\" y2=\"77\" />\n",
+       "  <line x1=\"62\" y1=\"52\" x2=\"62\" y2=\"77\" />\n",
        "  <line x1=\"65\" y1=\"55\" x2=\"65\" y2=\"81\" />\n",
        "  <line x1=\"69\" y1=\"59\" x2=\"69\" y2=\"84\" />\n",
        "  <line x1=\"73\" y1=\"63\" x2=\"73\" y2=\"88\" />\n",
@@ -4661,14 +4669,14 @@
        "  <line x1=\"24\" y1=\"14\" x2=\"50\" y2=\"14\" />\n",
        "  <line x1=\"28\" y1=\"18\" x2=\"53\" y2=\"18\" />\n",
        "  <line x1=\"32\" y1=\"22\" x2=\"57\" y2=\"22\" />\n",
-       "  <line x1=\"35\" y1=\"25\" x2=\"61\" y2=\"25\" />\n",
+       "  <line x1=\"36\" y1=\"26\" x2=\"61\" y2=\"26\" />\n",
        "  <line x1=\"39\" y1=\"29\" x2=\"65\" y2=\"29\" />\n",
        "  <line x1=\"43\" y1=\"33\" x2=\"68\" y2=\"33\" />\n",
        "  <line x1=\"47\" y1=\"37\" x2=\"72\" y2=\"37\" />\n",
        "  <line x1=\"50\" y1=\"40\" x2=\"76\" y2=\"40\" />\n",
-       "  <line x1=\"54\" y1=\"44\" x2=\"80\" y2=\"44\" />\n",
+       "  <line x1=\"54\" y1=\"44\" x2=\"79\" y2=\"44\" />\n",
        "  <line x1=\"58\" y1=\"48\" x2=\"83\" y2=\"48\" />\n",
-       "  <line x1=\"61\" y1=\"51\" x2=\"87\" y2=\"51\" />\n",
+       "  <line x1=\"62\" y1=\"52\" x2=\"87\" y2=\"52\" />\n",
        "  <line x1=\"65\" y1=\"55\" x2=\"91\" y2=\"55\" />\n",
        "  <line x1=\"69\" y1=\"59\" x2=\"94\" y2=\"59\" />\n",
        "  <line x1=\"73\" y1=\"63\" x2=\"98\" y2=\"63\" />\n",
@@ -4700,7 +4708,7 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mean_vertically_integrated_moisture_divergence</span></div><div class='xr-var-dims'>(time, longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(100, 64, 32), meta=np.ndarray&gt;</div><input id='attrs-067f18d0-087a-4d26-949c-0016ff2a64ef' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-067f18d0-087a-4d26-949c-0016ff2a64ef' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cc12a7c0-369a-4ed0-a8e0-0f0de1227d7f' class='xr-var-data-in' type='checkbox'><label for='data-cc12a7c0-369a-4ed0-a8e0-0f0de1227d7f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Mean vertically integrated moisture divergence</dd><dt><span>short_name :</span></dt><dd>mvimd</dd><dt><span>units :</span></dt><dd>kg m**-2 s**-1</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mean_vertically_integrated_moisture_divergence</span></div><div class='xr-var-dims'>(time, longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(48, 64, 32), meta=np.ndarray&gt;</div><input id='attrs-d2877072-624a-4712-947e-e109b6e03c33' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d2877072-624a-4712-947e-e109b6e03c33' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a083cb66-4f69-49ce-aae3-8f68593e32f8' class='xr-var-data-in' type='checkbox'><label for='data-a083cb66-4f69-49ce-aae3-8f68593e32f8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Mean vertically integrated moisture divergence</dd><dt><span>short_name :</span></dt><dd>mvimd</dd><dt><span>units :</span></dt><dd>kg m**-2 s**-1</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -4716,17 +4724,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 730.81 MiB </td>\n",
-       "                        <td> 800.00 kiB </td>\n",
+       "                        <td> 384.00 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (93544, 64, 32) </td>\n",
-       "                        <td> (100, 64, 32) </td>\n",
+       "                        <td> (48, 64, 32) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 936 chunks in 2 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 1949 chunks in 2 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -4750,14 +4758,14 @@
        "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"40\" />\n",
        "  <line x1=\"28\" y1=\"18\" x2=\"28\" y2=\"43\" />\n",
        "  <line x1=\"32\" y1=\"22\" x2=\"32\" y2=\"47\" />\n",
-       "  <line x1=\"35\" y1=\"25\" x2=\"35\" y2=\"51\" />\n",
+       "  <line x1=\"36\" y1=\"26\" x2=\"36\" y2=\"51\" />\n",
        "  <line x1=\"39\" y1=\"29\" x2=\"39\" y2=\"55\" />\n",
        "  <line x1=\"43\" y1=\"33\" x2=\"43\" y2=\"58\" />\n",
        "  <line x1=\"47\" y1=\"37\" x2=\"47\" y2=\"62\" />\n",
        "  <line x1=\"50\" y1=\"40\" x2=\"50\" y2=\"66\" />\n",
-       "  <line x1=\"54\" y1=\"44\" x2=\"54\" y2=\"70\" />\n",
+       "  <line x1=\"54\" y1=\"44\" x2=\"54\" y2=\"69\" />\n",
        "  <line x1=\"58\" y1=\"48\" x2=\"58\" y2=\"73\" />\n",
-       "  <line x1=\"61\" y1=\"51\" x2=\"61\" y2=\"77\" />\n",
+       "  <line x1=\"62\" y1=\"52\" x2=\"62\" y2=\"77\" />\n",
        "  <line x1=\"65\" y1=\"55\" x2=\"65\" y2=\"81\" />\n",
        "  <line x1=\"69\" y1=\"59\" x2=\"69\" y2=\"84\" />\n",
        "  <line x1=\"73\" y1=\"63\" x2=\"73\" y2=\"88\" />\n",
@@ -4775,14 +4783,14 @@
        "  <line x1=\"24\" y1=\"14\" x2=\"50\" y2=\"14\" />\n",
        "  <line x1=\"28\" y1=\"18\" x2=\"53\" y2=\"18\" />\n",
        "  <line x1=\"32\" y1=\"22\" x2=\"57\" y2=\"22\" />\n",
-       "  <line x1=\"35\" y1=\"25\" x2=\"61\" y2=\"25\" />\n",
+       "  <line x1=\"36\" y1=\"26\" x2=\"61\" y2=\"26\" />\n",
        "  <line x1=\"39\" y1=\"29\" x2=\"65\" y2=\"29\" />\n",
        "  <line x1=\"43\" y1=\"33\" x2=\"68\" y2=\"33\" />\n",
        "  <line x1=\"47\" y1=\"37\" x2=\"72\" y2=\"37\" />\n",
        "  <line x1=\"50\" y1=\"40\" x2=\"76\" y2=\"40\" />\n",
-       "  <line x1=\"54\" y1=\"44\" x2=\"80\" y2=\"44\" />\n",
+       "  <line x1=\"54\" y1=\"44\" x2=\"79\" y2=\"44\" />\n",
        "  <line x1=\"58\" y1=\"48\" x2=\"83\" y2=\"48\" />\n",
-       "  <line x1=\"61\" y1=\"51\" x2=\"87\" y2=\"51\" />\n",
+       "  <line x1=\"62\" y1=\"52\" x2=\"87\" y2=\"52\" />\n",
        "  <line x1=\"65\" y1=\"55\" x2=\"91\" y2=\"55\" />\n",
        "  <line x1=\"69\" y1=\"59\" x2=\"94\" y2=\"59\" />\n",
        "  <line x1=\"73\" y1=\"63\" x2=\"98\" y2=\"63\" />\n",
@@ -4814,7 +4822,7 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>potential_vorticity</span></div><div class='xr-var-dims'>(time, level, longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(100, 13, 64, 32), meta=np.ndarray&gt;</div><input id='attrs-933f2b93-4555-4d78-a22a-bc19e347296c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-933f2b93-4555-4d78-a22a-bc19e347296c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-50ea2655-582f-4b60-86b0-c1d15b7602d8' class='xr-var-data-in' type='checkbox'><label for='data-50ea2655-582f-4b60-86b0-c1d15b7602d8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Potential vorticity</dd><dt><span>short_name :</span></dt><dd>pv</dd><dt><span>units :</span></dt><dd>K m**2 kg**-1 s**-1</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>potential_vorticity</span></div><div class='xr-var-dims'>(time, level, longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(48, 13, 64, 32), meta=np.ndarray&gt;</div><input id='attrs-a3829cd5-f630-4365-9e36-4603349e61ac' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a3829cd5-f630-4365-9e36-4603349e61ac' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cd66a78a-4150-467c-8990-5b405984d2ee' class='xr-var-data-in' type='checkbox'><label for='data-cd66a78a-4150-467c-8990-5b405984d2ee' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Potential vorticity</dd><dt><span>short_name :</span></dt><dd>pv</dd><dt><span>units :</span></dt><dd>K m**2 kg**-1 s**-1</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -4830,17 +4838,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 9.28 GiB </td>\n",
-       "                        <td> 10.16 MiB </td>\n",
+       "                        <td> 4.88 MiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (93544, 13, 64, 32) </td>\n",
-       "                        <td> (100, 13, 64, 32) </td>\n",
+       "                        <td> (48, 13, 64, 32) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 936 chunks in 2 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 1949 chunks in 2 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -4861,31 +4869,31 @@
        "  <line x1=\"3\" y1=\"0\" x2=\"3\" y2=\"25\" />\n",
        "  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n",
        "  <line x1=\"11\" y1=\"0\" x2=\"11\" y2=\"25\" />\n",
-       "  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n",
+       "  <line x1=\"14\" y1=\"0\" x2=\"14\" y2=\"25\" />\n",
        "  <line x1=\"18\" y1=\"0\" x2=\"18\" y2=\"25\" />\n",
        "  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n",
        "  <line x1=\"26\" y1=\"0\" x2=\"26\" y2=\"25\" />\n",
-       "  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n",
+       "  <line x1=\"29\" y1=\"0\" x2=\"29\" y2=\"25\" />\n",
        "  <line x1=\"33\" y1=\"0\" x2=\"33\" y2=\"25\" />\n",
        "  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n",
        "  <line x1=\"41\" y1=\"0\" x2=\"41\" y2=\"25\" />\n",
-       "  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n",
+       "  <line x1=\"44\" y1=\"0\" x2=\"44\" y2=\"25\" />\n",
        "  <line x1=\"48\" y1=\"0\" x2=\"48\" y2=\"25\" />\n",
        "  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n",
        "  <line x1=\"56\" y1=\"0\" x2=\"56\" y2=\"25\" />\n",
-       "  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n",
+       "  <line x1=\"59\" y1=\"0\" x2=\"59\" y2=\"25\" />\n",
        "  <line x1=\"63\" y1=\"0\" x2=\"63\" y2=\"25\" />\n",
        "  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n",
        "  <line x1=\"71\" y1=\"0\" x2=\"71\" y2=\"25\" />\n",
-       "  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n",
+       "  <line x1=\"74\" y1=\"0\" x2=\"74\" y2=\"25\" />\n",
        "  <line x1=\"78\" y1=\"0\" x2=\"78\" y2=\"25\" />\n",
        "  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n",
        "  <line x1=\"86\" y1=\"0\" x2=\"86\" y2=\"25\" />\n",
-       "  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n",
+       "  <line x1=\"89\" y1=\"0\" x2=\"89\" y2=\"25\" />\n",
        "  <line x1=\"93\" y1=\"0\" x2=\"93\" y2=\"25\" />\n",
        "  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n",
        "  <line x1=\"101\" y1=\"0\" x2=\"101\" y2=\"25\" />\n",
-       "  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n",
+       "  <line x1=\"104\" y1=\"0\" x2=\"104\" y2=\"25\" />\n",
        "  <line x1=\"108\" y1=\"0\" x2=\"108\" y2=\"25\" />\n",
        "  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n",
        "  <line x1=\"116\" y1=\"0\" x2=\"116\" y2=\"25\" />\n",
@@ -4939,7 +4947,7 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>relative_humidity</span></div><div class='xr-var-dims'>(time, level, longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(100, 13, 64, 32), meta=np.ndarray&gt;</div><input id='attrs-f0cf23d0-2a93-465a-b074-37ea1d5dec48' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f0cf23d0-2a93-465a-b074-37ea1d5dec48' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dff25af8-57d5-44bb-b3e2-a66ca7493d31' class='xr-var-data-in' type='checkbox'><label for='data-dff25af8-57d5-44bb-b3e2-a66ca7493d31' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>relative_humidity</span></div><div class='xr-var-dims'>(time, level, longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(48, 13, 64, 32), meta=np.ndarray&gt;</div><input id='attrs-df7db05e-3ef6-454a-b67f-3d3021ca7d71' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-df7db05e-3ef6-454a-b67f-3d3021ca7d71' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7a4ee94d-dafe-4982-a619-0ec05ff7c084' class='xr-var-data-in' type='checkbox'><label for='data-7a4ee94d-dafe-4982-a619-0ec05ff7c084' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -4955,17 +4963,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 9.28 GiB </td>\n",
-       "                        <td> 10.16 MiB </td>\n",
+       "                        <td> 4.88 MiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (93544, 13, 64, 32) </td>\n",
-       "                        <td> (100, 13, 64, 32) </td>\n",
+       "                        <td> (48, 13, 64, 32) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 936 chunks in 2 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 1949 chunks in 2 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -4986,31 +4994,31 @@
        "  <line x1=\"3\" y1=\"0\" x2=\"3\" y2=\"25\" />\n",
        "  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n",
        "  <line x1=\"11\" y1=\"0\" x2=\"11\" y2=\"25\" />\n",
-       "  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n",
+       "  <line x1=\"14\" y1=\"0\" x2=\"14\" y2=\"25\" />\n",
        "  <line x1=\"18\" y1=\"0\" x2=\"18\" y2=\"25\" />\n",
        "  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n",
        "  <line x1=\"26\" y1=\"0\" x2=\"26\" y2=\"25\" />\n",
-       "  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n",
+       "  <line x1=\"29\" y1=\"0\" x2=\"29\" y2=\"25\" />\n",
        "  <line x1=\"33\" y1=\"0\" x2=\"33\" y2=\"25\" />\n",
        "  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n",
        "  <line x1=\"41\" y1=\"0\" x2=\"41\" y2=\"25\" />\n",
-       "  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n",
+       "  <line x1=\"44\" y1=\"0\" x2=\"44\" y2=\"25\" />\n",
        "  <line x1=\"48\" y1=\"0\" x2=\"48\" y2=\"25\" />\n",
        "  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n",
        "  <line x1=\"56\" y1=\"0\" x2=\"56\" y2=\"25\" />\n",
-       "  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n",
+       "  <line x1=\"59\" y1=\"0\" x2=\"59\" y2=\"25\" />\n",
        "  <line x1=\"63\" y1=\"0\" x2=\"63\" y2=\"25\" />\n",
        "  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n",
        "  <line x1=\"71\" y1=\"0\" x2=\"71\" y2=\"25\" />\n",
-       "  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n",
+       "  <line x1=\"74\" y1=\"0\" x2=\"74\" y2=\"25\" />\n",
        "  <line x1=\"78\" y1=\"0\" x2=\"78\" y2=\"25\" />\n",
        "  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n",
        "  <line x1=\"86\" y1=\"0\" x2=\"86\" y2=\"25\" />\n",
-       "  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n",
+       "  <line x1=\"89\" y1=\"0\" x2=\"89\" y2=\"25\" />\n",
        "  <line x1=\"93\" y1=\"0\" x2=\"93\" y2=\"25\" />\n",
        "  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n",
        "  <line x1=\"101\" y1=\"0\" x2=\"101\" y2=\"25\" />\n",
-       "  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n",
+       "  <line x1=\"104\" y1=\"0\" x2=\"104\" y2=\"25\" />\n",
        "  <line x1=\"108\" y1=\"0\" x2=\"108\" y2=\"25\" />\n",
        "  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n",
        "  <line x1=\"116\" y1=\"0\" x2=\"116\" y2=\"25\" />\n",
@@ -5064,7 +5072,7 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sea_ice_cover</span></div><div class='xr-var-dims'>(time, longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(100, 64, 32), meta=np.ndarray&gt;</div><input id='attrs-3779050e-3a84-42e8-a843-8d5da40417ff' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3779050e-3a84-42e8-a843-8d5da40417ff' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-171262d3-3dd9-4089-9255-29d876d58d08' class='xr-var-data-in' type='checkbox'><label for='data-171262d3-3dd9-4089-9255-29d876d58d08' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Sea ice area fraction</dd><dt><span>short_name :</span></dt><dd>siconc</dd><dt><span>standard_name :</span></dt><dd>sea_ice_area_fraction</dd><dt><span>units :</span></dt><dd>(0 - 1)</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sea_ice_cover</span></div><div class='xr-var-dims'>(time, longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(48, 64, 32), meta=np.ndarray&gt;</div><input id='attrs-4d4c7fdb-f1e5-47f9-91f0-914a638fefb8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4d4c7fdb-f1e5-47f9-91f0-914a638fefb8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5545c098-5848-407c-a627-2ffc291cde46' class='xr-var-data-in' type='checkbox'><label for='data-5545c098-5848-407c-a627-2ffc291cde46' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Sea ice area fraction</dd><dt><span>short_name :</span></dt><dd>siconc</dd><dt><span>standard_name :</span></dt><dd>sea_ice_area_fraction</dd><dt><span>units :</span></dt><dd>(0 - 1)</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -5080,17 +5088,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 730.81 MiB </td>\n",
-       "                        <td> 800.00 kiB </td>\n",
+       "                        <td> 384.00 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (93544, 64, 32) </td>\n",
-       "                        <td> (100, 64, 32) </td>\n",
+       "                        <td> (48, 64, 32) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 936 chunks in 2 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 1949 chunks in 2 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -5114,14 +5122,14 @@
        "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"40\" />\n",
        "  <line x1=\"28\" y1=\"18\" x2=\"28\" y2=\"43\" />\n",
        "  <line x1=\"32\" y1=\"22\" x2=\"32\" y2=\"47\" />\n",
-       "  <line x1=\"35\" y1=\"25\" x2=\"35\" y2=\"51\" />\n",
+       "  <line x1=\"36\" y1=\"26\" x2=\"36\" y2=\"51\" />\n",
        "  <line x1=\"39\" y1=\"29\" x2=\"39\" y2=\"55\" />\n",
        "  <line x1=\"43\" y1=\"33\" x2=\"43\" y2=\"58\" />\n",
        "  <line x1=\"47\" y1=\"37\" x2=\"47\" y2=\"62\" />\n",
        "  <line x1=\"50\" y1=\"40\" x2=\"50\" y2=\"66\" />\n",
-       "  <line x1=\"54\" y1=\"44\" x2=\"54\" y2=\"70\" />\n",
+       "  <line x1=\"54\" y1=\"44\" x2=\"54\" y2=\"69\" />\n",
        "  <line x1=\"58\" y1=\"48\" x2=\"58\" y2=\"73\" />\n",
-       "  <line x1=\"61\" y1=\"51\" x2=\"61\" y2=\"77\" />\n",
+       "  <line x1=\"62\" y1=\"52\" x2=\"62\" y2=\"77\" />\n",
        "  <line x1=\"65\" y1=\"55\" x2=\"65\" y2=\"81\" />\n",
        "  <line x1=\"69\" y1=\"59\" x2=\"69\" y2=\"84\" />\n",
        "  <line x1=\"73\" y1=\"63\" x2=\"73\" y2=\"88\" />\n",
@@ -5139,14 +5147,14 @@
        "  <line x1=\"24\" y1=\"14\" x2=\"50\" y2=\"14\" />\n",
        "  <line x1=\"28\" y1=\"18\" x2=\"53\" y2=\"18\" />\n",
        "  <line x1=\"32\" y1=\"22\" x2=\"57\" y2=\"22\" />\n",
-       "  <line x1=\"35\" y1=\"25\" x2=\"61\" y2=\"25\" />\n",
+       "  <line x1=\"36\" y1=\"26\" x2=\"61\" y2=\"26\" />\n",
        "  <line x1=\"39\" y1=\"29\" x2=\"65\" y2=\"29\" />\n",
        "  <line x1=\"43\" y1=\"33\" x2=\"68\" y2=\"33\" />\n",
        "  <line x1=\"47\" y1=\"37\" x2=\"72\" y2=\"37\" />\n",
        "  <line x1=\"50\" y1=\"40\" x2=\"76\" y2=\"40\" />\n",
-       "  <line x1=\"54\" y1=\"44\" x2=\"80\" y2=\"44\" />\n",
+       "  <line x1=\"54\" y1=\"44\" x2=\"79\" y2=\"44\" />\n",
        "  <line x1=\"58\" y1=\"48\" x2=\"83\" y2=\"48\" />\n",
-       "  <line x1=\"61\" y1=\"51\" x2=\"87\" y2=\"51\" />\n",
+       "  <line x1=\"62\" y1=\"52\" x2=\"87\" y2=\"52\" />\n",
        "  <line x1=\"65\" y1=\"55\" x2=\"91\" y2=\"55\" />\n",
        "  <line x1=\"69\" y1=\"59\" x2=\"94\" y2=\"59\" />\n",
        "  <line x1=\"73\" y1=\"63\" x2=\"98\" y2=\"63\" />\n",
@@ -5178,7 +5186,7 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sea_surface_temperature</span></div><div class='xr-var-dims'>(time, longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(100, 64, 32), meta=np.ndarray&gt;</div><input id='attrs-025b4c2f-b8f4-4773-b24a-014cd7b9e716' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-025b4c2f-b8f4-4773-b24a-014cd7b9e716' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-50dd482d-b7c5-4293-91e9-d1440fe01b20' class='xr-var-data-in' type='checkbox'><label for='data-50dd482d-b7c5-4293-91e9-d1440fe01b20' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Sea surface temperature</dd><dt><span>short_name :</span></dt><dd>sst</dd><dt><span>units :</span></dt><dd>K</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sea_surface_temperature</span></div><div class='xr-var-dims'>(time, longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(48, 64, 32), meta=np.ndarray&gt;</div><input id='attrs-52d75eb4-4414-4ec6-8410-0da135f46394' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-52d75eb4-4414-4ec6-8410-0da135f46394' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8436a2c6-d3ea-41e6-857a-efdc49ac6ba3' class='xr-var-data-in' type='checkbox'><label for='data-8436a2c6-d3ea-41e6-857a-efdc49ac6ba3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Sea surface temperature</dd><dt><span>short_name :</span></dt><dd>sst</dd><dt><span>units :</span></dt><dd>K</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -5194,17 +5202,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 730.81 MiB </td>\n",
-       "                        <td> 800.00 kiB </td>\n",
+       "                        <td> 384.00 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (93544, 64, 32) </td>\n",
-       "                        <td> (100, 64, 32) </td>\n",
+       "                        <td> (48, 64, 32) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 936 chunks in 2 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 1949 chunks in 2 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -5228,14 +5236,14 @@
        "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"40\" />\n",
        "  <line x1=\"28\" y1=\"18\" x2=\"28\" y2=\"43\" />\n",
        "  <line x1=\"32\" y1=\"22\" x2=\"32\" y2=\"47\" />\n",
-       "  <line x1=\"35\" y1=\"25\" x2=\"35\" y2=\"51\" />\n",
+       "  <line x1=\"36\" y1=\"26\" x2=\"36\" y2=\"51\" />\n",
        "  <line x1=\"39\" y1=\"29\" x2=\"39\" y2=\"55\" />\n",
        "  <line x1=\"43\" y1=\"33\" x2=\"43\" y2=\"58\" />\n",
        "  <line x1=\"47\" y1=\"37\" x2=\"47\" y2=\"62\" />\n",
        "  <line x1=\"50\" y1=\"40\" x2=\"50\" y2=\"66\" />\n",
-       "  <line x1=\"54\" y1=\"44\" x2=\"54\" y2=\"70\" />\n",
+       "  <line x1=\"54\" y1=\"44\" x2=\"54\" y2=\"69\" />\n",
        "  <line x1=\"58\" y1=\"48\" x2=\"58\" y2=\"73\" />\n",
-       "  <line x1=\"61\" y1=\"51\" x2=\"61\" y2=\"77\" />\n",
+       "  <line x1=\"62\" y1=\"52\" x2=\"62\" y2=\"77\" />\n",
        "  <line x1=\"65\" y1=\"55\" x2=\"65\" y2=\"81\" />\n",
        "  <line x1=\"69\" y1=\"59\" x2=\"69\" y2=\"84\" />\n",
        "  <line x1=\"73\" y1=\"63\" x2=\"73\" y2=\"88\" />\n",
@@ -5253,14 +5261,14 @@
        "  <line x1=\"24\" y1=\"14\" x2=\"50\" y2=\"14\" />\n",
        "  <line x1=\"28\" y1=\"18\" x2=\"53\" y2=\"18\" />\n",
        "  <line x1=\"32\" y1=\"22\" x2=\"57\" y2=\"22\" />\n",
-       "  <line x1=\"35\" y1=\"25\" x2=\"61\" y2=\"25\" />\n",
+       "  <line x1=\"36\" y1=\"26\" x2=\"61\" y2=\"26\" />\n",
        "  <line x1=\"39\" y1=\"29\" x2=\"65\" y2=\"29\" />\n",
        "  <line x1=\"43\" y1=\"33\" x2=\"68\" y2=\"33\" />\n",
        "  <line x1=\"47\" y1=\"37\" x2=\"72\" y2=\"37\" />\n",
        "  <line x1=\"50\" y1=\"40\" x2=\"76\" y2=\"40\" />\n",
-       "  <line x1=\"54\" y1=\"44\" x2=\"80\" y2=\"44\" />\n",
+       "  <line x1=\"54\" y1=\"44\" x2=\"79\" y2=\"44\" />\n",
        "  <line x1=\"58\" y1=\"48\" x2=\"83\" y2=\"48\" />\n",
-       "  <line x1=\"61\" y1=\"51\" x2=\"87\" y2=\"51\" />\n",
+       "  <line x1=\"62\" y1=\"52\" x2=\"87\" y2=\"52\" />\n",
        "  <line x1=\"65\" y1=\"55\" x2=\"91\" y2=\"55\" />\n",
        "  <line x1=\"69\" y1=\"59\" x2=\"94\" y2=\"59\" />\n",
        "  <line x1=\"73\" y1=\"63\" x2=\"98\" y2=\"63\" />\n",
@@ -5292,7 +5300,7 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>slope_of_sub_gridscale_orography</span></div><div class='xr-var-dims'>(longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(64, 32), meta=np.ndarray&gt;</div><input id='attrs-540ce475-deb4-40b4-9dbd-be9d96c84b59' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-540ce475-deb4-40b4-9dbd-be9d96c84b59' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2a2780f9-a95d-43ba-9d9c-0c6eb201ef0f' class='xr-var-data-in' type='checkbox'><label for='data-2a2780f9-a95d-43ba-9d9c-0c6eb201ef0f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Slope of sub-gridscale orography</dd><dt><span>short_name :</span></dt><dd>slor</dd><dt><span>units :</span></dt><dd>~</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>slope_of_sub_gridscale_orography</span></div><div class='xr-var-dims'>(longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(64, 32), meta=np.ndarray&gt;</div><input id='attrs-92f10b96-f257-4399-941b-e6d771d7fbf8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-92f10b96-f257-4399-941b-e6d771d7fbf8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cabd889c-f1d4-404a-a909-3cdb35b76b96' class='xr-var-data-in' type='checkbox'><label for='data-cabd889c-f1d4-404a-a909-3cdb35b76b96' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Slope of sub-gridscale orography</dd><dt><span>short_name :</span></dt><dd>slor</dd><dt><span>units :</span></dt><dd>~</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -5347,7 +5355,7 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>snow_depth</span></div><div class='xr-var-dims'>(time, longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(100, 64, 32), meta=np.ndarray&gt;</div><input id='attrs-1715a96c-08a6-4d39-9050-19b5cef678da' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1715a96c-08a6-4d39-9050-19b5cef678da' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2386c35c-6c83-4213-ada1-a9a49bc30d6d' class='xr-var-data-in' type='checkbox'><label for='data-2386c35c-6c83-4213-ada1-a9a49bc30d6d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Snow depth</dd><dt><span>short_name :</span></dt><dd>sd</dd><dt><span>standard_name :</span></dt><dd>lwe_thickness_of_surface_snow_amount</dd><dt><span>units :</span></dt><dd>m of water equivalent</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>snow_depth</span></div><div class='xr-var-dims'>(time, longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(48, 64, 32), meta=np.ndarray&gt;</div><input id='attrs-48281825-9e65-47d4-bdcf-4ab2938ea523' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-48281825-9e65-47d4-bdcf-4ab2938ea523' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9e4b979f-f3a3-4125-856b-178a1857cf57' class='xr-var-data-in' type='checkbox'><label for='data-9e4b979f-f3a3-4125-856b-178a1857cf57' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Snow depth</dd><dt><span>short_name :</span></dt><dd>sd</dd><dt><span>standard_name :</span></dt><dd>lwe_thickness_of_surface_snow_amount</dd><dt><span>units :</span></dt><dd>m of water equivalent</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -5363,17 +5371,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 730.81 MiB </td>\n",
-       "                        <td> 800.00 kiB </td>\n",
+       "                        <td> 384.00 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (93544, 64, 32) </td>\n",
-       "                        <td> (100, 64, 32) </td>\n",
+       "                        <td> (48, 64, 32) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 936 chunks in 2 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 1949 chunks in 2 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -5397,14 +5405,14 @@
        "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"40\" />\n",
        "  <line x1=\"28\" y1=\"18\" x2=\"28\" y2=\"43\" />\n",
        "  <line x1=\"32\" y1=\"22\" x2=\"32\" y2=\"47\" />\n",
-       "  <line x1=\"35\" y1=\"25\" x2=\"35\" y2=\"51\" />\n",
+       "  <line x1=\"36\" y1=\"26\" x2=\"36\" y2=\"51\" />\n",
        "  <line x1=\"39\" y1=\"29\" x2=\"39\" y2=\"55\" />\n",
        "  <line x1=\"43\" y1=\"33\" x2=\"43\" y2=\"58\" />\n",
        "  <line x1=\"47\" y1=\"37\" x2=\"47\" y2=\"62\" />\n",
        "  <line x1=\"50\" y1=\"40\" x2=\"50\" y2=\"66\" />\n",
-       "  <line x1=\"54\" y1=\"44\" x2=\"54\" y2=\"70\" />\n",
+       "  <line x1=\"54\" y1=\"44\" x2=\"54\" y2=\"69\" />\n",
        "  <line x1=\"58\" y1=\"48\" x2=\"58\" y2=\"73\" />\n",
-       "  <line x1=\"61\" y1=\"51\" x2=\"61\" y2=\"77\" />\n",
+       "  <line x1=\"62\" y1=\"52\" x2=\"62\" y2=\"77\" />\n",
        "  <line x1=\"65\" y1=\"55\" x2=\"65\" y2=\"81\" />\n",
        "  <line x1=\"69\" y1=\"59\" x2=\"69\" y2=\"84\" />\n",
        "  <line x1=\"73\" y1=\"63\" x2=\"73\" y2=\"88\" />\n",
@@ -5422,14 +5430,14 @@
        "  <line x1=\"24\" y1=\"14\" x2=\"50\" y2=\"14\" />\n",
        "  <line x1=\"28\" y1=\"18\" x2=\"53\" y2=\"18\" />\n",
        "  <line x1=\"32\" y1=\"22\" x2=\"57\" y2=\"22\" />\n",
-       "  <line x1=\"35\" y1=\"25\" x2=\"61\" y2=\"25\" />\n",
+       "  <line x1=\"36\" y1=\"26\" x2=\"61\" y2=\"26\" />\n",
        "  <line x1=\"39\" y1=\"29\" x2=\"65\" y2=\"29\" />\n",
        "  <line x1=\"43\" y1=\"33\" x2=\"68\" y2=\"33\" />\n",
        "  <line x1=\"47\" y1=\"37\" x2=\"72\" y2=\"37\" />\n",
        "  <line x1=\"50\" y1=\"40\" x2=\"76\" y2=\"40\" />\n",
-       "  <line x1=\"54\" y1=\"44\" x2=\"80\" y2=\"44\" />\n",
+       "  <line x1=\"54\" y1=\"44\" x2=\"79\" y2=\"44\" />\n",
        "  <line x1=\"58\" y1=\"48\" x2=\"83\" y2=\"48\" />\n",
-       "  <line x1=\"61\" y1=\"51\" x2=\"87\" y2=\"51\" />\n",
+       "  <line x1=\"62\" y1=\"52\" x2=\"87\" y2=\"52\" />\n",
        "  <line x1=\"65\" y1=\"55\" x2=\"91\" y2=\"55\" />\n",
        "  <line x1=\"69\" y1=\"59\" x2=\"94\" y2=\"59\" />\n",
        "  <line x1=\"73\" y1=\"63\" x2=\"98\" y2=\"63\" />\n",
@@ -5461,7 +5469,7 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>soil_type</span></div><div class='xr-var-dims'>(longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(64, 32), meta=np.ndarray&gt;</div><input id='attrs-4ade55ee-11ba-41ed-920a-05cb59494012' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4ade55ee-11ba-41ed-920a-05cb59494012' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-88ff3b51-cc9b-4282-b073-b94241860859' class='xr-var-data-in' type='checkbox'><label for='data-88ff3b51-cc9b-4282-b073-b94241860859' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Soil type</dd><dt><span>short_name :</span></dt><dd>slt</dd><dt><span>units :</span></dt><dd>~</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>soil_type</span></div><div class='xr-var-dims'>(longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(64, 32), meta=np.ndarray&gt;</div><input id='attrs-52da63fc-0bc8-433b-88b6-e5f87f89ec37' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-52da63fc-0bc8-433b-88b6-e5f87f89ec37' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8925ef07-f95f-4ec8-8d2b-dd648fedfab6' class='xr-var-data-in' type='checkbox'><label for='data-8925ef07-f95f-4ec8-8d2b-dd648fedfab6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Soil type</dd><dt><span>short_name :</span></dt><dd>slt</dd><dt><span>units :</span></dt><dd>~</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -5516,7 +5524,7 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>specific_humidity</span></div><div class='xr-var-dims'>(time, level, longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(100, 13, 64, 32), meta=np.ndarray&gt;</div><input id='attrs-0ed5013e-85ee-4b4e-8127-e2c4146d90a1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0ed5013e-85ee-4b4e-8127-e2c4146d90a1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-59bd4fb9-b5cc-4594-8939-159b45ae946d' class='xr-var-data-in' type='checkbox'><label for='data-59bd4fb9-b5cc-4594-8939-159b45ae946d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Specific humidity</dd><dt><span>short_name :</span></dt><dd>q</dd><dt><span>standard_name :</span></dt><dd>specific_humidity</dd><dt><span>units :</span></dt><dd>kg kg**-1</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>specific_humidity</span></div><div class='xr-var-dims'>(time, level, longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(48, 13, 64, 32), meta=np.ndarray&gt;</div><input id='attrs-135a2a38-326f-4686-970a-76f6c6e049a7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-135a2a38-326f-4686-970a-76f6c6e049a7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d32add53-6e07-4078-95b0-7628af36a31e' class='xr-var-data-in' type='checkbox'><label for='data-d32add53-6e07-4078-95b0-7628af36a31e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Specific humidity</dd><dt><span>short_name :</span></dt><dd>q</dd><dt><span>standard_name :</span></dt><dd>specific_humidity</dd><dt><span>units :</span></dt><dd>kg kg**-1</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -5532,17 +5540,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 9.28 GiB </td>\n",
-       "                        <td> 10.16 MiB </td>\n",
+       "                        <td> 4.88 MiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (93544, 13, 64, 32) </td>\n",
-       "                        <td> (100, 13, 64, 32) </td>\n",
+       "                        <td> (48, 13, 64, 32) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 936 chunks in 2 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 1949 chunks in 2 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -5563,31 +5571,31 @@
        "  <line x1=\"3\" y1=\"0\" x2=\"3\" y2=\"25\" />\n",
        "  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n",
        "  <line x1=\"11\" y1=\"0\" x2=\"11\" y2=\"25\" />\n",
-       "  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n",
+       "  <line x1=\"14\" y1=\"0\" x2=\"14\" y2=\"25\" />\n",
        "  <line x1=\"18\" y1=\"0\" x2=\"18\" y2=\"25\" />\n",
        "  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n",
        "  <line x1=\"26\" y1=\"0\" x2=\"26\" y2=\"25\" />\n",
-       "  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n",
+       "  <line x1=\"29\" y1=\"0\" x2=\"29\" y2=\"25\" />\n",
        "  <line x1=\"33\" y1=\"0\" x2=\"33\" y2=\"25\" />\n",
        "  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n",
        "  <line x1=\"41\" y1=\"0\" x2=\"41\" y2=\"25\" />\n",
-       "  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n",
+       "  <line x1=\"44\" y1=\"0\" x2=\"44\" y2=\"25\" />\n",
        "  <line x1=\"48\" y1=\"0\" x2=\"48\" y2=\"25\" />\n",
        "  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n",
        "  <line x1=\"56\" y1=\"0\" x2=\"56\" y2=\"25\" />\n",
-       "  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n",
+       "  <line x1=\"59\" y1=\"0\" x2=\"59\" y2=\"25\" />\n",
        "  <line x1=\"63\" y1=\"0\" x2=\"63\" y2=\"25\" />\n",
        "  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n",
        "  <line x1=\"71\" y1=\"0\" x2=\"71\" y2=\"25\" />\n",
-       "  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n",
+       "  <line x1=\"74\" y1=\"0\" x2=\"74\" y2=\"25\" />\n",
        "  <line x1=\"78\" y1=\"0\" x2=\"78\" y2=\"25\" />\n",
        "  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n",
        "  <line x1=\"86\" y1=\"0\" x2=\"86\" y2=\"25\" />\n",
-       "  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n",
+       "  <line x1=\"89\" y1=\"0\" x2=\"89\" y2=\"25\" />\n",
        "  <line x1=\"93\" y1=\"0\" x2=\"93\" y2=\"25\" />\n",
        "  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n",
        "  <line x1=\"101\" y1=\"0\" x2=\"101\" y2=\"25\" />\n",
-       "  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n",
+       "  <line x1=\"104\" y1=\"0\" x2=\"104\" y2=\"25\" />\n",
        "  <line x1=\"108\" y1=\"0\" x2=\"108\" y2=\"25\" />\n",
        "  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n",
        "  <line x1=\"116\" y1=\"0\" x2=\"116\" y2=\"25\" />\n",
@@ -5641,7 +5649,7 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>standard_deviation_of_filtered_subgrid_orography</span></div><div class='xr-var-dims'>(longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(64, 32), meta=np.ndarray&gt;</div><input id='attrs-5977c0e4-c927-4f80-bd28-d80c925052be' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5977c0e4-c927-4f80-bd28-d80c925052be' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-69d6c01a-f409-4f3d-968e-8108df1018a7' class='xr-var-data-in' type='checkbox'><label for='data-69d6c01a-f409-4f3d-968e-8108df1018a7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Standard deviation of filtered subgrid orography</dd><dt><span>short_name :</span></dt><dd>sdfor</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>standard_deviation_of_filtered_subgrid_orography</span></div><div class='xr-var-dims'>(longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(64, 32), meta=np.ndarray&gt;</div><input id='attrs-70032716-4e50-4b2b-9af8-56dfaba9d469' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-70032716-4e50-4b2b-9af8-56dfaba9d469' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ae06caa5-8e92-4773-8e58-7f9a35b15f65' class='xr-var-data-in' type='checkbox'><label for='data-ae06caa5-8e92-4773-8e58-7f9a35b15f65' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Standard deviation of filtered subgrid orography</dd><dt><span>short_name :</span></dt><dd>sdfor</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -5696,7 +5704,7 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>standard_deviation_of_orography</span></div><div class='xr-var-dims'>(longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(64, 32), meta=np.ndarray&gt;</div><input id='attrs-13cf41d5-6099-440d-9bef-96657f88c59a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-13cf41d5-6099-440d-9bef-96657f88c59a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5072e0e7-3e3c-4159-a121-e38ddfa3db4f' class='xr-var-data-in' type='checkbox'><label for='data-5072e0e7-3e3c-4159-a121-e38ddfa3db4f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Standard deviation of orography</dd><dt><span>short_name :</span></dt><dd>sdor</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>standard_deviation_of_orography</span></div><div class='xr-var-dims'>(longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(64, 32), meta=np.ndarray&gt;</div><input id='attrs-5ad1831a-e324-4c4b-b05b-783f2a6a6c1d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5ad1831a-e324-4c4b-b05b-783f2a6a6c1d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a53767dc-0556-418c-8533-74ee503c1303' class='xr-var-data-in' type='checkbox'><label for='data-a53767dc-0556-418c-8533-74ee503c1303' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Standard deviation of orography</dd><dt><span>short_name :</span></dt><dd>sdor</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -5751,7 +5759,7 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>surface_pressure</span></div><div class='xr-var-dims'>(time, longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(100, 64, 32), meta=np.ndarray&gt;</div><input id='attrs-00840fb0-b9ca-4f32-963d-d37cd72639ff' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-00840fb0-b9ca-4f32-963d-d37cd72639ff' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c10b1df4-1aa2-43cf-ae60-0eddca2e7137' class='xr-var-data-in' type='checkbox'><label for='data-c10b1df4-1aa2-43cf-ae60-0eddca2e7137' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Surface pressure</dd><dt><span>short_name :</span></dt><dd>sp</dd><dt><span>standard_name :</span></dt><dd>surface_air_pressure</dd><dt><span>units :</span></dt><dd>Pa</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>surface_pressure</span></div><div class='xr-var-dims'>(time, longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(48, 64, 32), meta=np.ndarray&gt;</div><input id='attrs-860fbd81-b076-4edc-8f44-26dd36481b9e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-860fbd81-b076-4edc-8f44-26dd36481b9e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9d135f53-54cb-4e4b-aca4-9ea2fc0e942e' class='xr-var-data-in' type='checkbox'><label for='data-9d135f53-54cb-4e4b-aca4-9ea2fc0e942e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Surface pressure</dd><dt><span>short_name :</span></dt><dd>sp</dd><dt><span>standard_name :</span></dt><dd>surface_air_pressure</dd><dt><span>units :</span></dt><dd>Pa</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -5767,17 +5775,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 730.81 MiB </td>\n",
-       "                        <td> 800.00 kiB </td>\n",
+       "                        <td> 384.00 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (93544, 64, 32) </td>\n",
-       "                        <td> (100, 64, 32) </td>\n",
+       "                        <td> (48, 64, 32) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 936 chunks in 2 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 1949 chunks in 2 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -5801,14 +5809,14 @@
        "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"40\" />\n",
        "  <line x1=\"28\" y1=\"18\" x2=\"28\" y2=\"43\" />\n",
        "  <line x1=\"32\" y1=\"22\" x2=\"32\" y2=\"47\" />\n",
-       "  <line x1=\"35\" y1=\"25\" x2=\"35\" y2=\"51\" />\n",
+       "  <line x1=\"36\" y1=\"26\" x2=\"36\" y2=\"51\" />\n",
        "  <line x1=\"39\" y1=\"29\" x2=\"39\" y2=\"55\" />\n",
        "  <line x1=\"43\" y1=\"33\" x2=\"43\" y2=\"58\" />\n",
        "  <line x1=\"47\" y1=\"37\" x2=\"47\" y2=\"62\" />\n",
        "  <line x1=\"50\" y1=\"40\" x2=\"50\" y2=\"66\" />\n",
-       "  <line x1=\"54\" y1=\"44\" x2=\"54\" y2=\"70\" />\n",
+       "  <line x1=\"54\" y1=\"44\" x2=\"54\" y2=\"69\" />\n",
        "  <line x1=\"58\" y1=\"48\" x2=\"58\" y2=\"73\" />\n",
-       "  <line x1=\"61\" y1=\"51\" x2=\"61\" y2=\"77\" />\n",
+       "  <line x1=\"62\" y1=\"52\" x2=\"62\" y2=\"77\" />\n",
        "  <line x1=\"65\" y1=\"55\" x2=\"65\" y2=\"81\" />\n",
        "  <line x1=\"69\" y1=\"59\" x2=\"69\" y2=\"84\" />\n",
        "  <line x1=\"73\" y1=\"63\" x2=\"73\" y2=\"88\" />\n",
@@ -5826,14 +5834,14 @@
        "  <line x1=\"24\" y1=\"14\" x2=\"50\" y2=\"14\" />\n",
        "  <line x1=\"28\" y1=\"18\" x2=\"53\" y2=\"18\" />\n",
        "  <line x1=\"32\" y1=\"22\" x2=\"57\" y2=\"22\" />\n",
-       "  <line x1=\"35\" y1=\"25\" x2=\"61\" y2=\"25\" />\n",
+       "  <line x1=\"36\" y1=\"26\" x2=\"61\" y2=\"26\" />\n",
        "  <line x1=\"39\" y1=\"29\" x2=\"65\" y2=\"29\" />\n",
        "  <line x1=\"43\" y1=\"33\" x2=\"68\" y2=\"33\" />\n",
        "  <line x1=\"47\" y1=\"37\" x2=\"72\" y2=\"37\" />\n",
        "  <line x1=\"50\" y1=\"40\" x2=\"76\" y2=\"40\" />\n",
-       "  <line x1=\"54\" y1=\"44\" x2=\"80\" y2=\"44\" />\n",
+       "  <line x1=\"54\" y1=\"44\" x2=\"79\" y2=\"44\" />\n",
        "  <line x1=\"58\" y1=\"48\" x2=\"83\" y2=\"48\" />\n",
-       "  <line x1=\"61\" y1=\"51\" x2=\"87\" y2=\"51\" />\n",
+       "  <line x1=\"62\" y1=\"52\" x2=\"87\" y2=\"52\" />\n",
        "  <line x1=\"65\" y1=\"55\" x2=\"91\" y2=\"55\" />\n",
        "  <line x1=\"69\" y1=\"59\" x2=\"94\" y2=\"59\" />\n",
        "  <line x1=\"73\" y1=\"63\" x2=\"98\" y2=\"63\" />\n",
@@ -5865,7 +5873,7 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>temperature</span></div><div class='xr-var-dims'>(time, level, longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(100, 13, 64, 32), meta=np.ndarray&gt;</div><input id='attrs-f4d48550-226a-4e2c-aa65-1cbae0235996' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f4d48550-226a-4e2c-aa65-1cbae0235996' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-700e9344-4105-407b-a6e9-77714664581f' class='xr-var-data-in' type='checkbox'><label for='data-700e9344-4105-407b-a6e9-77714664581f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Temperature</dd><dt><span>short_name :</span></dt><dd>t</dd><dt><span>standard_name :</span></dt><dd>air_temperature</dd><dt><span>units :</span></dt><dd>K</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>temperature</span></div><div class='xr-var-dims'>(time, level, longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(48, 13, 64, 32), meta=np.ndarray&gt;</div><input id='attrs-058a7c9b-23b2-4a71-b66f-62fe0c83d9df' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-058a7c9b-23b2-4a71-b66f-62fe0c83d9df' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6321e7fb-9298-483a-9699-687823e00d94' class='xr-var-data-in' type='checkbox'><label for='data-6321e7fb-9298-483a-9699-687823e00d94' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Temperature</dd><dt><span>short_name :</span></dt><dd>t</dd><dt><span>standard_name :</span></dt><dd>air_temperature</dd><dt><span>units :</span></dt><dd>K</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -5881,17 +5889,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 9.28 GiB </td>\n",
-       "                        <td> 10.16 MiB </td>\n",
+       "                        <td> 4.88 MiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (93544, 13, 64, 32) </td>\n",
-       "                        <td> (100, 13, 64, 32) </td>\n",
+       "                        <td> (48, 13, 64, 32) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 936 chunks in 2 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 1949 chunks in 2 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -5912,31 +5920,31 @@
        "  <line x1=\"3\" y1=\"0\" x2=\"3\" y2=\"25\" />\n",
        "  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n",
        "  <line x1=\"11\" y1=\"0\" x2=\"11\" y2=\"25\" />\n",
-       "  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n",
+       "  <line x1=\"14\" y1=\"0\" x2=\"14\" y2=\"25\" />\n",
        "  <line x1=\"18\" y1=\"0\" x2=\"18\" y2=\"25\" />\n",
        "  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n",
        "  <line x1=\"26\" y1=\"0\" x2=\"26\" y2=\"25\" />\n",
-       "  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n",
+       "  <line x1=\"29\" y1=\"0\" x2=\"29\" y2=\"25\" />\n",
        "  <line x1=\"33\" y1=\"0\" x2=\"33\" y2=\"25\" />\n",
        "  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n",
        "  <line x1=\"41\" y1=\"0\" x2=\"41\" y2=\"25\" />\n",
-       "  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n",
+       "  <line x1=\"44\" y1=\"0\" x2=\"44\" y2=\"25\" />\n",
        "  <line x1=\"48\" y1=\"0\" x2=\"48\" y2=\"25\" />\n",
        "  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n",
        "  <line x1=\"56\" y1=\"0\" x2=\"56\" y2=\"25\" />\n",
-       "  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n",
+       "  <line x1=\"59\" y1=\"0\" x2=\"59\" y2=\"25\" />\n",
        "  <line x1=\"63\" y1=\"0\" x2=\"63\" y2=\"25\" />\n",
        "  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n",
        "  <line x1=\"71\" y1=\"0\" x2=\"71\" y2=\"25\" />\n",
-       "  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n",
+       "  <line x1=\"74\" y1=\"0\" x2=\"74\" y2=\"25\" />\n",
        "  <line x1=\"78\" y1=\"0\" x2=\"78\" y2=\"25\" />\n",
        "  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n",
        "  <line x1=\"86\" y1=\"0\" x2=\"86\" y2=\"25\" />\n",
-       "  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n",
+       "  <line x1=\"89\" y1=\"0\" x2=\"89\" y2=\"25\" />\n",
        "  <line x1=\"93\" y1=\"0\" x2=\"93\" y2=\"25\" />\n",
        "  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n",
        "  <line x1=\"101\" y1=\"0\" x2=\"101\" y2=\"25\" />\n",
-       "  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n",
+       "  <line x1=\"104\" y1=\"0\" x2=\"104\" y2=\"25\" />\n",
        "  <line x1=\"108\" y1=\"0\" x2=\"108\" y2=\"25\" />\n",
        "  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n",
        "  <line x1=\"116\" y1=\"0\" x2=\"116\" y2=\"25\" />\n",
@@ -5990,7 +5998,7 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>total_cloud_cover</span></div><div class='xr-var-dims'>(time, longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(100, 64, 32), meta=np.ndarray&gt;</div><input id='attrs-b280954f-8fb0-4f9c-b520-eebf2922ab67' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b280954f-8fb0-4f9c-b520-eebf2922ab67' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1a7a2572-90d3-4d07-ada5-bbee2d1ad17d' class='xr-var-data-in' type='checkbox'><label for='data-1a7a2572-90d3-4d07-ada5-bbee2d1ad17d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Total cloud cover</dd><dt><span>short_name :</span></dt><dd>tcc</dd><dt><span>standard_name :</span></dt><dd>cloud_area_fraction</dd><dt><span>units :</span></dt><dd>(0 - 1)</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>total_cloud_cover</span></div><div class='xr-var-dims'>(time, longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(48, 64, 32), meta=np.ndarray&gt;</div><input id='attrs-e86754d8-0c04-4244-89e4-86c4e0ccd570' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e86754d8-0c04-4244-89e4-86c4e0ccd570' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-84b0115a-64e9-4ba7-9f28-49a62d92f03e' class='xr-var-data-in' type='checkbox'><label for='data-84b0115a-64e9-4ba7-9f28-49a62d92f03e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Total cloud cover</dd><dt><span>short_name :</span></dt><dd>tcc</dd><dt><span>standard_name :</span></dt><dd>cloud_area_fraction</dd><dt><span>units :</span></dt><dd>(0 - 1)</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -6006,17 +6014,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 730.81 MiB </td>\n",
-       "                        <td> 800.00 kiB </td>\n",
+       "                        <td> 384.00 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (93544, 64, 32) </td>\n",
-       "                        <td> (100, 64, 32) </td>\n",
+       "                        <td> (48, 64, 32) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 936 chunks in 2 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 1949 chunks in 2 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -6040,14 +6048,14 @@
        "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"40\" />\n",
        "  <line x1=\"28\" y1=\"18\" x2=\"28\" y2=\"43\" />\n",
        "  <line x1=\"32\" y1=\"22\" x2=\"32\" y2=\"47\" />\n",
-       "  <line x1=\"35\" y1=\"25\" x2=\"35\" y2=\"51\" />\n",
+       "  <line x1=\"36\" y1=\"26\" x2=\"36\" y2=\"51\" />\n",
        "  <line x1=\"39\" y1=\"29\" x2=\"39\" y2=\"55\" />\n",
        "  <line x1=\"43\" y1=\"33\" x2=\"43\" y2=\"58\" />\n",
        "  <line x1=\"47\" y1=\"37\" x2=\"47\" y2=\"62\" />\n",
        "  <line x1=\"50\" y1=\"40\" x2=\"50\" y2=\"66\" />\n",
-       "  <line x1=\"54\" y1=\"44\" x2=\"54\" y2=\"70\" />\n",
+       "  <line x1=\"54\" y1=\"44\" x2=\"54\" y2=\"69\" />\n",
        "  <line x1=\"58\" y1=\"48\" x2=\"58\" y2=\"73\" />\n",
-       "  <line x1=\"61\" y1=\"51\" x2=\"61\" y2=\"77\" />\n",
+       "  <line x1=\"62\" y1=\"52\" x2=\"62\" y2=\"77\" />\n",
        "  <line x1=\"65\" y1=\"55\" x2=\"65\" y2=\"81\" />\n",
        "  <line x1=\"69\" y1=\"59\" x2=\"69\" y2=\"84\" />\n",
        "  <line x1=\"73\" y1=\"63\" x2=\"73\" y2=\"88\" />\n",
@@ -6065,14 +6073,14 @@
        "  <line x1=\"24\" y1=\"14\" x2=\"50\" y2=\"14\" />\n",
        "  <line x1=\"28\" y1=\"18\" x2=\"53\" y2=\"18\" />\n",
        "  <line x1=\"32\" y1=\"22\" x2=\"57\" y2=\"22\" />\n",
-       "  <line x1=\"35\" y1=\"25\" x2=\"61\" y2=\"25\" />\n",
+       "  <line x1=\"36\" y1=\"26\" x2=\"61\" y2=\"26\" />\n",
        "  <line x1=\"39\" y1=\"29\" x2=\"65\" y2=\"29\" />\n",
        "  <line x1=\"43\" y1=\"33\" x2=\"68\" y2=\"33\" />\n",
        "  <line x1=\"47\" y1=\"37\" x2=\"72\" y2=\"37\" />\n",
        "  <line x1=\"50\" y1=\"40\" x2=\"76\" y2=\"40\" />\n",
-       "  <line x1=\"54\" y1=\"44\" x2=\"80\" y2=\"44\" />\n",
+       "  <line x1=\"54\" y1=\"44\" x2=\"79\" y2=\"44\" />\n",
        "  <line x1=\"58\" y1=\"48\" x2=\"83\" y2=\"48\" />\n",
-       "  <line x1=\"61\" y1=\"51\" x2=\"87\" y2=\"51\" />\n",
+       "  <line x1=\"62\" y1=\"52\" x2=\"87\" y2=\"52\" />\n",
        "  <line x1=\"65\" y1=\"55\" x2=\"91\" y2=\"55\" />\n",
        "  <line x1=\"69\" y1=\"59\" x2=\"94\" y2=\"59\" />\n",
        "  <line x1=\"73\" y1=\"63\" x2=\"98\" y2=\"63\" />\n",
@@ -6104,7 +6112,7 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>total_column_vapor</span></div><div class='xr-var-dims'>(time, longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(100, 64, 32), meta=np.ndarray&gt;</div><input id='attrs-2f5c4eae-6e05-40df-9dad-38dc95e5888e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2f5c4eae-6e05-40df-9dad-38dc95e5888e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b42006f7-dc63-4935-8400-fc20bc74a871' class='xr-var-data-in' type='checkbox'><label for='data-b42006f7-dc63-4935-8400-fc20bc74a871' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>total_column_vapor</span></div><div class='xr-var-dims'>(time, longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(48, 64, 32), meta=np.ndarray&gt;</div><input id='attrs-58ce27f4-9eaf-4512-92e0-932447664f74' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-58ce27f4-9eaf-4512-92e0-932447664f74' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cff87b41-dc53-4d43-9f47-b00b51a592ce' class='xr-var-data-in' type='checkbox'><label for='data-cff87b41-dc53-4d43-9f47-b00b51a592ce' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -6120,17 +6128,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 730.81 MiB </td>\n",
-       "                        <td> 800.00 kiB </td>\n",
+       "                        <td> 384.00 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (93544, 64, 32) </td>\n",
-       "                        <td> (100, 64, 32) </td>\n",
+       "                        <td> (48, 64, 32) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 936 chunks in 2 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 1949 chunks in 2 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -6154,14 +6162,14 @@
        "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"40\" />\n",
        "  <line x1=\"28\" y1=\"18\" x2=\"28\" y2=\"43\" />\n",
        "  <line x1=\"32\" y1=\"22\" x2=\"32\" y2=\"47\" />\n",
-       "  <line x1=\"35\" y1=\"25\" x2=\"35\" y2=\"51\" />\n",
+       "  <line x1=\"36\" y1=\"26\" x2=\"36\" y2=\"51\" />\n",
        "  <line x1=\"39\" y1=\"29\" x2=\"39\" y2=\"55\" />\n",
        "  <line x1=\"43\" y1=\"33\" x2=\"43\" y2=\"58\" />\n",
        "  <line x1=\"47\" y1=\"37\" x2=\"47\" y2=\"62\" />\n",
        "  <line x1=\"50\" y1=\"40\" x2=\"50\" y2=\"66\" />\n",
-       "  <line x1=\"54\" y1=\"44\" x2=\"54\" y2=\"70\" />\n",
+       "  <line x1=\"54\" y1=\"44\" x2=\"54\" y2=\"69\" />\n",
        "  <line x1=\"58\" y1=\"48\" x2=\"58\" y2=\"73\" />\n",
-       "  <line x1=\"61\" y1=\"51\" x2=\"61\" y2=\"77\" />\n",
+       "  <line x1=\"62\" y1=\"52\" x2=\"62\" y2=\"77\" />\n",
        "  <line x1=\"65\" y1=\"55\" x2=\"65\" y2=\"81\" />\n",
        "  <line x1=\"69\" y1=\"59\" x2=\"69\" y2=\"84\" />\n",
        "  <line x1=\"73\" y1=\"63\" x2=\"73\" y2=\"88\" />\n",
@@ -6179,14 +6187,14 @@
        "  <line x1=\"24\" y1=\"14\" x2=\"50\" y2=\"14\" />\n",
        "  <line x1=\"28\" y1=\"18\" x2=\"53\" y2=\"18\" />\n",
        "  <line x1=\"32\" y1=\"22\" x2=\"57\" y2=\"22\" />\n",
-       "  <line x1=\"35\" y1=\"25\" x2=\"61\" y2=\"25\" />\n",
+       "  <line x1=\"36\" y1=\"26\" x2=\"61\" y2=\"26\" />\n",
        "  <line x1=\"39\" y1=\"29\" x2=\"65\" y2=\"29\" />\n",
        "  <line x1=\"43\" y1=\"33\" x2=\"68\" y2=\"33\" />\n",
        "  <line x1=\"47\" y1=\"37\" x2=\"72\" y2=\"37\" />\n",
        "  <line x1=\"50\" y1=\"40\" x2=\"76\" y2=\"40\" />\n",
-       "  <line x1=\"54\" y1=\"44\" x2=\"80\" y2=\"44\" />\n",
+       "  <line x1=\"54\" y1=\"44\" x2=\"79\" y2=\"44\" />\n",
        "  <line x1=\"58\" y1=\"48\" x2=\"83\" y2=\"48\" />\n",
-       "  <line x1=\"61\" y1=\"51\" x2=\"87\" y2=\"51\" />\n",
+       "  <line x1=\"62\" y1=\"52\" x2=\"87\" y2=\"52\" />\n",
        "  <line x1=\"65\" y1=\"55\" x2=\"91\" y2=\"55\" />\n",
        "  <line x1=\"69\" y1=\"59\" x2=\"94\" y2=\"59\" />\n",
        "  <line x1=\"73\" y1=\"63\" x2=\"98\" y2=\"63\" />\n",
@@ -6218,7 +6226,7 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>total_column_water</span></div><div class='xr-var-dims'>(time, longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(100, 64, 32), meta=np.ndarray&gt;</div><input id='attrs-dadfe20c-13e0-471e-801f-8060986ce151' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-dadfe20c-13e0-471e-801f-8060986ce151' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-077109eb-4fd2-4b76-9901-1e105eccdf4f' class='xr-var-data-in' type='checkbox'><label for='data-077109eb-4fd2-4b76-9901-1e105eccdf4f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Total column water</dd><dt><span>short_name :</span></dt><dd>tcw</dd><dt><span>units :</span></dt><dd>kg m**-2</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>total_column_water</span></div><div class='xr-var-dims'>(time, longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(48, 64, 32), meta=np.ndarray&gt;</div><input id='attrs-9a01faa8-7ff0-4703-b461-9898a489955f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9a01faa8-7ff0-4703-b461-9898a489955f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c4ebc04e-b6dd-4374-9c79-9ba489bc6c4f' class='xr-var-data-in' type='checkbox'><label for='data-c4ebc04e-b6dd-4374-9c79-9ba489bc6c4f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Total column water</dd><dt><span>short_name :</span></dt><dd>tcw</dd><dt><span>units :</span></dt><dd>kg m**-2</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -6234,17 +6242,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 730.81 MiB </td>\n",
-       "                        <td> 800.00 kiB </td>\n",
+       "                        <td> 384.00 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (93544, 64, 32) </td>\n",
-       "                        <td> (100, 64, 32) </td>\n",
+       "                        <td> (48, 64, 32) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 936 chunks in 2 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 1949 chunks in 2 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -6268,14 +6276,14 @@
        "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"40\" />\n",
        "  <line x1=\"28\" y1=\"18\" x2=\"28\" y2=\"43\" />\n",
        "  <line x1=\"32\" y1=\"22\" x2=\"32\" y2=\"47\" />\n",
-       "  <line x1=\"35\" y1=\"25\" x2=\"35\" y2=\"51\" />\n",
+       "  <line x1=\"36\" y1=\"26\" x2=\"36\" y2=\"51\" />\n",
        "  <line x1=\"39\" y1=\"29\" x2=\"39\" y2=\"55\" />\n",
        "  <line x1=\"43\" y1=\"33\" x2=\"43\" y2=\"58\" />\n",
        "  <line x1=\"47\" y1=\"37\" x2=\"47\" y2=\"62\" />\n",
        "  <line x1=\"50\" y1=\"40\" x2=\"50\" y2=\"66\" />\n",
-       "  <line x1=\"54\" y1=\"44\" x2=\"54\" y2=\"70\" />\n",
+       "  <line x1=\"54\" y1=\"44\" x2=\"54\" y2=\"69\" />\n",
        "  <line x1=\"58\" y1=\"48\" x2=\"58\" y2=\"73\" />\n",
-       "  <line x1=\"61\" y1=\"51\" x2=\"61\" y2=\"77\" />\n",
+       "  <line x1=\"62\" y1=\"52\" x2=\"62\" y2=\"77\" />\n",
        "  <line x1=\"65\" y1=\"55\" x2=\"65\" y2=\"81\" />\n",
        "  <line x1=\"69\" y1=\"59\" x2=\"69\" y2=\"84\" />\n",
        "  <line x1=\"73\" y1=\"63\" x2=\"73\" y2=\"88\" />\n",
@@ -6293,14 +6301,14 @@
        "  <line x1=\"24\" y1=\"14\" x2=\"50\" y2=\"14\" />\n",
        "  <line x1=\"28\" y1=\"18\" x2=\"53\" y2=\"18\" />\n",
        "  <line x1=\"32\" y1=\"22\" x2=\"57\" y2=\"22\" />\n",
-       "  <line x1=\"35\" y1=\"25\" x2=\"61\" y2=\"25\" />\n",
+       "  <line x1=\"36\" y1=\"26\" x2=\"61\" y2=\"26\" />\n",
        "  <line x1=\"39\" y1=\"29\" x2=\"65\" y2=\"29\" />\n",
        "  <line x1=\"43\" y1=\"33\" x2=\"68\" y2=\"33\" />\n",
        "  <line x1=\"47\" y1=\"37\" x2=\"72\" y2=\"37\" />\n",
        "  <line x1=\"50\" y1=\"40\" x2=\"76\" y2=\"40\" />\n",
-       "  <line x1=\"54\" y1=\"44\" x2=\"80\" y2=\"44\" />\n",
+       "  <line x1=\"54\" y1=\"44\" x2=\"79\" y2=\"44\" />\n",
        "  <line x1=\"58\" y1=\"48\" x2=\"83\" y2=\"48\" />\n",
-       "  <line x1=\"61\" y1=\"51\" x2=\"87\" y2=\"51\" />\n",
+       "  <line x1=\"62\" y1=\"52\" x2=\"87\" y2=\"52\" />\n",
        "  <line x1=\"65\" y1=\"55\" x2=\"91\" y2=\"55\" />\n",
        "  <line x1=\"69\" y1=\"59\" x2=\"94\" y2=\"59\" />\n",
        "  <line x1=\"73\" y1=\"63\" x2=\"98\" y2=\"63\" />\n",
@@ -6332,7 +6340,7 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>total_column_water_vapour</span></div><div class='xr-var-dims'>(time, longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(100, 64, 32), meta=np.ndarray&gt;</div><input id='attrs-53b24baf-7197-474b-bddb-2acb73c3f5cd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-53b24baf-7197-474b-bddb-2acb73c3f5cd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dfef7db6-bee7-4c6e-8300-47d38b06f9bb' class='xr-var-data-in' type='checkbox'><label for='data-dfef7db6-bee7-4c6e-8300-47d38b06f9bb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Total column vertically-integrated water vapour</dd><dt><span>short_name :</span></dt><dd>tcwv</dd><dt><span>standard_name :</span></dt><dd>lwe_thickness_of_atmosphere_mass_content_of_water_vapor</dd><dt><span>units :</span></dt><dd>kg m**-2</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>total_column_water_vapour</span></div><div class='xr-var-dims'>(time, longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(48, 64, 32), meta=np.ndarray&gt;</div><input id='attrs-417b97cc-8a4c-4d40-8892-33f9c30dd107' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-417b97cc-8a4c-4d40-8892-33f9c30dd107' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e98b2702-5719-429c-a4f9-bb81ceea9f5d' class='xr-var-data-in' type='checkbox'><label for='data-e98b2702-5719-429c-a4f9-bb81ceea9f5d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Total column vertically-integrated water vapour</dd><dt><span>short_name :</span></dt><dd>tcwv</dd><dt><span>standard_name :</span></dt><dd>lwe_thickness_of_atmosphere_mass_content_of_water_vapor</dd><dt><span>units :</span></dt><dd>kg m**-2</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -6348,17 +6356,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 730.81 MiB </td>\n",
-       "                        <td> 800.00 kiB </td>\n",
+       "                        <td> 384.00 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (93544, 64, 32) </td>\n",
-       "                        <td> (100, 64, 32) </td>\n",
+       "                        <td> (48, 64, 32) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 936 chunks in 2 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 1949 chunks in 2 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -6382,14 +6390,14 @@
        "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"40\" />\n",
        "  <line x1=\"28\" y1=\"18\" x2=\"28\" y2=\"43\" />\n",
        "  <line x1=\"32\" y1=\"22\" x2=\"32\" y2=\"47\" />\n",
-       "  <line x1=\"35\" y1=\"25\" x2=\"35\" y2=\"51\" />\n",
+       "  <line x1=\"36\" y1=\"26\" x2=\"36\" y2=\"51\" />\n",
        "  <line x1=\"39\" y1=\"29\" x2=\"39\" y2=\"55\" />\n",
        "  <line x1=\"43\" y1=\"33\" x2=\"43\" y2=\"58\" />\n",
        "  <line x1=\"47\" y1=\"37\" x2=\"47\" y2=\"62\" />\n",
        "  <line x1=\"50\" y1=\"40\" x2=\"50\" y2=\"66\" />\n",
-       "  <line x1=\"54\" y1=\"44\" x2=\"54\" y2=\"70\" />\n",
+       "  <line x1=\"54\" y1=\"44\" x2=\"54\" y2=\"69\" />\n",
        "  <line x1=\"58\" y1=\"48\" x2=\"58\" y2=\"73\" />\n",
-       "  <line x1=\"61\" y1=\"51\" x2=\"61\" y2=\"77\" />\n",
+       "  <line x1=\"62\" y1=\"52\" x2=\"62\" y2=\"77\" />\n",
        "  <line x1=\"65\" y1=\"55\" x2=\"65\" y2=\"81\" />\n",
        "  <line x1=\"69\" y1=\"59\" x2=\"69\" y2=\"84\" />\n",
        "  <line x1=\"73\" y1=\"63\" x2=\"73\" y2=\"88\" />\n",
@@ -6407,14 +6415,14 @@
        "  <line x1=\"24\" y1=\"14\" x2=\"50\" y2=\"14\" />\n",
        "  <line x1=\"28\" y1=\"18\" x2=\"53\" y2=\"18\" />\n",
        "  <line x1=\"32\" y1=\"22\" x2=\"57\" y2=\"22\" />\n",
-       "  <line x1=\"35\" y1=\"25\" x2=\"61\" y2=\"25\" />\n",
+       "  <line x1=\"36\" y1=\"26\" x2=\"61\" y2=\"26\" />\n",
        "  <line x1=\"39\" y1=\"29\" x2=\"65\" y2=\"29\" />\n",
        "  <line x1=\"43\" y1=\"33\" x2=\"68\" y2=\"33\" />\n",
        "  <line x1=\"47\" y1=\"37\" x2=\"72\" y2=\"37\" />\n",
        "  <line x1=\"50\" y1=\"40\" x2=\"76\" y2=\"40\" />\n",
-       "  <line x1=\"54\" y1=\"44\" x2=\"80\" y2=\"44\" />\n",
+       "  <line x1=\"54\" y1=\"44\" x2=\"79\" y2=\"44\" />\n",
        "  <line x1=\"58\" y1=\"48\" x2=\"83\" y2=\"48\" />\n",
-       "  <line x1=\"61\" y1=\"51\" x2=\"87\" y2=\"51\" />\n",
+       "  <line x1=\"62\" y1=\"52\" x2=\"87\" y2=\"52\" />\n",
        "  <line x1=\"65\" y1=\"55\" x2=\"91\" y2=\"55\" />\n",
        "  <line x1=\"69\" y1=\"59\" x2=\"94\" y2=\"59\" />\n",
        "  <line x1=\"73\" y1=\"63\" x2=\"98\" y2=\"63\" />\n",
@@ -6446,7 +6454,7 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>total_precipitation_12hr</span></div><div class='xr-var-dims'>(time, longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(100, 64, 32), meta=np.ndarray&gt;</div><input id='attrs-8e027e00-a620-42ec-9f96-6b0ad0b2c5ff' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8e027e00-a620-42ec-9f96-6b0ad0b2c5ff' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-86d622ff-1453-442c-8a99-33e15f077a29' class='xr-var-data-in' type='checkbox'><label for='data-86d622ff-1453-442c-8a99-33e15f077a29' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Total precipitation</dd><dt><span>short_name :</span></dt><dd>tp</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>total_precipitation_12hr</span></div><div class='xr-var-dims'>(time, longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(48, 64, 32), meta=np.ndarray&gt;</div><input id='attrs-b4240336-15a8-40e6-aa87-2c9c866804b3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b4240336-15a8-40e6-aa87-2c9c866804b3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c4581dd4-a0d9-4d90-84b1-764761931e3b' class='xr-var-data-in' type='checkbox'><label for='data-c4581dd4-a0d9-4d90-84b1-764761931e3b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Total precipitation</dd><dt><span>short_name :</span></dt><dd>tp</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -6462,17 +6470,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 730.81 MiB </td>\n",
-       "                        <td> 800.00 kiB </td>\n",
+       "                        <td> 384.00 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (93544, 64, 32) </td>\n",
-       "                        <td> (100, 64, 32) </td>\n",
+       "                        <td> (48, 64, 32) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 936 chunks in 2 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 1949 chunks in 2 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -6496,14 +6504,14 @@
        "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"40\" />\n",
        "  <line x1=\"28\" y1=\"18\" x2=\"28\" y2=\"43\" />\n",
        "  <line x1=\"32\" y1=\"22\" x2=\"32\" y2=\"47\" />\n",
-       "  <line x1=\"35\" y1=\"25\" x2=\"35\" y2=\"51\" />\n",
+       "  <line x1=\"36\" y1=\"26\" x2=\"36\" y2=\"51\" />\n",
        "  <line x1=\"39\" y1=\"29\" x2=\"39\" y2=\"55\" />\n",
        "  <line x1=\"43\" y1=\"33\" x2=\"43\" y2=\"58\" />\n",
        "  <line x1=\"47\" y1=\"37\" x2=\"47\" y2=\"62\" />\n",
        "  <line x1=\"50\" y1=\"40\" x2=\"50\" y2=\"66\" />\n",
-       "  <line x1=\"54\" y1=\"44\" x2=\"54\" y2=\"70\" />\n",
+       "  <line x1=\"54\" y1=\"44\" x2=\"54\" y2=\"69\" />\n",
        "  <line x1=\"58\" y1=\"48\" x2=\"58\" y2=\"73\" />\n",
-       "  <line x1=\"61\" y1=\"51\" x2=\"61\" y2=\"77\" />\n",
+       "  <line x1=\"62\" y1=\"52\" x2=\"62\" y2=\"77\" />\n",
        "  <line x1=\"65\" y1=\"55\" x2=\"65\" y2=\"81\" />\n",
        "  <line x1=\"69\" y1=\"59\" x2=\"69\" y2=\"84\" />\n",
        "  <line x1=\"73\" y1=\"63\" x2=\"73\" y2=\"88\" />\n",
@@ -6521,14 +6529,14 @@
        "  <line x1=\"24\" y1=\"14\" x2=\"50\" y2=\"14\" />\n",
        "  <line x1=\"28\" y1=\"18\" x2=\"53\" y2=\"18\" />\n",
        "  <line x1=\"32\" y1=\"22\" x2=\"57\" y2=\"22\" />\n",
-       "  <line x1=\"35\" y1=\"25\" x2=\"61\" y2=\"25\" />\n",
+       "  <line x1=\"36\" y1=\"26\" x2=\"61\" y2=\"26\" />\n",
        "  <line x1=\"39\" y1=\"29\" x2=\"65\" y2=\"29\" />\n",
        "  <line x1=\"43\" y1=\"33\" x2=\"68\" y2=\"33\" />\n",
        "  <line x1=\"47\" y1=\"37\" x2=\"72\" y2=\"37\" />\n",
        "  <line x1=\"50\" y1=\"40\" x2=\"76\" y2=\"40\" />\n",
-       "  <line x1=\"54\" y1=\"44\" x2=\"80\" y2=\"44\" />\n",
+       "  <line x1=\"54\" y1=\"44\" x2=\"79\" y2=\"44\" />\n",
        "  <line x1=\"58\" y1=\"48\" x2=\"83\" y2=\"48\" />\n",
-       "  <line x1=\"61\" y1=\"51\" x2=\"87\" y2=\"51\" />\n",
+       "  <line x1=\"62\" y1=\"52\" x2=\"87\" y2=\"52\" />\n",
        "  <line x1=\"65\" y1=\"55\" x2=\"91\" y2=\"55\" />\n",
        "  <line x1=\"69\" y1=\"59\" x2=\"94\" y2=\"59\" />\n",
        "  <line x1=\"73\" y1=\"63\" x2=\"98\" y2=\"63\" />\n",
@@ -6560,7 +6568,7 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>total_precipitation_24hr</span></div><div class='xr-var-dims'>(time, longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(100, 64, 32), meta=np.ndarray&gt;</div><input id='attrs-25c476d9-48b3-43ef-b3eb-d9e3b8323f63' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-25c476d9-48b3-43ef-b3eb-d9e3b8323f63' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-601fa678-c1d9-43b3-91c2-753f230ff5f6' class='xr-var-data-in' type='checkbox'><label for='data-601fa678-c1d9-43b3-91c2-753f230ff5f6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Total precipitation</dd><dt><span>short_name :</span></dt><dd>tp</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>total_precipitation_24hr</span></div><div class='xr-var-dims'>(time, longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(48, 64, 32), meta=np.ndarray&gt;</div><input id='attrs-b09e55f7-b5b4-4a6b-b381-81544588a61f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b09e55f7-b5b4-4a6b-b381-81544588a61f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-921fbdcf-cec1-4bf4-91bd-b1f9018b7127' class='xr-var-data-in' type='checkbox'><label for='data-921fbdcf-cec1-4bf4-91bd-b1f9018b7127' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Total precipitation</dd><dt><span>short_name :</span></dt><dd>tp</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -6576,17 +6584,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 730.81 MiB </td>\n",
-       "                        <td> 800.00 kiB </td>\n",
+       "                        <td> 384.00 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (93544, 64, 32) </td>\n",
-       "                        <td> (100, 64, 32) </td>\n",
+       "                        <td> (48, 64, 32) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 936 chunks in 2 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 1949 chunks in 2 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -6610,14 +6618,14 @@
        "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"40\" />\n",
        "  <line x1=\"28\" y1=\"18\" x2=\"28\" y2=\"43\" />\n",
        "  <line x1=\"32\" y1=\"22\" x2=\"32\" y2=\"47\" />\n",
-       "  <line x1=\"35\" y1=\"25\" x2=\"35\" y2=\"51\" />\n",
+       "  <line x1=\"36\" y1=\"26\" x2=\"36\" y2=\"51\" />\n",
        "  <line x1=\"39\" y1=\"29\" x2=\"39\" y2=\"55\" />\n",
        "  <line x1=\"43\" y1=\"33\" x2=\"43\" y2=\"58\" />\n",
        "  <line x1=\"47\" y1=\"37\" x2=\"47\" y2=\"62\" />\n",
        "  <line x1=\"50\" y1=\"40\" x2=\"50\" y2=\"66\" />\n",
-       "  <line x1=\"54\" y1=\"44\" x2=\"54\" y2=\"70\" />\n",
+       "  <line x1=\"54\" y1=\"44\" x2=\"54\" y2=\"69\" />\n",
        "  <line x1=\"58\" y1=\"48\" x2=\"58\" y2=\"73\" />\n",
-       "  <line x1=\"61\" y1=\"51\" x2=\"61\" y2=\"77\" />\n",
+       "  <line x1=\"62\" y1=\"52\" x2=\"62\" y2=\"77\" />\n",
        "  <line x1=\"65\" y1=\"55\" x2=\"65\" y2=\"81\" />\n",
        "  <line x1=\"69\" y1=\"59\" x2=\"69\" y2=\"84\" />\n",
        "  <line x1=\"73\" y1=\"63\" x2=\"73\" y2=\"88\" />\n",
@@ -6635,14 +6643,14 @@
        "  <line x1=\"24\" y1=\"14\" x2=\"50\" y2=\"14\" />\n",
        "  <line x1=\"28\" y1=\"18\" x2=\"53\" y2=\"18\" />\n",
        "  <line x1=\"32\" y1=\"22\" x2=\"57\" y2=\"22\" />\n",
-       "  <line x1=\"35\" y1=\"25\" x2=\"61\" y2=\"25\" />\n",
+       "  <line x1=\"36\" y1=\"26\" x2=\"61\" y2=\"26\" />\n",
        "  <line x1=\"39\" y1=\"29\" x2=\"65\" y2=\"29\" />\n",
        "  <line x1=\"43\" y1=\"33\" x2=\"68\" y2=\"33\" />\n",
        "  <line x1=\"47\" y1=\"37\" x2=\"72\" y2=\"37\" />\n",
        "  <line x1=\"50\" y1=\"40\" x2=\"76\" y2=\"40\" />\n",
-       "  <line x1=\"54\" y1=\"44\" x2=\"80\" y2=\"44\" />\n",
+       "  <line x1=\"54\" y1=\"44\" x2=\"79\" y2=\"44\" />\n",
        "  <line x1=\"58\" y1=\"48\" x2=\"83\" y2=\"48\" />\n",
-       "  <line x1=\"61\" y1=\"51\" x2=\"87\" y2=\"51\" />\n",
+       "  <line x1=\"62\" y1=\"52\" x2=\"87\" y2=\"52\" />\n",
        "  <line x1=\"65\" y1=\"55\" x2=\"91\" y2=\"55\" />\n",
        "  <line x1=\"69\" y1=\"59\" x2=\"94\" y2=\"59\" />\n",
        "  <line x1=\"73\" y1=\"63\" x2=\"98\" y2=\"63\" />\n",
@@ -6674,7 +6682,7 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>total_precipitation_6hr</span></div><div class='xr-var-dims'>(time, longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(100, 64, 32), meta=np.ndarray&gt;</div><input id='attrs-3c78ad4c-21e9-4eed-b2fe-d1d4e4a24de2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3c78ad4c-21e9-4eed-b2fe-d1d4e4a24de2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-573b0cf6-5cc2-4bd1-9703-cd942876b361' class='xr-var-data-in' type='checkbox'><label for='data-573b0cf6-5cc2-4bd1-9703-cd942876b361' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Total precipitation</dd><dt><span>short_name :</span></dt><dd>tp</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>total_precipitation_6hr</span></div><div class='xr-var-dims'>(time, longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(48, 64, 32), meta=np.ndarray&gt;</div><input id='attrs-fb41bf8e-a943-44b0-a0aa-e8adc5e5580c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fb41bf8e-a943-44b0-a0aa-e8adc5e5580c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6dc55bfc-471f-4e51-95fb-743122f918e0' class='xr-var-data-in' type='checkbox'><label for='data-6dc55bfc-471f-4e51-95fb-743122f918e0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Total precipitation</dd><dt><span>short_name :</span></dt><dd>tp</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -6690,17 +6698,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 730.81 MiB </td>\n",
-       "                        <td> 800.00 kiB </td>\n",
+       "                        <td> 384.00 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (93544, 64, 32) </td>\n",
-       "                        <td> (100, 64, 32) </td>\n",
+       "                        <td> (48, 64, 32) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 936 chunks in 2 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 1949 chunks in 2 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -6724,14 +6732,14 @@
        "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"40\" />\n",
        "  <line x1=\"28\" y1=\"18\" x2=\"28\" y2=\"43\" />\n",
        "  <line x1=\"32\" y1=\"22\" x2=\"32\" y2=\"47\" />\n",
-       "  <line x1=\"35\" y1=\"25\" x2=\"35\" y2=\"51\" />\n",
+       "  <line x1=\"36\" y1=\"26\" x2=\"36\" y2=\"51\" />\n",
        "  <line x1=\"39\" y1=\"29\" x2=\"39\" y2=\"55\" />\n",
        "  <line x1=\"43\" y1=\"33\" x2=\"43\" y2=\"58\" />\n",
        "  <line x1=\"47\" y1=\"37\" x2=\"47\" y2=\"62\" />\n",
        "  <line x1=\"50\" y1=\"40\" x2=\"50\" y2=\"66\" />\n",
-       "  <line x1=\"54\" y1=\"44\" x2=\"54\" y2=\"70\" />\n",
+       "  <line x1=\"54\" y1=\"44\" x2=\"54\" y2=\"69\" />\n",
        "  <line x1=\"58\" y1=\"48\" x2=\"58\" y2=\"73\" />\n",
-       "  <line x1=\"61\" y1=\"51\" x2=\"61\" y2=\"77\" />\n",
+       "  <line x1=\"62\" y1=\"52\" x2=\"62\" y2=\"77\" />\n",
        "  <line x1=\"65\" y1=\"55\" x2=\"65\" y2=\"81\" />\n",
        "  <line x1=\"69\" y1=\"59\" x2=\"69\" y2=\"84\" />\n",
        "  <line x1=\"73\" y1=\"63\" x2=\"73\" y2=\"88\" />\n",
@@ -6749,14 +6757,14 @@
        "  <line x1=\"24\" y1=\"14\" x2=\"50\" y2=\"14\" />\n",
        "  <line x1=\"28\" y1=\"18\" x2=\"53\" y2=\"18\" />\n",
        "  <line x1=\"32\" y1=\"22\" x2=\"57\" y2=\"22\" />\n",
-       "  <line x1=\"35\" y1=\"25\" x2=\"61\" y2=\"25\" />\n",
+       "  <line x1=\"36\" y1=\"26\" x2=\"61\" y2=\"26\" />\n",
        "  <line x1=\"39\" y1=\"29\" x2=\"65\" y2=\"29\" />\n",
        "  <line x1=\"43\" y1=\"33\" x2=\"68\" y2=\"33\" />\n",
        "  <line x1=\"47\" y1=\"37\" x2=\"72\" y2=\"37\" />\n",
        "  <line x1=\"50\" y1=\"40\" x2=\"76\" y2=\"40\" />\n",
-       "  <line x1=\"54\" y1=\"44\" x2=\"80\" y2=\"44\" />\n",
+       "  <line x1=\"54\" y1=\"44\" x2=\"79\" y2=\"44\" />\n",
        "  <line x1=\"58\" y1=\"48\" x2=\"83\" y2=\"48\" />\n",
-       "  <line x1=\"61\" y1=\"51\" x2=\"87\" y2=\"51\" />\n",
+       "  <line x1=\"62\" y1=\"52\" x2=\"87\" y2=\"52\" />\n",
        "  <line x1=\"65\" y1=\"55\" x2=\"91\" y2=\"55\" />\n",
        "  <line x1=\"69\" y1=\"59\" x2=\"94\" y2=\"59\" />\n",
        "  <line x1=\"73\" y1=\"63\" x2=\"98\" y2=\"63\" />\n",
@@ -6788,7 +6796,7 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>type_of_high_vegetation</span></div><div class='xr-var-dims'>(longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(64, 32), meta=np.ndarray&gt;</div><input id='attrs-cb245654-ee00-4afc-8f13-0837c86479e6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cb245654-ee00-4afc-8f13-0837c86479e6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7ba22be9-390c-4b2c-aecf-f5ccaf04f08a' class='xr-var-data-in' type='checkbox'><label for='data-7ba22be9-390c-4b2c-aecf-f5ccaf04f08a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Type of high vegetation</dd><dt><span>short_name :</span></dt><dd>tvh</dd><dt><span>units :</span></dt><dd>~</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>type_of_high_vegetation</span></div><div class='xr-var-dims'>(longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(64, 32), meta=np.ndarray&gt;</div><input id='attrs-70f4928d-682b-4c9e-852a-8742e99dd9ac' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-70f4928d-682b-4c9e-852a-8742e99dd9ac' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-03a6acc3-55a4-4198-8352-2803c3133dcc' class='xr-var-data-in' type='checkbox'><label for='data-03a6acc3-55a4-4198-8352-2803c3133dcc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Type of high vegetation</dd><dt><span>short_name :</span></dt><dd>tvh</dd><dt><span>units :</span></dt><dd>~</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -6843,7 +6851,7 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>type_of_low_vegetation</span></div><div class='xr-var-dims'>(longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(64, 32), meta=np.ndarray&gt;</div><input id='attrs-ddbd3d48-34d5-463b-be0a-6f64784ffbb5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ddbd3d48-34d5-463b-be0a-6f64784ffbb5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7ecab63d-39ff-4345-b4dc-35086e2a1042' class='xr-var-data-in' type='checkbox'><label for='data-7ecab63d-39ff-4345-b4dc-35086e2a1042' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Type of low vegetation</dd><dt><span>short_name :</span></dt><dd>tvl</dd><dt><span>units :</span></dt><dd>~</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>type_of_low_vegetation</span></div><div class='xr-var-dims'>(longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(64, 32), meta=np.ndarray&gt;</div><input id='attrs-969ed95b-255f-4fa7-b041-7bcc1d67eaf8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-969ed95b-255f-4fa7-b041-7bcc1d67eaf8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-42133efb-001b-4913-89bb-01121a537ba2' class='xr-var-data-in' type='checkbox'><label for='data-42133efb-001b-4913-89bb-01121a537ba2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Type of low vegetation</dd><dt><span>short_name :</span></dt><dd>tvl</dd><dt><span>units :</span></dt><dd>~</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -6898,7 +6906,7 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>u_component_of_wind</span></div><div class='xr-var-dims'>(time, level, longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(100, 13, 64, 32), meta=np.ndarray&gt;</div><input id='attrs-5a2ca166-c21e-4dc9-ac36-93ae6f7118d6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5a2ca166-c21e-4dc9-ac36-93ae6f7118d6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-281755f8-9cf6-47d5-b108-ff3677c39910' class='xr-var-data-in' type='checkbox'><label for='data-281755f8-9cf6-47d5-b108-ff3677c39910' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>U component of wind</dd><dt><span>short_name :</span></dt><dd>u</dd><dt><span>standard_name :</span></dt><dd>eastward_wind</dd><dt><span>units :</span></dt><dd>m s**-1</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>u_component_of_wind</span></div><div class='xr-var-dims'>(time, level, longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(48, 13, 64, 32), meta=np.ndarray&gt;</div><input id='attrs-f72727a1-4d16-469f-87bb-390678455e02' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f72727a1-4d16-469f-87bb-390678455e02' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-736c7fc3-9f6d-4456-8a58-982d704af7e0' class='xr-var-data-in' type='checkbox'><label for='data-736c7fc3-9f6d-4456-8a58-982d704af7e0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>U component of wind</dd><dt><span>short_name :</span></dt><dd>u</dd><dt><span>standard_name :</span></dt><dd>eastward_wind</dd><dt><span>units :</span></dt><dd>m s**-1</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -6914,17 +6922,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 9.28 GiB </td>\n",
-       "                        <td> 10.16 MiB </td>\n",
+       "                        <td> 4.88 MiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (93544, 13, 64, 32) </td>\n",
-       "                        <td> (100, 13, 64, 32) </td>\n",
+       "                        <td> (48, 13, 64, 32) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 936 chunks in 2 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 1949 chunks in 2 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -6945,31 +6953,31 @@
        "  <line x1=\"3\" y1=\"0\" x2=\"3\" y2=\"25\" />\n",
        "  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n",
        "  <line x1=\"11\" y1=\"0\" x2=\"11\" y2=\"25\" />\n",
-       "  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n",
+       "  <line x1=\"14\" y1=\"0\" x2=\"14\" y2=\"25\" />\n",
        "  <line x1=\"18\" y1=\"0\" x2=\"18\" y2=\"25\" />\n",
        "  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n",
        "  <line x1=\"26\" y1=\"0\" x2=\"26\" y2=\"25\" />\n",
-       "  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n",
+       "  <line x1=\"29\" y1=\"0\" x2=\"29\" y2=\"25\" />\n",
        "  <line x1=\"33\" y1=\"0\" x2=\"33\" y2=\"25\" />\n",
        "  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n",
        "  <line x1=\"41\" y1=\"0\" x2=\"41\" y2=\"25\" />\n",
-       "  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n",
+       "  <line x1=\"44\" y1=\"0\" x2=\"44\" y2=\"25\" />\n",
        "  <line x1=\"48\" y1=\"0\" x2=\"48\" y2=\"25\" />\n",
        "  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n",
        "  <line x1=\"56\" y1=\"0\" x2=\"56\" y2=\"25\" />\n",
-       "  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n",
+       "  <line x1=\"59\" y1=\"0\" x2=\"59\" y2=\"25\" />\n",
        "  <line x1=\"63\" y1=\"0\" x2=\"63\" y2=\"25\" />\n",
        "  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n",
        "  <line x1=\"71\" y1=\"0\" x2=\"71\" y2=\"25\" />\n",
-       "  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n",
+       "  <line x1=\"74\" y1=\"0\" x2=\"74\" y2=\"25\" />\n",
        "  <line x1=\"78\" y1=\"0\" x2=\"78\" y2=\"25\" />\n",
        "  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n",
        "  <line x1=\"86\" y1=\"0\" x2=\"86\" y2=\"25\" />\n",
-       "  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n",
+       "  <line x1=\"89\" y1=\"0\" x2=\"89\" y2=\"25\" />\n",
        "  <line x1=\"93\" y1=\"0\" x2=\"93\" y2=\"25\" />\n",
        "  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n",
        "  <line x1=\"101\" y1=\"0\" x2=\"101\" y2=\"25\" />\n",
-       "  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n",
+       "  <line x1=\"104\" y1=\"0\" x2=\"104\" y2=\"25\" />\n",
        "  <line x1=\"108\" y1=\"0\" x2=\"108\" y2=\"25\" />\n",
        "  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n",
        "  <line x1=\"116\" y1=\"0\" x2=\"116\" y2=\"25\" />\n",
@@ -7023,7 +7031,7 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>v_component_of_wind</span></div><div class='xr-var-dims'>(time, level, longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(100, 13, 64, 32), meta=np.ndarray&gt;</div><input id='attrs-6d633111-8814-459d-ac26-ab106d462455' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6d633111-8814-459d-ac26-ab106d462455' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a69fb79a-e601-485f-86e0-e77d65e9b4d8' class='xr-var-data-in' type='checkbox'><label for='data-a69fb79a-e601-485f-86e0-e77d65e9b4d8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>V component of wind</dd><dt><span>short_name :</span></dt><dd>v</dd><dt><span>standard_name :</span></dt><dd>northward_wind</dd><dt><span>units :</span></dt><dd>m s**-1</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>v_component_of_wind</span></div><div class='xr-var-dims'>(time, level, longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(48, 13, 64, 32), meta=np.ndarray&gt;</div><input id='attrs-f6fb00c8-b758-4377-839b-7b84fb8db2cb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f6fb00c8-b758-4377-839b-7b84fb8db2cb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-028dc0c0-99b1-412d-ac86-3ac6435ec6ef' class='xr-var-data-in' type='checkbox'><label for='data-028dc0c0-99b1-412d-ac86-3ac6435ec6ef' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>V component of wind</dd><dt><span>short_name :</span></dt><dd>v</dd><dt><span>standard_name :</span></dt><dd>northward_wind</dd><dt><span>units :</span></dt><dd>m s**-1</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -7039,17 +7047,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 9.28 GiB </td>\n",
-       "                        <td> 10.16 MiB </td>\n",
+       "                        <td> 4.88 MiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (93544, 13, 64, 32) </td>\n",
-       "                        <td> (100, 13, 64, 32) </td>\n",
+       "                        <td> (48, 13, 64, 32) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 936 chunks in 2 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 1949 chunks in 2 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -7070,31 +7078,31 @@
        "  <line x1=\"3\" y1=\"0\" x2=\"3\" y2=\"25\" />\n",
        "  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n",
        "  <line x1=\"11\" y1=\"0\" x2=\"11\" y2=\"25\" />\n",
-       "  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n",
+       "  <line x1=\"14\" y1=\"0\" x2=\"14\" y2=\"25\" />\n",
        "  <line x1=\"18\" y1=\"0\" x2=\"18\" y2=\"25\" />\n",
        "  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n",
        "  <line x1=\"26\" y1=\"0\" x2=\"26\" y2=\"25\" />\n",
-       "  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n",
+       "  <line x1=\"29\" y1=\"0\" x2=\"29\" y2=\"25\" />\n",
        "  <line x1=\"33\" y1=\"0\" x2=\"33\" y2=\"25\" />\n",
        "  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n",
        "  <line x1=\"41\" y1=\"0\" x2=\"41\" y2=\"25\" />\n",
-       "  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n",
+       "  <line x1=\"44\" y1=\"0\" x2=\"44\" y2=\"25\" />\n",
        "  <line x1=\"48\" y1=\"0\" x2=\"48\" y2=\"25\" />\n",
        "  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n",
        "  <line x1=\"56\" y1=\"0\" x2=\"56\" y2=\"25\" />\n",
-       "  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n",
+       "  <line x1=\"59\" y1=\"0\" x2=\"59\" y2=\"25\" />\n",
        "  <line x1=\"63\" y1=\"0\" x2=\"63\" y2=\"25\" />\n",
        "  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n",
        "  <line x1=\"71\" y1=\"0\" x2=\"71\" y2=\"25\" />\n",
-       "  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n",
+       "  <line x1=\"74\" y1=\"0\" x2=\"74\" y2=\"25\" />\n",
        "  <line x1=\"78\" y1=\"0\" x2=\"78\" y2=\"25\" />\n",
        "  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n",
        "  <line x1=\"86\" y1=\"0\" x2=\"86\" y2=\"25\" />\n",
-       "  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n",
+       "  <line x1=\"89\" y1=\"0\" x2=\"89\" y2=\"25\" />\n",
        "  <line x1=\"93\" y1=\"0\" x2=\"93\" y2=\"25\" />\n",
        "  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n",
        "  <line x1=\"101\" y1=\"0\" x2=\"101\" y2=\"25\" />\n",
-       "  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n",
+       "  <line x1=\"104\" y1=\"0\" x2=\"104\" y2=\"25\" />\n",
        "  <line x1=\"108\" y1=\"0\" x2=\"108\" y2=\"25\" />\n",
        "  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n",
        "  <line x1=\"116\" y1=\"0\" x2=\"116\" y2=\"25\" />\n",
@@ -7148,7 +7156,7 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vertical_velocity</span></div><div class='xr-var-dims'>(time, level, longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(100, 13, 64, 32), meta=np.ndarray&gt;</div><input id='attrs-666e39c5-e387-4301-b0c2-1f75577bb42e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-666e39c5-e387-4301-b0c2-1f75577bb42e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9df96f42-bbdc-402e-8950-c1ad1f44b8e7' class='xr-var-data-in' type='checkbox'><label for='data-9df96f42-bbdc-402e-8950-c1ad1f44b8e7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Vertical velocity</dd><dt><span>short_name :</span></dt><dd>w</dd><dt><span>standard_name :</span></dt><dd>lagrangian_tendency_of_air_pressure</dd><dt><span>units :</span></dt><dd>Pa s**-1</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vertical_velocity</span></div><div class='xr-var-dims'>(time, level, longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(48, 13, 64, 32), meta=np.ndarray&gt;</div><input id='attrs-ebcd1cf2-8b94-4fc2-adf9-a9e00a49d2c3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ebcd1cf2-8b94-4fc2-adf9-a9e00a49d2c3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c0dfc359-77ab-48e7-9042-667d7b241e99' class='xr-var-data-in' type='checkbox'><label for='data-c0dfc359-77ab-48e7-9042-667d7b241e99' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Vertical velocity</dd><dt><span>short_name :</span></dt><dd>w</dd><dt><span>standard_name :</span></dt><dd>lagrangian_tendency_of_air_pressure</dd><dt><span>units :</span></dt><dd>Pa s**-1</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -7164,17 +7172,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 9.28 GiB </td>\n",
-       "                        <td> 10.16 MiB </td>\n",
+       "                        <td> 4.88 MiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (93544, 13, 64, 32) </td>\n",
-       "                        <td> (100, 13, 64, 32) </td>\n",
+       "                        <td> (48, 13, 64, 32) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 936 chunks in 2 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 1949 chunks in 2 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -7195,31 +7203,31 @@
        "  <line x1=\"3\" y1=\"0\" x2=\"3\" y2=\"25\" />\n",
        "  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n",
        "  <line x1=\"11\" y1=\"0\" x2=\"11\" y2=\"25\" />\n",
-       "  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n",
+       "  <line x1=\"14\" y1=\"0\" x2=\"14\" y2=\"25\" />\n",
        "  <line x1=\"18\" y1=\"0\" x2=\"18\" y2=\"25\" />\n",
        "  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n",
        "  <line x1=\"26\" y1=\"0\" x2=\"26\" y2=\"25\" />\n",
-       "  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n",
+       "  <line x1=\"29\" y1=\"0\" x2=\"29\" y2=\"25\" />\n",
        "  <line x1=\"33\" y1=\"0\" x2=\"33\" y2=\"25\" />\n",
        "  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n",
        "  <line x1=\"41\" y1=\"0\" x2=\"41\" y2=\"25\" />\n",
-       "  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n",
+       "  <line x1=\"44\" y1=\"0\" x2=\"44\" y2=\"25\" />\n",
        "  <line x1=\"48\" y1=\"0\" x2=\"48\" y2=\"25\" />\n",
        "  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n",
        "  <line x1=\"56\" y1=\"0\" x2=\"56\" y2=\"25\" />\n",
-       "  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n",
+       "  <line x1=\"59\" y1=\"0\" x2=\"59\" y2=\"25\" />\n",
        "  <line x1=\"63\" y1=\"0\" x2=\"63\" y2=\"25\" />\n",
        "  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n",
        "  <line x1=\"71\" y1=\"0\" x2=\"71\" y2=\"25\" />\n",
-       "  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n",
+       "  <line x1=\"74\" y1=\"0\" x2=\"74\" y2=\"25\" />\n",
        "  <line x1=\"78\" y1=\"0\" x2=\"78\" y2=\"25\" />\n",
        "  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n",
        "  <line x1=\"86\" y1=\"0\" x2=\"86\" y2=\"25\" />\n",
-       "  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n",
+       "  <line x1=\"89\" y1=\"0\" x2=\"89\" y2=\"25\" />\n",
        "  <line x1=\"93\" y1=\"0\" x2=\"93\" y2=\"25\" />\n",
        "  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n",
        "  <line x1=\"101\" y1=\"0\" x2=\"101\" y2=\"25\" />\n",
-       "  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n",
+       "  <line x1=\"104\" y1=\"0\" x2=\"104\" y2=\"25\" />\n",
        "  <line x1=\"108\" y1=\"0\" x2=\"108\" y2=\"25\" />\n",
        "  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n",
        "  <line x1=\"116\" y1=\"0\" x2=\"116\" y2=\"25\" />\n",
@@ -7273,7 +7281,7 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>volumetric_soil_water_layer_1</span></div><div class='xr-var-dims'>(time, longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(100, 64, 32), meta=np.ndarray&gt;</div><input id='attrs-68f80a67-8ef6-428d-ae99-dc22dfcbb630' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-68f80a67-8ef6-428d-ae99-dc22dfcbb630' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e8b21339-d7ae-4bb3-9208-3f3622bc936a' class='xr-var-data-in' type='checkbox'><label for='data-e8b21339-d7ae-4bb3-9208-3f3622bc936a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Volumetric soil water layer 1</dd><dt><span>short_name :</span></dt><dd>swvl1</dd><dt><span>units :</span></dt><dd>m**3 m**-3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>volumetric_soil_water_layer_1</span></div><div class='xr-var-dims'>(time, longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(48, 64, 32), meta=np.ndarray&gt;</div><input id='attrs-e0a0bff4-658b-4808-ac69-96eacb509dc3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e0a0bff4-658b-4808-ac69-96eacb509dc3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-60f0c7c3-b69a-4e84-a348-e6e7e0c4d6aa' class='xr-var-data-in' type='checkbox'><label for='data-60f0c7c3-b69a-4e84-a348-e6e7e0c4d6aa' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Volumetric soil water layer 1</dd><dt><span>short_name :</span></dt><dd>swvl1</dd><dt><span>units :</span></dt><dd>m**3 m**-3</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -7289,17 +7297,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 730.81 MiB </td>\n",
-       "                        <td> 800.00 kiB </td>\n",
+       "                        <td> 384.00 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (93544, 64, 32) </td>\n",
-       "                        <td> (100, 64, 32) </td>\n",
+       "                        <td> (48, 64, 32) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 936 chunks in 2 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 1949 chunks in 2 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -7323,14 +7331,14 @@
        "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"40\" />\n",
        "  <line x1=\"28\" y1=\"18\" x2=\"28\" y2=\"43\" />\n",
        "  <line x1=\"32\" y1=\"22\" x2=\"32\" y2=\"47\" />\n",
-       "  <line x1=\"35\" y1=\"25\" x2=\"35\" y2=\"51\" />\n",
+       "  <line x1=\"36\" y1=\"26\" x2=\"36\" y2=\"51\" />\n",
        "  <line x1=\"39\" y1=\"29\" x2=\"39\" y2=\"55\" />\n",
        "  <line x1=\"43\" y1=\"33\" x2=\"43\" y2=\"58\" />\n",
        "  <line x1=\"47\" y1=\"37\" x2=\"47\" y2=\"62\" />\n",
        "  <line x1=\"50\" y1=\"40\" x2=\"50\" y2=\"66\" />\n",
-       "  <line x1=\"54\" y1=\"44\" x2=\"54\" y2=\"70\" />\n",
+       "  <line x1=\"54\" y1=\"44\" x2=\"54\" y2=\"69\" />\n",
        "  <line x1=\"58\" y1=\"48\" x2=\"58\" y2=\"73\" />\n",
-       "  <line x1=\"61\" y1=\"51\" x2=\"61\" y2=\"77\" />\n",
+       "  <line x1=\"62\" y1=\"52\" x2=\"62\" y2=\"77\" />\n",
        "  <line x1=\"65\" y1=\"55\" x2=\"65\" y2=\"81\" />\n",
        "  <line x1=\"69\" y1=\"59\" x2=\"69\" y2=\"84\" />\n",
        "  <line x1=\"73\" y1=\"63\" x2=\"73\" y2=\"88\" />\n",
@@ -7348,14 +7356,14 @@
        "  <line x1=\"24\" y1=\"14\" x2=\"50\" y2=\"14\" />\n",
        "  <line x1=\"28\" y1=\"18\" x2=\"53\" y2=\"18\" />\n",
        "  <line x1=\"32\" y1=\"22\" x2=\"57\" y2=\"22\" />\n",
-       "  <line x1=\"35\" y1=\"25\" x2=\"61\" y2=\"25\" />\n",
+       "  <line x1=\"36\" y1=\"26\" x2=\"61\" y2=\"26\" />\n",
        "  <line x1=\"39\" y1=\"29\" x2=\"65\" y2=\"29\" />\n",
        "  <line x1=\"43\" y1=\"33\" x2=\"68\" y2=\"33\" />\n",
        "  <line x1=\"47\" y1=\"37\" x2=\"72\" y2=\"37\" />\n",
        "  <line x1=\"50\" y1=\"40\" x2=\"76\" y2=\"40\" />\n",
-       "  <line x1=\"54\" y1=\"44\" x2=\"80\" y2=\"44\" />\n",
+       "  <line x1=\"54\" y1=\"44\" x2=\"79\" y2=\"44\" />\n",
        "  <line x1=\"58\" y1=\"48\" x2=\"83\" y2=\"48\" />\n",
-       "  <line x1=\"61\" y1=\"51\" x2=\"87\" y2=\"51\" />\n",
+       "  <line x1=\"62\" y1=\"52\" x2=\"87\" y2=\"52\" />\n",
        "  <line x1=\"65\" y1=\"55\" x2=\"91\" y2=\"55\" />\n",
        "  <line x1=\"69\" y1=\"59\" x2=\"94\" y2=\"59\" />\n",
        "  <line x1=\"73\" y1=\"63\" x2=\"98\" y2=\"63\" />\n",
@@ -7387,7 +7395,7 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>volumetric_soil_water_layer_2</span></div><div class='xr-var-dims'>(time, longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(100, 64, 32), meta=np.ndarray&gt;</div><input id='attrs-d7024c37-3eae-45eb-a334-7ea6bb8d4650' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d7024c37-3eae-45eb-a334-7ea6bb8d4650' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e524157f-56b2-4557-92b6-7a56d369e8f7' class='xr-var-data-in' type='checkbox'><label for='data-e524157f-56b2-4557-92b6-7a56d369e8f7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Volumetric soil water layer 2</dd><dt><span>short_name :</span></dt><dd>swvl2</dd><dt><span>units :</span></dt><dd>m**3 m**-3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>volumetric_soil_water_layer_2</span></div><div class='xr-var-dims'>(time, longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(48, 64, 32), meta=np.ndarray&gt;</div><input id='attrs-c9db47b1-7c2f-4c4c-a8d5-971dfe9a10f7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c9db47b1-7c2f-4c4c-a8d5-971dfe9a10f7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ff0b689a-b41c-4788-86d0-c9ecadc091a7' class='xr-var-data-in' type='checkbox'><label for='data-ff0b689a-b41c-4788-86d0-c9ecadc091a7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Volumetric soil water layer 2</dd><dt><span>short_name :</span></dt><dd>swvl2</dd><dt><span>units :</span></dt><dd>m**3 m**-3</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -7403,17 +7411,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 730.81 MiB </td>\n",
-       "                        <td> 800.00 kiB </td>\n",
+       "                        <td> 384.00 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (93544, 64, 32) </td>\n",
-       "                        <td> (100, 64, 32) </td>\n",
+       "                        <td> (48, 64, 32) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 936 chunks in 2 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 1949 chunks in 2 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -7437,14 +7445,14 @@
        "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"40\" />\n",
        "  <line x1=\"28\" y1=\"18\" x2=\"28\" y2=\"43\" />\n",
        "  <line x1=\"32\" y1=\"22\" x2=\"32\" y2=\"47\" />\n",
-       "  <line x1=\"35\" y1=\"25\" x2=\"35\" y2=\"51\" />\n",
+       "  <line x1=\"36\" y1=\"26\" x2=\"36\" y2=\"51\" />\n",
        "  <line x1=\"39\" y1=\"29\" x2=\"39\" y2=\"55\" />\n",
        "  <line x1=\"43\" y1=\"33\" x2=\"43\" y2=\"58\" />\n",
        "  <line x1=\"47\" y1=\"37\" x2=\"47\" y2=\"62\" />\n",
        "  <line x1=\"50\" y1=\"40\" x2=\"50\" y2=\"66\" />\n",
-       "  <line x1=\"54\" y1=\"44\" x2=\"54\" y2=\"70\" />\n",
+       "  <line x1=\"54\" y1=\"44\" x2=\"54\" y2=\"69\" />\n",
        "  <line x1=\"58\" y1=\"48\" x2=\"58\" y2=\"73\" />\n",
-       "  <line x1=\"61\" y1=\"51\" x2=\"61\" y2=\"77\" />\n",
+       "  <line x1=\"62\" y1=\"52\" x2=\"62\" y2=\"77\" />\n",
        "  <line x1=\"65\" y1=\"55\" x2=\"65\" y2=\"81\" />\n",
        "  <line x1=\"69\" y1=\"59\" x2=\"69\" y2=\"84\" />\n",
        "  <line x1=\"73\" y1=\"63\" x2=\"73\" y2=\"88\" />\n",
@@ -7462,14 +7470,14 @@
        "  <line x1=\"24\" y1=\"14\" x2=\"50\" y2=\"14\" />\n",
        "  <line x1=\"28\" y1=\"18\" x2=\"53\" y2=\"18\" />\n",
        "  <line x1=\"32\" y1=\"22\" x2=\"57\" y2=\"22\" />\n",
-       "  <line x1=\"35\" y1=\"25\" x2=\"61\" y2=\"25\" />\n",
+       "  <line x1=\"36\" y1=\"26\" x2=\"61\" y2=\"26\" />\n",
        "  <line x1=\"39\" y1=\"29\" x2=\"65\" y2=\"29\" />\n",
        "  <line x1=\"43\" y1=\"33\" x2=\"68\" y2=\"33\" />\n",
        "  <line x1=\"47\" y1=\"37\" x2=\"72\" y2=\"37\" />\n",
        "  <line x1=\"50\" y1=\"40\" x2=\"76\" y2=\"40\" />\n",
-       "  <line x1=\"54\" y1=\"44\" x2=\"80\" y2=\"44\" />\n",
+       "  <line x1=\"54\" y1=\"44\" x2=\"79\" y2=\"44\" />\n",
        "  <line x1=\"58\" y1=\"48\" x2=\"83\" y2=\"48\" />\n",
-       "  <line x1=\"61\" y1=\"51\" x2=\"87\" y2=\"51\" />\n",
+       "  <line x1=\"62\" y1=\"52\" x2=\"87\" y2=\"52\" />\n",
        "  <line x1=\"65\" y1=\"55\" x2=\"91\" y2=\"55\" />\n",
        "  <line x1=\"69\" y1=\"59\" x2=\"94\" y2=\"59\" />\n",
        "  <line x1=\"73\" y1=\"63\" x2=\"98\" y2=\"63\" />\n",
@@ -7501,7 +7509,7 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>volumetric_soil_water_layer_3</span></div><div class='xr-var-dims'>(time, longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(100, 64, 32), meta=np.ndarray&gt;</div><input id='attrs-a775efc8-b3a5-4cd5-904a-6eb5fabe06d0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a775efc8-b3a5-4cd5-904a-6eb5fabe06d0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7b921dcd-ead4-4cad-9998-76e6925e28cd' class='xr-var-data-in' type='checkbox'><label for='data-7b921dcd-ead4-4cad-9998-76e6925e28cd' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Volumetric soil water layer 3</dd><dt><span>short_name :</span></dt><dd>swvl3</dd><dt><span>units :</span></dt><dd>m**3 m**-3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>volumetric_soil_water_layer_3</span></div><div class='xr-var-dims'>(time, longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(48, 64, 32), meta=np.ndarray&gt;</div><input id='attrs-3c22a7e8-0022-4ddb-943b-254d882ac080' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3c22a7e8-0022-4ddb-943b-254d882ac080' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-47db574a-be7e-4237-93a0-9e95df88d80f' class='xr-var-data-in' type='checkbox'><label for='data-47db574a-be7e-4237-93a0-9e95df88d80f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Volumetric soil water layer 3</dd><dt><span>short_name :</span></dt><dd>swvl3</dd><dt><span>units :</span></dt><dd>m**3 m**-3</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -7517,17 +7525,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 730.81 MiB </td>\n",
-       "                        <td> 800.00 kiB </td>\n",
+       "                        <td> 384.00 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (93544, 64, 32) </td>\n",
-       "                        <td> (100, 64, 32) </td>\n",
+       "                        <td> (48, 64, 32) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 936 chunks in 2 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 1949 chunks in 2 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -7551,14 +7559,14 @@
        "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"40\" />\n",
        "  <line x1=\"28\" y1=\"18\" x2=\"28\" y2=\"43\" />\n",
        "  <line x1=\"32\" y1=\"22\" x2=\"32\" y2=\"47\" />\n",
-       "  <line x1=\"35\" y1=\"25\" x2=\"35\" y2=\"51\" />\n",
+       "  <line x1=\"36\" y1=\"26\" x2=\"36\" y2=\"51\" />\n",
        "  <line x1=\"39\" y1=\"29\" x2=\"39\" y2=\"55\" />\n",
        "  <line x1=\"43\" y1=\"33\" x2=\"43\" y2=\"58\" />\n",
        "  <line x1=\"47\" y1=\"37\" x2=\"47\" y2=\"62\" />\n",
        "  <line x1=\"50\" y1=\"40\" x2=\"50\" y2=\"66\" />\n",
-       "  <line x1=\"54\" y1=\"44\" x2=\"54\" y2=\"70\" />\n",
+       "  <line x1=\"54\" y1=\"44\" x2=\"54\" y2=\"69\" />\n",
        "  <line x1=\"58\" y1=\"48\" x2=\"58\" y2=\"73\" />\n",
-       "  <line x1=\"61\" y1=\"51\" x2=\"61\" y2=\"77\" />\n",
+       "  <line x1=\"62\" y1=\"52\" x2=\"62\" y2=\"77\" />\n",
        "  <line x1=\"65\" y1=\"55\" x2=\"65\" y2=\"81\" />\n",
        "  <line x1=\"69\" y1=\"59\" x2=\"69\" y2=\"84\" />\n",
        "  <line x1=\"73\" y1=\"63\" x2=\"73\" y2=\"88\" />\n",
@@ -7576,14 +7584,14 @@
        "  <line x1=\"24\" y1=\"14\" x2=\"50\" y2=\"14\" />\n",
        "  <line x1=\"28\" y1=\"18\" x2=\"53\" y2=\"18\" />\n",
        "  <line x1=\"32\" y1=\"22\" x2=\"57\" y2=\"22\" />\n",
-       "  <line x1=\"35\" y1=\"25\" x2=\"61\" y2=\"25\" />\n",
+       "  <line x1=\"36\" y1=\"26\" x2=\"61\" y2=\"26\" />\n",
        "  <line x1=\"39\" y1=\"29\" x2=\"65\" y2=\"29\" />\n",
        "  <line x1=\"43\" y1=\"33\" x2=\"68\" y2=\"33\" />\n",
        "  <line x1=\"47\" y1=\"37\" x2=\"72\" y2=\"37\" />\n",
        "  <line x1=\"50\" y1=\"40\" x2=\"76\" y2=\"40\" />\n",
-       "  <line x1=\"54\" y1=\"44\" x2=\"80\" y2=\"44\" />\n",
+       "  <line x1=\"54\" y1=\"44\" x2=\"79\" y2=\"44\" />\n",
        "  <line x1=\"58\" y1=\"48\" x2=\"83\" y2=\"48\" />\n",
-       "  <line x1=\"61\" y1=\"51\" x2=\"87\" y2=\"51\" />\n",
+       "  <line x1=\"62\" y1=\"52\" x2=\"87\" y2=\"52\" />\n",
        "  <line x1=\"65\" y1=\"55\" x2=\"91\" y2=\"55\" />\n",
        "  <line x1=\"69\" y1=\"59\" x2=\"94\" y2=\"59\" />\n",
        "  <line x1=\"73\" y1=\"63\" x2=\"98\" y2=\"63\" />\n",
@@ -7615,7 +7623,7 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>volumetric_soil_water_layer_4</span></div><div class='xr-var-dims'>(time, longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(100, 64, 32), meta=np.ndarray&gt;</div><input id='attrs-dd12e885-f244-4890-8a93-835a82acf16f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-dd12e885-f244-4890-8a93-835a82acf16f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-211b4b32-e116-4e22-b2f2-70ddf86b66e3' class='xr-var-data-in' type='checkbox'><label for='data-211b4b32-e116-4e22-b2f2-70ddf86b66e3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Volumetric soil water layer 4</dd><dt><span>short_name :</span></dt><dd>swvl4</dd><dt><span>units :</span></dt><dd>m**3 m**-3</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>volumetric_soil_water_layer_4</span></div><div class='xr-var-dims'>(time, longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(48, 64, 32), meta=np.ndarray&gt;</div><input id='attrs-3fe89b2b-dacb-4783-9104-25359fd13c4e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3fe89b2b-dacb-4783-9104-25359fd13c4e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-00f15fe8-06c4-45c1-bf6b-a64ad31b4621' class='xr-var-data-in' type='checkbox'><label for='data-00f15fe8-06c4-45c1-bf6b-a64ad31b4621' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Volumetric soil water layer 4</dd><dt><span>short_name :</span></dt><dd>swvl4</dd><dt><span>units :</span></dt><dd>m**3 m**-3</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -7631,17 +7639,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 730.81 MiB </td>\n",
-       "                        <td> 800.00 kiB </td>\n",
+       "                        <td> 384.00 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (93544, 64, 32) </td>\n",
-       "                        <td> (100, 64, 32) </td>\n",
+       "                        <td> (48, 64, 32) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 936 chunks in 2 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 1949 chunks in 2 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -7665,14 +7673,14 @@
        "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"40\" />\n",
        "  <line x1=\"28\" y1=\"18\" x2=\"28\" y2=\"43\" />\n",
        "  <line x1=\"32\" y1=\"22\" x2=\"32\" y2=\"47\" />\n",
-       "  <line x1=\"35\" y1=\"25\" x2=\"35\" y2=\"51\" />\n",
+       "  <line x1=\"36\" y1=\"26\" x2=\"36\" y2=\"51\" />\n",
        "  <line x1=\"39\" y1=\"29\" x2=\"39\" y2=\"55\" />\n",
        "  <line x1=\"43\" y1=\"33\" x2=\"43\" y2=\"58\" />\n",
        "  <line x1=\"47\" y1=\"37\" x2=\"47\" y2=\"62\" />\n",
        "  <line x1=\"50\" y1=\"40\" x2=\"50\" y2=\"66\" />\n",
-       "  <line x1=\"54\" y1=\"44\" x2=\"54\" y2=\"70\" />\n",
+       "  <line x1=\"54\" y1=\"44\" x2=\"54\" y2=\"69\" />\n",
        "  <line x1=\"58\" y1=\"48\" x2=\"58\" y2=\"73\" />\n",
-       "  <line x1=\"61\" y1=\"51\" x2=\"61\" y2=\"77\" />\n",
+       "  <line x1=\"62\" y1=\"52\" x2=\"62\" y2=\"77\" />\n",
        "  <line x1=\"65\" y1=\"55\" x2=\"65\" y2=\"81\" />\n",
        "  <line x1=\"69\" y1=\"59\" x2=\"69\" y2=\"84\" />\n",
        "  <line x1=\"73\" y1=\"63\" x2=\"73\" y2=\"88\" />\n",
@@ -7690,14 +7698,14 @@
        "  <line x1=\"24\" y1=\"14\" x2=\"50\" y2=\"14\" />\n",
        "  <line x1=\"28\" y1=\"18\" x2=\"53\" y2=\"18\" />\n",
        "  <line x1=\"32\" y1=\"22\" x2=\"57\" y2=\"22\" />\n",
-       "  <line x1=\"35\" y1=\"25\" x2=\"61\" y2=\"25\" />\n",
+       "  <line x1=\"36\" y1=\"26\" x2=\"61\" y2=\"26\" />\n",
        "  <line x1=\"39\" y1=\"29\" x2=\"65\" y2=\"29\" />\n",
        "  <line x1=\"43\" y1=\"33\" x2=\"68\" y2=\"33\" />\n",
        "  <line x1=\"47\" y1=\"37\" x2=\"72\" y2=\"37\" />\n",
        "  <line x1=\"50\" y1=\"40\" x2=\"76\" y2=\"40\" />\n",
-       "  <line x1=\"54\" y1=\"44\" x2=\"80\" y2=\"44\" />\n",
+       "  <line x1=\"54\" y1=\"44\" x2=\"79\" y2=\"44\" />\n",
        "  <line x1=\"58\" y1=\"48\" x2=\"83\" y2=\"48\" />\n",
-       "  <line x1=\"61\" y1=\"51\" x2=\"87\" y2=\"51\" />\n",
+       "  <line x1=\"62\" y1=\"52\" x2=\"87\" y2=\"52\" />\n",
        "  <line x1=\"65\" y1=\"55\" x2=\"91\" y2=\"55\" />\n",
        "  <line x1=\"69\" y1=\"59\" x2=\"94\" y2=\"59\" />\n",
        "  <line x1=\"73\" y1=\"63\" x2=\"98\" y2=\"63\" />\n",
@@ -7729,7 +7737,7 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vorticity</span></div><div class='xr-var-dims'>(time, level, longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(100, 13, 64, 32), meta=np.ndarray&gt;</div><input id='attrs-8f591d59-42a0-48ab-a957-66f8c549ed36' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8f591d59-42a0-48ab-a957-66f8c549ed36' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9734042d-b332-476a-ae6f-2b997cb9d20b' class='xr-var-data-in' type='checkbox'><label for='data-9734042d-b332-476a-ae6f-2b997cb9d20b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vorticity</span></div><div class='xr-var-dims'>(time, level, longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(48, 13, 64, 32), meta=np.ndarray&gt;</div><input id='attrs-72684cff-cb1f-42d4-9df9-d120ed4a39ff' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-72684cff-cb1f-42d4-9df9-d120ed4a39ff' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dd5d4f6b-b445-4d50-99e9-d8ae0e23734a' class='xr-var-data-in' type='checkbox'><label for='data-dd5d4f6b-b445-4d50-99e9-d8ae0e23734a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -7745,17 +7753,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 9.28 GiB </td>\n",
-       "                        <td> 10.16 MiB </td>\n",
+       "                        <td> 4.88 MiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (93544, 13, 64, 32) </td>\n",
-       "                        <td> (100, 13, 64, 32) </td>\n",
+       "                        <td> (48, 13, 64, 32) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 936 chunks in 2 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 1949 chunks in 2 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -7776,31 +7784,31 @@
        "  <line x1=\"3\" y1=\"0\" x2=\"3\" y2=\"25\" />\n",
        "  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n",
        "  <line x1=\"11\" y1=\"0\" x2=\"11\" y2=\"25\" />\n",
-       "  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n",
+       "  <line x1=\"14\" y1=\"0\" x2=\"14\" y2=\"25\" />\n",
        "  <line x1=\"18\" y1=\"0\" x2=\"18\" y2=\"25\" />\n",
        "  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n",
        "  <line x1=\"26\" y1=\"0\" x2=\"26\" y2=\"25\" />\n",
-       "  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n",
+       "  <line x1=\"29\" y1=\"0\" x2=\"29\" y2=\"25\" />\n",
        "  <line x1=\"33\" y1=\"0\" x2=\"33\" y2=\"25\" />\n",
        "  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n",
        "  <line x1=\"41\" y1=\"0\" x2=\"41\" y2=\"25\" />\n",
-       "  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n",
+       "  <line x1=\"44\" y1=\"0\" x2=\"44\" y2=\"25\" />\n",
        "  <line x1=\"48\" y1=\"0\" x2=\"48\" y2=\"25\" />\n",
        "  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n",
        "  <line x1=\"56\" y1=\"0\" x2=\"56\" y2=\"25\" />\n",
-       "  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n",
+       "  <line x1=\"59\" y1=\"0\" x2=\"59\" y2=\"25\" />\n",
        "  <line x1=\"63\" y1=\"0\" x2=\"63\" y2=\"25\" />\n",
        "  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n",
        "  <line x1=\"71\" y1=\"0\" x2=\"71\" y2=\"25\" />\n",
-       "  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n",
+       "  <line x1=\"74\" y1=\"0\" x2=\"74\" y2=\"25\" />\n",
        "  <line x1=\"78\" y1=\"0\" x2=\"78\" y2=\"25\" />\n",
        "  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n",
        "  <line x1=\"86\" y1=\"0\" x2=\"86\" y2=\"25\" />\n",
-       "  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n",
+       "  <line x1=\"89\" y1=\"0\" x2=\"89\" y2=\"25\" />\n",
        "  <line x1=\"93\" y1=\"0\" x2=\"93\" y2=\"25\" />\n",
        "  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n",
        "  <line x1=\"101\" y1=\"0\" x2=\"101\" y2=\"25\" />\n",
-       "  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n",
+       "  <line x1=\"104\" y1=\"0\" x2=\"104\" y2=\"25\" />\n",
        "  <line x1=\"108\" y1=\"0\" x2=\"108\" y2=\"25\" />\n",
        "  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n",
        "  <line x1=\"116\" y1=\"0\" x2=\"116\" y2=\"25\" />\n",
@@ -7854,7 +7862,7 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>wind_speed</span></div><div class='xr-var-dims'>(time, level, longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(100, 13, 64, 32), meta=np.ndarray&gt;</div><input id='attrs-9d13ed3d-01f0-4d65-bbe1-0ee337e5ce15' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9d13ed3d-01f0-4d65-bbe1-0ee337e5ce15' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c3243064-849b-4fa8-9378-9d716c570da3' class='xr-var-data-in' type='checkbox'><label for='data-c3243064-849b-4fa8-9378-9d716c570da3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>wind_speed</span></div><div class='xr-var-dims'>(time, level, longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(48, 13, 64, 32), meta=np.ndarray&gt;</div><input id='attrs-dd920e16-41e0-45c5-90b9-4c872c175f1f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-dd920e16-41e0-45c5-90b9-4c872c175f1f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-63fb6394-34ff-459c-b4c7-edd713c32eb1' class='xr-var-data-in' type='checkbox'><label for='data-63fb6394-34ff-459c-b4c7-edd713c32eb1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -7870,17 +7878,17 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 9.28 GiB </td>\n",
-       "                        <td> 10.16 MiB </td>\n",
+       "                        <td> 4.88 MiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (93544, 13, 64, 32) </td>\n",
-       "                        <td> (100, 13, 64, 32) </td>\n",
+       "                        <td> (48, 13, 64, 32) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 936 chunks in 2 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 1949 chunks in 2 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -7901,31 +7909,31 @@
        "  <line x1=\"3\" y1=\"0\" x2=\"3\" y2=\"25\" />\n",
        "  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n",
        "  <line x1=\"11\" y1=\"0\" x2=\"11\" y2=\"25\" />\n",
-       "  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n",
+       "  <line x1=\"14\" y1=\"0\" x2=\"14\" y2=\"25\" />\n",
        "  <line x1=\"18\" y1=\"0\" x2=\"18\" y2=\"25\" />\n",
        "  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n",
        "  <line x1=\"26\" y1=\"0\" x2=\"26\" y2=\"25\" />\n",
-       "  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n",
+       "  <line x1=\"29\" y1=\"0\" x2=\"29\" y2=\"25\" />\n",
        "  <line x1=\"33\" y1=\"0\" x2=\"33\" y2=\"25\" />\n",
        "  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n",
        "  <line x1=\"41\" y1=\"0\" x2=\"41\" y2=\"25\" />\n",
-       "  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n",
+       "  <line x1=\"44\" y1=\"0\" x2=\"44\" y2=\"25\" />\n",
        "  <line x1=\"48\" y1=\"0\" x2=\"48\" y2=\"25\" />\n",
        "  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n",
        "  <line x1=\"56\" y1=\"0\" x2=\"56\" y2=\"25\" />\n",
-       "  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n",
+       "  <line x1=\"59\" y1=\"0\" x2=\"59\" y2=\"25\" />\n",
        "  <line x1=\"63\" y1=\"0\" x2=\"63\" y2=\"25\" />\n",
        "  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n",
        "  <line x1=\"71\" y1=\"0\" x2=\"71\" y2=\"25\" />\n",
-       "  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n",
+       "  <line x1=\"74\" y1=\"0\" x2=\"74\" y2=\"25\" />\n",
        "  <line x1=\"78\" y1=\"0\" x2=\"78\" y2=\"25\" />\n",
        "  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n",
        "  <line x1=\"86\" y1=\"0\" x2=\"86\" y2=\"25\" />\n",
-       "  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n",
+       "  <line x1=\"89\" y1=\"0\" x2=\"89\" y2=\"25\" />\n",
        "  <line x1=\"93\" y1=\"0\" x2=\"93\" y2=\"25\" />\n",
        "  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n",
        "  <line x1=\"101\" y1=\"0\" x2=\"101\" y2=\"25\" />\n",
-       "  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n",
+       "  <line x1=\"104\" y1=\"0\" x2=\"104\" y2=\"25\" />\n",
        "  <line x1=\"108\" y1=\"0\" x2=\"108\" y2=\"25\" />\n",
        "  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n",
        "  <line x1=\"116\" y1=\"0\" x2=\"116\" y2=\"25\" />\n",
@@ -7979,7 +7987,7 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-0c123ec2-16d4-4dc0-b135-2b08ea076cbe' class='xr-section-summary-in' type='checkbox'  ><label for='section-0c123ec2-16d4-4dc0-b135-2b08ea076cbe' class='xr-section-summary' >Indexes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>latitude</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-3c4a142c-c9e2-4064-9d05-a6aad6c866bc' class='xr-index-data-in' type='checkbox'/><label for='index-3c4a142c-c9e2-4064-9d05-a6aad6c866bc' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ -87.18750000000003,  -81.56250000000001,            -75.9375,\n",
+       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-d61acea1-2686-44d5-bbaa-d169c4bc594d' class='xr-section-summary-in' type='checkbox'  ><label for='section-d61acea1-2686-44d5-bbaa-d169c4bc594d' class='xr-section-summary' >Indexes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>latitude</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-f02961dd-c718-4eb2-89a5-461e69102fac' class='xr-index-data-in' type='checkbox'/><label for='index-f02961dd-c718-4eb2-89a5-461e69102fac' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ -87.18750000000003,  -81.56250000000001,            -75.9375,\n",
        "        -70.31249999999999,  -64.68750000000001,            -59.0625,\n",
        "                  -53.4375,            -47.8125,            -42.1875,\n",
        "                  -36.5625, -30.937499999999996, -25.312500000000004,\n",
@@ -7990,7 +7998,7 @@
        "                   47.8125,             53.4375,  59.062500000000014,\n",
        "         64.68750000000001,             70.3125,             75.9375,\n",
        "         81.56249999999997,   87.18750000000003],\n",
-       "      dtype=&#x27;float64&#x27;, name=&#x27;latitude&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>level</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-bc41b355-d509-4fd0-8e73-9796df3fa430' class='xr-index-data-in' type='checkbox'/><label for='index-bc41b355-d509-4fd0-8e73-9796df3fa430' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([50, 100, 150, 200, 250, 300, 400, 500, 600, 700, 850, 925, 1000], dtype=&#x27;int64&#x27;, name=&#x27;level&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>longitude</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-90e8b5f6-4fcd-4abf-a239-256b9cd24b08' class='xr-index-data-in' type='checkbox'/><label for='index-90e8b5f6-4fcd-4abf-a239-256b9cd24b08' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([               0.0,              5.625,              11.25,\n",
+       "      dtype=&#x27;float64&#x27;, name=&#x27;latitude&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>level</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-cdf5dd03-6bf8-484e-9bd3-2bf313cd6e4e' class='xr-index-data-in' type='checkbox'/><label for='index-cdf5dd03-6bf8-484e-9bd3-2bf313cd6e4e' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([50, 100, 150, 200, 250, 300, 400, 500, 600, 700, 850, 925, 1000], dtype=&#x27;int64&#x27;, name=&#x27;level&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>longitude</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-bbd87c8d-14e9-4059-9066-74920a9e34a0' class='xr-index-data-in' type='checkbox'/><label for='index-bbd87c8d-14e9-4059-9066-74920a9e34a0' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([               0.0,              5.625,              11.25,\n",
        "                   16.875,               22.5,             28.125,\n",
        "                    33.75,             39.375,               45.0,\n",
        "                   50.625,              56.25,  61.87499999999999,\n",
@@ -8012,7 +8020,7 @@
        "                  320.625,             326.25,            331.875,\n",
        "                    337.5,            343.125,             348.75,\n",
        "                  354.375],\n",
-       "      dtype=&#x27;float64&#x27;, name=&#x27;longitude&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-a84bf387-7e62-4903-8b38-4288e1547ca1' class='xr-index-data-in' type='checkbox'/><label for='index-a84bf387-7e62-4903-8b38-4288e1547ca1' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;1959-01-01 00:00:00&#x27;, &#x27;1959-01-01 06:00:00&#x27;,\n",
+       "      dtype=&#x27;float64&#x27;, name=&#x27;longitude&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-ff079fe4-a667-4ecd-888f-4915c75dba54' class='xr-index-data-in' type='checkbox'/><label for='index-ff079fe4-a667-4ecd-888f-4915c75dba54' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;1959-01-01 00:00:00&#x27;, &#x27;1959-01-01 06:00:00&#x27;,\n",
        "               &#x27;1959-01-01 12:00:00&#x27;, &#x27;1959-01-01 18:00:00&#x27;,\n",
        "               &#x27;1959-01-02 00:00:00&#x27;, &#x27;1959-01-02 06:00:00&#x27;,\n",
        "               &#x27;1959-01-02 12:00:00&#x27;, &#x27;1959-01-02 18:00:00&#x27;,\n",
@@ -8023,7 +8031,7 @@
        "               &#x27;2023-01-09 12:00:00&#x27;, &#x27;2023-01-09 18:00:00&#x27;,\n",
        "               &#x27;2023-01-10 00:00:00&#x27;, &#x27;2023-01-10 06:00:00&#x27;,\n",
        "               &#x27;2023-01-10 12:00:00&#x27;, &#x27;2023-01-10 18:00:00&#x27;],\n",
-       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=93544, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-c9bab06f-89aa-4f9e-9dc3-b83fec8a222b' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-c9bab06f-89aa-4f9e-9dc3-b83fec8a222b' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
+       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=93544, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-d95e6bd8-cfb1-4fa5-93fa-88aac2e0ea50' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-d95e6bd8-cfb1-4fa5-93fa-88aac2e0ea50' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
       ],
       "text/plain": [
        "<xarray.Dataset> Size: 175GB\n",
@@ -8036,19 +8044,19 @@
        "  * longitude                                         (longitude) float64 512B ...\n",
        "  * time                                              (time) datetime64[ns] 748kB ...\n",
        "Data variables: (12/62)\n",
-       "    10m_u_component_of_wind                           (time, longitude, latitude) float32 766MB dask.array<chunksize=(100, 64, 32), meta=np.ndarray>\n",
-       "    10m_v_component_of_wind                           (time, longitude, latitude) float32 766MB dask.array<chunksize=(100, 64, 32), meta=np.ndarray>\n",
-       "    10m_wind_speed                                    (time, longitude, latitude) float32 766MB dask.array<chunksize=(100, 64, 32), meta=np.ndarray>\n",
-       "    2m_dewpoint_temperature                           (time, longitude, latitude) float32 766MB dask.array<chunksize=(100, 64, 32), meta=np.ndarray>\n",
-       "    2m_temperature                                    (time, longitude, latitude) float32 766MB dask.array<chunksize=(100, 64, 32), meta=np.ndarray>\n",
-       "    above_ground                                      (time, level, longitude, latitude) float32 10GB dask.array<chunksize=(100, 13, 64, 32), meta=np.ndarray>\n",
+       "    10m_u_component_of_wind                           (time, longitude, latitude) float32 766MB dask.array<chunksize=(48, 64, 32), meta=np.ndarray>\n",
+       "    10m_v_component_of_wind                           (time, longitude, latitude) float32 766MB dask.array<chunksize=(48, 64, 32), meta=np.ndarray>\n",
+       "    10m_wind_speed                                    (time, longitude, latitude) float32 766MB dask.array<chunksize=(48, 64, 32), meta=np.ndarray>\n",
+       "    2m_dewpoint_temperature                           (time, longitude, latitude) float32 766MB dask.array<chunksize=(48, 64, 32), meta=np.ndarray>\n",
+       "    2m_temperature                                    (time, longitude, latitude) float32 766MB dask.array<chunksize=(48, 64, 32), meta=np.ndarray>\n",
+       "    above_ground                                      (time, level, longitude, latitude) float32 10GB dask.array<chunksize=(48, 13, 64, 32), meta=np.ndarray>\n",
        "    ...                                                ...\n",
-       "    volumetric_soil_water_layer_1                     (time, longitude, latitude) float32 766MB dask.array<chunksize=(100, 64, 32), meta=np.ndarray>\n",
-       "    volumetric_soil_water_layer_2                     (time, longitude, latitude) float32 766MB dask.array<chunksize=(100, 64, 32), meta=np.ndarray>\n",
-       "    volumetric_soil_water_layer_3                     (time, longitude, latitude) float32 766MB dask.array<chunksize=(100, 64, 32), meta=np.ndarray>\n",
-       "    volumetric_soil_water_layer_4                     (time, longitude, latitude) float32 766MB dask.array<chunksize=(100, 64, 32), meta=np.ndarray>\n",
-       "    vorticity                                         (time, level, longitude, latitude) float32 10GB dask.array<chunksize=(100, 13, 64, 32), meta=np.ndarray>\n",
-       "    wind_speed                                        (time, level, longitude, latitude) float32 10GB dask.array<chunksize=(100, 13, 64, 32), meta=np.ndarray>"
+       "    volumetric_soil_water_layer_1                     (time, longitude, latitude) float32 766MB dask.array<chunksize=(48, 64, 32), meta=np.ndarray>\n",
+       "    volumetric_soil_water_layer_2                     (time, longitude, latitude) float32 766MB dask.array<chunksize=(48, 64, 32), meta=np.ndarray>\n",
+       "    volumetric_soil_water_layer_3                     (time, longitude, latitude) float32 766MB dask.array<chunksize=(48, 64, 32), meta=np.ndarray>\n",
+       "    volumetric_soil_water_layer_4                     (time, longitude, latitude) float32 766MB dask.array<chunksize=(48, 64, 32), meta=np.ndarray>\n",
+       "    vorticity                                         (time, level, longitude, latitude) float32 10GB dask.array<chunksize=(48, 13, 64, 32), meta=np.ndarray>\n",
+       "    wind_speed                                        (time, level, longitude, latitude) float32 10GB dask.array<chunksize=(48, 13, 64, 32), meta=np.ndarray>"
       ]
      },
      "execution_count": 5,
@@ -8057,7 +8065,7 @@
     }
    ],
    "source": [
-    "ds_boundary = xr.open_zarr(url_boundary)\n",
+    "ds_boundary = xr.open_zarr(url_boundary, consolidated=True, chunks={\"time\": 48})\n",
     "ds_boundary"
    ]
   },
@@ -8086,7 +8094,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
@@ -8095,7 +8103,7 @@
        "((2048, 2), (4661, 2))"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -8111,16 +8119,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.collections.PathCollection at 0x13ecca270>"
+       "<matplotlib.collections.PathCollection at 0x29f73da90>"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -8144,58 +8152,137 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# latlon to (x, y, z) on unit sphere\n",
+    "pts_bnd_x = np.array(sg.vector.lonlat_to_vector(points_bnd[:,0], points_bnd[:,1])).T\n",
+    "pts_lam_x = np.array(sg.vector.lonlat_to_vector(points_lam[:,0], points_lam[:,1])).T\n",
+    "\n",
+    "chull_lam = SphericalPolygon.convex_hull(pts_lam_x)\n",
+    "\n",
+    "# Finding the boundary points inside the convec hull is what takes time. Possibly speed up by restricting to a subset of points_bnd that are within some distance from area center or similar?\n",
+    "msk_in = np.array([chull_lam.contains_lonlat(x[0], x[1]) for x in points_bnd])\n",
+    "\n",
+    "# Distances from all boundary points outside the region to the closest point inside\n",
+    "d = np.array([np.min(np.arccos(np.dot(pts_lam_x, x))) for x in pts_bnd_x[~msk_in]])\n",
+    "\n",
+    "# Define boundary distance and mask boundary points outside that are closer\n",
+    "# This is now in radians but should be defined as meters based on Earth radius\n",
+    "ii = d < 0.2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ds_bound_stacked = ds_boundary.stack(points=['latitude', 'longitude'])\n",
+    "ds_bound_stacked['msk'] = xr.DataArray(np.zeros(ds_bound_stacked.sizes['points']), dims='points', coords={'points': ds_bound_stacked['points']})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "xr.plot.scatter(ds_bound_stacked, x='longitude', y='latitude', hue='msk')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x13eee94f0>]"
+       "((2048,), (2035,))"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 29,
      "metadata": {},
      "output_type": "execute_result"
-    },
+    }
+   ],
+   "source": [
+    "msk_in.shape, ii.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Plot boundary valid boundary points outside and the LAM interior points (subset)\n",
+    "fig, ax = plt.subplots(subplot_kw={'projection': ccrs.Orthographic(-10, 45)}, figsize=(10, 10))\n",
+    "ax.coastlines()\n",
+    "# xr.plot.scatter(ds_boundary, x='longitude', y='latitude', color='red', s=1, ax=ax, transform=ccrs.Geodetic())\n",
+    "xr.plot.scatter(ds_lam_subsampled, x='lon', y='lat', color='blue', s=1, ax=ax, transform=ccrs.Geodetic(), label='Interior (DANRA)')\n",
+    "ax.plot(points_bnd[~msk_in,0][ii], points_bnd[~msk_in,1][ii], '.', color='green', markersize=4, transform=ccrs.Geodetic(), label='Boundary (ERA5)')\n",
+    "ax.legend()\n",
+    "ax.gridlines(draw_labels=True)\n",
+    "ax.set_title(\"\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "TypeError",
+     "evalue": "cond argument is array([False, False, False, ..., False, False, False]) but must be a <class 'xarray.core.dataset.Dataset'> or <class 'xarray.core.dataarray.DataArray'> (or a callable than returns one).",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[18], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mds_boundary\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstack\u001b[49m\u001b[43m(\u001b[49m\u001b[43mgrid_point\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mlatitude\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mlongitude\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mwhere\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmsk_in\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdrop\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/git-repos/mllam/mllam-data-prep/.venv/lib/python3.12/site-packages/xarray/core/common.py:1219\u001b[0m, in \u001b[0;36mDataWithCoords.where\u001b[0;34m(self, cond, other, drop)\u001b[0m\n\u001b[1;32m   1217\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m drop:\n\u001b[1;32m   1218\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(cond, (Dataset, DataArray)):\n\u001b[0;32m-> 1219\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\n\u001b[1;32m   1220\u001b[0m             \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcond argument is \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mcond\u001b[38;5;132;01m!r}\u001b[39;00m\u001b[38;5;124m but must be a \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mDataset\u001b[38;5;132;01m!r}\u001b[39;00m\u001b[38;5;124m or \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mDataArray\u001b[38;5;132;01m!r}\u001b[39;00m\u001b[38;5;124m (or a callable than returns one).\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m   1221\u001b[0m         )\n\u001b[1;32m   1223\u001b[0m     \u001b[38;5;28mself\u001b[39m, cond \u001b[38;5;241m=\u001b[39m align(\u001b[38;5;28mself\u001b[39m, cond)\n\u001b[1;32m   1225\u001b[0m     \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_dataarray_indexer\u001b[39m(dim: Hashable) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m DataArray:\n",
+      "\u001b[0;31mTypeError\u001b[0m: cond argument is array([False, False, False, ..., False, False, False]) but must be a <class 'xarray.core.dataset.Dataset'> or <class 'xarray.core.dataarray.DataArray'> (or a callable than returns one)."
+     ]
+    }
+   ],
+   "source": [
+    "ds_boundary.stack(grid_point=('latitude', 'longitude')).where(msk_in, drop=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
     {
      "data": {
-      "image/png": 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",
       "text/plain": [
-       "<Figure size 1000x1000 with 1 Axes>"
+       "[SingleSphericalPolygon(array([[ 0.49958987, -0.105781  ,  0.85977924],\n",
+       "       [ 0.39711363,  0.17672289,  0.90059413],\n",
+       "       [ 0.55692436,  0.25118806,  0.791669  ],\n",
+       "       [ 0.55972804,  0.2524979 ,  0.78927139],\n",
+       "       [ 0.56252231,  0.25380347,  0.7868624 ],\n",
+       "       [ 0.56530712,  0.25510474,  0.78444212],\n",
+       "       [ 0.56808237,  0.25640168,  0.78201061],\n",
+       "       [ 0.57084802,  0.25769424,  0.77956797],\n",
+       "       [ 0.67322075, -0.02461529,  0.73903174],\n",
+       "       [ 0.49958987, -0.105781  ,  0.85977924]]), array([0.55411136, 0.24987399, 0.79405515]))]"
       ]
      },
+     "execution_count": 11,
      "metadata": {},
-     "output_type": "display_data"
+     "output_type": "execute_result"
     }
    ],
    "source": [
-    "# latlon to cartesian coords\n",
-    "pts_bnd_x = np.array(sg.vector.lonlat_to_vector(points_bnd[:,0], points_bnd[:,1])).T\n",
-    "pts_lam_x = np.array(sg.vector.lonlat_to_vector(points_lam[:,0], points_lam[:,1])).T\n",
-    "\n",
-    "chull_lam = SphericalPolygon.convex_hull(pts_lam_x)\n",
-    "\n",
-    "# Finding the boundary points inside the convec hull is what takes time. Possibly speed up by restricting to a subset of points_bnd that are within some distance from area center or similar?\n",
-    "msk_in = np.array([chull_lam.contains_lonlat(x[1], x[0]) for x in points_bnd])\n",
-    "\n",
-    "# Distances from all boundary points outside the region to the closest point inside\n",
-    "d = np.array([np.min(np.arccos(np.dot(pts_lam_x, x))) for x in pts_bnd_x[~msk_in]])\n",
-    "\n",
-    "# Define boundary distance and mask boundary points outside that are closer\n",
-    "# This is now in radians but should be defined as meters based on Earth radius\n",
-    "ii = d < 0.2\n",
-    "\n",
-    "# Plot boundary valid boundary points outside and the LAM interior points (subset)\n",
-    "fig, ax = plt.subplots(subplot_kw={'projection': ccrs.PlateCarree()}, figsize=(10, 10))\n",
-    "ax.coastlines()\n",
-    "# xr.plot.scatter(ds_boundary, x='longitude', y='latitude', color='red', s=1, ax=ax, transform=ccrs.Geodetic())\n",
-    "xr.plot.scatter(ds_lam_subsampled, x='lon', y='lat', color='blue', s=1, ax=ax, transform=ccrs.Geodetic())\n",
-    "ax.plot(points_bnd[~msk_in,0][ii], points_bnd[~msk_in,1][ii], '.', color='green', markersize=1, transform=ccrs.Geodetic())"
+    "chull_lam"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
@@ -8204,7 +8291,7 @@
        "array([False, False, False, ..., False, False, False])"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
diff --git a/mllam_data_prep/ops/cropping.py b/mllam_data_prep/ops/cropping.py
new file mode 100644
index 0000000..629fa08
--- /dev/null
+++ b/mllam_data_prep/ops/cropping.py
@@ -0,0 +1,113 @@
+import numpy as np
+import pyproj
+import spherical_geometry as sg
+import xarray as xr
+from loguru import logger
+from spherical_geometry.polygon import SphericalPolygon
+
+
+def _get_latlon_coords(da):
+    """
+    Get the latlon coordinates of a DataArray.
+
+    Parameters
+    ----------
+    da : xarray.DataArray
+        The data.
+
+    Returns
+    -------
+    tuple (xarray.DataArray, xarray.DataArray)
+        The latitude and longitude coordinates.
+    """
+    if "latitude" in da.coords and "longitude" in da.coords:
+        return (da.latitude, da.longitude)
+    elif "lat" in da.coords and "lon" in da.coords:
+        return (da.lat, da.lon)
+    else:
+        raise Exception()
+
+
+def _add_latlon(ds):
+    logger.warning(
+        "Could not find lat/lon coordinates. Assuming equal area projection "
+        "centered on (lat, lon) = (0, 0) for now. This should be replaced!"
+    )
+
+    # create a local equal area projection centered on Denmark (Høje Taastrup)
+    # for now
+    da_x = ds.coords["x"]
+    da_y = ds.coords["y"]
+    proj = pyproj.Proj(proj="laea", lon_0=12.25, lat_0=55.65)
+    lon, lat = proj(da_x.values, da_y.values, inverse=True)
+    da_lat = xr.DataArray(lat, coords=da_x.coords, dims=da_x.dims)
+    da_lon = xr.DataArray(lon, coords=da_y.coords, dims=da_y.dims)
+
+    ds.coords["lon"] = da_lon
+    ds.coords["lat"] = da_lat
+
+    return ds
+
+
+def create_convex_hull_mask(ds, ds_reference):
+    """
+    Create a grid-point mask for lat/lon coordinates in `da` indicating which
+    points are interior to the convex hull of the lat/lon coordinates of
+    `da_ref`.
+    """
+    # TODO: this should be replaced with a function to get the true lat/lon coordinates
+    _add_latlon(ds)
+    _add_latlon(ds_reference)
+
+    da_lat, da_lon = _get_latlon_coords(ds)
+    da_lat_ref, da_lon_ref = _get_latlon_coords(ds_reference)
+
+    assert da_lat.dims == da_lon.dims == da_lat_ref.dims == da_lon_ref.dims
+
+    # latlon to (x, y, z) on unit sphere
+    pts_ref_xyz = np.array(sg.vector.lonlat_to_vector(da_lon_ref, da_lat_ref)).T
+
+    chull_lam = SphericalPolygon.convex_hull(pts_ref_xyz)
+
+    da_interior_mask = xr.apply_ufunc(
+        chull_lam.contains_lonlat, da_lon, da_lat, vectorize=True
+    )
+    da_interior_mask.attrs[
+        "long_name"
+    ] = "contained in convex hull of source dataset (da_ref)"
+
+    return da_interior_mask
+
+
+def boundary_crop_gridpoints_with_latlon_convex_hull(
+    ds, ds_reference, grid_index_dim="grid_index", max_dist=0.2
+):
+    raise NotImplementedError()
+    # da_interior_mask = create_convex_hull_mask(ds=ds, ds_reference=ds_reference)
+
+    # points = _get_latlon_coords(ds)
+    # points_lam = _get_latlon_coords(ds_reference)
+
+    # latlon to (x, y, z) on unit sphere
+    # pts_xyz = np.array(sg.vector.lonlat_to_vector(points[:, 0], points[:, 1])).T
+    # pts_lam_xyz = np.array(
+    #     sg.vector.lonlat_to_vector(points_lam[:, 0], points_lam[:, 1])
+    # ).T
+
+    # da_xyz = xr.DataArray(pts_xyz, coords=ds.coords, dims=[grid_index_dim, "xyz"])
+
+    # chull_lam = SphericalPolygon.convex_hull(pts_lam_xyz)
+
+    # # Finding the boundary points inside the convec hull is what takes time. Possibly speed up by restricting to a subset of points_ that are within some distance from area center or similar?
+    # msk_in = np.array([chull_lam.contains_lonlat(x[0], x[1]) for x in points])
+    # da_interior_mask = xr.DataArray(msk_in, coords=ds.coords, dims=[grid_index_dim])
+    # ds_reference = ds.where(da_interior_mask, drop=True)
+
+    # Distances from all boundary points outside the region to the closest point inside
+    # d = np.array(
+    #     [np.min(np.arccos(np.dot(pts_lam_xyz, pt_xyz))) for pt_xyz in pts_xyz[~msk_in]]
+    # )
+
+    # Define boundary distance and mask boundary points outside that are closer
+    # This is now in radians but should be defined as meters based on Earth radius
+    # ii = d < max_dist
diff --git a/tests/test_latlon_convex_hull_cropping.py b/tests/test_latlon_convex_hull_cropping.py
new file mode 100644
index 0000000..3c8c23e
--- /dev/null
+++ b/tests/test_latlon_convex_hull_cropping.py
@@ -0,0 +1,31 @@
+import tempfile
+
+import mllam_data_prep as mdp
+import tests.data as testdata
+from mllam_data_prep.ops import cropping
+
+
+def test_create_convex_hull_mask():
+    tmpdir = tempfile.TemporaryDirectory()
+    domain_size = 500 * 1.0e3  # length and width of domain in meters
+    config_lam = testdata.create_input_datasets_and_config(
+        identifier="lam",
+        data_categories=["state"],
+        tmpdir=tmpdir,
+        xlim=[-domain_size / 2.0, domain_size / 2.0],
+        ylim=[-domain_size / 2.0, domain_size / 2.0],
+    )
+    # make the global domain twice as large as the LAM domain so that the lam
+    # domain is contained within the global domain
+    config_global = testdata.create_input_datasets_and_config(
+        identifier="global",
+        data_categories=["state"],
+        tmpdir=tmpdir,
+        xlim=[-domain_size, domain_size],
+        ylim=[-domain_size, domain_size],
+    )
+
+    ds_lam = mdp.create_dataset(config=config_lam)
+    ds_global = mdp.create_dataset(config=config_global)
+
+    cropping.create_convex_hull_mask(ds=ds_global, ds_reference=ds_lam)

From bb80db77eff14efff15ab199ca718991d289ac8d Mon Sep 17 00:00:00 2001
From: Leif Denby <leif@denby.eu>
Date: Thu, 12 Dec 2024 12:11:27 +0100
Subject: [PATCH 04/28] refactor complete

---
 docs/domain-cropping-mllam.ipynb          | 1731 -----
 docs/domain-cropping.ipynb                | 8304 +--------------------
 mllam_data_prep/create_dataset.py         |    8 +
 mllam_data_prep/ops/cropping.py           |  205 +-
 tests/data.py                             |   90 +-
 tests/test_convex_hull_cropping.py        |   73 +
 tests/test_latlon_convex_hull_cropping.py |   31 -
 7 files changed, 417 insertions(+), 10025 deletions(-)
 delete mode 100644 docs/domain-cropping-mllam.ipynb
 create mode 100644 tests/test_convex_hull_cropping.py
 delete mode 100644 tests/test_latlon_convex_hull_cropping.py

diff --git a/docs/domain-cropping-mllam.ipynb b/docs/domain-cropping-mllam.ipynb
deleted file mode 100644
index 95def46..0000000
--- a/docs/domain-cropping-mllam.ipynb
+++ /dev/null
@@ -1,1731 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import tests.data as testdata\n",
-    "import tempfile\n",
-    "\n",
-    "import xarray as xr\n",
-    "import matplotlib.pyplot as plt\n",
-    "import cartopy.crs as ccrs\n",
-    "\n",
-    "import mllam_data_prep as mdp\n",
-    "import tests.data as testdata\n",
-    "from mllam_data_prep.ops import cropping    "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tmpdir = tempfile.TemporaryDirectory()\n",
-    "l = 500 * 1.0e3  # length and width of domain in meters\n",
-    "config_lam = testdata.create_input_datasets_and_config(\n",
-    "    identifier=\"lam\",\n",
-    "    data_categories=[\"state\"],\n",
-    "    tmpdir=tmpdir,\n",
-    "    xlim=[-l/2.0, l/2.0],\n",
-    "    ylim=[-l/2.0, l/2.0],\n",
-    ")\n",
-    "# make the global domain twice as large as the LAM domain so that the lam\n",
-    "# domain is contained within the global domain\n",
-    "config_global = testdata.create_input_datasets_and_config(\n",
-    "    identifier=\"global\",\n",
-    "    data_categories=[\"state\"],\n",
-    "    tmpdir=tmpdir,\n",
-    "    xlim=[-l, l],\n",
-    "    ylim=[-l, l]\n",
-    ")\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\u001b[32m2024-12-10 17:02:17.477\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmllam_data_prep.create_dataset\u001b[0m:\u001b[36mcreate_dataset\u001b[0m:\u001b[36m135\u001b[0m - \u001b[1mLoading dataset lam_surface_analysis from /var/folders/n8/kcprfkd918967drydbc_bphw0000gr/T/tmp6h_c0_ze/lam/surface_analysis_ca45dbbf-5bbd-42f3-980f-55f6abe03466.zarr\u001b[0m\n",
-      "\u001b[32m2024-12-10 17:02:17.481\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmllam_data_prep.create_dataset\u001b[0m:\u001b[36mcreate_dataset\u001b[0m:\u001b[36m160\u001b[0m - \u001b[1mMapping dimensions and variables for dataset lam_surface_analysis to state\u001b[0m\n",
-      "\u001b[32m2024-12-10 17:02:17.489\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmllam_data_prep.create_dataset\u001b[0m:\u001b[36m_merge_dataarrays_by_target\u001b[0m:\u001b[36m48\u001b[0m - \u001b[1mMerging dataarrays for target variable `state`\u001b[0m\n",
-      "\u001b[32m2024-12-10 17:02:17.499\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmllam_data_prep.create_dataset\u001b[0m:\u001b[36mcreate_dataset\u001b[0m:\u001b[36m195\u001b[0m - \u001b[1mChunking dataset with {}\u001b[0m\n",
-      "\u001b[32m2024-12-10 17:02:17.500\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmllam_data_prep.create_dataset\u001b[0m:\u001b[36mcreate_dataset\u001b[0m:\u001b[36m135\u001b[0m - \u001b[1mLoading dataset global_surface_analysis from /var/folders/n8/kcprfkd918967drydbc_bphw0000gr/T/tmp6h_c0_ze/global/surface_analysis_c93d0634-bd93-4b36-9708-d180d7f57fe0.zarr\u001b[0m\n",
-      "\u001b[32m2024-12-10 17:02:17.510\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmllam_data_prep.create_dataset\u001b[0m:\u001b[36mcreate_dataset\u001b[0m:\u001b[36m160\u001b[0m - \u001b[1mMapping dimensions and variables for dataset global_surface_analysis to state\u001b[0m\n",
-      "\u001b[32m2024-12-10 17:02:17.513\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmllam_data_prep.create_dataset\u001b[0m:\u001b[36m_merge_dataarrays_by_target\u001b[0m:\u001b[36m48\u001b[0m - \u001b[1mMerging dataarrays for target variable `state`\u001b[0m\n",
-      "\u001b[32m2024-12-10 17:02:17.516\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmllam_data_prep.create_dataset\u001b[0m:\u001b[36mcreate_dataset\u001b[0m:\u001b[36m195\u001b[0m - \u001b[1mChunking dataset with {}\u001b[0m\n"
-     ]
-    }
-   ],
-   "source": [
-    "ds_lam = mdp.create_dataset(config=config_lam)\n",
-    "ds_global = mdp.create_dataset(config=config_global)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
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-       "    x                             (grid_index) float64 640B dask.array&lt;chunksize=(80,), meta=np.ndarray&gt;\n",
-       "    y                             (grid_index) float64 640B dask.array&lt;chunksize=(80,), meta=np.ndarray&gt;\n",
-       "  * state_feature                 (state_feature) &lt;U15 60B &#x27;pres_seasurface&#x27;\n",
-       "    state_feature_units           (state_feature) &lt;U1 4B dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;\n",
-       "    state_feature_long_name       (state_feature) &lt;U1 4B dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;\n",
-       "    state_feature_source_dataset  (state_feature) &lt;U20 80B dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;\n",
-       "Dimensions without coordinates: grid_index\n",
-       "Data variables:\n",
-       "    state                         (state_feature, time, grid_index) float64 3kB dask.array&lt;chunksize=(1, 5, 80), meta=np.ndarray&gt;\n",
-       "Attributes:\n",
-       "    schema_version:   v0.5.0\n",
-       "    dataset_version:  v0.1.0\n",
-       "    created_on:       2024-12-10T17:02:17\n",
-       "    created_with:     mllam-data-prep (https://github.com/mllam/mllam-data-prep)\n",
-       "    mdp_version:      v0.5.0</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-bd4acf93-d08d-472f-bd32-502780773664' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-bd4acf93-d08d-472f-bd32-502780773664' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 5</li><li><span>grid_index</span>: 80</li><li><span class='xr-has-index'>state_feature</span>: 1</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-36da9a63-86e0-4c8b-b364-56f5d772c314' class='xr-section-summary-in' type='checkbox'  checked><label for='section-36da9a63-86e0-4c8b-b364-56f5d772c314' class='xr-section-summary' >Coordinates: <span>(7)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2000-01-01 ... 2000-01-02</div><input id='attrs-ac004efc-31d3-4c55-9bc1-e1bd22155cdd' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ac004efc-31d3-4c55-9bc1-e1bd22155cdd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-639ef3ee-26bf-4b1e-9788-ab7d1b6bb590' class='xr-var-data-in' type='checkbox'><label for='data-639ef3ee-26bf-4b1e-9788-ab7d1b6bb590' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;2000-01-01T00:00:00.000000000&#x27;, &#x27;2000-01-01T06:00:00.000000000&#x27;,\n",
-       "       &#x27;2000-01-01T12:00:00.000000000&#x27;, &#x27;2000-01-01T18:00:00.000000000&#x27;,\n",
-       "       &#x27;2000-01-02T00:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>x</span></div><div class='xr-var-dims'>(grid_index)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(80,), meta=np.ndarray&gt;</div><input id='attrs-888c2688-34fc-44a7-a225-8e44e95449d8' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-888c2688-34fc-44a7-a225-8e44e95449d8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-258f52a6-89ee-42c5-983c-2c35d355e5d9' class='xr-var-data-in' type='checkbox'><label for='data-258f52a6-89ee-42c5-983c-2c35d355e5d9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table style=\"border-collapse: collapse;\">\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 640 B </td>\n",
-       "                        <td> 640 B </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (80,) </td>\n",
-       "                        <td> (80,) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 1 chunks in 1 graph layer </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
-       "        <td>\n",
-       "        <svg width=\"170\" height=\"76\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
-       "\n",
-       "  <!-- Horizontal lines -->\n",
-       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"0\" y1=\"26\" x2=\"120\" y2=\"26\" style=\"stroke-width:2\" />\n",
-       "\n",
-       "  <!-- Vertical lines -->\n",
-       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"26\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"26\" style=\"stroke-width:2\" />\n",
-       "\n",
-       "  <!-- Colored Rectangle -->\n",
-       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,26.512733859679475 0.0,26.512733859679475\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
-       "\n",
-       "  <!-- Text -->\n",
-       "  <text x=\"60.000000\" y=\"46.512734\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >80</text>\n",
-       "  <text x=\"140.000000\" y=\"13.256367\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,13.256367)\">1</text>\n",
-       "</svg>\n",
-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>y</span></div><div class='xr-var-dims'>(grid_index)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(80,), meta=np.ndarray&gt;</div><input id='attrs-a2fc4064-f71e-49d6-b7ac-10d147b661ba' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a2fc4064-f71e-49d6-b7ac-10d147b661ba' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a5babf03-3391-42ff-983a-6ee54a4fc4f6' class='xr-var-data-in' type='checkbox'><label for='data-a5babf03-3391-42ff-983a-6ee54a4fc4f6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table style=\"border-collapse: collapse;\">\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 640 B </td>\n",
-       "                        <td> 640 B </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (80,) </td>\n",
-       "                        <td> (80,) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 1 chunks in 1 graph layer </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
-       "        <td>\n",
-       "        <svg width=\"170\" height=\"76\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
-       "\n",
-       "  <!-- Horizontal lines -->\n",
-       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"0\" y1=\"26\" x2=\"120\" y2=\"26\" style=\"stroke-width:2\" />\n",
-       "\n",
-       "  <!-- Vertical lines -->\n",
-       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"26\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"26\" style=\"stroke-width:2\" />\n",
-       "\n",
-       "  <!-- Colored Rectangle -->\n",
-       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,26.512733859679475 0.0,26.512733859679475\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
-       "\n",
-       "  <!-- Text -->\n",
-       "  <text x=\"60.000000\" y=\"46.512734\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >80</text>\n",
-       "  <text x=\"140.000000\" y=\"13.256367\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,13.256367)\">1</text>\n",
-       "</svg>\n",
-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>state_feature</span></div><div class='xr-var-dims'>(state_feature)</div><div class='xr-var-dtype'>&lt;U15</div><div class='xr-var-preview xr-preview'>&#x27;pres_seasurface&#x27;</div><input id='attrs-76d039e4-886b-4fdb-80dd-b65084f63dde' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-76d039e4-886b-4fdb-80dd-b65084f63dde' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0a32e3c6-fd46-46b6-912c-4e74a0a8929e' class='xr-var-data-in' type='checkbox'><label for='data-0a32e3c6-fd46-46b6-912c-4e74a0a8929e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;pres_seasurface&#x27;], dtype=&#x27;&lt;U15&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>state_feature_units</span></div><div class='xr-var-dims'>(state_feature)</div><div class='xr-var-dtype'>&lt;U1</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;</div><input id='attrs-95043526-328b-4695-b4cc-b65fd419a93c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-95043526-328b-4695-b4cc-b65fd419a93c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-50ba9af0-9724-4804-b0a6-9e3e0d10530a' class='xr-var-data-in' type='checkbox'><label for='data-50ba9af0-9724-4804-b0a6-9e3e0d10530a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table style=\"border-collapse: collapse;\">\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 4 B </td>\n",
-       "                        <td> 4 B </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (1,) </td>\n",
-       "                        <td> (1,) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 1 chunks in 1 graph layer </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> <U1 numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
-       "        <td>\n",
-       "        <svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
-       "\n",
-       "  <!-- Horizontal lines -->\n",
-       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"0\" y1=\"120\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
-       "\n",
-       "  <!-- Vertical lines -->\n",
-       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
-       "\n",
-       "  <!-- Colored Rectangle -->\n",
-       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
-       "\n",
-       "  <!-- Text -->\n",
-       "  <text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1</text>\n",
-       "  <text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,60.000000)\">1</text>\n",
-       "</svg>\n",
-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>state_feature_long_name</span></div><div class='xr-var-dims'>(state_feature)</div><div class='xr-var-dtype'>&lt;U1</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;</div><input id='attrs-c82e228a-8da6-44a6-850f-e4bdae3dd371' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c82e228a-8da6-44a6-850f-e4bdae3dd371' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a6098df1-6903-499c-8627-b7a78342a7a2' class='xr-var-data-in' type='checkbox'><label for='data-a6098df1-6903-499c-8627-b7a78342a7a2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table style=\"border-collapse: collapse;\">\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 4 B </td>\n",
-       "                        <td> 4 B </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (1,) </td>\n",
-       "                        <td> (1,) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 1 chunks in 1 graph layer </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> <U1 numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
-       "        <td>\n",
-       "        <svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
-       "\n",
-       "  <!-- Horizontal lines -->\n",
-       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"0\" y1=\"120\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
-       "\n",
-       "  <!-- Vertical lines -->\n",
-       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
-       "\n",
-       "  <!-- Colored Rectangle -->\n",
-       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
-       "\n",
-       "  <!-- Text -->\n",
-       "  <text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1</text>\n",
-       "  <text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,60.000000)\">1</text>\n",
-       "</svg>\n",
-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>state_feature_source_dataset</span></div><div class='xr-var-dims'>(state_feature)</div><div class='xr-var-dtype'>&lt;U20</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;</div><input id='attrs-56cd4af8-a4c0-4f1b-a8c1-fb192655f82c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-56cd4af8-a4c0-4f1b-a8c1-fb192655f82c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6e58fca1-1d5d-4169-a439-b976c2002aab' class='xr-var-data-in' type='checkbox'><label for='data-6e58fca1-1d5d-4169-a439-b976c2002aab' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table style=\"border-collapse: collapse;\">\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 80 B </td>\n",
-       "                        <td> 80 B </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (1,) </td>\n",
-       "                        <td> (1,) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 1 chunks in 1 graph layer </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> <U20 numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
-       "        <td>\n",
-       "        <svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
-       "\n",
-       "  <!-- Horizontal lines -->\n",
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-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-b958e553-8c03-49e9-a1ab-c6b38c23123b' class='xr-section-summary-in' type='checkbox'  checked><label for='section-b958e553-8c03-49e9-a1ab-c6b38c23123b' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>state</span></div><div class='xr-var-dims'>(state_feature, time, grid_index)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 5, 80), meta=np.ndarray&gt;</div><input id='attrs-5b3a306e-b2f7-4c08-ae70-23303a86ec49' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5b3a306e-b2f7-4c08-ae70-23303a86ec49' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-de259f88-7136-4207-9ee0-9cfd8cdb28b2' class='xr-var-data-in' type='checkbox'><label for='data-de259f88-7136-4207-9ee0-9cfd8cdb28b2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>variables_mapping_dim :</span></dt><dd>state_feature</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table style=\"border-collapse: collapse;\">\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 3.12 kiB </td>\n",
-       "                        <td> 3.12 kiB </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (1, 5, 80) </td>\n",
-       "                        <td> (1, 5, 80) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 1 chunks in 4 graph layers </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
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-       "\n",
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-       "  <text x=\"85.595726\" y=\"71.455273\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >80</text>\n",
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-       "  <text x=\"7.797863\" y=\"63.657410\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,7.797863,63.657410)\">1</text>\n",
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-       "               &#x27;2000-01-01 12:00:00&#x27;, &#x27;2000-01-01 18:00:00&#x27;,\n",
-       "               &#x27;2000-01-02 00:00:00&#x27;],\n",
-       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>state_feature</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-2f80c03b-504c-4517-a3dc-d09fa3b66250' class='xr-index-data-in' type='checkbox'/><label for='index-2f80c03b-504c-4517-a3dc-d09fa3b66250' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;pres_seasurface&#x27;], dtype=&#x27;object&#x27;, name=&#x27;state_feature&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-dde30d88-c881-4a5a-8166-f96574213c31' class='xr-section-summary-in' type='checkbox'  checked><label for='section-dde30d88-c881-4a5a-8166-f96574213c31' class='xr-section-summary' >Attributes: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>schema_version :</span></dt><dd>v0.5.0</dd><dt><span>dataset_version :</span></dt><dd>v0.1.0</dd><dt><span>created_on :</span></dt><dd>2024-12-10T17:02:17</dd><dt><span>created_with :</span></dt><dd>mllam-data-prep (https://github.com/mllam/mllam-data-prep)</dd><dt><span>mdp_version :</span></dt><dd>v0.5.0</dd></dl></div></li></ul></div></div>"
-      ],
-      "text/plain": [
-       "<xarray.Dataset> Size: 5kB\n",
-       "Dimensions:                       (time: 5, grid_index: 80, state_feature: 1)\n",
-       "Coordinates:\n",
-       "  * time                          (time) datetime64[ns] 40B 2000-01-01 ... 20...\n",
-       "    x                             (grid_index) float64 640B dask.array<chunksize=(80,), meta=np.ndarray>\n",
-       "    y                             (grid_index) float64 640B dask.array<chunksize=(80,), meta=np.ndarray>\n",
-       "  * state_feature                 (state_feature) <U15 60B 'pres_seasurface'\n",
-       "    state_feature_units           (state_feature) <U1 4B dask.array<chunksize=(1,), meta=np.ndarray>\n",
-       "    state_feature_long_name       (state_feature) <U1 4B dask.array<chunksize=(1,), meta=np.ndarray>\n",
-       "    state_feature_source_dataset  (state_feature) <U20 80B dask.array<chunksize=(1,), meta=np.ndarray>\n",
-       "Dimensions without coordinates: grid_index\n",
-       "Data variables:\n",
-       "    state                         (state_feature, time, grid_index) float64 3kB dask.array<chunksize=(1, 5, 80), meta=np.ndarray>\n",
-       "Attributes:\n",
-       "    schema_version:   v0.5.0\n",
-       "    dataset_version:  v0.1.0\n",
-       "    created_on:       2024-12-10T17:02:17\n",
-       "    created_with:     mllam-data-prep (https://github.com/mllam/mllam-data-prep)\n",
-       "    mdp_version:      v0.5.0"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "ds_lam"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
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-       "}\n",
-       "\n",
-       ".xr-has-index {\n",
-       "  font-weight: bold;\n",
-       "}\n",
-       "\n",
-       ".xr-var-list,\n",
-       ".xr-var-item {\n",
-       "  display: contents;\n",
-       "}\n",
-       "\n",
-       ".xr-var-item > div,\n",
-       ".xr-var-item label,\n",
-       ".xr-var-item > .xr-var-name span {\n",
-       "  background-color: var(--xr-background-color-row-even);\n",
-       "  margin-bottom: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-var-item > .xr-var-name:hover span {\n",
-       "  padding-right: 5px;\n",
-       "}\n",
-       "\n",
-       ".xr-var-list > li:nth-child(odd) > div,\n",
-       ".xr-var-list > li:nth-child(odd) > label,\n",
-       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
-       "  background-color: var(--xr-background-color-row-odd);\n",
-       "}\n",
-       "\n",
-       ".xr-var-name {\n",
-       "  grid-column: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-var-dims {\n",
-       "  grid-column: 2;\n",
-       "}\n",
-       "\n",
-       ".xr-var-dtype {\n",
-       "  grid-column: 3;\n",
-       "  text-align: right;\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-var-preview {\n",
-       "  grid-column: 4;\n",
-       "}\n",
-       "\n",
-       ".xr-index-preview {\n",
-       "  grid-column: 2 / 5;\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-var-name,\n",
-       ".xr-var-dims,\n",
-       ".xr-var-dtype,\n",
-       ".xr-preview,\n",
-       ".xr-attrs dt {\n",
-       "  white-space: nowrap;\n",
-       "  overflow: hidden;\n",
-       "  text-overflow: ellipsis;\n",
-       "  padding-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-var-name:hover,\n",
-       ".xr-var-dims:hover,\n",
-       ".xr-var-dtype:hover,\n",
-       ".xr-attrs dt:hover {\n",
-       "  overflow: visible;\n",
-       "  width: auto;\n",
-       "  z-index: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-var-attrs,\n",
-       ".xr-var-data,\n",
-       ".xr-index-data {\n",
-       "  display: none;\n",
-       "  background-color: var(--xr-background-color) !important;\n",
-       "  padding-bottom: 5px !important;\n",
-       "}\n",
-       "\n",
-       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
-       ".xr-var-data-in:checked ~ .xr-var-data,\n",
-       ".xr-index-data-in:checked ~ .xr-index-data {\n",
-       "  display: block;\n",
-       "}\n",
-       "\n",
-       ".xr-var-data > table {\n",
-       "  float: right;\n",
-       "}\n",
-       "\n",
-       ".xr-var-name span,\n",
-       ".xr-var-data,\n",
-       ".xr-index-name div,\n",
-       ".xr-index-data,\n",
-       ".xr-attrs {\n",
-       "  padding-left: 25px !important;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs,\n",
-       ".xr-var-attrs,\n",
-       ".xr-var-data,\n",
-       ".xr-index-data {\n",
-       "  grid-column: 1 / -1;\n",
-       "}\n",
-       "\n",
-       "dl.xr-attrs {\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "  display: grid;\n",
-       "  grid-template-columns: 125px auto;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt,\n",
-       ".xr-attrs dd {\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "  float: left;\n",
-       "  padding-right: 10px;\n",
-       "  width: auto;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt {\n",
-       "  font-weight: normal;\n",
-       "  grid-column: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt:hover span {\n",
-       "  display: inline-block;\n",
-       "  background: var(--xr-background-color);\n",
-       "  padding-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dd {\n",
-       "  grid-column: 2;\n",
-       "  white-space: pre-wrap;\n",
-       "  word-break: break-all;\n",
-       "}\n",
-       "\n",
-       ".xr-icon-database,\n",
-       ".xr-icon-file-text2,\n",
-       ".xr-no-icon {\n",
-       "  display: inline-block;\n",
-       "  vertical-align: middle;\n",
-       "  width: 1em;\n",
-       "  height: 1.5em !important;\n",
-       "  stroke-width: 0;\n",
-       "  stroke: currentColor;\n",
-       "  fill: currentColor;\n",
-       "}\n",
-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 5kB\n",
-       "Dimensions:                       (time: 5, grid_index: 80, state_feature: 1)\n",
-       "Coordinates:\n",
-       "  * time                          (time) datetime64[ns] 40B 2000-01-01 ... 20...\n",
-       "    x                             (grid_index) float64 640B dask.array&lt;chunksize=(80,), meta=np.ndarray&gt;\n",
-       "    y                             (grid_index) float64 640B dask.array&lt;chunksize=(80,), meta=np.ndarray&gt;\n",
-       "  * state_feature                 (state_feature) &lt;U15 60B &#x27;pres_seasurface&#x27;\n",
-       "    state_feature_units           (state_feature) &lt;U1 4B dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;\n",
-       "    state_feature_long_name       (state_feature) &lt;U1 4B dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;\n",
-       "    state_feature_source_dataset  (state_feature) &lt;U23 92B dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;\n",
-       "Dimensions without coordinates: grid_index\n",
-       "Data variables:\n",
-       "    state                         (state_feature, time, grid_index) float64 3kB dask.array&lt;chunksize=(1, 5, 80), meta=np.ndarray&gt;\n",
-       "Attributes:\n",
-       "    schema_version:   v0.5.0\n",
-       "    dataset_version:  v0.1.0\n",
-       "    created_on:       2024-12-10T17:02:17\n",
-       "    created_with:     mllam-data-prep (https://github.com/mllam/mllam-data-prep)\n",
-       "    mdp_version:      v0.5.0</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-75ac721c-8fb2-476c-8b90-99a03fede3d9' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-75ac721c-8fb2-476c-8b90-99a03fede3d9' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 5</li><li><span>grid_index</span>: 80</li><li><span class='xr-has-index'>state_feature</span>: 1</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-3ebbe816-f2ac-4457-9397-e8f9fce98b67' class='xr-section-summary-in' type='checkbox'  checked><label for='section-3ebbe816-f2ac-4457-9397-e8f9fce98b67' class='xr-section-summary' >Coordinates: <span>(7)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2000-01-01 ... 2000-01-02</div><input id='attrs-9b8417fe-6a2a-402b-afd1-0e9db36da452' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9b8417fe-6a2a-402b-afd1-0e9db36da452' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5b36a42b-3572-467c-9fbd-a546ce7e7e27' class='xr-var-data-in' type='checkbox'><label for='data-5b36a42b-3572-467c-9fbd-a546ce7e7e27' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;2000-01-01T00:00:00.000000000&#x27;, &#x27;2000-01-01T06:00:00.000000000&#x27;,\n",
-       "       &#x27;2000-01-01T12:00:00.000000000&#x27;, &#x27;2000-01-01T18:00:00.000000000&#x27;,\n",
-       "       &#x27;2000-01-02T00:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>x</span></div><div class='xr-var-dims'>(grid_index)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(80,), meta=np.ndarray&gt;</div><input id='attrs-8e50702e-f810-4721-90c4-372f33e09fd4' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8e50702e-f810-4721-90c4-372f33e09fd4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-22fcf2aa-f6a6-49c9-9da2-8755bca10c83' class='xr-var-data-in' type='checkbox'><label for='data-22fcf2aa-f6a6-49c9-9da2-8755bca10c83' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table style=\"border-collapse: collapse;\">\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 640 B </td>\n",
-       "                        <td> 640 B </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (80,) </td>\n",
-       "                        <td> (80,) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 1 chunks in 1 graph layer </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
-       "        <td>\n",
-       "        <svg width=\"170\" height=\"76\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
-       "\n",
-       "  <!-- Horizontal lines -->\n",
-       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"0\" y1=\"26\" x2=\"120\" y2=\"26\" style=\"stroke-width:2\" />\n",
-       "\n",
-       "  <!-- Vertical lines -->\n",
-       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"26\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"26\" style=\"stroke-width:2\" />\n",
-       "\n",
-       "  <!-- Colored Rectangle -->\n",
-       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,26.512733859679475 0.0,26.512733859679475\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
-       "\n",
-       "  <!-- Text -->\n",
-       "  <text x=\"60.000000\" y=\"46.512734\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >80</text>\n",
-       "  <text x=\"140.000000\" y=\"13.256367\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,13.256367)\">1</text>\n",
-       "</svg>\n",
-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>y</span></div><div class='xr-var-dims'>(grid_index)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(80,), meta=np.ndarray&gt;</div><input id='attrs-418540bf-c945-48e2-9b2a-f0e3c2926c8d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-418540bf-c945-48e2-9b2a-f0e3c2926c8d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5555329d-c4b6-4aed-9905-750491de1295' class='xr-var-data-in' type='checkbox'><label for='data-5555329d-c4b6-4aed-9905-750491de1295' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table style=\"border-collapse: collapse;\">\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 640 B </td>\n",
-       "                        <td> 640 B </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (80,) </td>\n",
-       "                        <td> (80,) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 1 chunks in 1 graph layer </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
-       "        <td>\n",
-       "        <svg width=\"170\" height=\"76\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
-       "\n",
-       "  <!-- Horizontal lines -->\n",
-       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"0\" y1=\"26\" x2=\"120\" y2=\"26\" style=\"stroke-width:2\" />\n",
-       "\n",
-       "  <!-- Vertical lines -->\n",
-       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"26\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"26\" style=\"stroke-width:2\" />\n",
-       "\n",
-       "  <!-- Colored Rectangle -->\n",
-       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,26.512733859679475 0.0,26.512733859679475\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
-       "\n",
-       "  <!-- Text -->\n",
-       "  <text x=\"60.000000\" y=\"46.512734\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >80</text>\n",
-       "  <text x=\"140.000000\" y=\"13.256367\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,13.256367)\">1</text>\n",
-       "</svg>\n",
-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>state_feature</span></div><div class='xr-var-dims'>(state_feature)</div><div class='xr-var-dtype'>&lt;U15</div><div class='xr-var-preview xr-preview'>&#x27;pres_seasurface&#x27;</div><input id='attrs-8751b2fc-6c00-498b-8a93-eb9ac851c26a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8751b2fc-6c00-498b-8a93-eb9ac851c26a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8da1cb7b-caf8-4996-8d1a-084281f94795' class='xr-var-data-in' type='checkbox'><label for='data-8da1cb7b-caf8-4996-8d1a-084281f94795' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;pres_seasurface&#x27;], dtype=&#x27;&lt;U15&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>state_feature_units</span></div><div class='xr-var-dims'>(state_feature)</div><div class='xr-var-dtype'>&lt;U1</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;</div><input id='attrs-78a8becd-a073-4a22-9e07-a7403f3a8b5f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-78a8becd-a073-4a22-9e07-a7403f3a8b5f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-10c51f6e-bca6-48a0-ae62-ef4da4f564bc' class='xr-var-data-in' type='checkbox'><label for='data-10c51f6e-bca6-48a0-ae62-ef4da4f564bc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table style=\"border-collapse: collapse;\">\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 4 B </td>\n",
-       "                        <td> 4 B </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (1,) </td>\n",
-       "                        <td> (1,) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 1 chunks in 1 graph layer </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> <U1 numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
-       "        <td>\n",
-       "        <svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
-       "\n",
-       "  <!-- Horizontal lines -->\n",
-       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"0\" y1=\"120\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
-       "\n",
-       "  <!-- Vertical lines -->\n",
-       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
-       "\n",
-       "  <!-- Colored Rectangle -->\n",
-       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
-       "\n",
-       "  <!-- Text -->\n",
-       "  <text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1</text>\n",
-       "  <text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,60.000000)\">1</text>\n",
-       "</svg>\n",
-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>state_feature_long_name</span></div><div class='xr-var-dims'>(state_feature)</div><div class='xr-var-dtype'>&lt;U1</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;</div><input id='attrs-cb7e35ad-81dd-4cab-a370-1be912b9fa33' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-cb7e35ad-81dd-4cab-a370-1be912b9fa33' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-73f27cf7-0088-4587-8b6c-9752e578a618' class='xr-var-data-in' type='checkbox'><label for='data-73f27cf7-0088-4587-8b6c-9752e578a618' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table style=\"border-collapse: collapse;\">\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 4 B </td>\n",
-       "                        <td> 4 B </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (1,) </td>\n",
-       "                        <td> (1,) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 1 chunks in 1 graph layer </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> <U1 numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
-       "        <td>\n",
-       "        <svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
-       "\n",
-       "  <!-- Horizontal lines -->\n",
-       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"0\" y1=\"120\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
-       "\n",
-       "  <!-- Vertical lines -->\n",
-       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
-       "\n",
-       "  <!-- Colored Rectangle -->\n",
-       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
-       "\n",
-       "  <!-- Text -->\n",
-       "  <text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1</text>\n",
-       "  <text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,60.000000)\">1</text>\n",
-       "</svg>\n",
-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>state_feature_source_dataset</span></div><div class='xr-var-dims'>(state_feature)</div><div class='xr-var-dtype'>&lt;U23</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;</div><input id='attrs-8745f29a-c36e-45cf-af58-03c2c6ea2a93' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8745f29a-c36e-45cf-af58-03c2c6ea2a93' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f5c8c75a-b82a-4561-9062-4dff840499f3' class='xr-var-data-in' type='checkbox'><label for='data-f5c8c75a-b82a-4561-9062-4dff840499f3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table style=\"border-collapse: collapse;\">\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 92 B </td>\n",
-       "                        <td> 92 B </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (1,) </td>\n",
-       "                        <td> (1,) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 1 chunks in 1 graph layer </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> <U23 numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
-       "        <td>\n",
-       "        <svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
-       "\n",
-       "  <!-- Horizontal lines -->\n",
-       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"0\" y1=\"120\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
-       "\n",
-       "  <!-- Vertical lines -->\n",
-       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
-       "\n",
-       "  <!-- Colored Rectangle -->\n",
-       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
-       "\n",
-       "  <!-- Text -->\n",
-       "  <text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1</text>\n",
-       "  <text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,60.000000)\">1</text>\n",
-       "</svg>\n",
-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-00106609-43ed-441a-9b8e-bb96fffc6276' class='xr-section-summary-in' type='checkbox'  checked><label for='section-00106609-43ed-441a-9b8e-bb96fffc6276' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>state</span></div><div class='xr-var-dims'>(state_feature, time, grid_index)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 5, 80), meta=np.ndarray&gt;</div><input id='attrs-4a13f8c6-e4f7-4d79-bfde-4a949f53ef17' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4a13f8c6-e4f7-4d79-bfde-4a949f53ef17' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dcae873f-05cd-480b-8e85-95c59259bd11' class='xr-var-data-in' type='checkbox'><label for='data-dcae873f-05cd-480b-8e85-95c59259bd11' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>variables_mapping_dim :</span></dt><dd>state_feature</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table style=\"border-collapse: collapse;\">\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 3.12 kiB </td>\n",
-       "                        <td> 3.12 kiB </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (1, 5, 80) </td>\n",
-       "                        <td> (1, 5, 80) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 1 chunks in 4 graph layers </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
-       "        <td>\n",
-       "        <svg width=\"195\" height=\"101\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
-       "\n",
-       "  <!-- Horizontal lines -->\n",
-       "  <line x1=\"10\" y1=\"0\" x2=\"25\" y2=\"15\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"10\" y1=\"35\" x2=\"25\" y2=\"51\" style=\"stroke-width:2\" />\n",
-       "\n",
-       "  <!-- Vertical lines -->\n",
-       "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"35\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"25\" y1=\"15\" x2=\"25\" y2=\"51\" style=\"stroke-width:2\" />\n",
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-       "\n",
-       "  <!-- Horizontal lines -->\n",
-       "  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"25\" y1=\"15\" x2=\"145\" y2=\"15\" style=\"stroke-width:2\" />\n",
-       "\n",
-       "  <!-- Vertical lines -->\n",
-       "  <line x1=\"10\" y1=\"0\" x2=\"25\" y2=\"15\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"130\" y1=\"0\" x2=\"145\" y2=\"15\" style=\"stroke-width:2\" />\n",
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-       "\n",
-       "  <!-- Horizontal lines -->\n",
-       "  <line x1=\"25\" y1=\"15\" x2=\"145\" y2=\"15\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"25\" y1=\"51\" x2=\"145\" y2=\"51\" style=\"stroke-width:2\" />\n",
-       "\n",
-       "  <!-- Vertical lines -->\n",
-       "  <line x1=\"25\" y1=\"15\" x2=\"25\" y2=\"51\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"145\" y1=\"15\" x2=\"145\" y2=\"51\" style=\"stroke-width:2\" />\n",
-       "\n",
-       "  <!-- Colored Rectangle -->\n",
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-       "\n",
-       "  <!-- Text -->\n",
-       "  <text x=\"85.595726\" y=\"71.455273\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >80</text>\n",
-       "  <text x=\"165.595726\" y=\"33.525499\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,165.595726,33.525499)\">5</text>\n",
-       "  <text x=\"7.797863\" y=\"63.657410\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,7.797863,63.657410)\">1</text>\n",
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-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-576631bc-7cfe-41f7-a835-27fa382bfdd1' class='xr-section-summary-in' type='checkbox'  ><label for='section-576631bc-7cfe-41f7-a835-27fa382bfdd1' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-e66009e2-450e-441f-86b7-2e415b9c9d96' class='xr-index-data-in' type='checkbox'/><label for='index-e66009e2-450e-441f-86b7-2e415b9c9d96' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2000-01-01 00:00:00&#x27;, &#x27;2000-01-01 06:00:00&#x27;,\n",
-       "               &#x27;2000-01-01 12:00:00&#x27;, &#x27;2000-01-01 18:00:00&#x27;,\n",
-       "               &#x27;2000-01-02 00:00:00&#x27;],\n",
-       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>state_feature</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-3ad22165-b896-40f4-b501-d53f43f2b75b' class='xr-index-data-in' type='checkbox'/><label for='index-3ad22165-b896-40f4-b501-d53f43f2b75b' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;pres_seasurface&#x27;], dtype=&#x27;object&#x27;, name=&#x27;state_feature&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-acc2ff92-4a8b-4521-9c9a-0521390223ae' class='xr-section-summary-in' type='checkbox'  checked><label for='section-acc2ff92-4a8b-4521-9c9a-0521390223ae' class='xr-section-summary' >Attributes: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>schema_version :</span></dt><dd>v0.5.0</dd><dt><span>dataset_version :</span></dt><dd>v0.1.0</dd><dt><span>created_on :</span></dt><dd>2024-12-10T17:02:17</dd><dt><span>created_with :</span></dt><dd>mllam-data-prep (https://github.com/mllam/mllam-data-prep)</dd><dt><span>mdp_version :</span></dt><dd>v0.5.0</dd></dl></div></li></ul></div></div>"
-      ],
-      "text/plain": [
-       "<xarray.Dataset> Size: 5kB\n",
-       "Dimensions:                       (time: 5, grid_index: 80, state_feature: 1)\n",
-       "Coordinates:\n",
-       "  * time                          (time) datetime64[ns] 40B 2000-01-01 ... 20...\n",
-       "    x                             (grid_index) float64 640B dask.array<chunksize=(80,), meta=np.ndarray>\n",
-       "    y                             (grid_index) float64 640B dask.array<chunksize=(80,), meta=np.ndarray>\n",
-       "  * state_feature                 (state_feature) <U15 60B 'pres_seasurface'\n",
-       "    state_feature_units           (state_feature) <U1 4B dask.array<chunksize=(1,), meta=np.ndarray>\n",
-       "    state_feature_long_name       (state_feature) <U1 4B dask.array<chunksize=(1,), meta=np.ndarray>\n",
-       "    state_feature_source_dataset  (state_feature) <U23 92B dask.array<chunksize=(1,), meta=np.ndarray>\n",
-       "Dimensions without coordinates: grid_index\n",
-       "Data variables:\n",
-       "    state                         (state_feature, time, grid_index) float64 3kB dask.array<chunksize=(1, 5, 80), meta=np.ndarray>\n",
-       "Attributes:\n",
-       "    schema_version:   v0.5.0\n",
-       "    dataset_version:  v0.1.0\n",
-       "    created_on:       2024-12-10T17:02:17\n",
-       "    created_with:     mllam-data-prep (https://github.com/mllam/mllam-data-prep)\n",
-       "    mdp_version:      v0.5.0"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "ds_global"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\u001b[32m2024-12-10 17:02:17.553\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mmllam_data_prep.ops.cropping\u001b[0m:\u001b[36m_add_latlon\u001b[0m:\u001b[36m30\u001b[0m - \u001b[33m\u001b[1mCould not find lat/lon coordinates. Assuming equal area projection centered on (lat, lon) = (0, 0) for now. This should be replaced!\u001b[0m\n",
-      "\u001b[32m2024-12-10 17:02:17.557\u001b[0m | \u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36mmllam_data_prep.ops.cropping\u001b[0m:\u001b[36m_add_latlon\u001b[0m:\u001b[36m30\u001b[0m - \u001b[33m\u001b[1mCould not find lat/lon coordinates. Assuming equal area projection centered on (lat, lon) = (0, 0) for now. This should be replaced!\u001b[0m\n"
-     ]
-    }
-   ],
-   "source": [
-    "da_interior_mask = cropping.create_convex_hull_mask(ds=ds_global, ds_reference=ds_lam)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "da_interior_mask = da_interior_mask.where(da_interior_mask, drop=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "ds_reference = ds_lam\n",
-    "ds = ds_global\n",
-    "\n",
-    "fig, ax = plt.subplots(subplot_kw=dict(projection=ccrs.PlateCarree()))\n",
-    "xr.plot.scatter(ax=ax, ds=ds, x=\"lon\", y=\"lat\", label=\"ds\", transform=ccrs.PlateCarree())\n",
-    "xr.plot.scatter(ax=ax, ds=ds_reference, x=\"lon\", y=\"lat\", label=\"ds_ref\", transform=ccrs.PlateCarree())\n",
-    "xr.plot.scatter(ax=ax, c=da_interior_mask, ds=da_interior_mask.to_dataset(name=\"mask\"), x=\"lon\", y=\"lat\", marker=\"x\", label=\"ds (interior)\")\n",
-    "ax.coastlines()\n",
-    "ax.gridlines(draw_labels=True)\n",
-    "ax.legend()\n",
-    "plt.show()\n",
-    "    "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "\n",
-    "    \n",
-    "    import matplotlib.pyplot as plt\n",
-    "    import cartopy.crs as ccrs\n",
-    "\n",
-    "    # fig, ax = plt.subplots()\n",
-    "    # xr.plot.scatter(ax=ax, ds=ds, x=\"x\", y=\"y\", label=\"ds\")\n",
-    "    # xr.plot.scatter(ax=ax, ds=ds_reference, x=\"x\", y=\"y\", label=\"ds_ref\")\n",
-    "    # xr.plot.scatter(ax=ax, ds=da_interior_mask.to_dataset(name=\"mask\"), x=\"x\", y=\"y\", color=\"blue\", label=\"mask\")\n",
-    "    # ax.legend()\n",
-    "    # plt.show()"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": ".venv",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.12.4"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/docs/domain-cropping.ipynb b/docs/domain-cropping.ipynb
index b12e6f1..305b889 100644
--- a/docs/domain-cropping.ipynb
+++ b/docs/domain-cropping.ipynb
@@ -1,8215 +1,160 @@
 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Cropping a domain using the lat/lon convex hull of another domain\n",
+    "\n",
+    "This notebook demonstrates how to crop a domain using the lat/lon convex hull of another domain. This is useful when a) you have two datasets on two different overlapping domains where you only want to keep the overlapping part, or b) in the case where you want to run limited-area simulations and need to create a dataset that provides the boundary conditions for the limited-area domain."
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
+    "import tests.data as testdata\n",
+    "import tempfile\n",
+    "\n",
+    "import xarray as xr\n",
     "import numpy as np\n",
     "import matplotlib.pyplot as plt\n",
     "import cartopy.crs as ccrs\n",
-    "import spherical_geometry as sg\n",
-    "from spherical_geometry.polygon import SphericalPolygon"
+    "\n",
+    "import mllam_data_prep as mdp\n",
+    "import tests.data as testdata\n",
+    "from mllam_data_prep.ops import cropping    "
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 2,
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [],
    "source": [
-    "import xarray as xr"
+    "We will start by creating the some synthetic data on two different domains where one sits within the other, mimicking the scenario where you have a high-resolution dataset within a coarser dataset."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
-    "url_lam = \"https://mllam-test-data.s3.eu-north-1.amazonaws.com/height_levels.zarr\"\n",
-    "url_boundary = \"gs://weatherbench2/datasets/era5/1959-2023_01_10-6h-64x32_equiangular_conservative.zarr\""
+    "tmpdir = tempfile.TemporaryDirectory()\n",
+    "l = 500 * 1.0e3  # length and width of domain in meters\n",
+    "N = 50  # number of grid points in each direction\n",
+    "config_lam = testdata.create_input_datasets_and_config(\n",
+    "    identifier=\"lam\",\n",
+    "    data_categories=[\"state\"],\n",
+    "    tmpdir=tmpdir,\n",
+    "    xlim=[-l/2.0, l/2.0],\n",
+    "    ylim=[-l/2.0, l/2.0],\n",
+    "    nx=N, ny=N,\n",
+    "    add_latlon=True\n",
+    "\n",
+    ")\n",
+    "# make the global domain twice as large as the LAM domain so that the lam\n",
+    "# domain is contained within the global domain, but half the number of grid\n",
+    "# points in each direction\n",
+    "config_global = testdata.create_input_datasets_and_config(\n",
+    "    identifier=\"global\",\n",
+    "    data_categories=[\"state\"],\n",
+    "    tmpdir=tmpdir,\n",
+    "    xlim=[-l, l],\n",
+    "    ylim=[-l, l],\n",
+    "    nx=N//2, ny=N//2,\n",
+    "    add_latlon=True\n",
+    ")\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
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-       "Dimensions:   (altitude: 1, y: 589, x: 789, time: 100)\n",
-       "Coordinates:\n",
-       "  * altitude  (altitude) int64 8B 100\n",
-       "    lat       (y, x) float64 4MB dask.array&lt;chunksize=(256, 256), meta=np.ndarray&gt;\n",
-       "    lon       (y, x) float64 4MB dask.array&lt;chunksize=(256, 256), meta=np.ndarray&gt;\n",
-       "  * time      (time) datetime64[ns] 800B 1990-09-01 ... 1990-09-13T09:00:00\n",
-       "  * x         (x) float64 6kB -1.999e+06 -1.997e+06 ... -3.175e+04 -2.925e+04\n",
-       "  * y         (y) float64 5kB -6.095e+05 -6.07e+05 ... 8.58e+05 8.605e+05\n",
-       "Data variables:\n",
-       "    r         (altitude, time, y, x) float64 372MB dask.array&lt;chunksize=(1, 100, 256, 256), meta=np.ndarray&gt;\n",
-       "    t         (altitude, time, y, x) float64 372MB dask.array&lt;chunksize=(1, 100, 256, 256), meta=np.ndarray&gt;\n",
-       "    u         (altitude, time, y, x) float64 372MB dask.array&lt;chunksize=(1, 100, 256, 256), meta=np.ndarray&gt;\n",
-       "    v         (altitude, time, y, x) float64 372MB dask.array&lt;chunksize=(1, 100, 256, 256), meta=np.ndarray&gt;\n",
-       "Attributes:\n",
-       "    description:  All prognostic variables for 10-year period on reduced levels</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-9f257df3-6cae-4853-9da5-b638ba9b8322' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-9f257df3-6cae-4853-9da5-b638ba9b8322' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>altitude</span>: 1</li><li><span class='xr-has-index'>y</span>: 589</li><li><span class='xr-has-index'>x</span>: 789</li><li><span class='xr-has-index'>time</span>: 100</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-f86ee731-6581-4ce5-ad26-fab85ffb9d3d' class='xr-section-summary-in' type='checkbox'  checked><label for='section-f86ee731-6581-4ce5-ad26-fab85ffb9d3d' class='xr-section-summary' >Coordinates: <span>(6)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>altitude</span></div><div class='xr-var-dims'>(altitude)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>100</div><input id='attrs-ccc0c7c5-fd9f-4560-a8f2-f6244ee6a32b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ccc0c7c5-fd9f-4560-a8f2-f6244ee6a32b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e2b9a677-9011-4017-9d8e-e409e60e6010' class='xr-var-data-in' type='checkbox'><label for='data-e2b9a677-9011-4017-9d8e-e409e60e6010' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><pre>array([100])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lat</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 256), meta=np.ndarray&gt;</div><input id='attrs-2bdf6b28-6f3a-4ded-bbef-3472e131b097' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2bdf6b28-6f3a-4ded-bbef-3472e131b097' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-253c5792-1529-4812-a72a-bf9c876e3791' class='xr-var-data-in' type='checkbox'><label for='data-253c5792-1529-4812-a72a-bf9c876e3791' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><table>\n",
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-       "  <text x=\"60.000000\" y=\"109.581749\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >789</text>\n",
-       "  <text x=\"140.000000\" y=\"44.790875\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,44.790875)\">589</text>\n",
-       "</svg>\n",
-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(256, 256), meta=np.ndarray&gt;</div><input id='attrs-8a70168e-ede7-40ae-be11-156970c32541' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8a70168e-ede7-40ae-be11-156970c32541' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4f3b8236-0e20-4d56-9f0c-92402aa6a25c' class='xr-var-data-in' type='checkbox'><label for='data-4f3b8236-0e20-4d56-9f0c-92402aa6a25c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table style=\"border-collapse: collapse;\">\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 3.55 MiB </td>\n",
-       "                        <td> 512.00 kiB </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (589, 789) </td>\n",
-       "                        <td> (256, 256) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 12 chunks in 2 graph layers </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
-       "        <td>\n",
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-       "        </td>\n",
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-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1990-09-01 ... 1990-09-13T09:00:00</div><input id='attrs-13408428-09a3-4052-9c30-1d2f6ea36976' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-13408428-09a3-4052-9c30-1d2f6ea36976' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ee053363-c038-4251-9c21-5b7ee108ff6d' class='xr-var-data-in' type='checkbox'><label for='data-ee053363-c038-4251-9c21-5b7ee108ff6d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;1990-09-01T00:00:00.000000000&#x27;, &#x27;1990-09-01T03:00:00.000000000&#x27;,\n",
-       "       &#x27;1990-09-01T06:00:00.000000000&#x27;, &#x27;1990-09-01T09:00:00.000000000&#x27;,\n",
-       "       &#x27;1990-09-01T12:00:00.000000000&#x27;, &#x27;1990-09-01T15:00:00.000000000&#x27;,\n",
-       "       &#x27;1990-09-01T18:00:00.000000000&#x27;, &#x27;1990-09-01T21:00:00.000000000&#x27;,\n",
-       "       &#x27;1990-09-02T00:00:00.000000000&#x27;, &#x27;1990-09-02T03:00:00.000000000&#x27;,\n",
-       "       &#x27;1990-09-02T06:00:00.000000000&#x27;, &#x27;1990-09-02T09:00:00.000000000&#x27;,\n",
-       "       &#x27;1990-09-02T12:00:00.000000000&#x27;, &#x27;1990-09-02T15:00:00.000000000&#x27;,\n",
-       "       &#x27;1990-09-02T18:00:00.000000000&#x27;, &#x27;1990-09-02T21:00:00.000000000&#x27;,\n",
-       "       &#x27;1990-09-03T00:00:00.000000000&#x27;, &#x27;1990-09-03T03:00:00.000000000&#x27;,\n",
-       "       &#x27;1990-09-03T06:00:00.000000000&#x27;, &#x27;1990-09-03T09:00:00.000000000&#x27;,\n",
-       "       &#x27;1990-09-03T12:00:00.000000000&#x27;, &#x27;1990-09-03T15:00:00.000000000&#x27;,\n",
-       "       &#x27;1990-09-03T18:00:00.000000000&#x27;, &#x27;1990-09-03T21:00:00.000000000&#x27;,\n",
-       "       &#x27;1990-09-04T00:00:00.000000000&#x27;, &#x27;1990-09-04T03:00:00.000000000&#x27;,\n",
-       "       &#x27;1990-09-04T06:00:00.000000000&#x27;, &#x27;1990-09-04T09:00:00.000000000&#x27;,\n",
-       "       &#x27;1990-09-04T12:00:00.000000000&#x27;, &#x27;1990-09-04T15:00:00.000000000&#x27;,\n",
-       "       &#x27;1990-09-04T18:00:00.000000000&#x27;, &#x27;1990-09-04T21:00:00.000000000&#x27;,\n",
-       "       &#x27;1990-09-05T00:00:00.000000000&#x27;, &#x27;1990-09-05T03:00:00.000000000&#x27;,\n",
-       "       &#x27;1990-09-05T06:00:00.000000000&#x27;, &#x27;1990-09-05T09:00:00.000000000&#x27;,\n",
-       "       &#x27;1990-09-05T12:00:00.000000000&#x27;, &#x27;1990-09-05T15:00:00.000000000&#x27;,\n",
-       "       &#x27;1990-09-05T18:00:00.000000000&#x27;, &#x27;1990-09-05T21:00:00.000000000&#x27;,\n",
-       "       &#x27;1990-09-06T00:00:00.000000000&#x27;, &#x27;1990-09-06T03:00:00.000000000&#x27;,\n",
-       "       &#x27;1990-09-06T06:00:00.000000000&#x27;, &#x27;1990-09-06T09:00:00.000000000&#x27;,\n",
-       "       &#x27;1990-09-06T12:00:00.000000000&#x27;, &#x27;1990-09-06T15:00:00.000000000&#x27;,\n",
-       "       &#x27;1990-09-06T18:00:00.000000000&#x27;, &#x27;1990-09-06T21:00:00.000000000&#x27;,\n",
-       "       &#x27;1990-09-07T00:00:00.000000000&#x27;, &#x27;1990-09-07T03:00:00.000000000&#x27;,\n",
-       "       &#x27;1990-09-07T06:00:00.000000000&#x27;, &#x27;1990-09-07T09:00:00.000000000&#x27;,\n",
-       "       &#x27;1990-09-07T12:00:00.000000000&#x27;, &#x27;1990-09-07T15:00:00.000000000&#x27;,\n",
-       "       &#x27;1990-09-07T18:00:00.000000000&#x27;, &#x27;1990-09-07T21:00:00.000000000&#x27;,\n",
-       "       &#x27;1990-09-08T00:00:00.000000000&#x27;, &#x27;1990-09-08T03:00:00.000000000&#x27;,\n",
-       "       &#x27;1990-09-08T06:00:00.000000000&#x27;, &#x27;1990-09-08T09:00:00.000000000&#x27;,\n",
-       "       &#x27;1990-09-08T12:00:00.000000000&#x27;, &#x27;1990-09-08T15:00:00.000000000&#x27;,\n",
-       "       &#x27;1990-09-08T18:00:00.000000000&#x27;, &#x27;1990-09-08T21:00:00.000000000&#x27;,\n",
-       "       &#x27;1990-09-09T00:00:00.000000000&#x27;, &#x27;1990-09-09T03:00:00.000000000&#x27;,\n",
-       "       &#x27;1990-09-09T06:00:00.000000000&#x27;, &#x27;1990-09-09T09:00:00.000000000&#x27;,\n",
-       "       &#x27;1990-09-09T12:00:00.000000000&#x27;, &#x27;1990-09-09T15:00:00.000000000&#x27;,\n",
-       "       &#x27;1990-09-09T18:00:00.000000000&#x27;, &#x27;1990-09-09T21:00:00.000000000&#x27;,\n",
-       "       &#x27;1990-09-10T00:00:00.000000000&#x27;, &#x27;1990-09-10T03:00:00.000000000&#x27;,\n",
-       "       &#x27;1990-09-10T06:00:00.000000000&#x27;, &#x27;1990-09-10T09:00:00.000000000&#x27;,\n",
-       "       &#x27;1990-09-10T12:00:00.000000000&#x27;, &#x27;1990-09-10T15:00:00.000000000&#x27;,\n",
-       "       &#x27;1990-09-10T18:00:00.000000000&#x27;, &#x27;1990-09-10T21:00:00.000000000&#x27;,\n",
-       "       &#x27;1990-09-11T00:00:00.000000000&#x27;, &#x27;1990-09-11T03:00:00.000000000&#x27;,\n",
-       "       &#x27;1990-09-11T06:00:00.000000000&#x27;, &#x27;1990-09-11T09:00:00.000000000&#x27;,\n",
-       "       &#x27;1990-09-11T12:00:00.000000000&#x27;, &#x27;1990-09-11T15:00:00.000000000&#x27;,\n",
-       "       &#x27;1990-09-11T18:00:00.000000000&#x27;, &#x27;1990-09-11T21:00:00.000000000&#x27;,\n",
-       "       &#x27;1990-09-12T00:00:00.000000000&#x27;, &#x27;1990-09-12T03:00:00.000000000&#x27;,\n",
-       "       &#x27;1990-09-12T06:00:00.000000000&#x27;, &#x27;1990-09-12T09:00:00.000000000&#x27;,\n",
-       "       &#x27;1990-09-12T12:00:00.000000000&#x27;, &#x27;1990-09-12T15:00:00.000000000&#x27;,\n",
-       "       &#x27;1990-09-12T18:00:00.000000000&#x27;, &#x27;1990-09-12T21:00:00.000000000&#x27;,\n",
-       "       &#x27;1990-09-13T00:00:00.000000000&#x27;, &#x27;1990-09-13T03:00:00.000000000&#x27;,\n",
-       "       &#x27;1990-09-13T06:00:00.000000000&#x27;, &#x27;1990-09-13T09:00:00.000000000&#x27;],\n",
-       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-1.999e+06 ... -2.925e+04</div><input id='attrs-a6c7d444-9427-4ae8-a067-829124b04464' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a6c7d444-9427-4ae8-a067-829124b04464' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fc319e59-664b-4d60-9acf-dc308c7f5531' class='xr-var-data-in' type='checkbox'><label for='data-fc319e59-664b-4d60-9acf-dc308c7f5531' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([-1999248.193226, -1996748.193226, -1994248.193226, ...,   -34248.193226,\n",
-       "         -31748.193226,   -29248.193226])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-6.095e+05 -6.07e+05 ... 8.605e+05</div><input id='attrs-13eb6525-069c-44dd-bada-d5332bfb81fc' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-13eb6525-069c-44dd-bada-d5332bfb81fc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f86831ed-3636-403a-ba5a-6794149c1b6b' class='xr-var-data-in' type='checkbox'><label for='data-f86831ed-3636-403a-ba5a-6794149c1b6b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([-609541.209133, -607041.209133, -604541.209133, ...,  855458.790867,\n",
-       "        857958.790867,  860458.790867])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-5224701a-3b18-4f04-abb8-e7e30330716f' class='xr-section-summary-in' type='checkbox'  checked><label for='section-5224701a-3b18-4f04-abb8-e7e30330716f' class='xr-section-summary' >Data variables: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>r</span></div><div class='xr-var-dims'>(altitude, time, y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 256, 256), meta=np.ndarray&gt;</div><input id='attrs-5c6afe49-690d-4796-b16c-9dfc815a9836' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5c6afe49-690d-4796-b16c-9dfc815a9836' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a8474546-490c-481c-a98a-8d57a0342894' class='xr-var-data-in' type='checkbox'><label for='data-a8474546-490c-481c-a98a-8d57a0342894' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>level_type :</span></dt><dd>heightAboveGround</dd><dt><span>long_name :</span></dt><dd>Relative humidity</dd><dt><span>shortName :</span></dt><dd>r</dd><dt><span>stepType :</span></dt><dd>instant</dd><dt><span>stepUnits :</span></dt><dd>1</dd><dt><span>typeOfLevel :</span></dt><dd>heightAboveGround</dd><dt><span>units :</span></dt><dd>%</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table style=\"border-collapse: collapse;\">\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 354.55 MiB </td>\n",
-       "                        <td> 50.00 MiB </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (1, 100, 589, 789) </td>\n",
-       "                        <td> (1, 100, 256, 256) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 12 chunks in 2 graph layers </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
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-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>t</span></div><div class='xr-var-dims'>(altitude, time, y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 256, 256), meta=np.ndarray&gt;</div><input id='attrs-0f9c07cf-637c-4fc1-8289-9488b698d208' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0f9c07cf-637c-4fc1-8289-9488b698d208' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-135a2950-9c46-4b28-88c8-a77d7513354d' class='xr-var-data-in' type='checkbox'><label for='data-135a2950-9c46-4b28-88c8-a77d7513354d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>level_type :</span></dt><dd>heightAboveGround</dd><dt><span>long_name :</span></dt><dd>Temperature</dd><dt><span>shortName :</span></dt><dd>t</dd><dt><span>stepType :</span></dt><dd>instant</dd><dt><span>stepUnits :</span></dt><dd>1</dd><dt><span>typeOfLevel :</span></dt><dd>heightAboveGround</dd><dt><span>units :</span></dt><dd>K</dd></dl></div><div class='xr-var-data'><table>\n",
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-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
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-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 354.55 MiB </td>\n",
-       "                        <td> 50.00 MiB </td>\n",
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-       "                        <th> Shape </th>\n",
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-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
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-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
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-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>u</span></div><div class='xr-var-dims'>(altitude, time, y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 256, 256), meta=np.ndarray&gt;</div><input id='attrs-0c105a73-758a-4360-b3b5-a7f5bc832383' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0c105a73-758a-4360-b3b5-a7f5bc832383' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2900e441-f08d-4847-9b39-201e18af2506' class='xr-var-data-in' type='checkbox'><label for='data-2900e441-f08d-4847-9b39-201e18af2506' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>level_type :</span></dt><dd>heightAboveGround</dd><dt><span>long_name :</span></dt><dd>u-component of wind</dd><dt><span>shortName :</span></dt><dd>u</dd><dt><span>stepType :</span></dt><dd>instant</dd><dt><span>stepUnits :</span></dt><dd>1</dd><dt><span>typeOfLevel :</span></dt><dd>heightAboveGround</dd><dt><span>units :</span></dt><dd>m s**-1</dd></dl></div><div class='xr-var-data'><table>\n",
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-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 354.55 MiB </td>\n",
-       "                        <td> 50.00 MiB </td>\n",
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-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (1, 100, 589, 789) </td>\n",
-       "                        <td> (1, 100, 256, 256) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 12 chunks in 2 graph layers </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
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-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>v</span></div><div class='xr-var-dims'>(altitude, time, y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 256, 256), meta=np.ndarray&gt;</div><input id='attrs-f7c55f69-8c64-492c-a65b-07c1f3cd2a68' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f7c55f69-8c64-492c-a65b-07c1f3cd2a68' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1dcf886f-c4e9-4e04-bbd3-64bce5ce1cf3' class='xr-var-data-in' type='checkbox'><label for='data-1dcf886f-c4e9-4e04-bbd3-64bce5ce1cf3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>level_type :</span></dt><dd>heightAboveGround</dd><dt><span>long_name :</span></dt><dd>v-component of wind</dd><dt><span>shortName :</span></dt><dd>v</dd><dt><span>stepType :</span></dt><dd>instant</dd><dt><span>stepUnits :</span></dt><dd>1</dd><dt><span>typeOfLevel :</span></dt><dd>heightAboveGround</dd><dt><span>units :</span></dt><dd>m s**-1</dd></dl></div><div class='xr-var-data'><table>\n",
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-       "        <td>\n",
-       "            <table style=\"border-collapse: collapse;\">\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
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-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 354.55 MiB </td>\n",
-       "                        <td> 50.00 MiB </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (1, 100, 589, 789) </td>\n",
-       "                        <td> (1, 100, 256, 256) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 12 chunks in 2 graph layers </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
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-       "               &#x27;1990-09-01 06:00:00&#x27;, &#x27;1990-09-01 09:00:00&#x27;,\n",
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-       "               &#x27;1990-09-04 00:00:00&#x27;, &#x27;1990-09-04 03:00:00&#x27;,\n",
-       "               &#x27;1990-09-04 06:00:00&#x27;, &#x27;1990-09-04 09:00:00&#x27;,\n",
-       "               &#x27;1990-09-04 12:00:00&#x27;, &#x27;1990-09-04 15:00:00&#x27;,\n",
-       "               &#x27;1990-09-04 18:00:00&#x27;, &#x27;1990-09-04 21:00:00&#x27;,\n",
-       "               &#x27;1990-09-05 00:00:00&#x27;, &#x27;1990-09-05 03:00:00&#x27;,\n",
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-       "               &#x27;1990-09-07 00:00:00&#x27;, &#x27;1990-09-07 03:00:00&#x27;,\n",
-       "               &#x27;1990-09-07 06:00:00&#x27;, &#x27;1990-09-07 09:00:00&#x27;,\n",
-       "               &#x27;1990-09-07 12:00:00&#x27;, &#x27;1990-09-07 15:00:00&#x27;,\n",
-       "               &#x27;1990-09-07 18:00:00&#x27;, &#x27;1990-09-07 21:00:00&#x27;,\n",
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-       "               &#x27;1990-09-08 18:00:00&#x27;, &#x27;1990-09-08 21:00:00&#x27;,\n",
-       "               &#x27;1990-09-09 00:00:00&#x27;, &#x27;1990-09-09 03:00:00&#x27;,\n",
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-       "               &#x27;1990-09-09 18:00:00&#x27;, &#x27;1990-09-09 21:00:00&#x27;,\n",
-       "               &#x27;1990-09-10 00:00:00&#x27;, &#x27;1990-09-10 03:00:00&#x27;,\n",
-       "               &#x27;1990-09-10 06:00:00&#x27;, &#x27;1990-09-10 09:00:00&#x27;,\n",
-       "               &#x27;1990-09-10 12:00:00&#x27;, &#x27;1990-09-10 15:00:00&#x27;,\n",
-       "               &#x27;1990-09-10 18:00:00&#x27;, &#x27;1990-09-10 21:00:00&#x27;,\n",
-       "               &#x27;1990-09-11 00:00:00&#x27;, &#x27;1990-09-11 03:00:00&#x27;,\n",
-       "               &#x27;1990-09-11 06:00:00&#x27;, &#x27;1990-09-11 09:00:00&#x27;,\n",
-       "               &#x27;1990-09-11 12:00:00&#x27;, &#x27;1990-09-11 15:00:00&#x27;,\n",
-       "               &#x27;1990-09-11 18:00:00&#x27;, &#x27;1990-09-11 21:00:00&#x27;,\n",
-       "               &#x27;1990-09-12 00:00:00&#x27;, &#x27;1990-09-12 03:00:00&#x27;,\n",
-       "               &#x27;1990-09-12 06:00:00&#x27;, &#x27;1990-09-12 09:00:00&#x27;,\n",
-       "               &#x27;1990-09-12 12:00:00&#x27;, &#x27;1990-09-12 15:00:00&#x27;,\n",
-       "               &#x27;1990-09-12 18:00:00&#x27;, &#x27;1990-09-12 21:00:00&#x27;,\n",
-       "               &#x27;1990-09-13 00:00:00&#x27;, &#x27;1990-09-13 03:00:00&#x27;,\n",
-       "               &#x27;1990-09-13 06:00:00&#x27;, &#x27;1990-09-13 09:00:00&#x27;],\n",
-       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>x</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-ca3ade16-ecca-4c51-a57b-e674418d705e' class='xr-index-data-in' type='checkbox'/><label for='index-ca3ade16-ecca-4c51-a57b-e674418d705e' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([-1999248.1932260487, -1996748.1932260487, -1994248.1932260487,\n",
-       "       -1991748.1932260487, -1989248.1932260487, -1986748.1932260487,\n",
-       "       -1984248.1932260487, -1981748.1932260487, -1979248.1932260487,\n",
-       "       -1976748.1932260487,\n",
-       "       ...\n",
-       "        -51748.19322604872,  -49248.19322604872,  -46748.19322604872,\n",
-       "        -44248.19322604872,  -41748.19322604872,  -39248.19322604872,\n",
-       "        -36748.19322604872,  -34248.19322604872, -31748.193226048723,\n",
-       "       -29248.193226048723],\n",
-       "      dtype=&#x27;float64&#x27;, name=&#x27;x&#x27;, length=789))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>y</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-3ddbec6c-f148-464e-b423-b64064a8444f' class='xr-index-data-in' type='checkbox'/><label for='index-3ddbec6c-f148-464e-b423-b64064a8444f' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([-609541.2091329489, -607041.2091329489, -604541.2091329489,\n",
-       "       -602041.2091329489, -599541.2091329489, -597041.2091329489,\n",
-       "       -594541.2091329489, -592041.2091329489, -589541.2091329489,\n",
-       "       -587041.2091329489,\n",
-       "       ...\n",
-       "        837958.7908670511,  840458.7908670511,  842958.7908670511,\n",
-       "        845458.7908670511,  847958.7908670511,  850458.7908670511,\n",
-       "        852958.7908670511,  855458.7908670511,  857958.7908670511,\n",
-       "        860458.7908670511],\n",
-       "      dtype=&#x27;float64&#x27;, name=&#x27;y&#x27;, length=589))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-3ab224de-ed1b-4450-ac27-4c4e744cfd61' class='xr-section-summary-in' type='checkbox'  checked><label for='section-3ab224de-ed1b-4450-ac27-4c4e744cfd61' class='xr-section-summary' >Attributes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>description :</span></dt><dd>All prognostic variables for 10-year period on reduced levels</dd></dl></div></li></ul></div></div>"
-      ],
-      "text/plain": [
-       "<xarray.Dataset> Size: 1GB\n",
-       "Dimensions:   (altitude: 1, y: 589, x: 789, time: 100)\n",
-       "Coordinates:\n",
-       "  * altitude  (altitude) int64 8B 100\n",
-       "    lat       (y, x) float64 4MB dask.array<chunksize=(256, 256), meta=np.ndarray>\n",
-       "    lon       (y, x) float64 4MB dask.array<chunksize=(256, 256), meta=np.ndarray>\n",
-       "  * time      (time) datetime64[ns] 800B 1990-09-01 ... 1990-09-13T09:00:00\n",
-       "  * x         (x) float64 6kB -1.999e+06 -1.997e+06 ... -3.175e+04 -2.925e+04\n",
-       "  * y         (y) float64 5kB -6.095e+05 -6.07e+05 ... 8.58e+05 8.605e+05\n",
-       "Data variables:\n",
-       "    r         (altitude, time, y, x) float64 372MB dask.array<chunksize=(1, 100, 256, 256), meta=np.ndarray>\n",
-       "    t         (altitude, time, y, x) float64 372MB dask.array<chunksize=(1, 100, 256, 256), meta=np.ndarray>\n",
-       "    u         (altitude, time, y, x) float64 372MB dask.array<chunksize=(1, 100, 256, 256), meta=np.ndarray>\n",
-       "    v         (altitude, time, y, x) float64 372MB dask.array<chunksize=(1, 100, 256, 256), meta=np.ndarray>\n",
-       "Attributes:\n",
-       "    description:  All prognostic variables for 10-year period on reduced levels"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "ds_lam = xr.open_zarr(url_lam)\n",
-    "ds_lam"
+    "ds_lam = mdp.create_dataset(config=config_lam)\n",
+    "ds_global = mdp.create_dataset(config=config_global)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/B280936/git-repos/mllam/mllam-data-prep/.venv/lib/python3.12/site-packages/xarray/core/dataset.py:274: UserWarning: The specified chunks separate the stored chunks along dimension \"time\" starting at index 48. This could degrade performance. Instead, consider rechunking after loading.\n",
-      "  warnings.warn(\n"
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-       ".xr-text-repr-fallback {\n",
-       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       ".xr-header {\n",
-       "  padding-top: 6px;\n",
-       "  padding-bottom: 6px;\n",
-       "  margin-bottom: 4px;\n",
-       "  border-bottom: solid 1px var(--xr-border-color);\n",
-       "}\n",
-       "\n",
-       ".xr-header > div,\n",
-       ".xr-header > ul {\n",
-       "  display: inline;\n",
-       "  margin-top: 0;\n",
-       "  margin-bottom: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-obj-type,\n",
-       ".xr-array-name {\n",
-       "  margin-left: 2px;\n",
-       "  margin-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-obj-type {\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-sections {\n",
-       "  padding-left: 0 !important;\n",
-       "  display: grid;\n",
-       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
-       "}\n",
-       "\n",
-       ".xr-section-item {\n",
-       "  display: contents;\n",
-       "}\n",
-       "\n",
-       ".xr-section-item input {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       ".xr-section-item input + label {\n",
-       "  color: var(--xr-disabled-color);\n",
-       "}\n",
-       "\n",
-       ".xr-section-item input:enabled + label {\n",
-       "  cursor: pointer;\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-section-item input:enabled + label:hover {\n",
-       "  color: var(--xr-font-color0);\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary {\n",
-       "  grid-column: 1;\n",
-       "  color: var(--xr-font-color2);\n",
-       "  font-weight: 500;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary > span {\n",
-       "  display: inline-block;\n",
-       "  padding-left: 0.5em;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:disabled + label {\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in + label:before {\n",
-       "  display: inline-block;\n",
-       "  content: '►';\n",
-       "  font-size: 11px;\n",
-       "  width: 15px;\n",
-       "  text-align: center;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:disabled + label:before {\n",
-       "  color: var(--xr-disabled-color);\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:checked + label:before {\n",
-       "  content: '▼';\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:checked + label > span {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary,\n",
-       ".xr-section-inline-details {\n",
-       "  padding-top: 4px;\n",
-       "  padding-bottom: 4px;\n",
-       "}\n",
-       "\n",
-       ".xr-section-inline-details {\n",
-       "  grid-column: 2 / -1;\n",
-       "}\n",
-       "\n",
-       ".xr-section-details {\n",
-       "  display: none;\n",
-       "  grid-column: 1 / -1;\n",
-       "  margin-bottom: 5px;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
-       "  display: contents;\n",
-       "}\n",
-       "\n",
-       ".xr-array-wrap {\n",
-       "  grid-column: 1 / -1;\n",
-       "  display: grid;\n",
-       "  grid-template-columns: 20px auto;\n",
-       "}\n",
-       "\n",
-       ".xr-array-wrap > label {\n",
-       "  grid-column: 1;\n",
-       "  vertical-align: top;\n",
-       "}\n",
-       "\n",
-       ".xr-preview {\n",
-       "  color: var(--xr-font-color3);\n",
-       "}\n",
-       "\n",
-       ".xr-array-preview,\n",
-       ".xr-array-data {\n",
-       "  padding: 0 5px !important;\n",
-       "  grid-column: 2;\n",
-       "}\n",
-       "\n",
-       ".xr-array-data,\n",
-       ".xr-array-in:checked ~ .xr-array-preview {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       ".xr-array-in:checked ~ .xr-array-data,\n",
-       ".xr-array-preview {\n",
-       "  display: inline-block;\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list {\n",
-       "  display: inline-block !important;\n",
-       "  list-style: none;\n",
-       "  padding: 0 !important;\n",
-       "  margin: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list li {\n",
-       "  display: inline-block;\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list:before {\n",
-       "  content: '(';\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list:after {\n",
-       "  content: ')';\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list li:not(:last-child):after {\n",
-       "  content: ',';\n",
-       "  padding-right: 5px;\n",
-       "}\n",
-       "\n",
-       ".xr-has-index {\n",
-       "  font-weight: bold;\n",
-       "}\n",
-       "\n",
-       ".xr-var-list,\n",
-       ".xr-var-item {\n",
-       "  display: contents;\n",
-       "}\n",
-       "\n",
-       ".xr-var-item > div,\n",
-       ".xr-var-item label,\n",
-       ".xr-var-item > .xr-var-name span {\n",
-       "  background-color: var(--xr-background-color-row-even);\n",
-       "  margin-bottom: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-var-item > .xr-var-name:hover span {\n",
-       "  padding-right: 5px;\n",
-       "}\n",
-       "\n",
-       ".xr-var-list > li:nth-child(odd) > div,\n",
-       ".xr-var-list > li:nth-child(odd) > label,\n",
-       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
-       "  background-color: var(--xr-background-color-row-odd);\n",
-       "}\n",
-       "\n",
-       ".xr-var-name {\n",
-       "  grid-column: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-var-dims {\n",
-       "  grid-column: 2;\n",
-       "}\n",
-       "\n",
-       ".xr-var-dtype {\n",
-       "  grid-column: 3;\n",
-       "  text-align: right;\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-var-preview {\n",
-       "  grid-column: 4;\n",
-       "}\n",
-       "\n",
-       ".xr-index-preview {\n",
-       "  grid-column: 2 / 5;\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-var-name,\n",
-       ".xr-var-dims,\n",
-       ".xr-var-dtype,\n",
-       ".xr-preview,\n",
-       ".xr-attrs dt {\n",
-       "  white-space: nowrap;\n",
-       "  overflow: hidden;\n",
-       "  text-overflow: ellipsis;\n",
-       "  padding-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-var-name:hover,\n",
-       ".xr-var-dims:hover,\n",
-       ".xr-var-dtype:hover,\n",
-       ".xr-attrs dt:hover {\n",
-       "  overflow: visible;\n",
-       "  width: auto;\n",
-       "  z-index: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-var-attrs,\n",
-       ".xr-var-data,\n",
-       ".xr-index-data {\n",
-       "  display: none;\n",
-       "  background-color: var(--xr-background-color) !important;\n",
-       "  padding-bottom: 5px !important;\n",
-       "}\n",
-       "\n",
-       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
-       ".xr-var-data-in:checked ~ .xr-var-data,\n",
-       ".xr-index-data-in:checked ~ .xr-index-data {\n",
-       "  display: block;\n",
-       "}\n",
-       "\n",
-       ".xr-var-data > table {\n",
-       "  float: right;\n",
-       "}\n",
-       "\n",
-       ".xr-var-name span,\n",
-       ".xr-var-data,\n",
-       ".xr-index-name div,\n",
-       ".xr-index-data,\n",
-       ".xr-attrs {\n",
-       "  padding-left: 25px !important;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs,\n",
-       ".xr-var-attrs,\n",
-       ".xr-var-data,\n",
-       ".xr-index-data {\n",
-       "  grid-column: 1 / -1;\n",
-       "}\n",
-       "\n",
-       "dl.xr-attrs {\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "  display: grid;\n",
-       "  grid-template-columns: 125px auto;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt,\n",
-       ".xr-attrs dd {\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "  float: left;\n",
-       "  padding-right: 10px;\n",
-       "  width: auto;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt {\n",
-       "  font-weight: normal;\n",
-       "  grid-column: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt:hover span {\n",
-       "  display: inline-block;\n",
-       "  background: var(--xr-background-color);\n",
-       "  padding-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dd {\n",
-       "  grid-column: 2;\n",
-       "  white-space: pre-wrap;\n",
-       "  word-break: break-all;\n",
-       "}\n",
-       "\n",
-       ".xr-icon-database,\n",
-       ".xr-icon-file-text2,\n",
-       ".xr-no-icon {\n",
-       "  display: inline-block;\n",
-       "  vertical-align: middle;\n",
-       "  width: 1em;\n",
-       "  height: 1.5em !important;\n",
-       "  stroke-width: 0;\n",
-       "  stroke: currentColor;\n",
-       "  fill: currentColor;\n",
-       "}\n",
-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 175GB\n",
-       "Dimensions:                                           (time: 93544,\n",
-       "                                                       longitude: 64,\n",
-       "                                                       latitude: 32, level: 13)\n",
-       "Coordinates:\n",
-       "  * latitude                                          (latitude) float64 256B ...\n",
-       "  * level                                             (level) int64 104B 50 ....\n",
-       "  * longitude                                         (longitude) float64 512B ...\n",
-       "  * time                                              (time) datetime64[ns] 748kB ...\n",
-       "Data variables: (12/62)\n",
-       "    10m_u_component_of_wind                           (time, longitude, latitude) float32 766MB dask.array&lt;chunksize=(48, 64, 32), meta=np.ndarray&gt;\n",
-       "    10m_v_component_of_wind                           (time, longitude, latitude) float32 766MB dask.array&lt;chunksize=(48, 64, 32), meta=np.ndarray&gt;\n",
-       "    10m_wind_speed                                    (time, longitude, latitude) float32 766MB dask.array&lt;chunksize=(48, 64, 32), meta=np.ndarray&gt;\n",
-       "    2m_dewpoint_temperature                           (time, longitude, latitude) float32 766MB dask.array&lt;chunksize=(48, 64, 32), meta=np.ndarray&gt;\n",
-       "    2m_temperature                                    (time, longitude, latitude) float32 766MB dask.array&lt;chunksize=(48, 64, 32), meta=np.ndarray&gt;\n",
-       "    above_ground                                      (time, level, longitude, latitude) float32 10GB dask.array&lt;chunksize=(48, 13, 64, 32), meta=np.ndarray&gt;\n",
-       "    ...                                                ...\n",
-       "    volumetric_soil_water_layer_1                     (time, longitude, latitude) float32 766MB dask.array&lt;chunksize=(48, 64, 32), meta=np.ndarray&gt;\n",
-       "    volumetric_soil_water_layer_2                     (time, longitude, latitude) float32 766MB dask.array&lt;chunksize=(48, 64, 32), meta=np.ndarray&gt;\n",
-       "    volumetric_soil_water_layer_3                     (time, longitude, latitude) float32 766MB dask.array&lt;chunksize=(48, 64, 32), meta=np.ndarray&gt;\n",
-       "    volumetric_soil_water_layer_4                     (time, longitude, latitude) float32 766MB dask.array&lt;chunksize=(48, 64, 32), meta=np.ndarray&gt;\n",
-       "    vorticity                                         (time, level, longitude, latitude) float32 10GB dask.array&lt;chunksize=(48, 13, 64, 32), meta=np.ndarray&gt;\n",
-       "    wind_speed                                        (time, level, longitude, latitude) float32 10GB dask.array&lt;chunksize=(48, 13, 64, 32), meta=np.ndarray&gt;</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-af5ca88f-347c-40e3-b01d-6a7688ec9d21' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-af5ca88f-347c-40e3-b01d-6a7688ec9d21' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 93544</li><li><span class='xr-has-index'>longitude</span>: 64</li><li><span class='xr-has-index'>latitude</span>: 32</li><li><span class='xr-has-index'>level</span>: 13</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-8c5fa463-0a2b-4503-9922-54d5003d98fc' class='xr-section-summary-in' type='checkbox'  checked><label for='section-8c5fa463-0a2b-4503-9922-54d5003d98fc' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>latitude</span></div><div class='xr-var-dims'>(latitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-87.19 -81.56 ... 81.56 87.19</div><input id='attrs-bfbf8daa-7071-46be-ae33-5205f82c8da7' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-bfbf8daa-7071-46be-ae33-5205f82c8da7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a2194b3e-ce3d-4e8d-990a-fd7f567669fc' class='xr-var-data-in' type='checkbox'><label for='data-a2194b3e-ce3d-4e8d-990a-fd7f567669fc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([-87.1875, -81.5625, -75.9375, -70.3125, -64.6875, -59.0625, -53.4375,\n",
-       "       -47.8125, -42.1875, -36.5625, -30.9375, -25.3125, -19.6875, -14.0625,\n",
-       "        -8.4375,  -2.8125,   2.8125,   8.4375,  14.0625,  19.6875,  25.3125,\n",
-       "        30.9375,  36.5625,  42.1875,  47.8125,  53.4375,  59.0625,  64.6875,\n",
-       "        70.3125,  75.9375,  81.5625,  87.1875])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>level</span></div><div class='xr-var-dims'>(level)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>50 100 150 200 ... 700 850 925 1000</div><input id='attrs-76cc0fbf-631e-45b5-8b40-e5ce5c19e53e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-76cc0fbf-631e-45b5-8b40-e5ce5c19e53e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-eb82b27a-5c5a-491c-b36f-a18d2d87b752' class='xr-var-data-in' type='checkbox'><label for='data-eb82b27a-5c5a-491c-b36f-a18d2d87b752' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  50,  100,  150,  200,  250,  300,  400,  500,  600,  700,  850,  925,\n",
-       "       1000])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>longitude</span></div><div class='xr-var-dims'>(longitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 5.625 11.25 ... 348.8 354.4</div><input id='attrs-2224ad9d-4634-4eeb-b4ed-30ce6a0a614a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2224ad9d-4634-4eeb-b4ed-30ce6a0a614a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-78e25346-ec58-46cb-96c0-f1043730114b' class='xr-var-data-in' type='checkbox'><label for='data-78e25346-ec58-46cb-96c0-f1043730114b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  0.   ,   5.625,  11.25 ,  16.875,  22.5  ,  28.125,  33.75 ,  39.375,\n",
-       "        45.   ,  50.625,  56.25 ,  61.875,  67.5  ,  73.125,  78.75 ,  84.375,\n",
-       "        90.   ,  95.625, 101.25 , 106.875, 112.5  , 118.125, 123.75 , 129.375,\n",
-       "       135.   , 140.625, 146.25 , 151.875, 157.5  , 163.125, 168.75 , 174.375,\n",
-       "       180.   , 185.625, 191.25 , 196.875, 202.5  , 208.125, 213.75 , 219.375,\n",
-       "       225.   , 230.625, 236.25 , 241.875, 247.5  , 253.125, 258.75 , 264.375,\n",
-       "       270.   , 275.625, 281.25 , 286.875, 292.5  , 298.125, 303.75 , 309.375,\n",
-       "       315.   , 320.625, 326.25 , 331.875, 337.5  , 343.125, 348.75 , 354.375])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1959-01-01 ... 2023-01-10T18:00:00</div><input id='attrs-85b442db-2df3-466b-b800-8ea724843565' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-85b442db-2df3-466b-b800-8ea724843565' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e9666272-6009-4e90-bf05-c7d31a5351c6' class='xr-var-data-in' type='checkbox'><label for='data-e9666272-6009-4e90-bf05-c7d31a5351c6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;1959-01-01T00:00:00.000000000&#x27;, &#x27;1959-01-01T06:00:00.000000000&#x27;,\n",
-       "       &#x27;1959-01-01T12:00:00.000000000&#x27;, ..., &#x27;2023-01-10T06:00:00.000000000&#x27;,\n",
-       "       &#x27;2023-01-10T12:00:00.000000000&#x27;, &#x27;2023-01-10T18:00:00.000000000&#x27;],\n",
-       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-00c57a2e-0612-4f56-8352-b46ceb3becfa' class='xr-section-summary-in' type='checkbox'  ><label for='section-00c57a2e-0612-4f56-8352-b46ceb3becfa' class='xr-section-summary' >Data variables: <span>(62)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>10m_u_component_of_wind</span></div><div class='xr-var-dims'>(time, longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(48, 64, 32), meta=np.ndarray&gt;</div><input id='attrs-3da1798e-163d-4ee1-a7f7-cce96f26c16f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3da1798e-163d-4ee1-a7f7-cce96f26c16f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bc2b5e06-7713-4e4a-8690-b5140f4d40c1' class='xr-var-data-in' type='checkbox'><label for='data-bc2b5e06-7713-4e4a-8690-b5140f4d40c1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>10 metre U wind component</dd><dt><span>short_name :</span></dt><dd>u10</dd><dt><span>units :</span></dt><dd>m s**-1</dd></dl></div><div class='xr-var-data'><table>\n",
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-       "                <tbody>\n",
-       "                    \n",
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-       "                        <th> Bytes </th>\n",
-       "                        <td> 9.28 GiB </td>\n",
-       "                        <td> 4.88 MiB </td>\n",
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-       "                        <th> Shape </th>\n",
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-       "                        <td> (48, 13, 64, 32) </td>\n",
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-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
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-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 9.28 GiB </td>\n",
-       "                        <td> 4.88 MiB </td>\n",
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-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
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-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
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-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>angle_of_sub_gridscale_orography</span></div><div class='xr-var-dims'>(longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(64, 32), meta=np.ndarray&gt;</div><input id='attrs-bd0ce40a-aa2a-4d07-8ef2-205ae291999c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bd0ce40a-aa2a-4d07-8ef2-205ae291999c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bfed1924-df0a-4160-868c-0b9f58685a6b' class='xr-var-data-in' type='checkbox'><label for='data-bfed1924-df0a-4160-868c-0b9f58685a6b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Angle of sub-gridscale orography</dd><dt><span>short_name :</span></dt><dd>anor</dd><dt><span>units :</span></dt><dd>radians</dd></dl></div><div class='xr-var-data'><table>\n",
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-       "                        <th> Chunk </th>\n",
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-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 8.00 kiB </td>\n",
-       "                        <td> 8.00 kiB </td>\n",
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-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (64, 32) </td>\n",
-       "                        <td> (64, 32) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
-       "        <td>\n",
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-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>anisotropy_of_sub_gridscale_orography</span></div><div class='xr-var-dims'>(longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(64, 32), meta=np.ndarray&gt;</div><input id='attrs-e28512b5-8df2-4338-b97f-4726624657b2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e28512b5-8df2-4338-b97f-4726624657b2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b5fe0375-0aca-4ddc-bf4d-08d2c9080fbe' class='xr-var-data-in' type='checkbox'><label for='data-b5fe0375-0aca-4ddc-bf4d-08d2c9080fbe' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Anisotropy of sub-gridscale orography</dd><dt><span>short_name :</span></dt><dd>isor</dd><dt><span>units :</span></dt><dd>~</dd></dl></div><div class='xr-var-data'><table>\n",
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-       "        <td>\n",
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-       "                        <th> Chunk </th>\n",
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-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 8.00 kiB </td>\n",
-       "                        <td> 8.00 kiB </td>\n",
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-       "                    \n",
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-       "                        <th> Shape </th>\n",
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-       "                        <td> (64, 32) </td>\n",
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-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
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-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 730.81 MiB </td>\n",
-       "                        <td> 384.00 kiB </td>\n",
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-       "                        <th> Shape </th>\n",
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-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 1949 chunks in 2 graph layers </td>\n",
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-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
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-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>divergence</span></div><div class='xr-var-dims'>(time, level, longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(48, 13, 64, 32), meta=np.ndarray&gt;</div><input id='attrs-433ab6ec-a276-478a-8b8e-7b633f1a3b5d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-433ab6ec-a276-478a-8b8e-7b633f1a3b5d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6c998a0e-380c-4dd9-8fbb-642d70df59b7' class='xr-var-data-in' type='checkbox'><label for='data-6c998a0e-380c-4dd9-8fbb-642d70df59b7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
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-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 9.28 GiB </td>\n",
-       "                        <td> 4.88 MiB </td>\n",
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-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
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-       "    <tr>\n",
-       "        <td>\n",
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-       "                <tbody>\n",
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-       "                    <tr>\n",
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-       "                        <th> Shape </th>\n",
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-       "                        <td> (64, 32) </td>\n",
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-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
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-       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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-       "                <tbody>\n",
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-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
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-       "                        <th> Dask graph </th>\n",
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-       "                        <td> 730.81 MiB </td>\n",
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-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>potential_vorticity</span></div><div class='xr-var-dims'>(time, level, longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(48, 13, 64, 32), meta=np.ndarray&gt;</div><input id='attrs-a3829cd5-f630-4365-9e36-4603349e61ac' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a3829cd5-f630-4365-9e36-4603349e61ac' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cd66a78a-4150-467c-8990-5b405984d2ee' class='xr-var-data-in' type='checkbox'><label for='data-cd66a78a-4150-467c-8990-5b405984d2ee' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Potential vorticity</dd><dt><span>short_name :</span></dt><dd>pv</dd><dt><span>units :</span></dt><dd>K m**2 kg**-1 s**-1</dd></dl></div><div class='xr-var-data'><table>\n",
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-       "                        <td> 9.28 GiB </td>\n",
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-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sea_ice_cover</span></div><div class='xr-var-dims'>(time, longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(48, 64, 32), meta=np.ndarray&gt;</div><input id='attrs-4d4c7fdb-f1e5-47f9-91f0-914a638fefb8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4d4c7fdb-f1e5-47f9-91f0-914a638fefb8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5545c098-5848-407c-a627-2ffc291cde46' class='xr-var-data-in' type='checkbox'><label for='data-5545c098-5848-407c-a627-2ffc291cde46' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Sea ice area fraction</dd><dt><span>short_name :</span></dt><dd>siconc</dd><dt><span>standard_name :</span></dt><dd>sea_ice_area_fraction</dd><dt><span>units :</span></dt><dd>(0 - 1)</dd></dl></div><div class='xr-var-data'><table>\n",
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-       "                        <th> Dask graph </th>\n",
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-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>standard_deviation_of_orography</span></div><div class='xr-var-dims'>(longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(64, 32), meta=np.ndarray&gt;</div><input id='attrs-5ad1831a-e324-4c4b-b05b-783f2a6a6c1d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5ad1831a-e324-4c4b-b05b-783f2a6a6c1d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a53767dc-0556-418c-8533-74ee503c1303' class='xr-var-data-in' type='checkbox'><label for='data-a53767dc-0556-418c-8533-74ee503c1303' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Standard deviation of orography</dd><dt><span>short_name :</span></dt><dd>sdor</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><table>\n",
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-       "                <tbody>\n",
-       "                    \n",
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-       "                        <th> Bytes </th>\n",
-       "                        <td> 730.81 MiB </td>\n",
-       "                        <td> 384.00 kiB </td>\n",
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-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 730.81 MiB </td>\n",
-       "                        <td> 384.00 kiB </td>\n",
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-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 730.81 MiB </td>\n",
-       "                        <td> 384.00 kiB </td>\n",
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-       "                        <th> Shape </th>\n",
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-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 1949 chunks in 2 graph layers </td>\n",
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-       "                        <th> Data type </th>\n",
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-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>total_precipitation_24hr</span></div><div class='xr-var-dims'>(time, longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(48, 64, 32), meta=np.ndarray&gt;</div><input id='attrs-b09e55f7-b5b4-4a6b-b381-81544588a61f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b09e55f7-b5b4-4a6b-b381-81544588a61f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-921fbdcf-cec1-4bf4-91bd-b1f9018b7127' class='xr-var-data-in' type='checkbox'><label for='data-921fbdcf-cec1-4bf4-91bd-b1f9018b7127' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Total precipitation</dd><dt><span>short_name :</span></dt><dd>tp</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
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-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 730.81 MiB </td>\n",
-       "                        <td> 384.00 kiB </td>\n",
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-       "                        <th> Shape </th>\n",
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-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
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-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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-       "                        <th> Bytes </th>\n",
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-       "                        <td> (64, 32) </td>\n",
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-       "                        <th> Dask graph </th>\n",
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-       "                <tbody>\n",
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-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 8.00 kiB </td>\n",
-       "                        <td> 8.00 kiB </td>\n",
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-       "                        <th> Shape </th>\n",
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-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
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-       "    <tr>\n",
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-       "                        <th> Bytes </th>\n",
-       "                        <td> 9.28 GiB </td>\n",
-       "                        <td> 4.88 MiB </td>\n",
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-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
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-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 9.28 GiB </td>\n",
-       "                        <td> 4.88 MiB </td>\n",
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-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
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-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 1949 chunks in 2 graph layers </td>\n",
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-       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vorticity</span></div><div class='xr-var-dims'>(time, level, longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(48, 13, 64, 32), meta=np.ndarray&gt;</div><input id='attrs-72684cff-cb1f-42d4-9df9-d120ed4a39ff' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-72684cff-cb1f-42d4-9df9-d120ed4a39ff' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dd5d4f6b-b445-4d50-99e9-d8ae0e23734a' class='xr-var-data-in' type='checkbox'><label for='data-dd5d4f6b-b445-4d50-99e9-d8ae0e23734a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table style=\"border-collapse: collapse;\">\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 9.28 GiB </td>\n",
-       "                        <td> 4.88 MiB </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (93544, 13, 64, 32) </td>\n",
-       "                        <td> (48, 13, 64, 32) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 1949 chunks in 2 graph layers </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>wind_speed</span></div><div class='xr-var-dims'>(time, level, longitude, latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(48, 13, 64, 32), meta=np.ndarray&gt;</div><input id='attrs-dd920e16-41e0-45c5-90b9-4c872c175f1f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-dd920e16-41e0-45c5-90b9-4c872c175f1f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-63fb6394-34ff-459c-b4c7-edd713c32eb1' class='xr-var-data-in' type='checkbox'><label for='data-63fb6394-34ff-459c-b4c7-edd713c32eb1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
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-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 9.28 GiB </td>\n",
-       "                        <td> 4.88 MiB </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (93544, 13, 64, 32) </td>\n",
-       "                        <td> (48, 13, 64, 32) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 1949 chunks in 2 graph layers </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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-       "  <text x=\"187.474299\" y=\"52.886915\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,187.474299,52.886915)\">13</text>\n",
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-       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-d61acea1-2686-44d5-bbaa-d169c4bc594d' class='xr-section-summary-in' type='checkbox'  ><label for='section-d61acea1-2686-44d5-bbaa-d169c4bc594d' class='xr-section-summary' >Indexes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>latitude</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-f02961dd-c718-4eb2-89a5-461e69102fac' class='xr-index-data-in' type='checkbox'/><label for='index-f02961dd-c718-4eb2-89a5-461e69102fac' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ -87.18750000000003,  -81.56250000000001,            -75.9375,\n",
-       "        -70.31249999999999,  -64.68750000000001,            -59.0625,\n",
-       "                  -53.4375,            -47.8125,            -42.1875,\n",
-       "                  -36.5625, -30.937499999999996, -25.312500000000004,\n",
-       "       -19.687499999999996, -14.062499999999991,  -8.437499999999996,\n",
-       "        -2.812500000000003,   2.812500000000003,   8.437500000000009,\n",
-       "        14.062500000000004,  19.687499999999996,  25.312500000000004,\n",
-       "         30.93750000000001,  36.562499999999986,             42.1875,\n",
-       "                   47.8125,             53.4375,  59.062500000000014,\n",
-       "         64.68750000000001,             70.3125,             75.9375,\n",
-       "         81.56249999999997,   87.18750000000003],\n",
-       "      dtype=&#x27;float64&#x27;, name=&#x27;latitude&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>level</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-cdf5dd03-6bf8-484e-9bd3-2bf313cd6e4e' class='xr-index-data-in' type='checkbox'/><label for='index-cdf5dd03-6bf8-484e-9bd3-2bf313cd6e4e' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([50, 100, 150, 200, 250, 300, 400, 500, 600, 700, 850, 925, 1000], dtype=&#x27;int64&#x27;, name=&#x27;level&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>longitude</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-bbd87c8d-14e9-4059-9066-74920a9e34a0' class='xr-index-data-in' type='checkbox'/><label for='index-bbd87c8d-14e9-4059-9066-74920a9e34a0' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([               0.0,              5.625,              11.25,\n",
-       "                   16.875,               22.5,             28.125,\n",
-       "                    33.75,             39.375,               45.0,\n",
-       "                   50.625,              56.25,  61.87499999999999,\n",
-       "                     67.5,             73.125,              78.75,\n",
-       "                   84.375,               90.0,             95.625,\n",
-       "                   101.25,            106.875,              112.5,\n",
-       "                  118.125, 123.74999999999999,            129.375,\n",
-       "                    135.0,            140.625,             146.25,\n",
-       "                  151.875,              157.5,            163.125,\n",
-       "                   168.75,            174.375,              180.0,\n",
-       "                  185.625,             191.25,            196.875,\n",
-       "                    202.5,            208.125,             213.75,\n",
-       "                  219.375,              225.0, 230.62499999999997,\n",
-       "                   236.25,            241.875, 247.49999999999997,\n",
-       "                  253.125,             258.75,            264.375,\n",
-       "                    270.0,            275.625,             281.25,\n",
-       "                  286.875,              292.5,            298.125,\n",
-       "                   303.75,            309.375,              315.0,\n",
-       "                  320.625,             326.25,            331.875,\n",
-       "                    337.5,            343.125,             348.75,\n",
-       "                  354.375],\n",
-       "      dtype=&#x27;float64&#x27;, name=&#x27;longitude&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-ff079fe4-a667-4ecd-888f-4915c75dba54' class='xr-index-data-in' type='checkbox'/><label for='index-ff079fe4-a667-4ecd-888f-4915c75dba54' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;1959-01-01 00:00:00&#x27;, &#x27;1959-01-01 06:00:00&#x27;,\n",
-       "               &#x27;1959-01-01 12:00:00&#x27;, &#x27;1959-01-01 18:00:00&#x27;,\n",
-       "               &#x27;1959-01-02 00:00:00&#x27;, &#x27;1959-01-02 06:00:00&#x27;,\n",
-       "               &#x27;1959-01-02 12:00:00&#x27;, &#x27;1959-01-02 18:00:00&#x27;,\n",
-       "               &#x27;1959-01-03 00:00:00&#x27;, &#x27;1959-01-03 06:00:00&#x27;,\n",
-       "               ...\n",
-       "               &#x27;2023-01-08 12:00:00&#x27;, &#x27;2023-01-08 18:00:00&#x27;,\n",
-       "               &#x27;2023-01-09 00:00:00&#x27;, &#x27;2023-01-09 06:00:00&#x27;,\n",
-       "               &#x27;2023-01-09 12:00:00&#x27;, &#x27;2023-01-09 18:00:00&#x27;,\n",
-       "               &#x27;2023-01-10 00:00:00&#x27;, &#x27;2023-01-10 06:00:00&#x27;,\n",
-       "               &#x27;2023-01-10 12:00:00&#x27;, &#x27;2023-01-10 18:00:00&#x27;],\n",
-       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=93544, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-d95e6bd8-cfb1-4fa5-93fa-88aac2e0ea50' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-d95e6bd8-cfb1-4fa5-93fa-88aac2e0ea50' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
-      ],
-      "text/plain": [
-       "<xarray.Dataset> Size: 175GB\n",
-       "Dimensions:                                           (time: 93544,\n",
-       "                                                       longitude: 64,\n",
-       "                                                       latitude: 32, level: 13)\n",
-       "Coordinates:\n",
-       "  * latitude                                          (latitude) float64 256B ...\n",
-       "  * level                                             (level) int64 104B 50 ....\n",
-       "  * longitude                                         (longitude) float64 512B ...\n",
-       "  * time                                              (time) datetime64[ns] 748kB ...\n",
-       "Data variables: (12/62)\n",
-       "    10m_u_component_of_wind                           (time, longitude, latitude) float32 766MB dask.array<chunksize=(48, 64, 32), meta=np.ndarray>\n",
-       "    10m_v_component_of_wind                           (time, longitude, latitude) float32 766MB dask.array<chunksize=(48, 64, 32), meta=np.ndarray>\n",
-       "    10m_wind_speed                                    (time, longitude, latitude) float32 766MB dask.array<chunksize=(48, 64, 32), meta=np.ndarray>\n",
-       "    2m_dewpoint_temperature                           (time, longitude, latitude) float32 766MB dask.array<chunksize=(48, 64, 32), meta=np.ndarray>\n",
-       "    2m_temperature                                    (time, longitude, latitude) float32 766MB dask.array<chunksize=(48, 64, 32), meta=np.ndarray>\n",
-       "    above_ground                                      (time, level, longitude, latitude) float32 10GB dask.array<chunksize=(48, 13, 64, 32), meta=np.ndarray>\n",
-       "    ...                                                ...\n",
-       "    volumetric_soil_water_layer_1                     (time, longitude, latitude) float32 766MB dask.array<chunksize=(48, 64, 32), meta=np.ndarray>\n",
-       "    volumetric_soil_water_layer_2                     (time, longitude, latitude) float32 766MB dask.array<chunksize=(48, 64, 32), meta=np.ndarray>\n",
-       "    volumetric_soil_water_layer_3                     (time, longitude, latitude) float32 766MB dask.array<chunksize=(48, 64, 32), meta=np.ndarray>\n",
-       "    volumetric_soil_water_layer_4                     (time, longitude, latitude) float32 766MB dask.array<chunksize=(48, 64, 32), meta=np.ndarray>\n",
-       "    vorticity                                         (time, level, longitude, latitude) float32 10GB dask.array<chunksize=(48, 13, 64, 32), meta=np.ndarray>\n",
-       "    wind_speed                                        (time, level, longitude, latitude) float32 10GB dask.array<chunksize=(48, 13, 64, 32), meta=np.ndarray>"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "ds_boundary = xr.open_zarr(url_boundary, consolidated=True, chunks={\"time\": 48})\n",
-    "ds_boundary"
+    "ds_lam"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
-    "def get_latlon_arrays(ds, lon='longitude', lat='latitude'):\n",
-    "    da_lon = ds[lon]\n",
-    "    da_lat = ds[lat]\n",
-    "\n",
-    "    # check if lat and lon dataarrays share dimensions, otherwise we need to\n",
-    "    # broadcast\n",
-    "    if da_lon.dims != da_lat.dims:\n",
-    "        lons, lats = np.meshgrid(da_lon.values, da_lat.values)\n",
-    "        lons = lons.flatten()\n",
-    "        lats = lats.flatten()\n",
-    "    else:\n",
-    "        lons = da_lon.values.flatten()\n",
-    "        lats = da_lat.values.flatten()\n",
-    "        \n",
-    "    return lons, lats\n"
+    "ds_global"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 12,
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "((2048, 2), (4661, 2))"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
    "source": [
-    "k = 10\n",
-    "ds_lam_subsampled = ds_lam.isel(x=slice(None, None, k), y=slice(None, None, k))\n",
-    "points_bnd = np.array(get_latlon_arrays(ds_boundary)).T\n",
-    "points_lam = np.array(get_latlon_arrays(ds_lam_subsampled, lon='lon', lat='lat')).T\n",
-    "\n",
-    "points_bnd.shape, points_lam.shape"
+    "Let's first make a mask that indicates which of the coarser domain grid-points are within the convex hull of the high-resolution grid points coordinates"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.collections.PathCollection at 0x29f73da90>"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "fig, ax = plt.subplots(subplot_kw={'projection': ccrs.PlateCarree()})\n",
-    "ax.coastlines()\n",
-    "ax.scatter(points_bnd[:, 0], points_bnd[:, 1], color='red', s=1)\n",
-    "ax.scatter(points_lam[:, 0], points_lam[:, 1], color='blue', s=1)"
+    "da_interior_mask = cropping.create_convex_hull_mask(ds=ds_global, ds_reference=ds_lam)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# latlon to (x, y, z) on unit sphere\n",
-    "pts_bnd_x = np.array(sg.vector.lonlat_to_vector(points_bnd[:,0], points_bnd[:,1])).T\n",
-    "pts_lam_x = np.array(sg.vector.lonlat_to_vector(points_lam[:,0], points_lam[:,1])).T\n",
-    "\n",
-    "chull_lam = SphericalPolygon.convex_hull(pts_lam_x)\n",
-    "\n",
-    "# Finding the boundary points inside the convec hull is what takes time. Possibly speed up by restricting to a subset of points_bnd that are within some distance from area center or similar?\n",
-    "msk_in = np.array([chull_lam.contains_lonlat(x[0], x[1]) for x in points_bnd])\n",
-    "\n",
-    "# Distances from all boundary points outside the region to the closest point inside\n",
-    "d = np.array([np.min(np.arccos(np.dot(pts_lam_x, x))) for x in pts_bnd_x[~msk_in]])\n",
-    "\n",
-    "# Define boundary distance and mask boundary points outside that are closer\n",
-    "# This is now in radians but should be defined as meters based on Earth radius\n",
-    "ii = d < 0.2"
+    "da_interior_mask = da_interior_mask.where(da_interior_mask, drop=True)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
-    "ds_bound_stacked = ds_boundary.stack(points=['latitude', 'longitude'])\n",
-    "ds_bound_stacked['msk'] = xr.DataArray(np.zeros(ds_bound_stacked.sizes['points']), dims='points', coords={'points': ds_bound_stacked['points']})"
+    "ds_reference = ds_lam\n",
+    "ds = ds_global\n",
+    "\n",
+    "fig, ax = plt.subplots(subplot_kw=dict(projection=ccrs.PlateCarree()))\n",
+    "xr.plot.scatter(ax=ax, ds=ds, x=\"lon\", y=\"lat\", label=\"ds\", transform=ccrs.PlateCarree())\n",
+    "xr.plot.scatter(ax=ax, ds=ds_reference, x=\"lon\", y=\"lat\", label=\"ds_ref\", transform=ccrs.PlateCarree())\n",
+    "xr.plot.scatter(ax=ax, c=da_interior_mask, ds=da_interior_mask.to_dataset(name=\"mask\"), x=\"lon\", y=\"lat\", marker=\"x\", label=\"ds (interior)\")\n",
+    "ax.coastlines()\n",
+    "ax.gridlines(draw_labels=True)\n",
+    "ax.legend()\n",
+    "plt.show()\n",
+    "    "
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 20,
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [],
    "source": [
-    "xr.plot.scatter(ds_bound_stacked, x='longitude', y='latitude', hue='msk')"
+    "Next we will for the points from the larger domain and exterior to this convex hull (i.e. points excluded by the mask created above) compute the distance to the nearest point of the inner domain."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "((2048,), (2035,))"
-      ]
-     },
-     "execution_count": 29,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "msk_in.shape, ii.shape"
+    "da_dist = cropping.distance_to_convex_hull_boundary(ds=ds_global, ds_reference=ds_lam)"
    ]
   },
   {
@@ -8218,87 +163,52 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Plot boundary valid boundary points outside and the LAM interior points (subset)\n",
-    "fig, ax = plt.subplots(subplot_kw={'projection': ccrs.Orthographic(-10, 45)}, figsize=(10, 10))\n",
+    "fig, ax = plt.subplots(subplot_kw=dict(projection=ccrs.PlateCarree()), figsize=(10, 4))\n",
+    "xr.plot.scatter(ax=ax, ds=ds_reference, x=\"lon\", y=\"lat\", label=\"ds_ref\", marker=\".\", transform=ccrs.PlateCarree())\n",
+    "xr.plot.scatter(ax=ax, hue=\"dist\", ds=da_dist.to_dataset(name=\"dist\"), x=\"lon\", y=\"lat\", marker=\"x\", label=\"ds (distance)\", add_colorbar=True)\n",
     "ax.coastlines()\n",
-    "# xr.plot.scatter(ds_boundary, x='longitude', y='latitude', color='red', s=1, ax=ax, transform=ccrs.Geodetic())\n",
-    "xr.plot.scatter(ds_lam_subsampled, x='lon', y='lat', color='blue', s=1, ax=ax, transform=ccrs.Geodetic(), label='Interior (DANRA)')\n",
-    "ax.plot(points_bnd[~msk_in,0][ii], points_bnd[~msk_in,1][ii], '.', color='green', markersize=4, transform=ccrs.Geodetic(), label='Boundary (ERA5)')\n",
-    "ax.legend()\n",
-    "ax.gridlines(draw_labels=True)\n",
-    "ax.set_title(\"\")"
+    "ax.gridlines(draw_labels=[\"top\", \"left\"])"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 18,
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [
-    {
-     "ename": "TypeError",
-     "evalue": "cond argument is array([False, False, False, ..., False, False, False]) but must be a <class 'xarray.core.dataset.Dataset'> or <class 'xarray.core.dataarray.DataArray'> (or a callable than returns one).",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[18], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mds_boundary\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstack\u001b[49m\u001b[43m(\u001b[49m\u001b[43mgrid_point\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mlatitude\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mlongitude\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mwhere\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmsk_in\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdrop\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m~/git-repos/mllam/mllam-data-prep/.venv/lib/python3.12/site-packages/xarray/core/common.py:1219\u001b[0m, in \u001b[0;36mDataWithCoords.where\u001b[0;34m(self, cond, other, drop)\u001b[0m\n\u001b[1;32m   1217\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m drop:\n\u001b[1;32m   1218\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(cond, (Dataset, DataArray)):\n\u001b[0;32m-> 1219\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\n\u001b[1;32m   1220\u001b[0m             \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcond argument is \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mcond\u001b[38;5;132;01m!r}\u001b[39;00m\u001b[38;5;124m but must be a \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mDataset\u001b[38;5;132;01m!r}\u001b[39;00m\u001b[38;5;124m or \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mDataArray\u001b[38;5;132;01m!r}\u001b[39;00m\u001b[38;5;124m (or a callable than returns one).\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m   1221\u001b[0m         )\n\u001b[1;32m   1223\u001b[0m     \u001b[38;5;28mself\u001b[39m, cond \u001b[38;5;241m=\u001b[39m align(\u001b[38;5;28mself\u001b[39m, cond)\n\u001b[1;32m   1225\u001b[0m     \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_dataarray_indexer\u001b[39m(dim: Hashable) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m DataArray:\n",
-      "\u001b[0;31mTypeError\u001b[0m: cond argument is array([False, False, False, ..., False, False, False]) but must be a <class 'xarray.core.dataset.Dataset'> or <class 'xarray.core.dataarray.DataArray'> (or a callable than returns one)."
-     ]
-    }
-   ],
    "source": [
-    "ds_boundary.stack(grid_point=('latitude', 'longitude')).where(msk_in, drop=True)"
+    "And finally we will use this distance to create a plot that includes a margin around the convex hull of the inner domain."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[SingleSphericalPolygon(array([[ 0.49958987, -0.105781  ,  0.85977924],\n",
-       "       [ 0.39711363,  0.17672289,  0.90059413],\n",
-       "       [ 0.55692436,  0.25118806,  0.791669  ],\n",
-       "       [ 0.55972804,  0.2524979 ,  0.78927139],\n",
-       "       [ 0.56252231,  0.25380347,  0.7868624 ],\n",
-       "       [ 0.56530712,  0.25510474,  0.78444212],\n",
-       "       [ 0.56808237,  0.25640168,  0.78201061],\n",
-       "       [ 0.57084802,  0.25769424,  0.77956797],\n",
-       "       [ 0.67322075, -0.02461529,  0.73903174],\n",
-       "       [ 0.49958987, -0.105781  ,  0.85977924]]), array([0.55411136, 0.24987399, 0.79405515]))]"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "chull_lam"
+    "max_dist = 1.5 # in degrees\n",
+    "da_global_margin_crop = cropping.crop_to_within_convex_hull_margin(\n",
+    "    ds=ds_global, ds_reference=ds_lam, max_dist=max_dist\n",
+    ")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([False, False, False, ..., False, False, False])"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "ii"
+    "fig, ax = plt.subplots(subplot_kw=dict(projection=ccrs.PlateCarree()), figsize=(10, 4))\n",
+    "xr.plot.scatter(ax=ax, ds=ds_reference, x=\"lon\", y=\"lat\", label=\"ds_ref\", marker=\".\", transform=ccrs.PlateCarree())\n",
+    "xr.plot.scatter(ax=ax, ds=da_global_margin_crop, x=\"lon\", y=\"lat\", marker=\"x\", label=f\"ds (margin crop, {max_dist}deg)\", transform=ccrs.PlateCarree())\n",
+    "ax.coastlines()\n",
+    "ax.legend()\n",
+    "ax.gridlines(draw_labels=[\"top\", \"left\"])"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
diff --git a/mllam_data_prep/create_dataset.py b/mllam_data_prep/create_dataset.py
index 73996cf..558da79 100644
--- a/mllam_data_prep/create_dataset.py
+++ b/mllam_data_prep/create_dataset.py
@@ -230,6 +230,14 @@ def create_dataset(config: Config):
         )
         ds["splits"] = da_splits
 
+    # ensure any dimensions for which coordinate values aren't yet set that
+    # these are given integer values. This will for example apply when stacking
+    # (x, y)-coordinates to a grid-index coordinate. These need unique values
+    # for later reference.
+    for d in ds.dims:
+        if d not in ds.coords:
+            ds[d] = np.arange(ds[d].size)
+
     ds.attrs = {}
     ds.attrs["schema_version"] = config.schema_version
     ds.attrs["dataset_version"] = config.dataset_version
diff --git a/mllam_data_prep/ops/cropping.py b/mllam_data_prep/ops/cropping.py
index 629fa08..ac576b3 100644
--- a/mllam_data_prep/ops/cropping.py
+++ b/mllam_data_prep/ops/cropping.py
@@ -1,12 +1,10 @@
 import numpy as np
-import pyproj
 import spherical_geometry as sg
 import xarray as xr
-from loguru import logger
 from spherical_geometry.polygon import SphericalPolygon
 
 
-def _get_latlon_coords(da):
+def _get_latlon_coords(da: xr.DataArray) -> tuple:
     """
     Get the latlon coordinates of a DataArray.
 
@@ -25,53 +23,42 @@ def _get_latlon_coords(da):
     elif "lat" in da.coords and "lon" in da.coords:
         return (da.lat, da.lon)
     else:
-        raise Exception()
+        raise Exception("Could not find lat/lon coordinates in DataArray.")
 
 
-def _add_latlon(ds):
-    logger.warning(
-        "Could not find lat/lon coordinates. Assuming equal area projection "
-        "centered on (lat, lon) = (0, 0) for now. This should be replaced!"
-    )
-
-    # create a local equal area projection centered on Denmark (Høje Taastrup)
-    # for now
-    da_x = ds.coords["x"]
-    da_y = ds.coords["y"]
-    proj = pyproj.Proj(proj="laea", lon_0=12.25, lat_0=55.65)
-    lon, lat = proj(da_x.values, da_y.values, inverse=True)
-    da_lat = xr.DataArray(lat, coords=da_x.coords, dims=da_x.dims)
-    da_lon = xr.DataArray(lon, coords=da_y.coords, dims=da_y.dims)
-
-    ds.coords["lon"] = da_lon
-    ds.coords["lat"] = da_lat
-
-    return ds
-
-
-def create_convex_hull_mask(ds, ds_reference):
+def create_convex_hull_mask(ds: xr.Dataset, ds_reference: xr.Dataset) -> xr.DataArray:
     """
     Create a grid-point mask for lat/lon coordinates in `da` indicating which
     points are interior to the convex hull of the lat/lon coordinates of
     `da_ref`.
-    """
-    # TODO: this should be replaced with a function to get the true lat/lon coordinates
-    _add_latlon(ds)
-    _add_latlon(ds_reference)
 
+    Parameters
+    ----------
+    ds : xarray.Dataset
+        The dataset for which to create the mask.
+    ds_reference : xarray.Dataset
+        The reference dataset from which to create the convex hull of the coordinates.
+
+    Returns
+    -------
+    xarray.DataArray
+        A boolean mask indicating which points are interior to the convex hull.
+    """
     da_lat, da_lon = _get_latlon_coords(ds)
     da_lat_ref, da_lon_ref = _get_latlon_coords(ds_reference)
 
-    assert da_lat.dims == da_lon.dims == da_lat_ref.dims == da_lon_ref.dims
+    assert da_lat.dims == da_lon.dims
+    assert da_lat_ref.dims == da_lon_ref.dims
 
     # latlon to (x, y, z) on unit sphere
-    pts_ref_xyz = np.array(sg.vector.lonlat_to_vector(da_lon_ref, da_lat_ref)).T
+    da_ref_xyz = _latlon_to_unit_sphere_xyz(da_lat=da_lat_ref, da_lon=da_lon_ref)
 
-    chull_lam = SphericalPolygon.convex_hull(pts_ref_xyz)
+    chull_lam = SphericalPolygon.convex_hull(da_ref_xyz.values)
 
+    # call .load() to avoid using dask arrays in the following apply_ufunc
     da_interior_mask = xr.apply_ufunc(
-        chull_lam.contains_lonlat, da_lon, da_lat, vectorize=True
-    )
+        chull_lam.contains_lonlat, da_lon.load(), da_lat.load(), vectorize=True
+    ).astype(bool)
     da_interior_mask.attrs[
         "long_name"
     ] = "contained in convex hull of source dataset (da_ref)"
@@ -79,35 +66,133 @@ def create_convex_hull_mask(ds, ds_reference):
     return da_interior_mask
 
 
-def boundary_crop_gridpoints_with_latlon_convex_hull(
-    ds, ds_reference, grid_index_dim="grid_index", max_dist=0.2
-):
-    raise NotImplementedError()
-    # da_interior_mask = create_convex_hull_mask(ds=ds, ds_reference=ds_reference)
+def _latlon_to_unit_sphere_xyz(
+    da_lat: xr.DataArray, da_lon: xr.DataArray
+) -> xr.DataArray:
+    """
+    Convert lat/lon coordinates to (x, y, z) on the unit sphere.
 
-    # points = _get_latlon_coords(ds)
-    # points_lam = _get_latlon_coords(ds_reference)
+    Parameters
+    ----------
+    da_lat : xarray.DataArray
+        Latitude coordinates.
+    da_lon : xarray.DataArray
+        Longitude coordinates.
 
-    # latlon to (x, y, z) on unit sphere
-    # pts_xyz = np.array(sg.vector.lonlat_to_vector(points[:, 0], points[:, 1])).T
-    # pts_lam_xyz = np.array(
-    #     sg.vector.lonlat_to_vector(points_lam[:, 0], points_lam[:, 1])
-    # ).T
+    Returns
+    -------
+    xr.DataArray
+        The (x, y, z) coordinates on the unit sphere as an xarray.DataArray
+        with dimensions (grid_index, component).
+    """
+    pts_xyz = np.array(sg.vector.lonlat_to_vector(da_lon, da_lat)).T
+    da_xyz = xr.DataArray(
+        pts_xyz, coords=da_lat.coords, dims=list(da_lat.dims) + ["xyz"]
+    )
+    return da_xyz
+
+
+def distance_to_convex_hull_boundary(
+    ds: xr.Dataset, ds_reference: xr.Dataset, grid_index_dim: str = "grid_index"
+) -> xr.DataArray:
+    """
+    For all points in `ds` that are external to the convex hull of the points in
+    `ds_reference`, calculate the minimum distance to the convex hull boundary.
+
+    Parameters
+    ----------
+    ds : xarray.Dataset
+        The dataset for which to calculate the distance.
+    ds_reference : xarray.Dataset
+        The reference dataset from which to calculate the convex hull boundary.
+    grid_index_dim : str, optional
+        The name of the grid index dimension in `ds` and `ds_reference`.
+
+    Returns
+    -------
+    xarray.DataArray
+        The minimum distance to the convex hull boundary.
+    """
+    # rename the grid index dimension in ds_reference to avoid conflicts (since
+    # the grid index dimension otherwise has the same name in both datasets,
+    # and later we will want to find the minimum distance for each point in ds
+    # to all points in ds_reference)
+    ds_reference_separate_gridindex = ds_reference.rename(
+        {grid_index_dim: "grid_index_ref"}
+    )
+
+    # create a mask from the convex hull of ds_reference for the grid points in ds
+    da_ch_mask = create_convex_hull_mask(
+        ds=ds, ds_reference=ds_reference_separate_gridindex
+    )
+
+    # only consider points that are external to the convex hull
+    ds_exterior = ds.where(~da_ch_mask, drop=True)
+
+    da_xyz = _latlon_to_unit_sphere_xyz(ds_exterior.lat, ds_exterior.lon)
+    da_xyz_ref = _latlon_to_unit_sphere_xyz(
+        ds_reference_separate_gridindex.lat, ds_reference_separate_gridindex.lon
+    )
+
+    def calc_dist(da_pt_xyz):
+        dotproduct = np.dot(da_xyz_ref, da_pt_xyz.T)
+        val = np.min(np.arccos(dotproduct))
+        return val
+
+    da_mindist_to_ref = xr.apply_ufunc(
+        calc_dist,
+        da_xyz,
+        input_core_dims=[["xyz"]],
+        output_core_dims=[[]],
+        vectorize=True,
+    )
+    da_mindist_to_ref.attrs[
+        "long_name"
+    ] = "minimum distance to convex hull boundary of reference dataset"
+    da_mindist_to_ref.attrs["units"] = "radians"
+
+    return da_mindist_to_ref
 
-    # da_xyz = xr.DataArray(pts_xyz, coords=ds.coords, dims=[grid_index_dim, "xyz"])
 
-    # chull_lam = SphericalPolygon.convex_hull(pts_lam_xyz)
+def crop_to_within_convex_hull_margin(
+    ds: xr.Dataset,
+    ds_reference: xr.Dataset,
+    grid_index_dim: str = "grid_index",
+    max_dist: float = 2.0,
+) -> xr.Dataset:
+    """
+    Crop grid points (with coordinates given in lat/lon) in `ds` that are
+    within a certain distance (within the margin of a given width) of the
+    convex hull boundary of the points in `ds_reference`. The margin is
+    measured in degrees.
+
+    ┌────── Margin ───────────┐
+    │                         │
+    │    ┌─ Convex hull ─┐    │
+    │    │               │    │
+    │    │               │    │
+    │    │               │    │
+    │    │               │    │
+    │    │               │    │
+    │    └───────────────┘    │
+    │    included in crop     │
+    └─────────────────────────┘
+
+    Parameters
+    ----------
+    ds : xarray.Dataset
+        The dataset to crop.
+    ds_reference : xarray.Dataset
+        The reference dataset from which to calculate the convex hull boundary.
+    grid_index_dim : str, optional
+        The name of the grid index dimension in `ds` and `ds_reference`.
+    """
+    da_min_dist_to_ref = distance_to_convex_hull_boundary(
+        ds, ds_reference, grid_index_dim=grid_index_dim
+    )
 
-    # # Finding the boundary points inside the convec hull is what takes time. Possibly speed up by restricting to a subset of points_ that are within some distance from area center or similar?
-    # msk_in = np.array([chull_lam.contains_lonlat(x[0], x[1]) for x in points])
-    # da_interior_mask = xr.DataArray(msk_in, coords=ds.coords, dims=[grid_index_dim])
-    # ds_reference = ds.where(da_interior_mask, drop=True)
+    max_dist_radians = max_dist * np.pi / 180.0
 
-    # Distances from all boundary points outside the region to the closest point inside
-    # d = np.array(
-    #     [np.min(np.arccos(np.dot(pts_lam_xyz, pt_xyz))) for pt_xyz in pts_xyz[~msk_in]]
-    # )
+    da_boundary_mask = da_min_dist_to_ref < max_dist_radians
 
-    # Define boundary distance and mask boundary points outside that are closer
-    # This is now in radians but should be defined as meters based on Earth radius
-    # ii = d < max_dist
+    return ds.where(da_boundary_mask, drop=True)
diff --git a/tests/data.py b/tests/data.py
index fdd2c77..3a332c5 100644
--- a/tests/data.py
+++ b/tests/data.py
@@ -7,6 +7,11 @@
 import pandas as pd
 import xarray as xr
 
+try:
+    import pyproj
+except ImportError:
+    pyproj = None
+
 import mllam_data_prep as mdp
 
 SCHEMA_VERSION = "v0.5.0"
@@ -295,7 +300,42 @@ def create_static_dataset(
     return ds
 
 
-def create_data_collection(data_kinds, fp_root, xlim=DEFAULT_XLIM, ylim=DEFAULT_YLIM):
+def _add_latlon(ds: xr.Dataset) -> xr.Dataset:
+    """
+    Add latitude and longitude coordinates to the dataset using a local equal
+    area projection centered on Denmark.
+    """
+    if pyproj is None:
+        raise ImportError("pyproj is required for this function")
+
+    da_x = ds.coords["x"]
+    da_y = ds.coords["y"]
+    xs, ys = np.meshgrid(da_x, da_y, indexing="ij")
+    proj = pyproj.Proj(proj="laea", lon_0=12.25, lat_0=55.65)
+    lon, lat = proj(xs, ys, inverse=True)
+    dims = da_x.dims + da_y.dims
+    coords = {d: ds.coords[d] for d in dims}
+    da_lat = xr.DataArray(lat, coords=coords, dims=dims)
+    da_lon = xr.DataArray(lon, coords=coords, dims=dims)
+
+    ds.coords["lon"] = da_lon
+    ds.coords["lat"] = da_lat
+
+    return ds
+
+
+def create_data_collection(
+    data_kinds,
+    fp_root,
+    xlim=DEFAULT_XLIM,
+    ylim=DEFAULT_YLIM,
+    nx=NX,
+    ny=NY,
+    nz=NZ,
+    nt_analysis=NT_ANALYSIS,
+    nt_forecast=NT_FORECAST,
+    add_latlon=False,
+):
     """
     Create a fake data collection with the given `data_kinds` and save it to `fp_root`, with
     each dataset having the `data_kind` name with a unique suffix and saved in `.zarr` format.
@@ -307,6 +347,20 @@ def create_data_collection(data_kinds, fp_root, xlim=DEFAULT_XLIM, ylim=DEFAULT_
         List of data kinds to create, e.g. ["surface_forecast", "static"]
     fp_root : str
         Root directory to save the data collection
+    xlim : tuple, optional
+        Tuple of the form (xmin, xmax) defining the x-limits, by default DEFAULT_XLIM
+    ylim : tuple, optional
+        Tuple of the form (ymin, ymax) defining the y-limits, by default DEFAULT_YLIM
+    nx : int, optional
+        Number of grid points in x-direction, by default NX
+    ny : int, optional
+        Number of grid points in y-direction, by default NY
+    nz : int, optional
+        Number of levels, by default NZ
+    nt_analysis : int, optional
+        Number of analysis times, by default NT_ANALYSIS
+    nt_forecast : int, optional
+        Number of forecast times, by default NT_FORECAST
 
     Returns
     -------
@@ -325,28 +379,42 @@ def create_data_collection(data_kinds, fp_root, xlim=DEFAULT_XLIM, ylim=DEFAULT_
     for data_kind in data_kinds:
         if data_kind == "surface_forecast":
             ds = create_surface_forecast_dataset(
-                NT_ANALYSIS, NT_FORECAST, NX, NY, xlim=xlim, ylim=ylim
+                nt_analysis=nt_analysis,
+                nt_forecast=nt_forecast,
+                nx=nx,
+                ny=ny,
+                xlim=xlim,
+                ylim=ylim,
             )
         elif data_kind == "surface_analysis":
             ds = create_surface_analysis_dataset(
-                NT_ANALYSIS, NX, NY, xlim=xlim, ylim=ylim
+                nt_analysis=nt_analysis, nx=nx, ny=ny, xlim=xlim, ylim=ylim
             )
         elif data_kind == "analysis_on_levels":
             ds = create_analysis_dataset_on_levels(
-                NT_ANALYSIS, NX, NY, NZ, xlim=xlim, ylim=ylim
+                nt_analysis=nt_analysis, nx=nx, ny=ny, nz=nz, xlim=xlim, ylim=ylim
             )
         elif data_kind == "forecast_on_levels":
             ds = create_forecast_dataset_on_levels(
-                NT_ANALYSIS, NT_FORECAST, NX, NY, NZ, xlim=xlim, ylim=ylim
+                nt_analysis=nt_analysis,
+                nt_forecast=nt_forecast,
+                nx=nx,
+                ny=ny,
+                nz=nz,
+                xlim=xlim,
+                ylim=ylim,
             )
         elif data_kind == "static":
-            ds = create_static_dataset(NX, NY, xlim=xlim, ylim=ylim)
+            ds = create_static_dataset(nx=nx, ny=ny, xlim=xlim, ylim=ylim)
         else:
             raise ValueError(f"Unknown data kind: {data_kind}")
 
         identifier = str(uuid.uuid4())
         dataset_name = f"{data_kind}_{identifier}"
 
+        if add_latlon:
+            ds = _add_latlon(ds)
+
         fp = f"{fp_root}/{dataset_name}.zarr"
         ds.to_zarr(fp, mode="w")
         datasets[data_kind] = fp
@@ -360,6 +428,9 @@ def create_input_datasets_and_config(
     data_categories: List[str],
     xlim: List[float] = DEFAULT_XLIM,
     ylim: List[float] = DEFAULT_YLIM,
+    nx: int = NX,
+    ny: int = NY,
+    add_latlon: bool = False,
 ):
     """
     Create a config and input datasets with test data for it with a given set
@@ -377,6 +448,10 @@ def create_input_datasets_and_config(
         List of the form [xmin, xmax] defining the x-limits, by default DEFAULT_XLIM
     ylim : List[float], optional
         List of the form [ymin, ymax] defining the y-limits, by default DEFAULT_YLIM
+    nx : int, optional
+        Number of grid points in x-direction, by default NX
+    ny : int, optional
+        Number of grid points in y-direction, by default NY
 
     Returns
     -------
@@ -408,6 +483,9 @@ def create_input_datasets_and_config(
             fp_root=Path(tmpdir.name) / identifier,
             xlim=xlim,
             ylim=ylim,
+            nx=nx,
+            ny=ny,
+            add_latlon=add_latlon,
         )
 
         assert len(datasets) == 1
diff --git a/tests/test_convex_hull_cropping.py b/tests/test_convex_hull_cropping.py
new file mode 100644
index 0000000..505976c
--- /dev/null
+++ b/tests/test_convex_hull_cropping.py
@@ -0,0 +1,73 @@
+import tempfile
+
+import numpy as np
+
+import mllam_data_prep as mdp
+import tests.data as testdata
+from mllam_data_prep.ops import cropping
+
+
+def test_create_convex_hull_mask():
+    tmpdir = tempfile.TemporaryDirectory()
+    domain_size = 500 * 1.0e3  # length and width of domain in meters
+    N = 200
+    config_lam = testdata.create_input_datasets_and_config(
+        identifier="lam",
+        data_categories=["state"],
+        tmpdir=tmpdir,
+        xlim=[-domain_size / 2.0, domain_size / 2.0],
+        ylim=[-domain_size / 2.0, domain_size / 2.0],
+        nx=N,
+        ny=N,
+        add_latlon=True,
+    )
+    # make the global domain twice as large as the LAM domain so that the lam
+    # domain is contained within the global domain
+    config_global = testdata.create_input_datasets_and_config(
+        identifier="global",
+        data_categories=["state"],
+        tmpdir=tmpdir,
+        xlim=[-domain_size, domain_size],
+        ylim=[-domain_size, domain_size],
+        add_latlon=True,
+        nx=N // 2,
+        ny=N // 2,
+    )
+
+    ds_lam = mdp.create_dataset(config=config_lam)
+    ds_global = mdp.create_dataset(config=config_global)
+
+    da_ch_mask = cropping.create_convex_hull_mask(ds=ds_global, ds_reference=ds_lam)
+
+    # Given that the outer domain is 4x larger than the inner domain and the
+    # inner domain sits completely within the outer domain, then approximately
+    # 1/4 of the outer domain points should be within the convex hull of the
+    # inner domain, and 3/4 outside.
+    n_inside = da_ch_mask.where(da_ch_mask).count().values
+    n_outside = da_ch_mask.where(~da_ch_mask).count().values
+    np.testing.assert_almost_equal(n_inside / n_outside, 1.0 / 3.0, decimal=2)
+
+    da_dist = cropping.distance_to_convex_hull_boundary(
+        ds=ds_global, ds_reference=ds_lam
+    )
+    da_dist_unstacked = da_dist.set_index(grid_index=["x", "y"]).unstack("grid_index")
+
+    # check that the distance decreases towards the middle of the domain in
+    # both x and y directions
+    da_dist_x = da_dist_unstacked.sel(y=0, method="nearest")
+    da_dist_change = da_dist_x.diff("x").dropna("x")
+    da_dist_x = da_dist_x.sel(x=da_dist_change.x)
+    np.testing.assert_array_equal(np.sign(da_dist_change), np.sign(da_dist_x.x))
+
+    da_dist_y = da_dist_unstacked.sel(x=0, method="nearest")
+    da_dist_change = da_dist_y.diff("y").dropna("y")
+    da_dist_y = da_dist_y.sel(y=da_dist_change.y)
+    np.testing.assert_array_equal(np.sign(da_dist_change), np.sign(da_dist_y.y))
+
+    da_convex_hull_margin_crop = cropping.crop_to_within_convex_hull_margin(
+        ds=ds_global, ds_reference=ds_lam, max_dist=2.0
+    )
+
+    # check that there are fewer points in this margin region
+    n_points_margin_region = da_convex_hull_margin_crop.count().values
+    assert n_points_margin_region < n_outside
diff --git a/tests/test_latlon_convex_hull_cropping.py b/tests/test_latlon_convex_hull_cropping.py
deleted file mode 100644
index 3c8c23e..0000000
--- a/tests/test_latlon_convex_hull_cropping.py
+++ /dev/null
@@ -1,31 +0,0 @@
-import tempfile
-
-import mllam_data_prep as mdp
-import tests.data as testdata
-from mllam_data_prep.ops import cropping
-
-
-def test_create_convex_hull_mask():
-    tmpdir = tempfile.TemporaryDirectory()
-    domain_size = 500 * 1.0e3  # length and width of domain in meters
-    config_lam = testdata.create_input_datasets_and_config(
-        identifier="lam",
-        data_categories=["state"],
-        tmpdir=tmpdir,
-        xlim=[-domain_size / 2.0, domain_size / 2.0],
-        ylim=[-domain_size / 2.0, domain_size / 2.0],
-    )
-    # make the global domain twice as large as the LAM domain so that the lam
-    # domain is contained within the global domain
-    config_global = testdata.create_input_datasets_and_config(
-        identifier="global",
-        data_categories=["state"],
-        tmpdir=tmpdir,
-        xlim=[-domain_size, domain_size],
-        ylim=[-domain_size, domain_size],
-    )
-
-    ds_lam = mdp.create_dataset(config=config_lam)
-    ds_global = mdp.create_dataset(config=config_global)
-
-    cropping.create_convex_hull_mask(ds=ds_global, ds_reference=ds_lam)

From aed4d22400f5141d3622a7d21806e0980df5decc Mon Sep 17 00:00:00 2001
From: Leif Denby <leif@denby.eu>
Date: Thu, 12 Dec 2024 12:13:41 +0100
Subject: [PATCH 05/28] add latlon domain crop deps to ci/cd action

---
 .github/workflows/python-package-pip.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/workflows/python-package-pip.yml b/.github/workflows/python-package-pip.yml
index 0a1c5bb..6e2c619 100644
--- a/.github/workflows/python-package-pip.yml
+++ b/.github/workflows/python-package-pip.yml
@@ -21,7 +21,7 @@ jobs:
       - uses: actions/checkout@v2
       - name: Install package with pip
         run: |
-          python -m pip install .
+          python -m pip install ".[latlon-domain-crop]"
           python -m pip install pytest
 
       - name: Run tests

From 972186da4854da3bfa8a1b15699bf51d0d8c6505 Mon Sep 17 00:00:00 2001
From: Leif Denby <leif@denby.eu>
Date: Thu, 12 Dec 2024 21:41:40 +0100
Subject: [PATCH 06/28] implement cropping during dataset creation

---
 example.era5_cropped.yaml         | 109 ++++++++++++++++++++++++++++++
 mllam_data_prep/config.py         |  20 ++++++
 mllam_data_prep/create_dataset.py |  21 ++++++
 mllam_data_prep/ops/cropping.py   |   3 +
 4 files changed, 153 insertions(+)
 create mode 100644 example.era5_cropped.yaml

diff --git a/example.era5_cropped.yaml b/example.era5_cropped.yaml
new file mode 100644
index 0000000..562e8df
--- /dev/null
+++ b/example.era5_cropped.yaml
@@ -0,0 +1,109 @@
+schema_version: v0.5.0
+dataset_version: v1.0.0
+
+output:
+  variables:
+    static: [grid_index, static_feature]
+    forcing: [time, grid_index, forcing_feature]
+  coord_ranges:
+    time:
+      start: 1990-09-03T00:00
+      end: 1990-09-09T00:00
+      step: PT6H
+  chunking:
+    time: 1
+  splitting:
+    dim: time
+    splits:
+      train:
+        start: 1990-09-03T00:00
+        end: 1990-09-06T00:00
+        compute_statistics:
+          ops: [mean, std, diff_mean, diff_std]
+          dims: [grid_index, time]
+      val:
+        start: 1990-09-06T00:00
+        end: 1990-09-07T00:00
+      test:
+        start: 1990-09-07T00:00
+        end: 1990-09-09T00:00
+  domain_cropping:
+    margin_width_degrees: 0.2
+    interior_dataset_config_path: example.danra.yaml
+
+inputs:
+  era_height_levels:
+    path: 'gs://weatherbench2/datasets/era5/1959-2023_01_10-6h-64x32_equiangular_conservative.zarr'
+    dims: [time, longitude, latitude, level]
+    variables:
+      u_component_of_wind:
+        level:
+          values: [1000,]
+          units: hPa
+    dim_mapping:
+      time:
+        method: rename
+        dim: time
+      x:
+        method: rename
+        dim: longitude
+      y:
+        method: rename
+        dim: latitude
+      forcing_feature:
+        method: stack_variables_by_var_name
+        dims: [level]
+        name_format: "{var_name}{level}hPa"
+      grid_index:
+        method: stack
+        dims: [x, y]
+    target_output_variable: forcing
+
+  era5_surface:
+    path: 'gs://weatherbench2/datasets/era5/1959-2023_01_10-6h-64x32_equiangular_conservative.zarr'
+    dims: [time, longitude, latitude, level]
+    variables:
+      - mean_sea_level_pressure
+    dim_mapping:
+      time:
+        method: rename
+        dim: time
+      x:
+        method: rename
+        dim: longitude
+      y:
+        method: rename
+        dim: latitude
+      forcing_feature:
+        method: stack_variables_by_var_name
+        name_format: "{var_name}"
+      grid_index:
+        method: stack
+        dims: [x, y]
+    target_output_variable: forcing
+
+  era5_static:
+    path: 'gs://weatherbench2/datasets/era5/1959-2023_01_10-6h-64x32_equiangular_conservative.zarr'
+    dims: [time, longitude, latitude, level]
+    variables:
+      - land_sea_mask
+    dim_mapping:
+      x:
+        method: rename
+        dim: longitude
+      y:
+        method: rename
+        dim: latitude
+      static_feature:
+        method: stack_variables_by_var_name
+        name_format: "{var_name}"
+      grid_index:
+        method: stack
+        dims: [x, y]
+    target_output_variable: static
+
+extra:
+  projection:
+    class_name: PlateCarree
+    kwargs:
+      central_longitude: 0.0
diff --git a/mllam_data_prep/config.py b/mllam_data_prep/config.py
index 14e80ef..07ec9ce 100644
--- a/mllam_data_prep/config.py
+++ b/mllam_data_prep/config.py
@@ -217,6 +217,25 @@ class Splitting:
     splits: Dict[str, Split]
 
 
+@dataclass
+class ConvexHullPaddingCrop:
+    """
+    Define the method for cropping the domain of the output dataset, this includes defining
+    the method to use for cropping the domain and the parameters for the method.
+
+    Attributes
+    ----------
+    margin_width_degrees: float
+        The width (in degrees) of the margin applied to the convex hull
+        boundary of the interior dataset used to define the cropping domain.
+    interior_dataset_config_path: str
+        The path to the configuration file for the dataset defining the interior domain
+    """
+
+    margin_width_degrees: float
+    interior_dataset_config_path: str
+
+
 @dataclass
 class Output:
     """
@@ -260,6 +279,7 @@ class Output:
     coord_ranges: Dict[str, Range] = None
     chunking: Dict[str, int] = None
     splitting: Splitting = None
+    domain_cropping: ConvexHullPaddingCrop = None
 
 
 @dataclass
diff --git a/mllam_data_prep/create_dataset.py b/mllam_data_prep/create_dataset.py
index 558da79..78f443d 100644
--- a/mllam_data_prep/create_dataset.py
+++ b/mllam_data_prep/create_dataset.py
@@ -10,6 +10,7 @@
 
 from . import __version__
 from .config import Config, InvalidConfigException
+from .ops.cropping import crop_to_within_convex_hull_margin
 from .ops.loading import load_and_subset_dataset
 from .ops.mapping import map_dims_and_variables
 from .ops.selection import select_by_kwargs
@@ -118,6 +119,14 @@ def create_dataset(config: Config):
             "update the schema version used in your config to v0.5.0."
         )
 
+    # parse the interior domain config already here if domain cropping is
+    # enabled, so that we can alert the user quickly if the config is invalid
+    ds_interior_domain = None
+    if config.output.domain_cropping is not None:
+        config_interior_domain = Config.from_yaml_file(
+            file=config.output.domain_cropping.interior_dataset_config_path
+        )
+
     output_config = config.output
     output_coord_ranges = output_config.coord_ranges
 
@@ -238,6 +247,18 @@ def create_dataset(config: Config):
         if d not in ds.coords:
             ds[d] = np.arange(ds[d].size)
 
+    if config.output.domain_cropping is not None:
+        domain_cropping = config.output.domain_cropping
+        ds_interior_domain = create_dataset(config=config_interior_domain)
+        logger.info(
+            f"Cropping dataset to within convex hull margin of {ds_interior_domain} with a margin of {domain_cropping.margin_width_degrees} degrees"
+        )
+        ds = crop_to_within_convex_hull_margin(
+            ds=ds,
+            ds_reference=ds_interior_domain,
+            max_dist=domain_cropping.margin_width_degrees,
+        )
+
     ds.attrs = {}
     ds.attrs["schema_version"] = config.schema_version
     ds.attrs["dataset_version"] = config.dataset_version
diff --git a/mllam_data_prep/ops/cropping.py b/mllam_data_prep/ops/cropping.py
index ac576b3..0798027 100644
--- a/mllam_data_prep/ops/cropping.py
+++ b/mllam_data_prep/ops/cropping.py
@@ -23,6 +23,9 @@ def _get_latlon_coords(da: xr.DataArray) -> tuple:
     elif "lat" in da.coords and "lon" in da.coords:
         return (da.lat, da.lon)
     else:
+        import ipdb
+
+        ipdb.set_trace()
         raise Exception("Could not find lat/lon coordinates in DataArray.")
 
 

From ba467183ec88959aa529fc0574daf1c82b5d1a1c Mon Sep 17 00:00:00 2001
From: Leif Denby <leif@denby.eu>
Date: Mon, 16 Dec 2024 14:16:02 +0100
Subject: [PATCH 07/28] fix tests, examples and missing use of lat/lon function

---
 example.era5_cropped.yaml          | 24 +++---------------------
 mllam_data_prep/ops/cropping.py    |  7 ++-----
 tests/test_convex_hull_cropping.py |  2 +-
 tests/test_from_config.py          | 22 ++++++++++++++++------
 4 files changed, 22 insertions(+), 33 deletions(-)

diff --git a/example.era5_cropped.yaml b/example.era5_cropped.yaml
index 562e8df..71a9284 100644
--- a/example.era5_cropped.yaml
+++ b/example.era5_cropped.yaml
@@ -44,19 +44,13 @@ inputs:
       time:
         method: rename
         dim: time
-      x:
-        method: rename
-        dim: longitude
-      y:
-        method: rename
-        dim: latitude
       forcing_feature:
         method: stack_variables_by_var_name
         dims: [level]
         name_format: "{var_name}{level}hPa"
       grid_index:
         method: stack
-        dims: [x, y]
+        dims: [longitude, latitude]
     target_output_variable: forcing
 
   era5_surface:
@@ -68,18 +62,12 @@ inputs:
       time:
         method: rename
         dim: time
-      x:
-        method: rename
-        dim: longitude
-      y:
-        method: rename
-        dim: latitude
       forcing_feature:
         method: stack_variables_by_var_name
         name_format: "{var_name}"
       grid_index:
         method: stack
-        dims: [x, y]
+        dims: [longitude, latitude]
     target_output_variable: forcing
 
   era5_static:
@@ -88,18 +76,12 @@ inputs:
     variables:
       - land_sea_mask
     dim_mapping:
-      x:
-        method: rename
-        dim: longitude
-      y:
-        method: rename
-        dim: latitude
       static_feature:
         method: stack_variables_by_var_name
         name_format: "{var_name}"
       grid_index:
         method: stack
-        dims: [x, y]
+        dims: [longitude, latitude]
     target_output_variable: static
 
 extra:
diff --git a/mllam_data_prep/ops/cropping.py b/mllam_data_prep/ops/cropping.py
index 0798027..0ce3e62 100644
--- a/mllam_data_prep/ops/cropping.py
+++ b/mllam_data_prep/ops/cropping.py
@@ -23,9 +23,6 @@ def _get_latlon_coords(da: xr.DataArray) -> tuple:
     elif "lat" in da.coords and "lon" in da.coords:
         return (da.lat, da.lon)
     else:
-        import ipdb
-
-        ipdb.set_trace()
         raise Exception("Could not find lat/lon coordinates in DataArray.")
 
 
@@ -132,9 +129,9 @@ def distance_to_convex_hull_boundary(
     # only consider points that are external to the convex hull
     ds_exterior = ds.where(~da_ch_mask, drop=True)
 
-    da_xyz = _latlon_to_unit_sphere_xyz(ds_exterior.lat, ds_exterior.lon)
+    da_xyz = _latlon_to_unit_sphere_xyz(*_get_latlon_coords(ds_exterior))
     da_xyz_ref = _latlon_to_unit_sphere_xyz(
-        ds_reference_separate_gridindex.lat, ds_reference_separate_gridindex.lon
+        *_get_latlon_coords(ds_reference_separate_gridindex)
     )
 
     def calc_dist(da_pt_xyz):
diff --git a/tests/test_convex_hull_cropping.py b/tests/test_convex_hull_cropping.py
index 505976c..4810fa7 100644
--- a/tests/test_convex_hull_cropping.py
+++ b/tests/test_convex_hull_cropping.py
@@ -69,5 +69,5 @@ def test_create_convex_hull_mask():
     )
 
     # check that there are fewer points in this margin region
-    n_points_margin_region = da_convex_hull_margin_crop.count().values
+    n_points_margin_region = da_convex_hull_margin_crop.count()
     assert n_points_margin_region < n_outside
diff --git a/tests/test_from_config.py b/tests/test_from_config.py
index 06cf640..df0aa4b 100644
--- a/tests/test_from_config.py
+++ b/tests/test_from_config.py
@@ -10,6 +10,22 @@
 import tests.data as testdata
 
 
+def _find_example_config_files():
+    fps = Path(__file__).parent.parent.glob("example.*.yaml")
+    examples = {fp.name.split(".")[1]: fp for fp in fps}
+    return examples
+
+
+EXAMPLE_YAML_FILES = _find_example_config_files()
+
+
+@pytest.mark.parametrize("example_name", EXAMPLE_YAML_FILES.keys())
+def test_yaml_example(example_name):
+    fp_config = EXAMPLE_YAML_FILES[example_name]
+    with tempfile.TemporaryDirectory(suffix=".zarr") as tmpdir:
+        mdp.create_dataset_zarr(fp_config=fp_config, fp_zarr=tmpdir)
+
+
 def test_gen_data():
     tmpdir = tempfile.TemporaryDirectory()
     testdata.create_data_collection(
@@ -277,12 +293,6 @@ def test_feature_collision(use_common_feature_var_name):
         mdp.create_dataset_zarr(fp_config=fp_config)
 
 
-def test_danra_example():
-    fp_config = Path(__file__).parent.parent / "example.danra.yaml"
-    with tempfile.TemporaryDirectory(suffix=".zarr") as tmpdir:
-        mdp.create_dataset_zarr(fp_config=fp_config, fp_zarr=tmpdir)
-
-
 @pytest.mark.parametrize("extra_content", [None, {"foobar": {"baz": 42}}])
 def test_optional_extra_section(extra_content):
     """

From 41a52bc868b4376981932ad36304d0c5a51a44a8 Mon Sep 17 00:00:00 2001
From: Leif Denby <leif@denby.eu>
Date: Wed, 8 Jan 2025 17:31:14 +0000
Subject: [PATCH 08/28] enable inclusion of convex hull interior

---
 docs/domain-cropping.ipynb        | 1935 ++++++++++++++++++++++++++++-
 mllam_data_prep/create_dataset.py |    7 +-
 mllam_data_prep/ops/cropping.py   |   90 +-
 3 files changed, 1983 insertions(+), 49 deletions(-)

diff --git a/docs/domain-cropping.ipynb b/docs/domain-cropping.ipynb
index 305b889..f358d62 100644
--- a/docs/domain-cropping.ipynb
+++ b/docs/domain-cropping.ipynb
@@ -6,12 +6,14 @@
    "source": [
     "# Cropping a domain using the lat/lon convex hull of another domain\n",
     "\n",
-    "This notebook demonstrates how to crop a domain using the lat/lon convex hull of another domain. This is useful when a) you have two datasets on two different overlapping domains where you only want to keep the overlapping part, or b) in the case where you want to run limited-area simulations and need to create a dataset that provides the boundary conditions for the limited-area domain."
+    "This notebook demonstrates how convex hull based cropping works in `mllam-data-prep`.\n",
+    "\n",
+    "This method crops a domain using the lat/lon convex hull of another domain. This is useful when a) you have two datasets on two different overlapping domains where you only want to keep the overlapping part, or b) in the case where you want to run limited-area simulations and need to create a dataset that provides the boundary conditions for the limited-area domain."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -37,7 +39,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -70,9 +72,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\u001b[32m2025-01-08 17:28:54.812\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmllam_data_prep.create_dataset\u001b[0m:\u001b[36mcreate_dataset\u001b[0m:\u001b[36m144\u001b[0m - \u001b[1mLoading dataset lam_surface_analysis from /var/folders/n8/kcprfkd918967drydbc_bphw0000gr/T/tmpu4sstlah/lam/surface_analysis_8633b219-3a8a-4986-9d6a-c5fa9d06a2ea.zarr\u001b[0m\n",
+      "\u001b[32m2025-01-08 17:28:54.858\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmllam_data_prep.create_dataset\u001b[0m:\u001b[36mcreate_dataset\u001b[0m:\u001b[36m169\u001b[0m - \u001b[1mMapping dimensions and variables for dataset lam_surface_analysis to state\u001b[0m\n",
+      "\u001b[32m2025-01-08 17:28:54.861\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmllam_data_prep.create_dataset\u001b[0m:\u001b[36m_merge_dataarrays_by_target\u001b[0m:\u001b[36m49\u001b[0m - \u001b[1mMerging dataarrays for target variable `state`\u001b[0m\n",
+      "\u001b[32m2025-01-08 17:28:54.862\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmllam_data_prep.create_dataset\u001b[0m:\u001b[36mcreate_dataset\u001b[0m:\u001b[36m204\u001b[0m - \u001b[1mChunking dataset with {}\u001b[0m\n",
+      "\u001b[32m2025-01-08 17:28:54.863\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmllam_data_prep.create_dataset\u001b[0m:\u001b[36mcreate_dataset\u001b[0m:\u001b[36m144\u001b[0m - \u001b[1mLoading dataset global_surface_analysis from /var/folders/n8/kcprfkd918967drydbc_bphw0000gr/T/tmpu4sstlah/global/surface_analysis_31c04db2-76af-4bcb-b2d1-b109001fb04a.zarr\u001b[0m\n",
+      "\u001b[32m2025-01-08 17:28:54.866\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmllam_data_prep.create_dataset\u001b[0m:\u001b[36mcreate_dataset\u001b[0m:\u001b[36m169\u001b[0m - \u001b[1mMapping dimensions and variables for dataset global_surface_analysis to state\u001b[0m\n",
+      "\u001b[32m2025-01-08 17:28:54.868\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmllam_data_prep.create_dataset\u001b[0m:\u001b[36m_merge_dataarrays_by_target\u001b[0m:\u001b[36m49\u001b[0m - \u001b[1mMerging dataarrays for target variable `state`\u001b[0m\n",
+      "\u001b[32m2025-01-08 17:28:54.869\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmllam_data_prep.create_dataset\u001b[0m:\u001b[36mcreate_dataset\u001b[0m:\u001b[36m204\u001b[0m - \u001b[1mChunking dataset with {}\u001b[0m\n"
+     ]
+    }
+   ],
    "source": [
     "ds_lam = mdp.create_dataset(config=config_lam)\n",
     "ds_global = mdp.create_dataset(config=config_global)"
@@ -80,18 +97,1794 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
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+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 200kB\n",
+       "Dimensions:                       (time: 5, grid_index: 2500, state_feature: 1)\n",
+       "Coordinates:\n",
+       "  * time                          (time) datetime64[ns] 40B 2000-01-01 ... 20...\n",
+       "    lat                           (grid_index) float64 20kB dask.array&lt;chunksize=(2500,), meta=np.ndarray&gt;\n",
+       "    lon                           (grid_index) float64 20kB dask.array&lt;chunksize=(2500,), meta=np.ndarray&gt;\n",
+       "    x                             (grid_index) float64 20kB dask.array&lt;chunksize=(2500,), meta=np.ndarray&gt;\n",
+       "    y                             (grid_index) float64 20kB dask.array&lt;chunksize=(2500,), meta=np.ndarray&gt;\n",
+       "  * state_feature                 (state_feature) &lt;U15 60B &#x27;pres_seasurface&#x27;\n",
+       "    state_feature_units           (state_feature) &lt;U1 4B dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;\n",
+       "    state_feature_long_name       (state_feature) &lt;U1 4B dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;\n",
+       "    state_feature_source_dataset  (state_feature) &lt;U20 80B dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;\n",
+       "  * grid_index                    (grid_index) int64 20kB 0 1 2 ... 2498 2499\n",
+       "Data variables:\n",
+       "    state                         (state_feature, time, grid_index) float64 100kB dask.array&lt;chunksize=(1, 5, 2500), meta=np.ndarray&gt;\n",
+       "Attributes:\n",
+       "    schema_version:   v0.5.0\n",
+       "    dataset_version:  v0.1.0\n",
+       "    created_on:       2025-01-08T17:28:54\n",
+       "    created_with:     mllam-data-prep (https://github.com/mllam/mllam-data-prep)\n",
+       "    mdp_version:      v0.5.0</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-0332cd33-acf2-4a76-b352-d6ea924e7127' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-0332cd33-acf2-4a76-b352-d6ea924e7127' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 5</li><li><span class='xr-has-index'>grid_index</span>: 2500</li><li><span class='xr-has-index'>state_feature</span>: 1</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-de7b73b6-58e1-401b-b433-19d6ab115769' class='xr-section-summary-in' type='checkbox'  checked><label for='section-de7b73b6-58e1-401b-b433-19d6ab115769' class='xr-section-summary' >Coordinates: <span>(10)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2000-01-01 ... 2000-01-02</div><input id='attrs-c2d6e351-cb01-4105-aab1-a0df605e76b9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c2d6e351-cb01-4105-aab1-a0df605e76b9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ee2a169a-5a18-415a-9816-187fdcc73bdb' class='xr-var-data-in' type='checkbox'><label for='data-ee2a169a-5a18-415a-9816-187fdcc73bdb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;2000-01-01T00:00:00.000000000&#x27;, &#x27;2000-01-01T06:00:00.000000000&#x27;,\n",
+       "       &#x27;2000-01-01T12:00:00.000000000&#x27;, &#x27;2000-01-01T18:00:00.000000000&#x27;,\n",
+       "       &#x27;2000-01-02T00:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lat</span></div><div class='xr-var-dims'>(grid_index)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(2500,), meta=np.ndarray&gt;</div><input id='attrs-805343c9-dd5f-4a4a-9893-a15a76a9b530' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-805343c9-dd5f-4a4a-9893-a15a76a9b530' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cb75b819-d292-414b-8045-e1d82c944b98' class='xr-var-data-in' type='checkbox'><label for='data-cb75b819-d292-414b-8045-e1d82c944b98' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 19.53 kiB </td>\n",
+       "                        <td> 19.53 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (2500,) </td>\n",
+       "                        <td> (2500,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2500</text>\n",
+       "  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon</span></div><div class='xr-var-dims'>(grid_index)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(2500,), meta=np.ndarray&gt;</div><input id='attrs-3928647a-7e8b-4f72-af1d-dcab6c46bd80' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3928647a-7e8b-4f72-af1d-dcab6c46bd80' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c60ab730-19f3-4d1b-8b0d-0222b412fe6e' class='xr-var-data-in' type='checkbox'><label for='data-c60ab730-19f3-4d1b-8b0d-0222b412fe6e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 19.53 kiB </td>\n",
+       "                        <td> 19.53 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (2500,) </td>\n",
+       "                        <td> (2500,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2500</text>\n",
+       "  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>x</span></div><div class='xr-var-dims'>(grid_index)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(2500,), meta=np.ndarray&gt;</div><input id='attrs-5921fc2c-799d-4b0e-9166-311e8d129723' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5921fc2c-799d-4b0e-9166-311e8d129723' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ac1ca605-e35a-4692-94f3-69e73615d15c' class='xr-var-data-in' type='checkbox'><label for='data-ac1ca605-e35a-4692-94f3-69e73615d15c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 19.53 kiB </td>\n",
+       "                        <td> 19.53 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (2500,) </td>\n",
+       "                        <td> (2500,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 1 graph layer </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2500</text>\n",
+       "  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>y</span></div><div class='xr-var-dims'>(grid_index)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(2500,), meta=np.ndarray&gt;</div><input id='attrs-b2d3eb55-3ecf-4e69-9608-19bbf7beb16c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b2d3eb55-3ecf-4e69-9608-19bbf7beb16c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-142bc523-5fb1-4600-b2fa-7455a597f5aa' class='xr-var-data-in' type='checkbox'><label for='data-142bc523-5fb1-4600-b2fa-7455a597f5aa' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 19.53 kiB </td>\n",
+       "                        <td> 19.53 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (2500,) </td>\n",
+       "                        <td> (2500,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 1 graph layer </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2500</text>\n",
+       "  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>state_feature</span></div><div class='xr-var-dims'>(state_feature)</div><div class='xr-var-dtype'>&lt;U15</div><div class='xr-var-preview xr-preview'>&#x27;pres_seasurface&#x27;</div><input id='attrs-909eabcc-1739-4245-a6b8-19131f3dc2d9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-909eabcc-1739-4245-a6b8-19131f3dc2d9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1a78fc8b-a065-414f-8ad3-1dd97a927144' class='xr-var-data-in' type='checkbox'><label for='data-1a78fc8b-a065-414f-8ad3-1dd97a927144' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;pres_seasurface&#x27;], dtype=&#x27;&lt;U15&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>state_feature_units</span></div><div class='xr-var-dims'>(state_feature)</div><div class='xr-var-dtype'>&lt;U1</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;</div><input id='attrs-7a0e33f7-e0ad-45d3-bbb0-3aba4b3e3bcb' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7a0e33f7-e0ad-45d3-bbb0-3aba4b3e3bcb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cb4003ae-0ee9-4967-8277-914fa7f19a8b' class='xr-var-data-in' type='checkbox'><label for='data-cb4003ae-0ee9-4967-8277-914fa7f19a8b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 4 B </td>\n",
+       "                        <td> 4 B </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (1,) </td>\n",
+       "                        <td> (1,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 1 graph layer </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> <U1 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"120\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1</text>\n",
+       "  <text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,60.000000)\">1</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>state_feature_long_name</span></div><div class='xr-var-dims'>(state_feature)</div><div class='xr-var-dtype'>&lt;U1</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;</div><input id='attrs-5a1a104b-00f7-4109-8bbc-437e02911a0d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5a1a104b-00f7-4109-8bbc-437e02911a0d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d292a00f-da13-4192-93b8-a90281f57e56' class='xr-var-data-in' type='checkbox'><label for='data-d292a00f-da13-4192-93b8-a90281f57e56' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 4 B </td>\n",
+       "                        <td> 4 B </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (1,) </td>\n",
+       "                        <td> (1,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 1 graph layer </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> <U1 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"120\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1</text>\n",
+       "  <text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,60.000000)\">1</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>state_feature_source_dataset</span></div><div class='xr-var-dims'>(state_feature)</div><div class='xr-var-dtype'>&lt;U20</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;</div><input id='attrs-58ac850e-85cd-442c-9555-3e3b72214494' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-58ac850e-85cd-442c-9555-3e3b72214494' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-02a01a5c-25ab-458b-81be-2a8b7a45937e' class='xr-var-data-in' type='checkbox'><label for='data-02a01a5c-25ab-458b-81be-2a8b7a45937e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 80 B </td>\n",
+       "                        <td> 80 B </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (1,) </td>\n",
+       "                        <td> (1,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 1 graph layer </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> <U20 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"120\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1</text>\n",
+       "  <text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,60.000000)\">1</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>grid_index</span></div><div class='xr-var-dims'>(grid_index)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 ... 2496 2497 2498 2499</div><input id='attrs-c7dff998-0817-4743-a984-e73885c29e6b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c7dff998-0817-4743-a984-e73885c29e6b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b51764f2-987f-44bf-901f-b1b6438e1c76' class='xr-var-data-in' type='checkbox'><label for='data-b51764f2-987f-44bf-901f-b1b6438e1c76' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([   0,    1,    2, ..., 2497, 2498, 2499])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e13f402f-3234-49df-8a35-101d0ff5cf08' class='xr-section-summary-in' type='checkbox'  checked><label for='section-e13f402f-3234-49df-8a35-101d0ff5cf08' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>state</span></div><div class='xr-var-dims'>(state_feature, time, grid_index)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 5, 2500), meta=np.ndarray&gt;</div><input id='attrs-fc10491f-b722-4d4a-9f17-af9eefeac541' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fc10491f-b722-4d4a-9f17-af9eefeac541' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dbe154b5-b3b3-4646-a037-3140ad7014fa' class='xr-var-data-in' type='checkbox'><label for='data-dbe154b5-b3b3-4646-a037-3140ad7014fa' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>variables_mapping_dim :</span></dt><dd>state_feature</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 97.66 kiB </td>\n",
+       "                        <td> 97.66 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (1, 5, 2500) </td>\n",
+       "                        <td> (1, 5, 2500) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 4 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"194\" height=\"90\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"24\" y2=\"14\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"10\" y1=\"25\" x2=\"24\" y2=\"40\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"40\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"10.0,0.0 24.9485979497544,14.948597949754403 24.9485979497544,40.36121446433689 10.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"24\" y1=\"14\" x2=\"144\" y2=\"14\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"24\" y2=\"14\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"130\" y1=\"0\" x2=\"144\" y2=\"14\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"10.0,0.0 130.0,0.0 144.9485979497544,14.948597949754403 24.9485979497544,14.948597949754403\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"24\" y1=\"14\" x2=\"144\" y2=\"14\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"24\" y1=\"40\" x2=\"144\" y2=\"40\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"40\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"144\" y1=\"14\" x2=\"144\" y2=\"40\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"24.9485979497544,14.948597949754403 144.9485979497544,14.948597949754403 144.9485979497544,40.36121446433689 24.9485979497544,40.36121446433689\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"84.948598\" y=\"60.361214\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2500</text>\n",
+       "  <text x=\"164.948598\" y=\"27.654906\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,164.948598,27.654906)\">5</text>\n",
+       "  <text x=\"7.474299\" y=\"52.886915\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,7.474299,52.886915)\">1</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-fe8e63dc-901f-4332-9041-e947a227dbdb' class='xr-section-summary-in' type='checkbox'  ><label for='section-fe8e63dc-901f-4332-9041-e947a227dbdb' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-d9b27e40-908d-4c40-9d5e-7635f4472d6d' class='xr-index-data-in' type='checkbox'/><label for='index-d9b27e40-908d-4c40-9d5e-7635f4472d6d' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2000-01-01 00:00:00&#x27;, &#x27;2000-01-01 06:00:00&#x27;,\n",
+       "               &#x27;2000-01-01 12:00:00&#x27;, &#x27;2000-01-01 18:00:00&#x27;,\n",
+       "               &#x27;2000-01-02 00:00:00&#x27;],\n",
+       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>state_feature</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-89ec57b5-201f-466e-b79c-c6e0f5ecbdbb' class='xr-index-data-in' type='checkbox'/><label for='index-89ec57b5-201f-466e-b79c-c6e0f5ecbdbb' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;pres_seasurface&#x27;], dtype=&#x27;object&#x27;, name=&#x27;state_feature&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>grid_index</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-2dd2ede7-978b-430d-9b2f-a35a2d8b3867' class='xr-index-data-in' type='checkbox'/><label for='index-2dd2ede7-978b-430d-9b2f-a35a2d8b3867' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([   0,    1,    2,    3,    4,    5,    6,    7,    8,    9,\n",
+       "       ...\n",
+       "       2490, 2491, 2492, 2493, 2494, 2495, 2496, 2497, 2498, 2499],\n",
+       "      dtype=&#x27;int64&#x27;, name=&#x27;grid_index&#x27;, length=2500))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-4541135c-155b-483f-912a-83592977d087' class='xr-section-summary-in' type='checkbox'  checked><label for='section-4541135c-155b-483f-912a-83592977d087' class='xr-section-summary' >Attributes: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>schema_version :</span></dt><dd>v0.5.0</dd><dt><span>dataset_version :</span></dt><dd>v0.1.0</dd><dt><span>created_on :</span></dt><dd>2025-01-08T17:28:54</dd><dt><span>created_with :</span></dt><dd>mllam-data-prep (https://github.com/mllam/mllam-data-prep)</dd><dt><span>mdp_version :</span></dt><dd>v0.5.0</dd></dl></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.Dataset> Size: 200kB\n",
+       "Dimensions:                       (time: 5, grid_index: 2500, state_feature: 1)\n",
+       "Coordinates:\n",
+       "  * time                          (time) datetime64[ns] 40B 2000-01-01 ... 20...\n",
+       "    lat                           (grid_index) float64 20kB dask.array<chunksize=(2500,), meta=np.ndarray>\n",
+       "    lon                           (grid_index) float64 20kB dask.array<chunksize=(2500,), meta=np.ndarray>\n",
+       "    x                             (grid_index) float64 20kB dask.array<chunksize=(2500,), meta=np.ndarray>\n",
+       "    y                             (grid_index) float64 20kB dask.array<chunksize=(2500,), meta=np.ndarray>\n",
+       "  * state_feature                 (state_feature) <U15 60B 'pres_seasurface'\n",
+       "    state_feature_units           (state_feature) <U1 4B dask.array<chunksize=(1,), meta=np.ndarray>\n",
+       "    state_feature_long_name       (state_feature) <U1 4B dask.array<chunksize=(1,), meta=np.ndarray>\n",
+       "    state_feature_source_dataset  (state_feature) <U20 80B dask.array<chunksize=(1,), meta=np.ndarray>\n",
+       "  * grid_index                    (grid_index) int64 20kB 0 1 2 ... 2498 2499\n",
+       "Data variables:\n",
+       "    state                         (state_feature, time, grid_index) float64 100kB dask.array<chunksize=(1, 5, 2500), meta=np.ndarray>\n",
+       "Attributes:\n",
+       "    schema_version:   v0.5.0\n",
+       "    dataset_version:  v0.1.0\n",
+       "    created_on:       2025-01-08T17:28:54\n",
+       "    created_with:     mllam-data-prep (https://github.com/mllam/mllam-data-prep)\n",
+       "    mdp_version:      v0.5.0"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "ds_lam"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
+       "<defs>\n",
+       "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
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+       "</symbol>\n",
+       "<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n",
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+       "<path d=\"M23 26h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+       "<path d=\"M23 22h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+       "<path d=\"M23 18h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+       "</symbol>\n",
+       "</defs>\n",
+       "</svg>\n",
+       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
+       " *\n",
+       " */\n",
+       "\n",
+       ":root {\n",
+       "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
+       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
+       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
+       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
+       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
+       "  --xr-background-color: var(--jp-layout-color0, white);\n",
+       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
+       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
+       "}\n",
+       "\n",
+       "html[theme=dark],\n",
+       "body[data-theme=dark],\n",
+       "body.vscode-dark {\n",
+       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
+       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
+       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
+       "  --xr-border-color: #1F1F1F;\n",
+       "  --xr-disabled-color: #515151;\n",
+       "  --xr-background-color: #111111;\n",
+       "  --xr-background-color-row-even: #111111;\n",
+       "  --xr-background-color-row-odd: #313131;\n",
+       "}\n",
+       "\n",
+       ".xr-wrap {\n",
+       "  display: block !important;\n",
+       "  min-width: 300px;\n",
+       "  max-width: 700px;\n",
+       "}\n",
+       "\n",
+       ".xr-text-repr-fallback {\n",
+       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-header {\n",
+       "  padding-top: 6px;\n",
+       "  padding-bottom: 6px;\n",
+       "  margin-bottom: 4px;\n",
+       "  border-bottom: solid 1px var(--xr-border-color);\n",
+       "}\n",
+       "\n",
+       ".xr-header > div,\n",
+       ".xr-header > ul {\n",
+       "  display: inline;\n",
+       "  margin-top: 0;\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type,\n",
+       ".xr-array-name {\n",
+       "  margin-left: 2px;\n",
+       "  margin-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-sections {\n",
+       "  padding-left: 0 !important;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input + label {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label {\n",
+       "  cursor: pointer;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label:hover {\n",
+       "  color: var(--xr-font-color0);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary {\n",
+       "  grid-column: 1;\n",
+       "  color: var(--xr-font-color2);\n",
+       "  font-weight: 500;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary > span {\n",
+       "  display: inline-block;\n",
+       "  padding-left: 0.5em;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in + label:before {\n",
+       "  display: inline-block;\n",
+       "  content: '►';\n",
+       "  font-size: 11px;\n",
+       "  width: 15px;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label:before {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label:before {\n",
+       "  content: '▼';\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label > span {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary,\n",
+       ".xr-section-inline-details {\n",
+       "  padding-top: 4px;\n",
+       "  padding-bottom: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-inline-details {\n",
+       "  grid-column: 2 / -1;\n",
+       "}\n",
+       "\n",
+       ".xr-section-details {\n",
+       "  display: none;\n",
+       "  grid-column: 1 / -1;\n",
+       "  margin-bottom: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap {\n",
+       "  grid-column: 1 / -1;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 20px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap > label {\n",
+       "  grid-column: 1;\n",
+       "  vertical-align: top;\n",
+       "}\n",
+       "\n",
+       ".xr-preview {\n",
+       "  color: var(--xr-font-color3);\n",
+       "}\n",
+       "\n",
+       ".xr-array-preview,\n",
+       ".xr-array-data {\n",
+       "  padding: 0 5px !important;\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-array-data,\n",
+       ".xr-array-in:checked ~ .xr-array-preview {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-array-in:checked ~ .xr-array-data,\n",
+       ".xr-array-preview {\n",
+       "  display: inline-block;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list {\n",
+       "  display: inline-block !important;\n",
+       "  list-style: none;\n",
+       "  padding: 0 !important;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li {\n",
+       "  display: inline-block;\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:before {\n",
+       "  content: '(';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:after {\n",
+       "  content: ')';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li:not(:last-child):after {\n",
+       "  content: ',';\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-has-index {\n",
+       "  font-weight: bold;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list,\n",
+       ".xr-var-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > div,\n",
+       ".xr-var-item label,\n",
+       ".xr-var-item > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-even);\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > .xr-var-name:hover span {\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list > li:nth-child(odd) > div,\n",
+       ".xr-var-list > li:nth-child(odd) > label,\n",
+       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-odd);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name {\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dims {\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dtype {\n",
+       "  grid-column: 3;\n",
+       "  text-align: right;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-preview {\n",
+       "  grid-column: 4;\n",
+       "}\n",
+       "\n",
+       ".xr-index-preview {\n",
+       "  grid-column: 2 / 5;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name,\n",
+       ".xr-var-dims,\n",
+       ".xr-var-dtype,\n",
+       ".xr-preview,\n",
+       ".xr-attrs dt {\n",
+       "  white-space: nowrap;\n",
+       "  overflow: hidden;\n",
+       "  text-overflow: ellipsis;\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name:hover,\n",
+       ".xr-var-dims:hover,\n",
+       ".xr-var-dtype:hover,\n",
+       ".xr-attrs dt:hover {\n",
+       "  overflow: visible;\n",
+       "  width: auto;\n",
+       "  z-index: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  display: none;\n",
+       "  background-color: var(--xr-background-color) !important;\n",
+       "  padding-bottom: 5px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+       ".xr-var-data-in:checked ~ .xr-var-data,\n",
+       ".xr-index-data-in:checked ~ .xr-index-data {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > table {\n",
+       "  float: right;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-index-name div,\n",
+       ".xr-index-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 125px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt,\n",
+       ".xr-attrs dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2,\n",
+       ".xr-no-icon {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 50kB\n",
+       "Dimensions:                       (time: 5, grid_index: 625, state_feature: 1)\n",
+       "Coordinates:\n",
+       "  * time                          (time) datetime64[ns] 40B 2000-01-01 ... 20...\n",
+       "    lat                           (grid_index) float64 5kB dask.array&lt;chunksize=(625,), meta=np.ndarray&gt;\n",
+       "    lon                           (grid_index) float64 5kB dask.array&lt;chunksize=(625,), meta=np.ndarray&gt;\n",
+       "    x                             (grid_index) float64 5kB dask.array&lt;chunksize=(625,), meta=np.ndarray&gt;\n",
+       "    y                             (grid_index) float64 5kB dask.array&lt;chunksize=(625,), meta=np.ndarray&gt;\n",
+       "  * state_feature                 (state_feature) &lt;U15 60B &#x27;pres_seasurface&#x27;\n",
+       "    state_feature_units           (state_feature) &lt;U1 4B dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;\n",
+       "    state_feature_long_name       (state_feature) &lt;U1 4B dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;\n",
+       "    state_feature_source_dataset  (state_feature) &lt;U23 92B dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;\n",
+       "  * grid_index                    (grid_index) int64 5kB 0 1 2 3 ... 622 623 624\n",
+       "Data variables:\n",
+       "    state                         (state_feature, time, grid_index) float64 25kB dask.array&lt;chunksize=(1, 5, 625), meta=np.ndarray&gt;\n",
+       "Attributes:\n",
+       "    schema_version:   v0.5.0\n",
+       "    dataset_version:  v0.1.0\n",
+       "    created_on:       2025-01-08T17:28:54\n",
+       "    created_with:     mllam-data-prep (https://github.com/mllam/mllam-data-prep)\n",
+       "    mdp_version:      v0.5.0</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-736b5ced-9462-486a-bdd7-34c9027c5981' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-736b5ced-9462-486a-bdd7-34c9027c5981' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 5</li><li><span class='xr-has-index'>grid_index</span>: 625</li><li><span class='xr-has-index'>state_feature</span>: 1</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-e4a4d801-ce30-4b33-a7ac-c6c23a92e49c' class='xr-section-summary-in' type='checkbox'  checked><label for='section-e4a4d801-ce30-4b33-a7ac-c6c23a92e49c' class='xr-section-summary' >Coordinates: <span>(10)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2000-01-01 ... 2000-01-02</div><input id='attrs-98e7bc32-b1f2-4f53-8221-f26b840906f8' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-98e7bc32-b1f2-4f53-8221-f26b840906f8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8420408c-efab-4edb-8ca3-f26ca212b93b' class='xr-var-data-in' type='checkbox'><label for='data-8420408c-efab-4edb-8ca3-f26ca212b93b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;2000-01-01T00:00:00.000000000&#x27;, &#x27;2000-01-01T06:00:00.000000000&#x27;,\n",
+       "       &#x27;2000-01-01T12:00:00.000000000&#x27;, &#x27;2000-01-01T18:00:00.000000000&#x27;,\n",
+       "       &#x27;2000-01-02T00:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lat</span></div><div class='xr-var-dims'>(grid_index)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(625,), meta=np.ndarray&gt;</div><input id='attrs-cf5cb4b1-e2a2-4d7d-8a31-dcafb9752eeb' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-cf5cb4b1-e2a2-4d7d-8a31-dcafb9752eeb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bb22c341-979e-40e8-8c72-06c8d488878f' class='xr-var-data-in' type='checkbox'><label for='data-bb22c341-979e-40e8-8c72-06c8d488878f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 4.88 kiB </td>\n",
+       "                        <td> 4.88 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (625,) </td>\n",
+       "                        <td> (625,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
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+       "\n",
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+       "\n",
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+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >625</text>\n",
+       "  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon</span></div><div class='xr-var-dims'>(grid_index)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(625,), meta=np.ndarray&gt;</div><input id='attrs-59b9ba76-72d9-42ae-aa58-96b0b55f46fd' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-59b9ba76-72d9-42ae-aa58-96b0b55f46fd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fa61f999-e88a-41c3-b363-d3a09490b06f' class='xr-var-data-in' type='checkbox'><label for='data-fa61f999-e88a-41c3-b363-d3a09490b06f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 4.88 kiB </td>\n",
+       "                        <td> 4.88 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (625,) </td>\n",
+       "                        <td> (625,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >625</text>\n",
+       "  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>x</span></div><div class='xr-var-dims'>(grid_index)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(625,), meta=np.ndarray&gt;</div><input id='attrs-93619d03-1079-43cd-820b-b41ffb5bda15' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-93619d03-1079-43cd-820b-b41ffb5bda15' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-034a4251-4751-4bcf-95c7-3abce9d1370a' class='xr-var-data-in' type='checkbox'><label for='data-034a4251-4751-4bcf-95c7-3abce9d1370a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 4.88 kiB </td>\n",
+       "                        <td> 4.88 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (625,) </td>\n",
+       "                        <td> (625,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 1 graph layer </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >625</text>\n",
+       "  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>y</span></div><div class='xr-var-dims'>(grid_index)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(625,), meta=np.ndarray&gt;</div><input id='attrs-4e93df29-39a8-4224-853f-80cfa213122b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4e93df29-39a8-4224-853f-80cfa213122b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-99c4e516-743d-41fd-973d-76565af3ebe0' class='xr-var-data-in' type='checkbox'><label for='data-99c4e516-743d-41fd-973d-76565af3ebe0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 4.88 kiB </td>\n",
+       "                        <td> 4.88 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (625,) </td>\n",
+       "                        <td> (625,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 1 graph layer </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >625</text>\n",
+       "  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>state_feature</span></div><div class='xr-var-dims'>(state_feature)</div><div class='xr-var-dtype'>&lt;U15</div><div class='xr-var-preview xr-preview'>&#x27;pres_seasurface&#x27;</div><input id='attrs-ecb21910-9bf6-45f0-8a7d-c4fb00506281' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ecb21910-9bf6-45f0-8a7d-c4fb00506281' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-47a3e2bc-c183-4ecb-a418-9f3473b10d9f' class='xr-var-data-in' type='checkbox'><label for='data-47a3e2bc-c183-4ecb-a418-9f3473b10d9f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;pres_seasurface&#x27;], dtype=&#x27;&lt;U15&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>state_feature_units</span></div><div class='xr-var-dims'>(state_feature)</div><div class='xr-var-dtype'>&lt;U1</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;</div><input id='attrs-aaebe91c-6d90-47b0-beb0-819ee88359d5' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-aaebe91c-6d90-47b0-beb0-819ee88359d5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8bdce67b-2004-468c-9ca4-0bf94c25fd54' class='xr-var-data-in' type='checkbox'><label for='data-8bdce67b-2004-468c-9ca4-0bf94c25fd54' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 4 B </td>\n",
+       "                        <td> 4 B </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (1,) </td>\n",
+       "                        <td> (1,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 1 graph layer </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> <U1 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"120\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1</text>\n",
+       "  <text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,60.000000)\">1</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>state_feature_long_name</span></div><div class='xr-var-dims'>(state_feature)</div><div class='xr-var-dtype'>&lt;U1</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;</div><input id='attrs-d7516ad1-f334-4a3b-8feb-b8e50baa0a64' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d7516ad1-f334-4a3b-8feb-b8e50baa0a64' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6dd4c213-aa2c-46e3-84ad-379e89d016ba' class='xr-var-data-in' type='checkbox'><label for='data-6dd4c213-aa2c-46e3-84ad-379e89d016ba' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 4 B </td>\n",
+       "                        <td> 4 B </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (1,) </td>\n",
+       "                        <td> (1,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 1 graph layer </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> <U1 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"120\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1</text>\n",
+       "  <text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,60.000000)\">1</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>state_feature_source_dataset</span></div><div class='xr-var-dims'>(state_feature)</div><div class='xr-var-dtype'>&lt;U23</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;</div><input id='attrs-eddb0aba-0f4a-4482-9d6e-4fb0a1c9e344' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-eddb0aba-0f4a-4482-9d6e-4fb0a1c9e344' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cd46ffb0-72dc-41b3-8bdd-476e4084dd08' class='xr-var-data-in' type='checkbox'><label for='data-cd46ffb0-72dc-41b3-8bdd-476e4084dd08' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 92 B </td>\n",
+       "                        <td> 92 B </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (1,) </td>\n",
+       "                        <td> (1,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 1 graph layer </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> <U23 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"120\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1</text>\n",
+       "  <text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,60.000000)\">1</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>grid_index</span></div><div class='xr-var-dims'>(grid_index)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 620 621 622 623 624</div><input id='attrs-11ea847f-6efa-4b1e-8b57-35792da61003' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-11ea847f-6efa-4b1e-8b57-35792da61003' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-15219975-d0e0-43e5-8a59-26703f74a65a' class='xr-var-data-in' type='checkbox'><label for='data-15219975-d0e0-43e5-8a59-26703f74a65a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  0,   1,   2, ..., 622, 623, 624])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e2279082-05fe-4e29-a4d1-b40df41acaf1' class='xr-section-summary-in' type='checkbox'  checked><label for='section-e2279082-05fe-4e29-a4d1-b40df41acaf1' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>state</span></div><div class='xr-var-dims'>(state_feature, time, grid_index)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 5, 625), meta=np.ndarray&gt;</div><input id='attrs-88c560be-e7b6-4253-8525-cc5f18ef32b2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-88c560be-e7b6-4253-8525-cc5f18ef32b2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cb0535dd-b8cf-4b0c-b905-a130a21e515f' class='xr-var-data-in' type='checkbox'><label for='data-cb0535dd-b8cf-4b0c-b905-a130a21e515f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>variables_mapping_dim :</span></dt><dd>state_feature</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 24.41 kiB </td>\n",
+       "                        <td> 24.41 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (1, 5, 625) </td>\n",
+       "                        <td> (1, 5, 625) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 4 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"194\" height=\"90\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"24\" y2=\"14\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"10\" y1=\"25\" x2=\"24\" y2=\"40\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"40\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"10.0,0.0 24.9485979497544,14.948597949754403 24.9485979497544,40.36121446433689 10.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"24\" y1=\"14\" x2=\"144\" y2=\"14\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"24\" y2=\"14\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"130\" y1=\"0\" x2=\"144\" y2=\"14\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"10.0,0.0 130.0,0.0 144.9485979497544,14.948597949754403 24.9485979497544,14.948597949754403\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"24\" y1=\"14\" x2=\"144\" y2=\"14\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"24\" y1=\"40\" x2=\"144\" y2=\"40\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"40\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"144\" y1=\"14\" x2=\"144\" y2=\"40\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"24.9485979497544,14.948597949754403 144.9485979497544,14.948597949754403 144.9485979497544,40.36121446433689 24.9485979497544,40.36121446433689\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"84.948598\" y=\"60.361214\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >625</text>\n",
+       "  <text x=\"164.948598\" y=\"27.654906\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,164.948598,27.654906)\">5</text>\n",
+       "  <text x=\"7.474299\" y=\"52.886915\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,7.474299,52.886915)\">1</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-7f97a916-d161-4706-bdd5-1d00896642fc' class='xr-section-summary-in' type='checkbox'  ><label for='section-7f97a916-d161-4706-bdd5-1d00896642fc' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-0db060b1-4c58-4c4e-8f48-44df4254c60a' class='xr-index-data-in' type='checkbox'/><label for='index-0db060b1-4c58-4c4e-8f48-44df4254c60a' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2000-01-01 00:00:00&#x27;, &#x27;2000-01-01 06:00:00&#x27;,\n",
+       "               &#x27;2000-01-01 12:00:00&#x27;, &#x27;2000-01-01 18:00:00&#x27;,\n",
+       "               &#x27;2000-01-02 00:00:00&#x27;],\n",
+       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>state_feature</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-2d59fd7b-2b00-4f95-bf81-14d9b5e01d7a' class='xr-index-data-in' type='checkbox'/><label for='index-2d59fd7b-2b00-4f95-bf81-14d9b5e01d7a' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;pres_seasurface&#x27;], dtype=&#x27;object&#x27;, name=&#x27;state_feature&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>grid_index</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-81e22433-16ea-4b6e-8d93-cb4a81e82594' class='xr-index-data-in' type='checkbox'/><label for='index-81e22433-16ea-4b6e-8d93-cb4a81e82594' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,\n",
+       "       ...\n",
+       "       615, 616, 617, 618, 619, 620, 621, 622, 623, 624],\n",
+       "      dtype=&#x27;int64&#x27;, name=&#x27;grid_index&#x27;, length=625))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-fc60bcdd-a3ce-40c7-b9bb-07a6030fe4b0' class='xr-section-summary-in' type='checkbox'  checked><label for='section-fc60bcdd-a3ce-40c7-b9bb-07a6030fe4b0' class='xr-section-summary' >Attributes: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>schema_version :</span></dt><dd>v0.5.0</dd><dt><span>dataset_version :</span></dt><dd>v0.1.0</dd><dt><span>created_on :</span></dt><dd>2025-01-08T17:28:54</dd><dt><span>created_with :</span></dt><dd>mllam-data-prep (https://github.com/mllam/mllam-data-prep)</dd><dt><span>mdp_version :</span></dt><dd>v0.5.0</dd></dl></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.Dataset> Size: 50kB\n",
+       "Dimensions:                       (time: 5, grid_index: 625, state_feature: 1)\n",
+       "Coordinates:\n",
+       "  * time                          (time) datetime64[ns] 40B 2000-01-01 ... 20...\n",
+       "    lat                           (grid_index) float64 5kB dask.array<chunksize=(625,), meta=np.ndarray>\n",
+       "    lon                           (grid_index) float64 5kB dask.array<chunksize=(625,), meta=np.ndarray>\n",
+       "    x                             (grid_index) float64 5kB dask.array<chunksize=(625,), meta=np.ndarray>\n",
+       "    y                             (grid_index) float64 5kB dask.array<chunksize=(625,), meta=np.ndarray>\n",
+       "  * state_feature                 (state_feature) <U15 60B 'pres_seasurface'\n",
+       "    state_feature_units           (state_feature) <U1 4B dask.array<chunksize=(1,), meta=np.ndarray>\n",
+       "    state_feature_long_name       (state_feature) <U1 4B dask.array<chunksize=(1,), meta=np.ndarray>\n",
+       "    state_feature_source_dataset  (state_feature) <U23 92B dask.array<chunksize=(1,), meta=np.ndarray>\n",
+       "  * grid_index                    (grid_index) int64 5kB 0 1 2 3 ... 622 623 624\n",
+       "Data variables:\n",
+       "    state                         (state_feature, time, grid_index) float64 25kB dask.array<chunksize=(1, 5, 625), meta=np.ndarray>\n",
+       "Attributes:\n",
+       "    schema_version:   v0.5.0\n",
+       "    dataset_version:  v0.1.0\n",
+       "    created_on:       2025-01-08T17:28:54\n",
+       "    created_with:     mllam-data-prep (https://github.com/mllam/mllam-data-prep)\n",
+       "    mdp_version:      v0.5.0"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "ds_global"
    ]
@@ -105,7 +1898,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -114,7 +1907,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -123,9 +1916,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "ds_reference = ds_lam\n",
     "ds = ds_global\n",
@@ -150,7 +1954,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -159,9 +1963,30 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<cartopy.mpl.gridliner.Gridliner at 0x165c4e300>"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x400 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "fig, ax = plt.subplots(subplot_kw=dict(projection=ccrs.PlateCarree()), figsize=(10, 4))\n",
     "xr.plot.scatter(ax=ax, ds=ds_reference, x=\"lon\", y=\"lat\", label=\"ds_ref\", marker=\".\", transform=ccrs.PlateCarree())\n",
@@ -179,21 +2004,42 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
     "max_dist = 1.5 # in degrees\n",
-    "da_global_margin_crop = cropping.crop_to_within_convex_hull_margin(\n",
-    "    ds=ds_global, ds_reference=ds_lam, max_dist=max_dist\n",
+    "da_global_margin_crop = cropping.crop_with_convex_hull(\n",
+    "    ds=ds_global, ds_reference=ds_lam, margin_thickness=max_dist\n",
     ")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 12,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<cartopy.mpl.gridliner.Gridliner at 0x165d07ad0>"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "fig, ax = plt.subplots(subplot_kw=dict(projection=ccrs.PlateCarree()), figsize=(10, 4))\n",
     "xr.plot.scatter(ax=ax, ds=ds_reference, x=\"lon\", y=\"lat\", label=\"ds_ref\", marker=\".\", transform=ccrs.PlateCarree())\n",
@@ -203,6 +2049,53 @@
     "ax.gridlines(draw_labels=[\"top\", \"left\"])"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "max_dist = 1.5 # in degrees\n",
+    "ds_global_margin_crop_without_interior = cropping.crop_with_convex_hull(\n",
+    "    ds=ds_global, ds_reference=ds_lam, margin_thickness=max_dist, include_interior=False\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<cartopy.mpl.gridliner.Gridliner at 0x165db85c0>"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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ATp06RYcOHWjUqBHLli3D2dkZY2NjVq9ezcaNG9XafPLkSfz8/Ni3bx8HDx5k8+bNNGvWjMOHD2NoaKiXMZQRjx49onnz5pQvX56FCxdSsmRJTExM2L9/P4sWLcqWhU+Zd968eRk6z87p2ltdWbBgAQMHDmTXrl0cPnyY0aNHq9a2urq6ZnruqFGjWL16NWPHjqVu3brY2toik8no2bOnTv2gzLN+/XqcnJw0jhsZ5f6xre/xJ/F+ISmGEnmKs7MzI0aMYMSIEQQHB1O9enVmzpypUgwzelht3boVDw8Ptm/frpZn+vTpavkyOl9p2TI2NqZFixb6aIoGnp6eHD16lPr162f6ED1x4gQhISFs376dRo0aqdKfPHmikTej9iitI+Hh4WrpT58+zVZ9r127RvPmzXP0tj9//nydlCM3N7c8cdr74MEDNWvgw4cPkcvlWS7Et7e3x9rampSUFJ3GgqWlJT169KBHjx4kJibSpUsXZs6cyaRJkzSmPzPCxMSE9u3b0759e+RyOSNGjOC3335j6tSpeHl54enpSXR0dJb12bZtG2ZmZhw6dAhTU1NV+urVqzXyGhgY0Lx5c5o3b87ChQuZNWsWkydPxs/PjxYtWuhlDGXEnj17SEhIYPfu3ZQqVUqVnn7aU7kh4ebNmxm+mCnz2NjY5Nm1qwuVKlWiUqVKTJkyhbNnz1K/fn1WrFjBjz/+CGR+7xowYAALFixQpcXHx2v0e0bnK9vv4OCQp/cufY8/yYL4/iCtMZTIE1JSUjSmTRwcHHBxcVGbjrC0tNQ6vWJoaAigNg1z4cIFzp07p5bPwsIC0HzYOTg40KRJE3777TdevnypUf7r16+z1yAtdO/enZSUFH744QeNY8nJyao6aWtLYmIiy5Yt0zgvo/5QPizSrv1JSUnJcGo0o/oGBgby+++/axyLi4sjJiYm0/MLeo3hr7/+qvZ9yZIlAFlanw0NDfnkk0/Ytm0bN2/e1DiediyEhISoHTMxMaFChQoIIdSWM2RG+jIMDAxUFknl2O/evTvnzp3j0KFDGueHh4eTnJysqrtMJlOz6vn7+2vsIA8NDdUoR2ltU8rUxxjKCG1jPCIiQkOBaNmyJdbW1syePVvDv6Ty3Bo1auDp6cn8+fO1Wqj1ce1mRmRkpKr/lVSqVAkDAwONe1f6+w4o+iJtP4BirKa3zFpaWgKa9y5fX19sbGyYNWuW1jGnr3uXPscfZNwfynXUb968UUu/e/cuz549U0t79uxZnqy5lsgeksVQIk+IiorC1dWVrl27UqVKFaysrDh69CiXLl1Se5OuUaMGmzdvZty4cdSqVQsrKyvat29Pu3bt2L59O507d6Zt27Y8efKEFStWUKFCBbWHhbm5ORUqVGDz5s2ULVuWokWL8tFHH/HRRx/x66+/0qBBAypVqsRnn32Gh4cHQUFBnDt3jufPn3Pt2rVctbFx48YMGzaM2bNnc/XqVVq2bImxsTEPHjzg77//5ueff6Zr167Uq1ePIkWKMGDAAEaPHo1MJmP9+vUaD4/M+qNixYrUqVOHSZMmERoaStGiRfnrr780HmCZ0a9fP7Zs2cLw4cPx8/Ojfv36pKSkcPfuXbZs2cKhQ4eoWbNmhucX9BrDJ0+e0KFDB1q1asW5c+fYsGEDvXv3pkqVKlmeO2fOHPz8/PDx8eGzzz6jQoUKhIaGcvnyZY4ePapSrFq2bImTkxP169fH0dGRO3fusHTpUtq2bYu1tbVO9RwyZAihoaE0a9YMV1dXnj59ypIlS6hatarKHcn48ePZvXs37dq1Y+DAgdSoUYOYmBhu3LjB1q1b8ff3p3jx4rRt25aFCxfSqlUrevfuTXBwML/++iteXl5cv35dJfP777/n5MmTtG3bFjc3N4KDg1m2bBmurq40aNAAQC9jKCNatmypspIOGzaM6Ohofv/9dxwcHNRezGxsbFi0aBFDhgyhVq1a9O7dmyJFinDt2jViY2NZu3YtBgYG/PHHH7Ru3ZqKFSsyaNAgSpQoQWBgIH5+ftjY2LBnz54c1VNpXc7Mon38+HFGjhxJt27dKFu2LMnJyaxfv171gqGkRo0aHD16lIULF+Li4kLp0qXx8fGhXbt2rF+/HltbWypUqMC5c+c4evQoxYoVU5NTtWpVDA0NmTt3LhEREZiamtKsWTMcHBxYvnw5/fr1o3r16vTs2RN7e3uePXvGvn37qF+/PkuXLs1R+5Xoe/xl1h8XL16kadOmTJ8+nRkzZqjye3t707hxY7Vwpv379+eff/7Rem+UyEcKZjO0xPtOQkKCGD9+vKhSpYqwtrYWlpaWokqVKhoOb6Ojo0Xv3r2FnZ2dmqsTuVwuZs2aJdzc3ISpqamoVq2a2Lt3rxgwYICGO5SzZ8+KGjVqCBMTEw3XNY8ePRL9+/cXTk5OwtjYWJQoUUK0a9dObN26NVvt0eauRsnKlStFjRo1hLm5ubC2thaVKlUS33zzjXjx4oUqz5kzZ0SdOnWEubm5cHFxEd988404dOiQhruKjPpD2ZYWLVoIU1NT4ejoKL799ltx5MiRDJ0TayMxMVHMnTtXVKxYUZiamooiRYqIGjVqiO+++05ERERkq09yQ07c1dy+fVt07dpVWFtbiyJFioiRI0dm6OBaG0FBQeKLL74QJUuWFMbGxsLJyUk0b95crFy5UpXnt99+E40aNRLFihUTpqamwtPTU4wfPz5bfbN161bRsmVLlSPtUqVKiWHDhomXL1+q5YuKihKTJk0SXl5ewsTERBQvXlzUq1dPzJ8/XyQmJqryrVq1SpQpU0aYmpqK8uXLi9WrV6v6RMmxY8dEx44dhYuLizAxMREuLi6iV69eGu5I9DGGMnJXs3v3blG5cmVhZmYm3N3dxdy5c8X//vc/rS5ydu/eLerVqyfMzc2FjY2NqF27tti0aZNanitXroguXbqofgs3NzfRvXt3cezYsUz7X4k2dzXFixcXderUyfS8x48fi08//VR4enqqHFQ3bdpUHD16VC3f3bt3RaNGjYS5ubmaQ+ewsDAxaNAgUbx4cWFlZSV8fX3F3bt3hZubm4Y7l99//114eHgIQ0NDjd/Az89P+Pr6CltbW2FmZiY8PT3FwIEDxb///qtT+5VkdE3oc/xl1h9Kd0jpXYoBGvcApZskiYJFJoSkmktISOQvTZo0ISkpiV27dmFiYpLpTssZM2bw3Xff8fr1a4oXL56PtZR4F1FOhVavXp3KlSuzd+9eAG7fvk3FihXVHDNLSEhoIq0xlJCQKBDOnj2Lvb29zhFFJCR0oUmTJtjb2xMQEKCW7ufnR926dSWlUEIiC6Q1hhIfNK9evcr0uLm5uU6+yySyx4IFCwgLCwPIF9c2+iAlJSXLhf9WVlZ57kpFInN+++03oqKiAPWx9cUXX2g4SX9XSUxM1LrhKC22trbZcjkkIaFEUgwlPmicnZ0zPT5gwADWrFmTP5X5gKhRo0ZBVyHbBAQEZOlcPf0Ce4n8x8fHp6CrkOecPXuWpk2bZppn9erVDBw4MH8qJPFeIa0xlPigOXr0aKbHXVxc1MKrSXy4xMfHc/r06UzzFPTObYkPg7CwMP77779M81SsWDHLF18JCW1IiqGEhISEhISEhAQgbT6RkJCQkJCQkJB4i6QYSkhISEhISEhIAJJiKCEhISEhISEh8RZJMXwHCQwMpG/fvhQrVgxzc3MqVarEv//+qzr+6tUrWrdujYuLCyNHjkQul6uONWnSBJlMpvE3fPjwgmhKnpGSksLUqVMpXbo05ubmeHp68sMPP6iFWvoQ++nkyZO0b98eFxcXZDKZRsxTIQTTpk3D2dkZc3NzWrRowYMHD9TynDt3jqpVq+Lu7s6qVavUjmnrM5lMxl9//ZXXTdMrmfVTUlISEyZMoFKlSlhaWuLi4kL//v158eKFWhkfQj9B1mMqLcOHD0cmk7F48WK19A+hr3Tppzt37tChQwdsbW2xtLSkVq1aavGE7927R/369XF1deXHH39UO9fd3V1rP82ZMyevm6ZXsuqn6OhoRo4ciaurqyok6ooVK9TyfAj9lJdIiuE7RlhYGPXr18fY2JgDBw5w+/ZtFixYQJEiRVR5pk6dSs2aNTlw4ACPHz/WuIF+9tlnvHz5Uu3vp59+yu+m5Clz585l+fLlLF26lDt37jB37lx++uknlixZosrzIfZTTEwMVapU4ddff9V6/KeffuKXX35hxYoVXLhwAUtLS3x9fYmPj1flGTx4MFOnTmXjxo3Mnj1bw5Hw6tWrNfqtU6dOedksvZNZP8XGxnL58mWmTp3K5cuX2b59O/fu3aNDhw5q+T6EfoKsx5SSHTt2cP78eVxcXDSOfQh9lVU/PXr0iAYNGlC+fHlOnDjB9evXmTp1KmZmZqo8I0eOpG/fvuzatYtdu3Zx9uxZtTK+//57jX4aNWpUnrZL32TVT+PGjePgwYNs2LCBO3fuMHbsWEaOHMnu3btVeT6EfspLJD+G7xhz586lZMmSrF69WpWW3rdaWFgYH3/8MZUqVcLDw4Pw8HC14xYWFjg5OeVHdQuMs2fP0rFjR1WUA3d3dzZt2sTFixdVeT7EfmrdujWtW7fWekwIweLFi5kyZQodO3YEYN26dTg6OrJz50569uwJKG7c1atXx8HBgSJFiqicCSuxs7N75/sts36ytbXlyJEjamlLly6ldu3aPHv2jFKlSgEfRj9B5n2lJDAwkFGjRnHo0CGtkUc+hL7Kqp8mT55MmzZt1F4+PT091fKEhYVRo0YNKleujIuLi8Y9y9ra+r3vp7NnzzJgwACaNGkCwNChQ/ntt9+4ePGi6uXsQ+invESyGL5j7N69m5o1a9KtWzccHByoVq0av//+u1qeiRMnMmrUKExNTbl8+TL9+/cvoNoWHPXq1ePYsWPcv38fgGvXrnH69Gm1G47UT+o8efKEV69e0aJFC1Wara0tPj4+nDt3TpU2bdo0vL29sbW1pU6dOpKfRyAiIgKZTIadnZ0qTeonBXK5nH79+jF+/HgqVqyoNc+H3ldyuZx9+/ZRtmxZfH19cXBwwMfHR2Ma9fvvv6dFixZYWFhgYGCAr69vwVS4AKlXrx67d+8mMDAQIQR+fn7cv3+fli1bqvJI/ZRLhMQ7hampqTA1NRWTJk0Sly9fFr/99pswMzMTa9asUcuXlJQkXr58qXF+48aNhbGxsbC0tFT727BhQ341IV9ISUkREyZMEDKZTBgZGQmZTCZmzZqlke9D7idA7NixQ/X9zJkzAhAvXrxQy9etWzfRvXt3tbTo6GgRGhqqtUwzMzONfnv69GmetCE/SN9P6YmLixPVq1cXvXv31jj2IfWTENr7atasWeLjjz8WcrlcCCGEm5ubWLRokca5H1Jfpe+nly9fCkBYWFiIhQsXiitXrojZs2cLmUwmTpw4oXZufHy8CA4O1ijTzc1NmJiYaPTTyZMn87o5eYa28RQfHy/69+8vAGFkZCRMTEzE2rVrNc79kPpJ30hTye8YcrmcmjVrMmvWLACqVavGzZs3WbFiBQMGDFDlMzIyytBU3qdPHyZPnqyW5ujomHeVLgC2bNnCn3/+ycaNG6lYsSJXr15l7NixuLi4SP2kBywtLbG0tNR6bNGiRWpWR0DrurL3gaSkJLp3744QguXLl2sc/9D76b///uPnn3/m8uXLyGSyTPN+yH2l3PjWsWNHvvzySwCqVq3K2bNnWbFiBY0bN1blNTU1zTC++Pjx4zXC4JUoUSJvKl1ALFmyhPPnz7N7927c3Nw4efIkX3zxBS4uLmpj5EPvp9wgKYbvGM7OzhrTLN7e3mzbtk3nMmxtbfHy8tJ31QoV48ePZ+LEiap1cZUqVeLp06fMnj1bTTHMjA+hn9KiVJCDgoLUQmkFBQVRtWrVbJXzIfSbUil8+vQpx48fx8bGJlvnfwj9dOrUKYKDg1XrLkHhMeCrr75i8eLF+Pv761TO+95XxYsXx8jISOu9PaswjOnLeZ/7KS4ujm+//ZYdO3ao1qpWrlyZq1evMn/+fI2Xh4x43/spt0hrDN8x6tevz71799TS7t+/j5ubWwHVqHASGxuLgYH68DY0NFRzSSOhTunSpXFycuLYsWOqtMjISC5cuEDdunULsGaFD6VS+ODBA44ePUqxYsUKukqFkn79+nH9+nWuXr2q+nNxcWH8+PEcOnSooKtXaDAxMaFWrVrSvT0LkpKSSEpKku7teYxkMXzH+PLLL6lXrx6zZs2ie/fuXLx4kZUrV7Jy5Uqdy4iNjeXVq1dqaaampmoub9512rdvz8yZMylVqhQVK1bkypUrLFy4kE8//VTnMt7HfoqOjubhw4eq70+ePOHq1asULVqUUqVKMXbsWH788UfKlClD6dKlmTp1Ki4uLtlyDRIeHq7Rb9bW1hlOExZGMusnZ2dnunbtyuXLl9m7dy8pKSmq9hYtWhQTExOdZLwP/QRZj6n0SrOxsTFOTk6UK1dOZxnvQ19l1U/jx4+nR48eNGrUiKZNm3Lw4EH27NnDiRMndJYRFRWl0U8WFhbZtmYXJFn1U+PGjRk/fjzm5ua4ubnxzz//sG7dOhYuXKizjPehn/KUgl7kKJF99uzZIz766CNhamoqypcvL1auXKnzuY0bNxaAxp+vr28e1jj/iYyMFGPGjBGlSpUSZmZmwsPDQ0yePFkkJCTodP772k9+fn5a2zVgwAAhhBByuVxMnTpVODo6ClNTU9G8eXNx7949ncvXVjYgZs+enUctyhsy66cnT55k2E4/Pz+dyn9f+kmIrMdUejLafJIR70tf6dJPq1atEl5eXsLMzExUqVJF7Ny5U+fy3dzctJY/bNiwPGhN3pFVP718+VIMHDhQuLi4CDMzM1GuXDmxYMEC1eamrHhf+ikvkQmRJhSEhISEhISEhITEB4u0xlBCQkJCQkJCQgKQFEMJCQkJCQkJCYm3SIqhhISEhISEhIQEICmGEhISEhISEhISb5EUQwkJCQkJCQkJCUBSDCUkJCQkJCQkJN4iKYYSEhISEhISEhKApBhKSEhISEhISEi8RQqJB8THx5OYmFjQ1ZCQkJCQkJCQyDNMTEwwMzPLNM8HrxjGx8dTunRpjbiJEhISEhISEhLvE05OTjx58iRT5fCDVwwTExN59eoVAQEBegugLYRg4MCB7Ny5E4Bq1apRsWJFNmzYoMpjbW3NzZs3SUpK4s6dO0ybNo0rV64AEBoaiqGhYaYyHj9+zCeffMLjx48BKF68OJMmTeLGjRtcvHiRgIAAfvvtNy5cuECvXr3w9vbWS9veN5KSkjh8+DAtW7bE2Ni4oKtTaNGln6pXr86jR48wNzcnLi6OGjVq0KRJE5o0aUKjRo0Axdj29PTkhx9+IDExkblz5/LkyRMsLCxU5SxYsICUlBS++eabfGmbvpHGlG5I/aQbUj/pRkH0U3JyMleuXOHevXvcv3+fo0ePcuvWLQAsLS1xdHTk2bNnJCcnY2lpyc8//0y3bt3ypW7aiIyMpGTJkiQmJmZuNSzgWM0FTkREhABERESE3srcuXOnAETdunUFICwsLERCQoKYOHGiWtDu+/fvi3Xr1gkjIyNRrVo10b9/fwGICxcuiNu3b4s9e/aIFy9eqAUHl8vlYtKkScLIyEiUKFFCnDlzRjx48EDcuHFDrQ6JiYli586dIjExUW/teh+R+kk3dOmnvXv3ChsbG60B6r/88kvVOP7ss8+EhYWFWLBggTAwMBCAaNGiRX41Jc+RxpRuSP2kG1I/6UZ+9tPr16/Fli1bhL29veoe5+bmJrp37y78/PzEy5cvxY8//ii+/vprsWzZMnHs2DERExOT5/XKCl31nQ/eYpgX7Nq1i5IlS3Lu3DkAYmNjCQoK4vnz5wC0bNmSiRMncvXqVfr370+tWrU4c+YMt2/fZt26dWzbtk1lNUmLgYEBzs7OBAYGAhAYGEj9+vVVxwMCAnB1dc12fQ8fPsydO3cYNWoUBgbSfiSJnNG2bVvu3LnDgQMH+Oeff9i/fz8hISEALFq0iK5du1KvXj0WL17M8+fP+eqrr1TnHj16FLlcLo0/CQmJQk1YWBj29vaq78ePH8fHx0c16/Ho0SPWrl2Lp6cnTZs2xdHRsaCqmmOku3AeEBISgpubG02aNAHAxcWFkiVLsn79em7cuEFUVBTNmjWje/fuAFy6dIk7d+7w66+/ArBkyRLatm3Lo0eP2LZtG3/88Qd//PEHS5Ys4fXr11pl2traYm1tne26rl69Gl9fX8aOHUtERETOGiwh8RYXFxcGDx7MunXrCAoKYseOHaplEceOHQPAwsKCffv2cfbsWUaMGKE69/Tp0wVSZwkJCQldmDJlCkWLFlX73rRpUywsLLh48SJNmjTBy8uLiRMn0qtXL5ycnPj3338LsMY5Q1IM84BPPvmE06dPU6dOHQBevHgBKNZA9O3bl9DQUHbu3MmZM2dU5zx58oT//e9/lCpViri4OGbMmIGHhwddunRh8ODBDB48mDZt2qh2Tzs6OiKXyxFCIIQgPDwcW1vbbNUzKiqKTz/9FIBNmzZRpEgRfTRfQgIAQ0NDOnXqRIsWLQDYsWOH6phMJqNu3br8+uuvNGzYEIDRo0cTGxtbIHWVkJCQyIp9+/apPtesWZNvvvmG6OhoPvvsM3x8fPjnn380zjl06FB+VlEvSFPJeUC/fv24fv06c+bMAWDOnDm8fPmS7t27c+vWLf755x/q1auHEAIXFxdMTEzo0KEDpUuXxtXVlWfPnrF3716qVaumVq6pqSnt27fHwMCA4cOHI5PJclXPPXv2qD5XqlRJ7VhiYiIvX74kMDCQoKAgKleujKenZ67kSXyYWFpaAnDz5k2EEGrjNi4ujmfPnlGsWDGuXbvG3r17VZZ0ifcDuVyu1R1YUlISRkZGxMfHayybkUhF6ifdyKt+evPmDTt27ODff/8lLCwMNzc35s+fT+PGjRk5cqRKGXRzc1M7b/78+ZQpU4ayZcsSHx+vt/pkhrGxcZYbV3VBUgzzAJlMxrx58+jSpQtOTk4sX76cypUrY2xsrFIKlfnu37/P77//TuPGjXn48CEVK1YEoGzZshrlOjs7s3v3btX3169fY2xsjJ2dXbbrKITgf//7H6CYhj59+jSTJk3i/PnzGU5X9+vXj3Xr1mVblsSHwcGDB2ndujWlS5fm008/5bPPPsPR0ZEnT54A4OnpqaYUXr9+nX79+vHq1Su+++47Jk6cqLKyS7wfJCYm8uTJE+RyucYxIQROTk4EBATk+iX3fUbqJ93QZz8pX2bi4uKIioqibNmyeHt7M2jQICwtLbGwsODZs2f06tWLXr16YWpqipWVFfHx8cjlcmxsbFS7fv39/fXQOt2xs7PDyckpV30gKYZ5hEwmo169egwYMEClTP3yyy8qpVDJd999x7x581Tfjxw5QsWKFenSpUuGZQsh+PLLL/n5558pWrQoV65coVSpUjrXTS6XM2HCBNWar4iICIYPH641b/ny5SlXrhzPnj1j8ODBOsuQ+PBwcXEBFMsipk6dyvfff0+FChW4du0aNjY2xMfHs3PnTmJjY/nrr784cOAA3t7eXLhwgZcvXwIKC6LE+4EQgpcvX2JoaEjJkiU1NhbJ5XKio6OxsrKSNh1lgtRPuqGPfoqPjycoKIiYmBhAMUtnaWlJUlKSyhKXkpKiuk9VrVoVW1vbQqGwCyGIjY0lODgYUBiScoqkGOYhUVFRKqXQ3t6eKlWqaOS5ffs2oPBrWK9ePRo0aMDQoUMz9cMkk8k4cuQIoNghVaVKFXbs2EHjxo01BmhiYiKnT5/mwYMHmJiY0KJFCxYvXsyCBQvU8rm5udGzZ0/q1KmDj49PrgaVxIdJ5cqVOXjwIH369CEkJISkpCSsra354YcfaNWqFS1btqRz584A+Pj4sGjRIj777DPV2zbAxx9/zK3dy7GOfQL1RmoKObsUHCqCV1P9VPqhHwTfkmTlgazk5GRiQ1/iUtJNzU+lEqVVxszMTFJ4MkHqJ93ITT/J5XJev37N8+fPMTExoWTJkiQkJBAcHExSUhJWVlaYm5urzaYVK1YMBweHQvWbmJubAxAcHIyDg0OOp5UlxTAPsbKyolSpUvTo0YOffvpJa54FCxbQqlUrlYk6My5cuMC9e/fo378/27Ztw9vbm2nTpnH8+HGaNm1KkSJFsLa2xsLCAlNTU7777jvs7e0ztMKUKVOGTZs2UaVKFYyMpKHwTpHfD/5XtwEtrpDSyfL19eXevXusX7+e+fPn8+rVKwYPHoyzszOvX78mJCSElJQUtRePp0+f0rSp4vyAgADGD+nMinbmIE+BBmNSZZ3+GY5OU3zuuzP37XvoBxs6KT5LsvQuK+Xi/8DAHZOYl2BlCWZ6CCAQHwnJ8WDloHksOhiMzPQjp6DkSeiVlJQUDAwMEEKQkpKCsbEx0dHR3L9/HyGE6oU0Li6O5ORkHBwcKFGiBOHh4QQEBKjKiY6OJjo6GgcHB5ycnDAxMSmoJmWJ8iUsrZUzu0jaQB4ik8koU6YMd+/ezTBPuXLlKFeunE7lTZs2jcOHDzNlyhQCAgIwMTGhe/fuTJkyhTJlyuDv709YWBiQ+uaQGZ988gk1atTQrTESWZNfylpBPPgNzKDKSvVjGcgqVqwYY8eOpWPHjjRs2BB3d3fVGp1q1arx+eefq4pISEigVatWREVFqdIiEoTig7LsBmPUZYGin3PbtuBbqZ8lWfqXdf53qL8AmQyFckUuFaj4SAh9pPgsBFin8Q8XFQRRCu8PFPXUnxKa3/IyUkIBEqMlJVRH5HI5L168ICgoCFNTU5KTk0lOTsbT05NHjx6p8qWkpGBqakrx4sUpUqQIiYmJREVFYWhoiEwmw9zcXOUpoWTJkln6JAwJCSE8PByZTIabm5teNoJkF31MaxceG+h7iqWlJXv27KFcuXKqRfj+/v5MmDCBmzdvAorp4ISEBLXzNm/eTL9+/Xj58iVnz57F1NSUw4cPA6jeZBITE3nx4gVRUVH4+/tjZ2dH79691ax/q1evpl27dhr18vT0ZNq0aRrp7x0P/RRKmTbOLoXHp/QnZ0MnODxZ8VBMy+mfFekbOiny5Zb0D2OlPG0Pfn3KAji3XGdZpUuX5vz58yxYsIBGjRrx+vVrpk+fTteuXVOLO3eOu3fvIoRCGSxSpAgTv/oytZCj02BdJ3VZLb7Xrnxnl3ojFWVJsvJWlqVDxspOdkhOs7Mz6oVCOQN1JS19vndFnlIJjQxMlaMk5u30ZehjRT59EB+psHhqIzpYf3IKgISEBGJiYnj16hWWlpYkJCSQnJwMKHYYp/X3W7x4cdzd3XF1dSU6OppHjx7x8OFDHj58iEwmUymFpqamODhkPobDw8N58uQJYWFhhIaGqmS+i0gWwzwkMDBQtYs4ICCA9u3bc+7cOVasWMFPP/3E1atX6devHwMHDqRjx45s27YNUCwi7dmzJwBnzpzBzs5O5e5h9OjRtG3bFrlczk8//cTHH3/M4MGDkclktGvXjtWrV7Nv3z5V/kGDBqmmkj08POjfvz/du3fH0dFRJ6viO40uljVtlrCckJ9WmnojFe1Rlnt0Gjz2U/wp0eeDX54Cx2cpvp+YCf7HdZbl6urKyJGKY0IIPv74Y7V1Oj4+PkyfPp1//vmHEydOMGPGDKoMHA2nS6S2L72stL9jblGWJcnKG1lmdmBZXD9yrBwUljulUhb1AhKiIDHV2oy1i36UUC3ymvi2pWqlj1g8PU3f6UteeiUUFBbKqCCIeokwd2PYNz+wbf9xwsLCuXLlClWrVs2ZrPy2hOYzd+7cUe2CNzAwoHjx4qp7TkREhJoV79mzZwA4ODhQrFgxteljU1NT4uLisLGxUbMUKl9i01vm3rx5o/b92bNneHl5FYqNKdlFUgzzkLRvGHFxcTx8+JA+ffqo5fnuu+9ISUnhwoULqjSZTMacOXOYOHEiT548YdOmTdStW5dSpUqpBpkQgpYtW7JgwQK++eYbOnfuzIYNGwgODiYiIkJN6evVqxfTpk2jXLlyyGQyhBAZuqR5r9BVWdMH+amsQf4/+OUyCCNXsn766SeOHTvG1q1bVWnm5ubMmDGDzZs3c+LEiVS3Jg3GaPafR1P9tkuJJCtvZLn66F+xUCoxSuUlvVJorefwY+nlpaTxx6hPeZkqvTKOHj3K2i17OHHiBB4eHhQvngtlO1MlNL0l9N1TDNMSGalp+Uzv4zAgIIBXr15hYWGhej7KZDLc3d25c+cOkZGRREZGYm5uTlxcHDKZDAMDA1xcXHBwcFBT/MzMzLCwsCA0NJSIiAgSExMxNTXN83bqG0kxzEP27t0LKHZali5dmvXr17N3717VG8e9e/dUawLbtm2rdq4QgmLFilG+fHl69erFnTt3GD16NP7+/kRHR3PhwgUsLS0JDw8HoFOnTtjb27N27VpatmzJgwcPVGVt2rSJFStWcPr0aRYtWkSRIkX43//+x7x58/jqq6+QyWQ8f/6cR48eER4erlqka2RkRNWqVd/dHcq6KGtNJqcqPLklP5U1pbz8evDX/Rz271dPy6asw4cPq9bzpHd03bJlS7y8vDh79iwDBgzgj8kDaZZ8jBouadboPPZTKPX6bt/pn9X7UJKlH1nPL0C5PJiStHbUtBSaWOtfKUwvLy16kpeYmJi6kSETpffJkyc4OztruDvLEfltec1nqlatSlJSEjdu3FClGRoaqimEtra2JCcnk5SUhLm5Oebm5rx69Up13MLCAnNzc4yNjUlKSgJSXWkJIbCxsSEgIIDk5GRKlCgBgJeXl+p8R0dHhBDvpFIIkmKYpygdXNasWZNx48Zx+vRp7t69y+bNm7GysqJNmzaqvK6uqTs+w8PD2bp1q8pZJ0DdunUJDw/H1NSUypUr8+mnn+Li4oKdnR2urq60adOGfv36sXz5cgwMDDSiR2gLlzd+/Hhu3brFvn37MrQgtm3bVqXgvpNkpaz5aFF4cisvv5S1/Hzwn1sOqHv2z66sn376if79+9OkSRN69erFn3/+qVIOixQpwoABA5g5cyb/nj7Ok5chAAytbsxvY9qktjOt5VcfpLceezSVZOlTVnw4xLwBMy072nNKVJC6EgOK71FBelMOY2Ji+Pzzz9m+fTvWVpZ8PVR9pmfZylUsWrWZgMAX2Nra0rBhQzVLeEY0adKEjz76CCMjIzZs2EClSpXw8/Pj5s2bjB8/nlOnTmFpYUbLhj4s+u4rihctwqBxP7Bu83YA1aaGXDtNzm/Laz5jamqqsvCBupXQ0dGRN2/eqNISExORyWRqSmBSUhKRkZGUKFGC+Ph4kpKSCAkJUSmYSj+H8fHxxMXFYWpqqua2JisPI4UdSTHMQ5RvgrNnz2bTpk2MGjWKr776iuTkZJo1a4abmxtPnz7FyMiI6dOn8/LlS4oXL87atWuJiopi7ty5DB06FFAM3q+//poffvhBpXBq46+//mLZsmU8evSIPn36ULJkSeLj41VrKdIOfoA1a9aoPvfp04fy5ctjYmJCUlISycnJahsF3lkyU9bS9IVeyC9lLb8f/Cdmpa7FzIYspSW6cuXK1KhRg6NHj+Li4sK2bdv49ddf1eJzjx49muf/HuTqv+eZ2sGMT3fH89igNPTfqd7eo9PAwDD30/Jnl2puyEi/1ECSlXNZ539XpMUEg6mJfixQ0a/VpztNrFOVmrTTorlk/Pjx/PPPP+zatAYHCznfzlnK5Rt3qVrpI/69dpvR0+ax/pcfqNf4Y0ITDTl1SvdNbGvXruXzzz/nzJkzgMIQ0KxZM4YMGcKiHycTFxLAhJm/0H3YBI7/vZLFM8bi6lmedevWcenSJf3tdM1vy2s+U758ebUNIEZGRkRFRWFjY0NQkGKDj4GBAXK5XDXzpiQxMZGHDx+qzjMyMsLa2hobGxsCAwNVlt6wsDDCwsIwNTWlYsWKhcqnYW6QFMM8ZPHixQA0aNAAX19fevbsyYEDB+jduzeXL1/mypUrnD59mqZNm7JkyRKWL19OfHw8zZo1Y86cObi5ual8EzZu3DhThRAUfg579+6Nr68vo0ePBqB9+/ZqzqyTtChCy5YtY/DgwYXaN1OuyExZ8xmhXzn5oawVxIPf4O3YazIZGo3WWVbdunV5/vy54tQmTQgKCqJo0aLs2bNHTSkEsLGxYcX8H2BDJ9puVOwGHDP97dhNb/l1qJi7dqUvI+1UvyRLP7JkaTa3GWV+78oKuRAYyGTILYtjkBwLsaGplq20a+OiXoBMlislNDo6mlWrVrHhj19pXkMRH37t4u9xrdUGzO14FpqApYU57Vo0xNrKEDebElSrNlrn8suUKaPm1/bHH3+kWrVqzJo0RlF/5/L8b8F0StZqzf1HT/HydMfKygpDA3BycspxuzTIB8trQWJoaKihRNvZ2an59TU0NMTZ2ZnY2FjVsi5ra2tkMplqfaLS1Y2joyP29vbY29sTHBzMixepLyjm5ubv5CaTjJAUwzykbNmyODs7q71Nrl27lhIlSvD8+XOOHDnChAkTsLCwYMSIEdy7d09jp3B2LHaBgYGAYrBfu3YNb29v/vjjD4yMjKhbt66qHs2bN2f48OHUqlVLbUPLe0lWyppchsYUaU7IT2WtoB78oFhrqKOsqKgonj9/TteuXWnTpg0rVqzgzp07zJkzJ+O1Ul5Nie68if3fKdbcTpgwIdXdUoMxiv7Tlz9Ir6YK/4vafE9KsnIvq0Z/uHsdbEvlahOKXAj+uhjAH6ceM6ShBz1rlcTAyAKs7BUZ0k+L5lIJffToEYmJifj41AUUm02KlvJW+Zv9uEM33GbPx6Nue1o1qUerdp3p3KO31ugu2kjvO/batWv4+flh5VQ6NfHtPflRcCxenm/T5CkKVzL6sLym32iSR5bXwoiZmRmurq48f/6cpKQk1XPT1dWV+Ph4IiIitBpQlG7gjIyMsLOzIyEhQRWCz93d/b16jkqKYR5y+fJlfHx81NJWrVqFiYkJFStWZMiQITRr1ox69eoxZ84c4uLiWLo0A597OtCxY0dWrFjBn3/+qTJpd+vWjfXr1/Pvv//Sp08fpk6dqrND7XceXZS1EzP1464mP5W1gnjwv7oNIemOZSIrNjZWNc5q167NoEGDGDRoEAkJCVkuyLas3JqiRYsSGhrK7du3efHiBS4uLiQnJ7M32JVKjqXwzOT8pKQkTpw4gZ2dHbVq1cq6fRn1k752kH/IskytwdQqV8UbyGT8ceoxj9/E8Mepx/T2KZWqFCqxdlQoU/qMRGJmDUXtNJxOW1tbc/nqdU4c2MFhv9NM+3E2M2bN5dKlS9jZ2WVZbPr1Z9HR0bRv04q549/Gord0BCvFrmNnZ2eQR6dmzqXSqxAYrK4U5pHltbAik8lwcnIiNjaW0NBQVfrz589xcHCgcuXKhIWF8fLlS+Li4ihatCju7u4YGBiQlJSEv78/ERERgCI+vDJG/PuEpBjmISVKlODixYskJydjZGSEXC7nwIEDdOzYEQ8PD7p27cqyZcuIjY3F0dGRkydP5kqeoaEhw4YNY9iwYSQlJbF//342btzIJ598wrJly7RuQHmv0VVZ0wf5qawp5eXng9+tgfZNOhnIWrt2LUFBQRw7dkwV7g7QaZeeTCbj/v37HDp0iD59+lCmTBnVJqi4uDhV9IKaNWvSrFkzfvjhB0xMTJg/fz6enp506dIFUNy0ldYAiXcXuRAMaeihshgqp5U10JMS4+npibGxMRcuXKBUt26ADWFhYdy/f5/GjRsDCqtRi/bdaNG+G9N/mIWdnR3Hjx9Xjb3sUL16dbZt24Z7lYYYkazRDrn87SySgbF+lN60ymXajSZ6trzmloiICCwsLDA2Ns6T8kuXLk2xYsUIDQ0lJETx1hscHEypUqUoWrQoRYsW1TgnrVIIFEhkk/zg/VgpWUj56quvePz4MT169GD+/PlUrVqVc+fO0aJFCwCWLFnC9u3buXnzJoGBgYwZo77+LDExkVevXmn4XdIFZVQVIQSbNm3Czs6OEiVKqBbdfhAolbWWMzXX9jUYo0jvsUm/8jJSyuqN1J9SWMhJSUlhz549NGjQgGbNmuVoiqVYsWL07NmTs2fP4ujoyN9//02TJk0AVCGtrKysWLBgAVWqVOH8+fOMHz9e7cH84sULfvzxR720SaLgMJDJ6Fm7JMe/bkLP2iW1K4V6xMrKisGDBzN+/HiOHz/OzZs3GThwoGoWZu/evfzyyy9cvXqVp0+fsm7dOuRyeY5nYr744gtCQ0PpNWgYl+485dGjRxw6dIhBgwap3/v11W4zG4XzapsSmtPF1o6K9ELg3PrBgwdcu3aN+Hg9RbJJh9JlVvqX1cyet5aWltjY2FC6dGmqVauWZYi8dxXJYpiHVKtWjU2bNjF69Gh2795Nq1atWLZsGQ0aNAAUO6I6d+7MrVu3ePr0KQ0bNlSde/z4cZo3b676Xr58eapUqcKzZ88oUqQIXbp0YdCgQaqblVwu59KlS/z777/4+/tz/PhxVcg7pd/EFy9eaF078V6TlWUtKQnu6tFdTSHk8ePHjBkzRvXg+uWXX5g+fTqTJ0/Wq5zjx4/j6upK//79uXDhgtZQjDrxNua0Qb2R1K1bl2vXrrF9+3bFlLJhGH8eucxHlSpz6NAhLl68SMOGDalbt67qdDs7O9Uuw2XLllG9enWioqKIjo7G0NAQb29vateurVBY8yu+dZp2fVCyEqIgIRqy2DiXFUplMK+VQiXz5s1TTPG2b4+1tTVfffWVylJkZ2fH9u3bmTFjBvHx8ZQpU4ZNmzZRsWLOlom4uLhw5swZJkyYQMuWLUlISMDNzY1WrVphYGCgun/rFTMbMnReXQimj+VyucrZdFBQEDKZjPj4eNzd3XO8STI+Pp7Xr18TFxeHra2tyhWcoaEhFhYWxMfHY2Njw+vXr7GyssLKSnMJxPs4bawNmciTUffuEBkZia2tLREREdjY5N0bUnqHvllx8eJFjfWJoNiV5uXlxenTp/n777/p2rUrr169omXLlty4cQNjY2NKlSpF2bJlGTZsGGFhYfj4+ODu7v7+h8DLAcop9zZt2uTZlEWeoePD/99//9W61m7Xrl0YGhpqOFfXRtK94+y/G6W9n84uJdrSA+uq6uVcvHgx6zV+6UkbxvDt9P+GDRvo16+fWraUe0eRlWnGrl272LRpEyVKlMDExIRu3bpRo0YN/vzzT/r27ZuhGHd3dy5tXkTx/QPUZKlIuw61785sKVFax5SWdulDllYKiaz4Myt5YuBG6RL2mDl4aVig5HI5kZGR2NjY6O7mIz5SY82fiuhg/a0xzC85OpCjfnqHef36tZoRw9ramqgoxcYYNzc37O3ttZ6nrZ8SEhIIDAwkPj5eFfc4PTVq1CA2NpbXr1+rhbWrXr36O9nf8fHxPHnyhNKlS2t4MtFV35EshvlEdqfTateujRCCkJAQzp8/z4kTJ9i6dSv+/v6qBbPFihUDYPPmzdy4cYOjR4/SuHFjjIyMVA+nPn366EXhSU5OJigoiJcvX/L06VM8PDyoVq1arst978gvS40ucaAB+u6kZs2mnDt3jhkzZnDo0CFVto4dOwKwf/9+mjRpgqGhofa38Yd+sLmXYpPOueUKdzVvST6xkITDM9h+R9MSnaObqpYwhm5u1dWy9PrICIM3d9h6PYxu3bqpHevduzdCCGJjYzEyMiI5OZmiRYvi5eWFp6cnMpmMjRs34u/vz4hJ37OlobqsPItvnZ+xtAuLrPO/Q/237ob0EV4tv2L85ncsYV2U0A8I5a5gpcUw7dSuLpt7EhISiI2NVUX0AoUrrLSKoZmZGcWKFcPW1hZ/f39CQkIwNjbG2dmZmJgYYmJiSElJeScVQ30gKYaFkEePHrF582a6dOlC+fLladu2LW3btuWnn37i33//5cCBA1StWlW1qL9OnTrIZDK2bt1Ko0aNAFSe2XUlJSWFs2fPcu3aNV6+fMnLly+5f/++yglreipUqMCtW7e0HtOZwjLdBfD4FJRrlnsZOipr+f3wr1OnDgcPHuTatWtUrVpVrShlBB5ra2uWLl1Kv3791F9k0sja+9t3tIiP46lLW/73w0j+t+sfIhJgSWvNh9eTJ080XHNkiZYwhg09mrK7pzmnnqVQwlrGyEmzod5I7s+aBcDy5cv5/HOFGx1LS0sGDhzIunXrGDZsGD/99JPGm3HlypWZOHEifx+/QuQ3c7A5Nyu1H/MqvnV+xtIuLLKsSirSLB30Mz2ZXzF+cyjn2bNnVKhQIcNib9++TalSpdQTdVJCZWDhnqOmaJCfltAcylI6lFY6p46NjVUFZshsglMZZ/3OnTuqz6ampnh5eSGTyVShOC0tLVUKX2BgoGrjiZeXF/fu3UMmk+Hl5fXuzSDpEUkxLIQsWLCA5cuXM3nyZHx8fBgwYAA9e/akSJEi1KpVS2N6zsfHhxUrVvDFF1+wZ88erK2tefr0KZs2bWL9+vUMGjRI9aBPSEjg8ePHPH36lKdPn+Lv78+FCxfw8/PTVhUNGjVqRLNmzTRC7mWb/FSiMpOlDPW2uRf0/it3svLTUpPDh3+VKlXYvXs3Xbt2JTExUZVuYmJC9erVGTBgAJcuXWLJkiXqspLlEAZ9dsQRt3EiMFGt3KW3bIB43NzcaN26NcuWLcu5Xy8tYQzblzOmfTljtenK4sUVLj2USuGpU6eYMmUKu3fvZsOGDfTp00ejaICBAwcycaKi/u1n7ef49zMw9JuhkqVC3/Gt8zOWdmGRZWYHlsX1Iye/YvzmUI6LiwtXr17NsFit69N0VUL1QX5aQnMhSy6Xqzx5WFlZERMTg6urK0+ePMnUgpd2/XyxYsVITEwkJiZGzYBhZGRElSpVVN+Vzq7t7Ox49uwZMpmMjz766INWCkHalVzo8PPzY/ny5arvxsbGjBo1CicnJ7p168bx48e1njd06FCuXLlCt27dqFSpkip95MiRrFmzhoSEBCZNmoSZmRkVKlSgdevWDB8+nDlz5qgpheU8StK5UWW+/fZblf/DqKgohBCIM0v4Z9UMpk+fjre3d+4aml6JOv2z4rM2JSq3ZCbrxEz9yao3UvHQTStrXSdNX4r6cifTYIy6PB0f/u3atWPChAkq64aBgQHx8fGcOHGChQsXsnTpUs0HnNKxNdC2jBGrO5phkEbvSzCwYObMmdy9e5fly5fn3tlrgzEKZ+RpSRdzeujQoRw5coQff/yRGzdu0KBBA27evElsbCx9+/ZVewCkxdHRkdOnTwNw8uRJNgXYZylLb+jQrvdGlquP/tfhWTsqlDIleRXjNwdyjIyM8PLyyvBP6SBZDSsHdTlRL+DNw3R+Bp310CA0ldCotx4qtFpCC06WUikzMzPDxsaG5ORkXr16hampqfY+BJKiw/B/rAhh5+npSWRkJFFRURrRlezs7NTuTY6Ojpibm6umr0uXLv3BK4UgWQwLHSVLllT73rdvX/7++282btzIL7/8ws6dO4mPj8fQ0JCzZ8/y5ZdfEhMTg7e3Nz179mTu3Llqu02/+uorGjdujLW1tdobVdGiRWnZsiWVKlXC29sbb29vPEUAxpu7AhHQwgEapFm8r28rXmGZ7kob6k0fsvLTUqOUl1EcaC0EBwdz9OhRfvjhB1XaF198gUwmIzAwkHHjxgHwww8/4O3tTUpKCsnJyQQEBNCrVy/KOFmxvnM8RcxlfOJtzB2Tajh+uk7/EXR0jDndokULlfun5ORknjx5ojqW9gUpPfXr1+fhw4esWrWKBqb34VbWsvRCfsXSLgyynl+AcpH6lQP5F+M3P+VAqsKUXgm1tIdIPfRjfllccyhLCMGTJ09Ua+iVoepAYUVMSEggKCgIBwcH9XtNfCQBTx+TmJQ6K6Z81imnic2MwMnRCZtiDrx8+ZLo6Gg1f4QVKlTQOXLNh4CkGBYyvLy81L6PGzeOsmXLMm7cOPz8/JDL5QwZMoTY2FiOHTtGSEgII0eO5Pz583Tt2pUyZcqo1owBdO/eHU/P1DgRy5cvp2PHjgqP+uk5ezT1c15PhSrLTiuroKa7QM0iphdZ2VDWckUmD/8N/sXYv38/LVu2pEiRIpw/f545c+YAqF4UNm/eTIcOHQCwsLCgUqVK3Lhxg4sXL/Lff/9hYGCAoaEh7u7uABzuAUVMFTdga1MZtbkKATvBTY9ty2HM6bCwMNUDYcqUKVSqVInY2NgMb/ienp7MauOYP/GtIf9iaRcmWfHhEPMGzFz1IwvyL8ZvfsYSzkwJfbteTm9yIGMlVJ/tyqYsIYTa2vgXL15gZmam2hSXkJBAQECAyt2MpaWlYrNccjwJKQKTt1MY5iRgZ2dHUQtDkmNCSZGDg6WMsJREbt68qVp/qMTBwUHy2JEOaSq5EDJq1CjV59jYWFasWAEoNqUEBASwZcsWQkNDqVixIitXrmTJkiVcunSJq1evYmJiwqZNm6hcuTKAyuVNp06dSEpKYvjw4dqVQsj/qVAo+Oku90b6l5OZpUbfctL8NlHODdh2O4kdd5J4tGUK/fr1Y9OmTQwaNIhOnTqplMKFCxcSGRlJXFycSikEKFKkCNevX0cIQUBAAP7+/jx+/JgHDx6wf2p7AIqav31TT9uPaafnc4u2MIb9d2qOy7OaoSPt7e05evQonTt35pdfflE5ls8LWdnmQ5YVE6zYbKAPtMX4VR1LM2X5rshJKy8jJVTfWDuqtwfyxhKahazExETVi1xsbCwvX75UcxgdExNDSEhIqgL4ljdv3vDo0SOuX7/O5cuXuekfTEwimBsr7k2WySF42UFRwnGwNMDZ2oBkC0f8X4VqVQr1PtvxHiBZDAshpUuXBqBMmTI0a9ZMtbg+ISEBgLlz5zJypKZyVqVKFfz8/OjXr59GeL1p06ZluD5DjfyeCi3o6S7/k1AlY393OZKRH5aatw/kh6FyfjiZwISvvqJi/zkZZt+1axcVKlSgVKlS2XcQe3apekxpbTGnj05ThP3L7UtDLmNON2zYkIYNG5KQkICNjU3mu/PzM771hyhLlsYK8y7F+M3vWMLalFClkqhKzzqUZLbk5ZclNBNZd58EkZiYiLm5OTKZTM2djJ2dHebm5rx8+RJ7e3tsbW0JDw8nJSUFFxcXDAwMeP78OXK5HEtLS5ycnChikkKUPI0MJdYuGFvaU7x4EsnJydja2mJubo6FhcUH644mKyTFsJAREBDAuHHjkMlkbN68Wc1X4KBBg5g6dSqdO3fO8Hx7e3sOHjzIuXPnCA5OfUuvXl3hC65cuXJYWVlhZ2fHuHHj1KadVeTXVGhhme4CDf98OUKb9SQPFSghBF8djmf3vWTWpVMKR9U2oXlpQ1p8u5UE1zpa435mR5aKJpOhwdt+ygtFQ08xp01NTfHw8GDPnj106tRJLTKKvmXpxIcoq0Z/uHsdbEu9WzF+8zOWsE5K6Ev9uavRRQnVl3KYhaziNpa8eJNIfHy8hhsa5ZKqEiVKqNI8PDyIiYnB2dmZxMRElY9Cd3d3ZDKZwhqYfi3mW+ukwdt8ErohqcuFDFdXV5ydnZkyZYqGA+nJkycTHBysdrFkRM2aNQG4fPkyn376qSr93r17/Pfffxw7doy2bduqrJBq5MdUaGGb7joxM/eyMrOepJWlJwXq/Eez2H0vWevhJRcTuVOyD5aV2+ROKXwrSxVTOv1aTGXMaX1sSEorTw8xp+fMmUNQUBBffvllnsvSiQ9Rlqk1mGqGFssR+RXjV4ucJk2aMHbs2FzJ6devH7Pe+t5UkZkSaq3FvU1u0KaEFvdSyVmzeTd2rl76mfbPQhaAPF6hJFarVo2KFSuq3E+l34CpxNbWFhcXF1V848qVK1OuXLnUaeCY15on5dV0fBb4+/sjk8kydV+UE+rUqcO2bdv0WqY2JIthIUPpw0nbmgeZTJZhOKCM8PT0ZNWqVfz+++/4+/urYsgKIbCzs9MIIJ5vVrzCMt0ll0GYlnw5IT8tNUCl1gNZtCiZS5cu4ejoiKWlJT/++KPqeOMun2ZydjbxaJhxTGl9rjnVIx07dqR79+6cOHGioKsioS/yK8avnuVcu3aN/fv3q7kiU8kp6qndEbS1o2K62sgM4vWwASULS2iPDi1p07yBfiyhWmTdunWLuLg4rMxNMSWRkDiFlfDx48d4eXnh7u6eLaueiYlJ6rKYqCB1y2oGltCZM2eyb98+1Xp85fMwMwYOHMjatWvV0nx9fTl48KDOddUXU6ZM4csvv6Rz5855Og0uKYaFiNu3bzNixAjevHmjtilAHxgYGODh4ZF5pvycCi0s0111P4f9+xUWMX3JyqgcPStQVlZWCisGigXZaV8alG4dPmSOHDnCihUrVH0k8Y4RHwEJ0WCrZYYkIlBhiTSzzf965YAlS5bQrVs3rKy0WE+zUkLlcoXD6BySmJioUKCyUELNZTLMsxn5RLmTOD4+Hltb21QfgFpkWVtbExcXR3RcAsLcBEODZOITEnLvN1BlnXxrTLF2znBNaGJiIt26daNu3bqsWrVKZxGtWrVi9erVqu8aBpV8onXr1gwZMoQDBw7oFOM+p0hTyYWEmJgYqlSpwj///MP+/fuzH0pMH+TnVCgUnukuUFjECiEbNmygXr16yGQyfH19mTp1KqtWrWL79u0cOnSITZs2MXv2bOrUqUOxYsVYt24d8fHxeacUXlipPf3sUkWEGX3x0C/jqX0dZN24cYOWLVsCMG/evDyTky3eR1mZybmyAZJy6Cw5PgI2fAJr2kDE87dpkQolIOK5In3DJ4p8oEjPhfKUlpiYGPr374+VlRXOzs4sWLBAI8+yZcsoU6YMZmZmODo60rVr1wzLS0lJYevWrbRv314t3d3dnR9//FEly83Njd27d/P69Ws6duyIlZUVlStX5t9//1WdExISQq9evShRooTKvdSmTZvUym3SpAkjR45k7NixFC9eHF9fXwB2795NmUo1MCteiqZNm7J27VpkMpnKarZm637snFJD9s2YMYOqVauyfv163N3dsbW1pWfPnkRFKSxxkZGR/Pfff9y9exd/f39u377NmzdvOHr0KI0aNcKiqBN2rmWpX78+J06cICQkhGHDhrFo0SJ++/1/fNyyJd9++y1ubm78888/1K5dG1NTU5ydnZk4caIqJF7aNo0cORJbW1uKFy/O1KlTFWsT01s4Ld++IKefjjcy47vvvuPLL7/M1MepNkxNTXFyclL9pXecffHiRapVq4aZmRk1a9bkypUrGmXcvHmT1q1bY2VlhaOjI/369ePNmzeq41FRUfTp0wdLS0ucnZ1ZtGhR6hKGtxgaGtKmTRv++uuvbNU/24gPnIiICAGIiIiIgq6KmDBhggDEkiVLcl1WYmKi2Llzp0hMTMzeiQ+OC3EmA/lnliiOv0fkuJ/ygTt37gggyz8LMxPRpUsXceXKFfUC9Ph7Jd49puin7xyEOLVY/eCpxUJMt1H86UPeg+Op5eVQ1qhRowQgpkyZIlJSUvJMjja0jqk8kqWV/JKVhZy4eRXF7bMHRVz4a62np6SkiLCwMO2/T/hzIRZXVpS9uLIQQXeECLwsxL2DQiysmJoe/lyIyFeKY4GXhYjL/X3886GDRamSruLo0aPi+vXrol27dsLa2lqMGTNGiKggcem0nzA0NBQbN24U/v7+4vLly+Lnn3/OsLzLly8LQLx69Uot3c3NTRQtWlSsWLFC3L9/X3z++efCxsZGtGrVSmzZskXcu3dPdOrUSXh7e4vQ0FCRkpIinj9/LubNmyeuXLkiHj16JH755RdhaGgoLly4oCq3cePGwsrKSowfP17cvXtX3L17Vzx+/FgYGxuLr7/+Wty9e1ds2rRJlChRQgAiLCxMCCHE6tWrha2traqc6dOnCysrK9GlSxdx48YNcfLkSeHk5CS+/fZbIYQQCQkJ4tq1a+LSpUvi0qVL4vLly2LDhg3CxMREfPLJJ+LPP/8Uf/31lxg/fry4evWqePnypahbt65G3Z4/fy4sLCzEiBEjxJ07d8SOHTtE8eLFxfTp0zXaNGbMGHH37l2xYcMGYWFhIVauXKnIEBchUiKDtI+nqCCNcZG+rZkxYMAAYWtrK+zt7UXZsmXF8OHDxZs3b1KLj4oS9vb2onfv3uLmzZtiz549wsPDQwCqe3JYWJiwt7cXkyZNEnfu3BGXL18WH3/8sWjatKmqnCFDhgg3Nzdx9OhRcePGDdG5c+fUcZeG5cuXCzc3twzrGxcXJ27fvi3i4uI0jumq70iKYSFSDIVQDA57e3vVxZpTCrPCU2BoUXpV/XRqqX6VXj0o2ImJiZkqhP4nNorIidZCPs06z5WNxFNLUxXDtIpAWjnTbTJuc3Y4s0S9zBzI2rFjh6qfLl26lGdytKH12ssjWfnZruzKUSmGb55rPT1TxVAIIcIDUpXDhRWF+G+dEPPLp34PD1BXCgMvK5SAXBD1OlCYmBiLLSvmKsoWQoSEhAhzc3Mx5vPPhAi8LLb9Pk/Y2NiIyMhIncrcsWOHMDQ0FHK5XC3dzc1N9O3VQ1Xnly9fCkBMnTr1bWWCxLkTRwUg7t69m2E/tW3bVnz11Veq740bNxbVqlVTyzPhq7Hiowrl1dImT56sUAwD7gsRF6FVMbSwsFBr5/jx40Xt2rXFq1evREhIiIiNjVUphpcuXRK+vr6idu3aIjo6WkRFRYmoqCgRExOTad2+/fZbUa5cObX++fXXX4WVlZWqzY0bNxbe3t5qeSZMmCC8vb1V37McT2nIjmK4adMmsWvXLnH9+nWxY8cO4e3tLWrVqiWSk5OFEEL89ttvolixYmqK2PLly9UUwx9++EG0bNlSrdyAgAABiHv37onIyEhhbGws/v77b9Xx8PBwYWFhoaEY7tq1SxgYGGTYTn0ohtIaw0LGlClT2LZtGwMHDmTHjh0fhuPNh37a1/+BYppKH2sNH/rBhk6Kz/IUzQ00x78Hebx+dtdmJisboQVfv9ayy+4ts2bNws04BN5GIcnzSDU+QxVrMZUUVAhDHWV16tSJQ4cO4evry5YtW6hWrRqGhoZ6l5OfbSp0snSRY2YHlsVyVr6tKwzcB2vaQpg/7H5bX2sXaLtAMU2dmOYa0UM4t0cP7pGYmIRP9Y9U69OKFnWkXBlPSIwG4ONGdXArWQIPDw9atWpFq1at6Ny5c4YRduLi4jA1NdW8lwtBZU8niAwEIVTOnStVqqRaH+doGgco7gVlypQhJSWFWbNmsWXLFgIDA0lMTCQhIQG5XM6///6LlZUVcXFxlC1blmfPnmFoaIgRyVy/dpXKFcoSFfQMQ+viGBoapi5XinoJBtGQFKdRd3d3d6ytUx1UOzg48Pz5cwICAlRpFhYWJCYmkpyczMOHD+nd/RMsRYz23yIliRpV1adx79y5Q926ddX6p379+kRHR/P8+XNKlVJMb9epU0ctT926dVmwYAEpKSma17Ye6dmzp+pzpUqVqFy5Mp6enpw4cYLmzZtz584dKleujJlZ6pR2evdY165dw8/PT+sa00ePHhEXF0dSUhK1a9dWpdva2lKuXDmN/Obm5qoQgXkVsSVbawxnzJiBTCZT+ytfvrzq+KtXr+jXrx9OTk5YWlpSvXr1LLdWZ1UmKFys1K9fH1dXV7Vdl5Dqw+j8+fNq6WPHjqVJkybZaV6hwM3NjT/++INdu3bly7b0DMnPNUobOsHhyZrucE7/rEjf0Cn38oJvpX5OG6nj3PKM8+lbljaFLRNcXFxISUkhKSmJrVu3UqtWLQA+/vhjhdPzgohU0yQ1DneehzBM27YcyGrRogUzZ85kwYIFTJkyJc/k6Mz7KCszOXVH5951jK0rdE63rrXptwqlIy/CuVmkc+0U9QLePISU1Djz1s5luHz1Ops2bcLZ2Zlp06ZRpUqVDHe4Fi9enNjYWBITE9MdERgrgw5EvUD21k2McXKMSilVKkLirZ+/efPm8fPPPzNhwgT8/Pw4cOAAderUUTmHjo6OVnm1CA4O5s2bNwS8fE1cMkQlCO4FBHP79m1u3LjB48ePAbjyKoV/X6QQGPQGuVzOw4cP8ff3V8USDgoKIiQkhPDwcBITEzV8DoJih3CpUqWwtbFCFh+mUHbTu4mJCoKUBCwNkvSzHjQxNuNjelxzmh4PDw+KFy/Ow4cPdT4nOjqa9u3bc/XqVbW/Bw8e0KhR9qJvhYaGYmlpmadh/LK9+aRixYq8fPlS9Xf69GnVsf79+3Pv3j12797NjRs36NKlC927d9e6EFPXMgFGjhxJ37592bVrF7t27eLs2bNqx83MzJgwYUJ2m1Jo6dy5M506daJfv35s3Lgx/yuQX8oa6E2JypKMlKgTM1PT9GmxyabC9u+//zJu3Dj27dvH/fv3OXnyJFevXuXHH3+kXLlydO3aFUtLS44dO8bhw4exs7NTnJifygYodnEXZAjDbMgyMDDg22+/pU6dOhw6dCjP5GSL91FWRnJq9M992RHPYcdQ9bQTs9V97ekxnJunpyfGxsZcuBOoSgsLDuT+46eKL28VUCMjI1q0aMFPP/3E9evX8ff35/jx41rLrFq1KqDwOqGGzABM0+yqVu6gjQtNTbNUt7qdOXOGjh070rdvX6pUqYKNjQ3Pnj3TkKnc6Vu0aFFkMhllvTy5f/eOWp709UlMSkYIQXh4OG/evCEmJobk5GSeP3/OkydPePjwoWqzhKWlJUWLFsXJyYny5ctToUIFHBwcqFzBm2OnL6W2R6kcpnd2nZy6Kcnb25tz586pKZxnzpzB2toaV9fUGNsXLlxIPT8+kvMnj1CmdEkMY1M3cAAKf4aRgRD6KE+Uw+fPnxMSEqIKLevt7c3169eJj09tU3pDVfXq1bl16xbu7u54eXmp/VlaWuLh4YGxsTGXLl1SnRMREcH9+/c15N+8eVPDx7G+ybZiaGRkpLY7R+mUEuDs2bOMGjWK2rVr4+HhwZQpU7Czs+O///7LcZkAYWFh1KhRg8qVK+Pi4qLxZjZ06FDOnz/P/rRTXe8wMpmMTZs20b17d/r06cM333xDSkqK4mB+WPLyS1mD/LV6ZaZENZlcoBabw4cPs2jRItq1a0e5cuVo3Lgx1apVY968eTRo0ICLFy/i5+dHs2bNtMvKL2Xj3PL8iQMNenO0bmJiwpUrVzR9yOlZjk68j7IykvPfutyVG/E8dRq5iDt8ehhsSyoe+nvGpiqHenRibGVlxeDBgxk/bSbHL9zk5t2HDPxyusJnnKEJWDuyd+9efvnlF65evcrTp09Zt24dcrlc67QfKKJRVa9eXcPgAYCZdcaOrK1dwErdb22ZMmU4cuQIZ8+e5c6dOyxZsoTw8HCsrKzw9vamZMmSWFhYULRoUSwsLAgKCkImk/HV+G947P+ULf9biklUAHcvHuXQgX0AVCztRKlSpbS6jTE3N6dGjRpUr16dKlWq4OLigomJCd7e3nh4eODq6qrmT2/StO+4dO02IybN5vrt+9y9cobl877jzdM0zwoTK7Vp5hEjRhAQEMCoUaO4e/cuu3btYvr06YwbN06t7GfPnjFu3Dju3bvHpk0bWfK/vxgzuJdm7Oqol6mfk9V3xT979oyrV6/y7NkzUlJSVJa76OhoVZ7y5cuzY8cOQGHpGz9+POf/OYb/rX85duwYHTt2xMvLS7Xbu3eHj5HJ4LPPPuP27dvs379fI0b7F198QWhoKL169eLSpUs8evSIQ4cOMWjQIFJSUrC2tmbAgAGMHz8ePz8/bt26xeDBgzEwMNBYgnDq1CmVx4W8ItuK4YMHD3BxccHDw4M+ffqova3Uq1ePzZs3ExqqCFb9119/ER8fn+WUbmZlAnz//fe0aNFCFdtQ+YMoKV26NMOHD2fSpEkaQbLfVczMzFizZg0LFy5k3rx59O/fP/8sefk9RZnfU2zplSjQjOiRV7IyUNjGjx+vcnkxefJkrly5wokTJwgKCmLt2rWqaWSt5KeykdbCmrZtaV8g9IE2R+s5lKUMB6nVIa0e5WTJ+ygrMznnfsm5xSYiUF0pHLhP8b/tAoXCFPUC9n6ZqhymVw5ywbx582hYpxbt+wylRc/PaVC7KjUqe0NKIkQFYWdnx/bt22nWrBne3t6sWLGCTZs2UbFixm68hgwZwp9//qn9oLWjwuqZlgysoFOmTKF69er4+vrSpEkTnJ2d6dSpE0ZGRlhaWuLo6Khab1emTBnc3d3x9vambNmybN26lR0HT1C7dU9WbdjGlNGDAbBxKo2DgwOurq4YGhpSvnx5nJycAIiNjeX58+cYGBhgbGyMkVHm2xLKli3L4cOHuXbPn9rt+lO3w0B27T+EkeHb8wxNwER9LWaJEiXYv38/Fy9epEqVKgwfPpzBgwdrLP/o378/cXFx1K5dmy+++pYxI4YytO8nioNRL/hu4lgqV66cpl8115xOmzaNatWqMX36dKKjo6lWrRrVqlVTcwl079491TS6oaEh169epsMn3ShbrS6DPx1IjRo1OHXqlMKXYVQQVvJw9vxvATeuX6NatWpMnjyZuXPnqsl1cXHhzJkzpKSk0LJlSypVqsTYsWOxs7NTKb8LFy6kbt26tGvXjhYtWlC/fn28vb3V1i4GBgZy9uxZBg0alOnvkFtkQtuCgQw4cOAA0dHRlCtXjpcvX/Ldd98RGBjIzZs3sba2Jjw8nB49enD48GGMjIywsLDg77//zlS7zapMJQkJCURGRmpE/nB3d2fs2LH06dMHT09Pfv31V/r168fYsWO5evVqllEPIiMjsbW1JSIiAhsbPcTzzANq1KjB5cuX6dmiOtW4RVC0oFxxA/qO+R6LFuM1b84tZ5JUaxj79++nTZs2OXcgmr5cJXkxRQkK5TN9fOb+O/UrI12bkgzM2F9lJW2KPMU4t7GSs5ClIoP+W7FiBZ9//jlhYWGpU8XZlZE+BrSefquk07+yP8SVNteGYiyP1+78HBTh8XL7wnB2qeIlR0kuZTk7O1OxYkV27Nihdk/RtxwlSUlJmtdeHsnSSn7JykJOvFVJntRfQOmyH2FWTNNJtVwuJzIyEhsbG80oDko/hjGvFUqhoYnCUggKZXDfV2DtBJ1WqK81tCmR+wgomcX4hRytZ4yLi6NcuXJs3rxZM253ennp5GTaT9khnZyZP//BivXbCLh3TaM9QggCAgIIDlYo3m5ubtjZ2WXvWfLmoXq/mVgrwuLlgCZNmlC1alUWL16sfiBNm/qPmUaysTUb5n+DgdLJtT6IDk4de6A9njXoZ+ylISYmhhIlSrBgwQIGD1Yo8RMmTCAsLIyVKzPwJwvEx8fz5MkTSpcuraZUgu76TrZ2Jbdu3Vr1uXLlyvj4+ODm5saWLVsYPHgwU6dOJTw8nKNHj1K8eHF27txJ9+7dOXXqVIYOJbMqU4mpqWmm4eDs7e35+uuvmTZtGj169MhOswDFzTwpKSnrjAXAP//8w4YNG1i0aBHH3phjb5rIyuuC/17/yKKA8xgHnAaDtwOgyWSoNUzVlly1yWcEPD4N/idT09wbKdL13VfnloP/udR2gOL7yV/0Z807txxOzEqV4d6IpGcXAUg6+daJbR7KUvXj8VmKUHxvZUVHR/Pzzz+zcOFCOnfujKWlpW6/24WVirLS/vZ1P38re2aqLIwUu4pzQVLR8hASTZKBmUKOz+eKMeAzQtEWpbxiFXI/NopVUG9TLmRduXKFiIgIqlSpgpmZmXq/6lFOWrRee3kkSyv5JSsLOUmX1iKQITcy1TqTo7RJCCE0j5tYQ++/FTuBrV3e7gh+O6XmXBUGHVBMSZrZKpRH5fShkZkiUkhOiXnztqw0UTQs7dVlKI9bFs+oFA1MTU1Zs2YNwcHB6m1VlStLbbcqlJtCnrBQyNHaTzoiooNZsfQXalatSLEitpy5cpd5K9bxxcCeyJXtslR/vrq6umJubk5AQAABAQE8f/4cOzs7SpQokaXlkJjX6r8ZKL5HBWnI0bkN2tpvaQ8J0YiESP459x/7Dh5GmFggt7TP3ThIi0VxEEL990+Ifvs7pRknFsVzJfPKlSvcvXuX2rVrExERwQ8//ABA+/btVe22t7dn7NixmY4DuVyOEIKkpCSN3dq66gPZshhqo1atWrRo0YIhQ4bg5eXFzZs31UzqLVq0wMvLixUrVmS7zNmzZ2eZV2kxHDt2LNHR0Xh5eTFx4kT8/f2zZTHcuHFjhu4GJCQkJCSyh3LteMmSJVNj2koUGN9++y07duwgLCwMV1dXevTowZdffpm1klcIaNeuHZUqVdJJJ3hXuX79OqNHj+bhw4cYGxtTtWpVfvzxx0yXKGgjMTGRgIAAXr16pRY9BhRLA3r37q1fi2F6oqOjefToEf369VNtl09v6jY0NMzWW07aMrOLlZUVU6dOZcaMGdmONdyyZctCO5WsDbGxFxfPnuDyixRiksCkeGlMfAYRGhrK3Llz+f3337GysuLjjz/O2VRyWqsTqFu8INU6lVsurFT4EExfbnr5zablzur1+BRs7qUuA8Ub1JEjR/j4xmjFFGmPTbkPj5eBLADOLSf68I/svJvMsgdO3H74lN69e/Ptt9+q7cDLlqzXd7T3zYWVYO+tl3B/qn7K6XgqQL7++mt+//13jI2NefXqlepBGBgYyP3792naNHVdXLVq1Xj8+DHh4eEkJydz//59ypQpky3F5l3uK30SHx9PQEAAVlZWGlNaoLAARUVFYW1tXbj8tSZGK3wkarMIxrwBYzOFtVIfckIVLmNUlkmVnFQLpSjiQVSCPEf9lJyczP17d0lMSmbIkCEMGTJE7Xjo6yDMZYkYG4KxtQPGFtaYmZkRERFBfHw8kZGRJCQkYGlpSZEiRTA2NsbGxibjesS8UZ9e1WZxBYUVOBsW15MnT2ompitTmNgQZVQU69hnyJBr9qk+CH2iOT1etLReim7QoAGXL1/OdTnx8fGYm5vTqFEjrVPJupAtxfDrr7+mffv2uLm58eLFC6ZPn46hoSG9evXCzs4OLy8vhg0bxvz58ylWrBg7d+7kyJEj7N27V1VG8+bN6dy5MyNHjsyyzJwwdOhQFi1axMaNG/Hx8dH5PGNj43fnJn76Z3hyhAbO0MBZmfgIqsmhwXSWLVvGjRs3qFu3bs7adXYpHJ+a+l3bGqXjU8HIIPfroZwqKBxLq+S8XefXaDQYiFR5ThUgN79PuWbQ+68MHWkbN/kGY6cK+onPnIEsIQTjdz/jl59DSUpOoXXrOvj9vkbNqWmOZJXTslMZoMEXOS83A96l68Tf35+RI0eyb98+bGxsuH//vsr31+nTp2nYUKEwp500uX//PklJSXz55ZcsW7YMULgbycrlljbepb7KC1JSUpDJZBgYGGhdG6c0GCjzFBrMbDL2v2itxxjkZjZQ1EOxczb92jRrR5DJwMgMuYkVJETmqJ9MTEz4qFJliI9EnhhLlDAnIiJCtXYwMiYOlaoQ8RJIVbRMTU0xNzfHyclJ5fYmS4zNUAQcQn0tpvK/Umk0NoPc/ObRwekUUBfF9HFkJDLkGCAUx2Uy/a37iwqCxHSKVWKkQkHV13pGPaDcyazt/qPr/ShbiuHz58/p1asXISEh2Nvb06BBA86fP69a+7d//34mTpxI+/btVdO6a9eupU2bNqoyHj16pBY4Oqsys4uxsTE//PADvXv3ztH5hZ7MNhu8TR8wYAC//fYbdevW5dGjR5QpUyZ7nuEd0piu025eUP5XynfInolbK15NFRFAtClsDcaAgaF+Ip8oZWVUjs/Q3CmeWcgSQjBixAhWrFjBtGnT+PTTT3Fzc9OfPAkNVq5cyb59+2jfvj3t27fH0dGR2NhYvvnmG3799VdAca2kpUWLFhw4cEClFIJiaYsQonBZtd4hcrla6f3GzAbIQAlVKjT6WCtnZoOBmQ22KCJqlCpVioSEBCIiIoiIiCAqKkqlqLu4uGBjY0NERARhYWE8efKE2NhYSpYsqVt7inpmqezm2vG5URpLmFIBVfaTtXOq0mikaanOEZltSFKmZ1M5TEpKwt/fX3GqtTUWFhaYmppiYmKSq3uNPq63XK8xfNd5F3Ylq9Bxt2FCkxkM/uM/unXrRq9evShZsiQ//fQTbdu21X09SX6EqSsEaN1Bqkf27NnDli1buHDhAg8ePGDx4sWMGZMHO7rzGFU/lbfGOOR23o8LPYw/pVWwTp06eHt7U6pUKc6dO8fhw4dVeczNzYm+ugeDN3dIqjUMR0dH7OzsePLkCQD3Nk2lbM1MXii0kOGYyq9rKj+v3UxkJZ1dwUOr2ri4eWJra6txPFu7beMjtSsboLAe6UPZKKTobVdyBiQmJmJgYEBsbCz379/HyMgIQ0NDEhISVHmKFStG6dL6mTLVG+nGhFo/xb7R35jIo13JiYmJXL9+HVBYzdOqYjVq1MixchgSEkJwcDBly5bVMAjlya5kiQJGR0ueqWtVVq8eyf79+9m+fTtz5syhU6dOODk54evrS/HixXF0dOSTTz7Bw8NDu6zMrGtvHwLR0dGEh4fnbG3ce8y9e/fo0aMHXk627DhyikqVKtOoUSOWLl2a6ropHx/QepW1uZdi6j8XMaCzRE+xpuvVqwcoohDExsaye/duQkJC1PLExcUxsk9rlrU154eVewgLCyMsLIwWLVqwdFAtyt79Ge7+DH13cl9egsDAQKysrIiNjWXjxo2sWrWKJUuWKEIU5kObsiS/5Oggy+jodCyqjue1aWeMjY01lBq5XE5iYiLx8fGZKzwJ0RDx1rdtQqL62rSYNxDz1pehbSkwzeXav4RoSE7QHt85JgSMTHMvI5tydO6nHJCcnMzdu3c10pRh9ZTKioGBAbdv36Zo0aK6u9LKc0zAyATeRhxR6yejt0pPmmgkOSbFAJLfKm2WDmBsqyjX2BZMk1LHX4pBtuU5ODgQHByMoaGh2kaRtEq5rgghiI2NJTg4GDs7u1zFj5YUw3eJ7Ey7vt2W3rx5c3x9fbly5Qpr167lwoULhIeH8/z5cyZOnIiPjw+VKlWib9++NGjQQKe3lKlTp7JmzRqeP38OwFdffaXh6b1Qk4dK1Llz5/jkk094+fIl165Bn0rGrFncH6Mm41Iz5fMDWq+ylCjL1OYfL/hW7uSkj7yTQzkGBgbMmzeP0NBQZs6ciUwmIzQ0lFmzZrFgwQJVvrtv5Iw7FM+i8wpL4vz58xlX2xDZsekkJAv+vp3EmfHTWLFTS+QKYN26dVkrhnpqU5bklxwdZMkA57v/40nJhjx9qnlfEUIQFxeHubl55vedhCiIC3v75TWY2SksQfGREB+emi80GUyttRSgI0nxqQ95pQwlaWVZOrxdS5c/cnTup8zkyZO0901CFMmJiYRHRmseS4Ny+debN29yvMwrr8l1P2VGkkzRhzFRQJT6sYRkMDBWrDXkdbaLNjAwICYmBgMDA0xNTTEzM1PNWOQEOzs7lYPynCIphu8aOljy0iOTyahevboqAgQoLCUbNmzAz8+Po0ePsnLlSj766CO6d++u8uhfpEgRrRfY/v37VUohwLVr11SfY2JiePDgASVLlqRYMS1vw5mRH1av9EqUz4jUY9lUooQQhIaGqhZmr1y5kmHDhlGrVi2urBxJkQtzMDGUwYnvwMiwwB7QepXVZHLqxqSj0xTrW9M71M7thqR6IxW/jbLuuZDz9ddfq30vWrQo8+fP58mTJ2zfvh0AP/8U/PwVIScn1jfhK/tTJBw6zh+Xk5h1Kp4X0WBgcBZra2sWL15MyZIladmyJZ07d2bUqFHUr18/X9tUKOToKMukwWjKVGtAYmKixulJSUmcPHmSRo0aZb2M4791ikgqSlx94Hma2Ll1R0P5xrlpDVzZAGcWq5dZo7+m7PpjoVrffJOTrX5Kz9MLcPgLdTlK0sgT7ZbScfQsjdi8derUUcX97d69O1OnTs24Dk8vQOgD7X1zZQMULQNuum8IzS656qf3BGNj41xZCpVIiuEHirm5OZ999hmfffYZcrmc48ePs3LlShYsWMC0aYobvYmJCU5OTrgWt6ZltVJ0Hj0HV1dX1Zb6cePGUVo8o0OnT9i/fz99+/YlLCxMJSNby1fzy+qVXomSywA3hXuc47opUUII3rx5w8KFC5kzZw4ODg6UL19e5VJBFS7JzrLQPKD1Jqvu5+q7xfMqfGH6jU56ltOtWzeVYti3b1/8r5/l9PXHRCQIduw7zE9nE7kUmEKZkg68iA6mYsWKpKSkMGXKFBYvXoyDgwO3bt3Kno++PG5TvsvRUZYBaHVXo5w+MzMzy/pBXn8oiLhUOXcD1OXUz50DdwDqDoGUmFQZR8bDo/2abao7RPv5eSQnW/2UnpAbEB2QKkfEaX9pDL1Jr169+PTTT1VJe/bsQQjBli1bEELg6+urHjkoLQ/9YFMnxeeUmLxfwqDFgKDqpxeXMM7IU4OEThQiHwES2eKhn8KKpo2zSxX+7XTEwMCAFi1asGXLFkJCQvjvv//YunUrCxYsoF/bhpSIv8fCDQeoUqWKmhVwxbKlxN/cg3vjXrRt21alFFatWpUjR45krz3pFTZlLFdtVq/ckC4OdMrxHwG4+GcaX4qZKFF+fn5UqVIFBwcH5syZA4CPjw8ODg74+Pjw+++/K5RCyP8Y0AUZbzqDGNCFVU7NmjWZO3cuQUFBrF+/nsoNWgGw/N8kumyJ48rLFEoVt+Dus2AqVarElStXOHDgAB4eHvTo0YMaNWoQFRXF5MmTic/OuqL3oO8KTFZ+yMmv6yi/5GQj7n3v3r3Zvn07X331FaCIuLF06VJu3brF7du3ad68ecZy8uv+rTQgHJ6sGe/73HLF/829FPlyS1bPWH3IKKRIFsN3EV2sawZmUCXjeIoZYWhoqD7tfBZw2ENCsuD0sxQiyvXApEoX3ENPU2nAXMan0f8OHz5M8+bNc7ZAOj+tXmmsHOeey6EafLw+lp2fyGg5bJbGTfn+/fuUK1dO9b1+/fps2rSJpKQkqlSpoh64XZus9O3Iywd0fsg6/bO6DFB8P/2zfmXloRwvLy+++eYb1fe+lUw46WDArWA5AqhfypCP7JMJrlyN7l9MxdDQkFKlSnH69Gl27txJ586dVS9TW7Zs4eXLl7qt63kP+q7AZOWXnPy6jvJTDmi16voV7ceZE1GM/CgcOzs7OnfuTOfOnZk0aRL29vYcPnwYGxsbSpTQjHWtRn7dvzNbNnNiVuozL7fLZgpi3XYhQrIYvovo+namD96+cZoayWjuYYTtvc3sWziKygPmqmVbuXIle/fupUKFChw6dChnsgrA6lW/ZOolMPKIjNAKmhF3Ll68qPq8ZcsWTp48Sc+ePenXr1/mSiFk/jDTN/kh69xyTT+aStKOxdyizV9nHsi5d+8ePZpXo97ni0lIhiWtzbj2QxOO9bfk59ZmbGrwiM72z9TO6dSpExYWFvj5Kfra1dVVtwX5+dSmfJOTn7Lyu035cc3m570hA2vrtvuKzYRFihRh1KhRqghmUVFRlC1bFoALFy6kLy1jGXl9/9bFAtpksv4V0LyygBZSJMXwXUTXi0NfvL3gI+IF7TbFsvvfp3jbK4ZOz+bVAMWGlPv373Pv3j1atWrFZ599RlBQEAChoaFcuHCB06dPa8Ru1CorP6al3t6UX6XZjPcgKIYhnRWy79+/zx9//MHt27cZNmwYVlZWnDhxgm7duuluEX0fH9BpwxS2+B7679QcixlNv+jK2aWaU115IGfevHlUrFiBc/9e44/2Ztz+wpIvvp1D5Sl+WcpKTk4mJSWFU6dOERAQkPWC73xqU77JyU9Z+dmm91HRVcrTooQu/KS0Kvzs0qVLcXd3Z/ny5ap7OaCKEKQTBT3dD/oJ1ZqNKfj3EUkxfFfJ6u1MHxfHW4QQrLxtToM/Fd/PD7bk1ggr4v9ox1/HrgCwb98+GjVqpDrnjz/+wMnJCZlMRrFixahTpw4NGzbM+u0zP96iT//M7U2T8d0Qg/ev6m4ahrg+4PWemTRu3JjPPvuMihUrEhsby8WLF2ncuLHuMt7HB3Ra0vvRTCsrtxFxMvPXqSc5MTExfPPNN3iWcuH+KCsGVzfBqOUP3CrSgpkzZ/LVjmfsNe+BXAjFJiqHioSGhrJq1SomTpyo2mVbrVq1QtOmfJWTn7LyS877qOhCpkqoyT/fs7zfR6rv0dHRjBo1Cl9fXwAOHjyYPfc0+WUF1aaAujfSnjc3MvJrBquQIa0xfJfJbI3KWz+G+mLYsGGAwp1HSVvF+0T0vRMUsbYgLCqWpKQkvv322yzLUQbxjo+P5/jx42zfvp2wsDBWrVqF3c21WYb7y/XFeHYp8QenUnFZDAArJ3YGoHwpR67c86ftxjjYOAV7Oyv279/PqlWraNasGeXLl8+enPwMK5ifsnpsAm2RT/QZvjAfwiRaWloyePBgVq1ahflMaFq9DJXCHvPLLx9ha2tL0aJFWfjkCZbmJpiZmVH+2FT+++8/EhMTVTszK1asiKWlZaFpU77KyU9Z+SUnv66j/LxetSmh6XYlW56Zyb1NU+k6ayc3b95kzpw5FCtWjCpVqlCzZk3dZekQrlVvypQ2BdT/JFTJhRshbeTnGvFChBQS710KiZeejNYUtvieJJ8R+gv1dvpn7v01meH74jnhn0KT8sWpbheJiSEYyGDW6VQfZaVLl87UOeevv/5KSEiIyiWOkhe7fsT58k/EJQnC4gUunX/U7lah5cwMzfcRERGsWLECDw8PLCwsOH/+PPfu3SMkJET1Fxryhti41F2kL1++5MKFCzRv3pzLa74l0O8Prgel0H/Kb3i3Gpiz/lJSSEKT6UNWXocOLCgWLlyo2oUJMHr0aObPn4+RkRE//fQTiYmJpKSk4Ofnh4uLCzNnzsTT0xOAGzdu8NFHH2mU+b72lb4plP1UCMMW5qqf0m6iSG/lSreJ4nKkLTVq1AAUSyWy5Q9Px3Ctmd2/dSYDBTTJwIz9VVbSpshTjBuNzp2MjGQpeUcthlJIvPedrN7OlP75csvbN85yxQ053t+CbUafsPJUIPvvXyMx8jVJKWBvIeN1rOL9YvDgwXTr1o1z585x7949Zs+erVbcF198oVVMraGLqWAZw5HHKchkMrbULUGVBw8oXecLxSDN4C06OjqayZMnU6dOHXr37q12zN7enkqVKlG8eHHKlStHsWLFFH/JQZS1TaLmp3NVgeNNTU1pNPIXqF6WXvq0qGTTGfk7Ies94tNPP+X69eu0adOGGTNmsG/fPm7cuKFyOSSE4P79+6qXnQ4dOmBtbY2Pj49WpVDiHSe/rqP8lJOFtfXCCwPunH7KoEGDADAyMiI6OlprfOsMyS8raGYW0OOzFGknZoKRQZ4poID+LaCFDEkxfBfRYXqAEzNz5K5GgzQXsuzjH+jaYAxdlQlp5D1v/jtJrrVUgdbLli1LbGwsNWrU4MCBAxw6dEgVLaVr165s3boVgHLlylG1alU2b96MW/WKTOzrwvI9F+nWrRuguEl5enriVawkplZF4cZSIHXtzcGDB4mNjeWXXxQe/D/55BNmzJiBtbU1pUqVyjI0klIxVCEpUR8UdnZ2rFmzhoEDB3Lnzh1AsVmqadPUh7atrS2NGjXi5MmTLFiwgCFDhrBq1aqCqrKERPbIRAkNdOtMnfrqse6Tk5MZNWoU69aty56Mgp7ul8sgTEu+nKDLM/boNEW73sNnhqQYvovo+namD3S84F21XPAWFhZ88sknfPLJJ4BCIdy2bRt79uwBYMaMGUyfPh1QTOk5Ozsjk8mYKZcTGBjI/fv3VbvjHj9+TFxyMsTFqcmoWbMmiYmJNGvWjBYtWqg90CUkdMXX15e1a9cCEBYWxpw5cyhTpgyXL1/Gx8dHtZu+cuXK/Pnnn3Ts2LEgqyshkW0iIyNp3LgxkZGRzJs3j86dO+Pk5ESVKlXUwppWrVqV7t27Z19AflhBM3se1f0c9u9XrIMupOtN4+LiuHTpEjExMZw6dQpbW1ueP3/OmDFjMDY2Jj4+Hjc3N62RgvITSTF8F9FFWStWAe5GaT09R/L0cMGPGjUKBwcHypYtS+3atfHxSY2b6eLiovpsYGBAyZIlKVmyZObe9iUk9ESHDh0oWrQoJiYm/PLLL5QuXZojR46wdOlSXF1dmThxIufPn2f//v0UKVKEuXPnZl2ohEQhIiAggKtXrwKKmZWWLVuyZMkSDhw4wNdff83GjRsBCA4Opm7dugVY0yzI7HkE4JEN9zqZydCzBfTNmzfUqVOHR48eAYrZipSUFKKioli6VH0H+tatW1UGlYJAUgzfVbJS1pKS4O7+/K1TFjRu3Dh7Ll8kJPIJS0tLhg8fzqxZs+jduzfe3t7cuHEDUGyYGjZsGLt27SIoKIg9e/bg7OxcwDWWkMgeFStWZMWKFYwZM4aEhAQOHz5MuXLl8PLy4ty5c/Tr149PP/2UFy9eMGnSJJYsWZIa3vNDJINnrBCCZyXaY2pqirZYRzExMRgaGqpZ/aKiohg8eDCvX7/mwoULFC1alKtXr/Lvv/9qfcmMSzczlt9IfgwlCh8fcIzKgkD+1jGBXIuDgoyOZTc9v87JTVnTZ8zgv//+45tvvsHZ2Zlt27bx4uVLRowYQVJyssoH56+//pplWdr4kPqysMnPrKz3ndjYWF69eoWnpyfDhw+ndevWajuOHz58SIsWLejatSsJCQkA/P7776rNKFnyAd2v//zzT9zd3XF3d8fZ2Zk+ffqQnJyMv78/1atXx9jYGCsrKzw8PJg0aRItWrSgYsWKdOjQgQMHDrBu3Tpq1arF8OHD6datG+vWreOzzz7j3Llz7Nmzhw0bNpCUlETfvnp2u5NNJMVQQnfy4waQWZD00z8r0jd0eidvNoXxASwXgr8uBtBs/gn+uhiQ4bFt/wVmeY6uZeXVObkta+vll1StVo2ZM2dy6NAhOnXujN/TRHy+Xk1xB2dCQ0MB6NS5c6ZltV9yWud+fl/7UtdzALb9F1hgbUn7OxU2JVcfCCGwtLTE2dmZx48fA3Dq1ClSUlLU8l27do2YmBjVGAfFWsMsyc/7dVbPn8enci8jE+bMmUPfvn3x8fFh7969LF++nI0bN/Lbb7+RlJTElStXsLKyAhRu0ObMmUNUVBS3b9/m9u3bmJubU6lSJVJSUnj16hUA169fZ+XKldSpU4d27drRp08fjIwKfiJXUgzfBzK7YC6sfLcUtgKKUZnXN/qCfgBnlG4gk/HHqcc8fhPDH6ceY5BmF3faY+vO+WtNT3uOrmXl1Tl5IX/5wSv899vXRIa9YfHixURERNCpY8dMy3oSEqP6XpjaUhjlA6w7519gbSmsSrbyWNr/WaVrOyaTyXB3d1fLExISwogRIzhy5Ah3795l48aNLF++nKtXrzJx4kRKlixJvXr1+PxzHaJn5df9Wpfnz+ZeuZORVtbb52l8fDxyuZwLFy4wadIkpg5sxZZZn9O2bVsGDhwIwIYNGxg6dCgA69at49q1a3z33Xds2bKFCxcuMHbsWIKDg4mPj6d27dqMHDmSFy9eUK5cOZWz/MKGpBi+62R2wQAc//7dUtgKKEZldqwWObnRF/QDOKN0uRAMaeiBR3FLhjT00GiL8lj/uu5a09Oeo2tZeXVOXsgP37+AlGiFFaVho0bY2NhkWVbpYpaq74WpLYVRPkD/uu4F1pbCqmSnvY/oYq3P7NiNmzfpMEDdf+yyZcsYN24cxYoVo1evXgwfPpwqVaowe/Zsnj17xpkzZ3RTWvLrfq3r8ye3PPQjYU1H5kz7Gp8KblhZWWFnZ0fbtm0pW9KeGaXOqJ6nZmZmqk1pJ06c4NixY7Rv357KlSszbdo0unXrhhCCDh060KFDBxITEwkJCWHNmjXY2tri5+dXaNdwSpFP8jLySX540c/A43zSyV/YH+ZGm2tDMZbH56nH+fSy9UI+eZxXRhVY/siGu8GxeBS35PjXTVTHm80/weM3MTqnZ3RMedP+49RjhjT0oGftkhjIZBmm5/c5yjyAhkXFQCYjITGRgwcOqKIvpD1HW/70ZcmFQAgwNJCRIhfIZKjyaUvPzTkGMhnJKXL+fRpG7dJFMz0nq7Lu3LnLCb/jLFy4kOTkZFatWoWZuTnOzi54epTWWpY8JZn9+/fTqnVrTE1MctSWvOiXgpIvF4KLT0Kp6VYEI0MD1W+kvPZ8W7XGzNQkw99S2xhTjq3Mxp+u6Xl9jeWkrLT3kfIOFnzuGam69rJ771GmPwh4hfmzcwQcXEl8fGr0p99++40hQ4ZgYJALO1F+3K+zeP4kNftB8czLRSSdB5un0XHkTB6GyunibUQj306EF6tG8NVDDHf4j/LF367NfPs8TUpKYsaMGcjlco1gDkIIxowZw5IlS7C2tiY6Ohp3d3dOnz6No6Mjhk9O5l9krLfoqu9IimFeKYbZCEWU6x9fywWT5H9OER7o2lCMm337zilsgOLNM63i6dFUEWxejygfTjEOlfnjzNM8vdHrS5lKe46+FKDjd4JZefIxnaqVUGvnrqsvOHn/NY28imLw/Apt2rTB0MgoNb2sPR2rumRZFkD3FecwMAC5HLYMT3WHkVF6Ts/5cvNVzj0KwcLEUO3hmBv5MaGvebZpKtevX1cdazX9T6ydSmuUZWIop5vDG40HVHbl67tfClJ+s/kniE1Moa5nMRb1qAqkXnubXxVHLjNQO0dt7KUZY8rra+eVQIY28qCZt4Pa2MvonMzGqz6U3MzKUh7LjpKrdh+p74Zl8HXVtZdbhbVLVUcmf/stCxcuVNXDw8ODTZs2Ubt2bXJMPtyv8zIM7PXr12nZsiVFzAR/t4nhI4e3SmAODCAPHz6kZs2aREREMGTIEFauXKkebCE/9YM06KrvSFPJeUV+rpVrMEbdnJ92EDeZrF+FrcEYxYWSlrwIKq4tSPpjP+3T5XqgS/USHP+6CT1qlVRL71GrpEa6gUxGz9qK9O41Xbn4JFRtSkfbOcqH05ebr7Lr6gu1/ABHbwfR87fzGtPPvVaep+fKc/RaeV5jCurLzVdpMNePb7ffUJu20nZOVmXN2n+Hi/6hGlNgS449YMeVQH7755FafmX6kmMPdCpLLgSdqpUgODKBjlVdOHvuHOPGjaPFxx9zddlIDs39nKIvzmlMjSnP6VSthNrUoLZ05bFa7kWxMDHUmE7MSVnKY72aVmXuTz+pjn1UqwGxJkW1lvUmKlH1Padt0bVeeXWOvssa0tADCxNDarkX1Rj77Sq7aJyT0RhTTr1e9A9l1v47GmMvo3MyG6/ZuV4MZDK+3X6DBnP9+HLzVZ3KyujaVypsPX87z9HbQaRHeR/pUr2E1vT095eLT0LpXtOV4183UVMY059jYmLCggULWLRoker448eP8fHxoXPnzhr10In8ul/n0fPnwoULNGnSBBcXF05euslHvWemHsxCKUxJSeHChQsEBqZO+U+ePJmIiAiqVatG//79NSNwFdBael2RLIZ5OZWcn1OvoPbGpgooru8A9YVgykCfsjKzWoB+LVP6mn7O7FieTY3l0moBkJSczIb16/n555+5du0axYoVIyQkRK0fXr95Q/FixVTfszs1mNGx3E6Z9u8/gPXrFSHCpk+fzqRvJ2NqYvxOTCXr28qVk99FW7ry2lP2U4YWs0K09CK308LZWZKivI8YCDk9nFIt0NmxyqYvK+05t2/fpnTp0vj6+nLqVOqO3myrBIVkiVFOLYYnTpygffv2VKlShX379qXGiNbRAvrbb78xfPhwDAwMqFGjBjKZjIsXL1KhQgVu3cpEsctv/QBpKlln8lQxhPybek0nR6UYFnmKcaPReSIjTwZyBmsmNWTrY80kOZtKhvx7oOjr4ZxbRSM+IZFDBzXXGOo6lX3g4EGGjxrLs4f3qNW4Jc3rVmfNmjW8efOG/v374/JRHa7EFad38xrap7KzMTWY0bGcTpnKk+PZNqqZWrqJpS2WxZyJCnpGckIsANu2beOvYOdCOZWcXaUhJ32c3XTltSd3rcbJh6Ea5xSkwpzd60uZDllMC+fknqDDS1l27y+vXr6kRAmFJbJOnTqcP39e1Y7bt2/j7e2NTuTn/ToP1hju27ePrl270qBBA3bu3ImlpaV2WUq0POMWLlzIV199xaJFi7h+/ToGBgY4ODjQpk0bGjRokL02ZSJHH+iq7xS8w5z3nQZjFIM3/ZtHHiqFeDQF/3OKzydmgoHQj8KWH0HF8yhGZVZ8UqMEveuWVruhK6eMe/uU0pj+AsX0TG+fUqTIRZbpactKTpFz8UmoSpnK6Bx9PpwNZDK6/6ZdacjoWNp0hdUCtXO0WUzTlzWtgTVff/01hw4dwrZ0JZz6LSAy/iULFiygfv36rFq1Cg8PD4XlJFKxM7O3TylVWUuOPeDxmxiuBYTTuVqJTNMzO6ac5lQ+HNM+3LWlpz+n75ipBF4/jZeXF58OHsz3i3/nwF+r1H73WrVrEx8oZ92ZR+CgfSpZV/m61kvXsoY09OCPU49VU7lZlZWTPs7J7wXw2z+PuBscq3FOVmMyJ+M4ffq3229oVZhzovymX/uYluzcK9JOC/f2KfV249f1TM9Jf69K/xKZ/piLiwuOjo4EBQVx/vx5KlWqpIr04+Xlhc7k1/1al+fPiZlQZaVOxcXFxTF//ny+//572rZty19//ZUaqSQzBVSZ/rZ9CQkJ/PHHH7Rr146xY8dmv135oR/kAEkxzGsyW3uhjx8/owvm5C8Q9jbtXVLY8iBGpS7sv/FKw2qh5OjtII3NFDl9cOmiTKUtS18P51wrGvXdIPiNSr5cKNbyXQsI1zinfSVH5v2xCaMHflT5wg8PDw+2bttGnHN1vp34DfdO/s3QoUNZunSpyvqoVFwyUmh0SVfWS9uxjB6cmT1Q1Y81Vju2+ufSlPh7Da3btGHZr79ia2uLjY0NPUsIulV3Zv/+/ZmUlbV83euVN2XlpI9z8nuBwl2N0lpfWBTmnCi/Srczb6ITaFHBUa2Pc6Ow/tSlYpZl6aqwNi1vz6ugIExNTQkKSl3XeO3aNc11cLqQX/drXZ8/WSCEYOvWrYwfP54XL14wbtw4fvzxx1QLYzYNID/++COPHj1i06ZNOWtXXusHOUV84ERERAhARERE6L/wU4uFmG6T+re2o/r3U4tzL+PBca3lJSYmip07d4rE7xwUxx4c14+sM0u0HzuzRD8y8hllP/kuOC7cJuwVTef5aeRpOs9P41iKXC7+PP9UNJ3nJ/48/1SkyOWZpufkHH2WpTym/J82Pf0xbenxCQli586dIjIyUsjfnp+cIhdxcXHi0eMn4vSZM2LRokWic5cuonjx4gIQNWvWFCt++00kJCSIFLlcrFmzVgBi0eLFIjklRU1GcopCTnKKXK0u2UnP6pztl5+LsX9dEdsvP88yXZdzGvb9UgCqvyf+/mL75efiq7/+Ezt37hTxCQk6l6XPeumrrIz68dyjNyIpOUVjvGT391KOqdi4+FyXpTyW1TjOTnpBXavp05X9lJiYmKkcbfep9OlXrlxRjdcePXqoPr969UoUerJ4/iTePabqJ21cvXpVNG7cWACiXbt24v79+9plZPR8Tvs8f3BcxMXFCUNDQwGItWvXCnm6MZQl+aEfpENXfUdaY5hXawzzc+2FFn+JyvU7bYo9x9ipgt4tbO8Lma0xhJyt/0ubDtr9Aqa3dGS2bspAlo9uadJZG7Zdesq82T8S8O8RViz9hV69ehEXF4eFhQWJckiOj1W1zdTUFB8fH15blKZkjSbYupZTWTSSk5MpUsIDG2c36g//qUBcqejLJ6Uy/WHga8K2fEv0i4cAvHr1il7r7xAYFs1PtVM01jplV35O66WvsvS1XjGjdOW1t+COJTVKF89VWektY7lxYyPPZO1hdq+7zMpSlpfVPSQlOVm1qcLwbbi0nK5jLGv0mlo1a6rk1K1bl3nz5lG/fn3edVTPvHTXXXh4OBMnTuT333+nbNmyLFq0iFatWmVckI7+h+VyORMmTGD+/PkAyOVy3a2u+byWXom0xrCgyc+1cl5NM1b8fIaCPnclv6co1xjqY/0faJ9+zugcfU8/Z1bWrP13VNNdWa3le/niBQPaNSLuTSBezXoA8PvvvxMbG0tMTAyXHr/mSrCcDnUr0KNxJSp4e2NsYqJ6ECndjxjIZJy/cIHo4ABKt/1cLV3Zl/mx/k5f09Kp6TBk+zHsQm/Rtk0b4uLjGdLQQ7HGkEiNNYZ5PV2u77L0tV4xs3RAzY1NTsvKaio3u9PCGa09zO20cE7WMbapaK/K/3cu1zF+/dUitWMJCQns3LmTmjVrFtooHLnh+fPntGrVioCAABYuXMiIESOy3piS2fM0jZJmYGDA7NmzWb58OV9//XX2puILaC29zujdVvmOkadTyQU49aqaSs7ArP5OkA/9l7afxv51RfjMPKoxFdNt+VnR47ezotvys2rpGU3dZHYsO9PSmR3L6ymtGzduiHLlyon/s3fdUU1sX3cndJAqoiBIVR8qKCqigL0h9g4W7PXZey/P/uy994JdngUL2AUVC4i9gA0VkSa9Jef7I2ZMQhKSkAT8fey1XJgzd+6duZm59+SUfcpXqkz1J+ygw+GxhZ4nedxzmZmZZG9vT61ataK8/HyZzlG2K1lVfZ2/cIEA0L3796mAw2WeKVFXsrqvq7T0xT8m+JdI+N0TfR4knSNJXhrfoaL6IpJtnRCcJ2lrjqR1SlD+6dMnGjRoEI0cOZLmzZtHAwYMIC0tLZo+fXqh/opEKQstErfntW7dmmxsbOjly5cqGfPs2bMEgK5cuSL/ySUwf2WuZBmhcrqaEoIks7pSoI5Sf2pihhfkUjsVGa8+XkAFytKJWlRU4X5OSf2JRf8sxIYNG+Dk5ISz586hWtWqyM7JxZXLl4Q45+Slkrl46RI6tG8PixoeWLRuBwa3qFnkOX9ChQ/9e9uxf/9+eHp6onL/VaWSrqYk+5L0/fLLLN7OsYFXtYrFpsSR9j7I6/5VlVtYVC7LOtHdrRIuXbwocY2Spy9R4msAWL58OWbNmoXz58/D19cXMkGdlTtk3G9E97x3796hatWqOHToEPr27Vu8axCD7OxsVKpUCdra2vj+/XvxSgqqCWWVT/4/4t113osiDuGbeMeVMcahLrz4CFFW+zvrefJfRcaLBTUzw/OzM0WrBgjKBaucSJLzIa46gTQ5IL76CX8jFK2awGYpvyqKz7yDsHGqge3bt2Px4sWIjo5GtapVMfFYFNquu8205f+Vt8pEOx8f1Bq8DN/fRmNC345IT0uTeg7fbVjaK3wEDBgAAAgPD0cD02ym8klGZmaJXldp6UvaswIA56K/iq1IIs/zVdT7IG91E3mrnkh6T6XJZa1UIgh5q55IWtcEMXHiRBAR2rdvj4SEhELHxUJd63Mx9pv09HQAgL29vexjybF/amhoIC0tDdnZ2Xjz5o1sY/whKIsxVBdUbWUT/AXH5QAeo38fU+YvONEFARAfNJvwvHjjeI7h3YcgRUDs9cKE2koKzOUS4bwEq5W0OL+Siv/jb8KS4sYkUclIituqyv2IFSuGw9rOAVcv3oajg4NQXy+/pDCf+X8ViWebPtQP600q4unWsRg0eDBOnTwJAopFMSMIWc9RZl+zZ22Frq4uVq5ciVE9m8H/2zc8fPgQla2sMH36dMyfP7/Y1yWq6JTGvoDCfHlFPSsAYF/eAAFexY+jVCaNjTrobWSNYzw8pD4j999xTylxjILgJ08AQEJCAiwshGMXxUJd67M8+42tMJl0rVq1YGZmhiNHjsDT01P6OKL7pwwWUG1tbYwdOxYbN26Es7MzHjx4gCdPniA1NRX9+vVDxYoVUQjq8LYpA0p3Yv9hUGmMIR9ypMArjLCNQqnueTfX8+Itbq4XToGXFNMgD9SZZi86lpLHkCV+R1lxRcqMXeIf4/8tDj3H9evXSU9Pj9q0aUM/09LpbkxioZgy0bi54sagbd++g4nNkdaXNJqVkOfx1HNreKH5UoSypTh9DR8+nJycnKiAw6HTj+PIsbYHBQUFUd9+/QgABQYGyn1dRV1vaeuL/5z23BpOIc/jZYo9FKRhURUljrS+BD8rQ66qtYA/Tzm5uUqNYyQiysrKYihrjh07Vuh4kVDx+ix2DAn7jbgYw3///ZdYLBZFRERIH0Nk/2SuX3Rskf2Tw+FQ7dq1hWirAFCFChXo27dvwmOoQw8oArLqO2UWQ3VAHVY20V9wfBb4GwLFwJVlYRPNnBL9lahMYk41McNLskQAillPlFX9RJockC/7WZz89q1baN++Pby9vVGt30K02XhfrPWTFzcn7EpWtDIFi0W4vmYbbG1t0bBhQ6l9SSP4lsfCqsq+Wlapgnfv3uH4sWPY9t4EX98+AwA0a9oUhw8dgr+/P1q1aqW0aiGKWKdU3RebpRjBMwAM2heBPA5bJguYvM+dItY0ReSClUpkWQskycX1xSkoYO5F3nVI0roWGxuL2NhYoVrl165dQ69evSAX1LE+F2O/GT9+PGbMmIE1a9Zg9+7d0NfXF99QQQsom81GVFQU0tPTsXHjRty4cQPdu3fHyJEjcfToUeFqKOrytikBZTGG6oDnGN5DxUfoPF6BblGG9eIqbd7jhccRhCoUNgeRh1cVpXykMcMrEYKxOKIxg/wNQdb4oaLinRSJ/5PU19Lgl4j4kIxdt2MLubWKis+6c/s2fH194enpiTNBQWhUzQr62hrMBsK/xy5ulZm4OVG5IjFocV/i8ePNY/yzaBEMDQ2l9jW0sQMczA2ErknaMXnlyujLxMQEANCnTx/8OPkPQFwAEKqG8PXbt1Jzvaroq6gxJH2/AJCYnif2exeksZGlL2nPkLi+ZH1PZJFLij2U972W1pe865CgkikYY3js2DE4OjqidevW8PPzA8Cjodq4cSPkRdrlFXgVEQoiAvHnVgXrs6L7jba2NtauXYujR49i7ty5RY8huH8WoYASEYKCguDj44PKlStj9uzZCA0NxciRI2FgYAB3d3fh/tWlBygDKrNZ/iFQiyuZD3WY3YmI9nemvIUWvyuf7O+s3P6JSpULoTgQdT9Ioqz50+lnROVzt50gAwMDatmyJWVmZjLHBf8KjiPOTSOLu02cG/Dr168EgM6eOyckJyo+NUoBh0MvX72mq1ev0sdPn4vVlzzyFStWMG4kPT09CgoKIj09PQJAFStWpNj370sdxYyyXbbyXq8kV7LgtYh7tkqb+1fV77ss4S7S5B5LQmnC0UhG5ufnRwCobdu2dPv2bQoJCSGOQCUiWVBQUEDT+rQgbY3f7tPOdSsRZ55hiYUUSaJoe/bsGQGgtWvXyjaW6D4jZv9MSEig1q1bEwDy9PSk5cuXU3BwMGVlZVF8fDzl5OQU615UhTJXcmmEOszufAsbW/e3jP8LTlnjyFFkXGHIWbNSGeCS5KQNeQLhVZUcoQz3c293G9jkfUS7dgPh4eGBs2fPQl9fv0j3Wdib72isi0JWCnkIvvnuPm5+NsBi4cevDEh53YNEhCdRUZh4IAyZiXF4H34BlcuxwWKx8P37d8THxwMAWGwN3Lp5A97e3jK5uOVxT4rKp06disGDB8N38jqYV+El7gQEBODWrVtITEyEW506cBuyFBWruyk8hjKvly9XpstWkesFCruSpT2LotVN+Cjq+VWl+1eaHBCfkCPPOsARuEdFic351tLMjAwYGxsDAB49egRvb+9C11sUvnz5gmE92uDK/ReY20QHz39wcOJFAf57HI+QPiPQNv2XpVxZ63Mx9pusrCzGci+12ongWJI8VN7jUVBQgOXLl2PNmjXQ0tLChQsXClH86OnpSR9DTeFRxUGZK1mdULVbVPQFEoQgpUBxIE5hCwgqbCKXlPYvK6QxwwuOpWRmeHloIqS5nwH56GckyfnjKMP9zGax0HradrRo3Rb6lavj3LlzTMxNUe6zc9Ffmc/S2vOPiXNxc3+5+5Jz2ahs54QHDx4IyYtyD2bn5CA4OBju7u6oW7cubq4bi4eHVsCyvAliYmLw5MkTRikEAOJyEBYWJnUMecaXJmexWDArXx4TRg8H15RHj7F+wwa8ePECkZGRyMrKQtqDoFJFMcNXHJTlslXkeoHCrmRpz+Ku27GI+JCMpcEvCz1zJen+FSfnK7J+2+8h9MV3CEKRdeDRx5Ri0dssXrQIFSpUwPbt2wEABw8ehCLw9/fH3RefcL6PPvq5aiH8hwEsLCwwZswYtJ28TbnrczH2m7dv38Le3h7Lli3DrFmz8Ndff0kfS5wCKjjGnfX4559/MHfuXNSvXx9PnjyRnfdRdBxV6gFKQJnFUF1QtZVN9AVqNhtI+fX32tzf4xT3F5y6Svk4NedRA4hL7fcez7sPJaf2K2oNkURZo6zkCGmWTHnoNm7fuYO7W6egnHV1LN9+GLoCv2yLsjqIlnmT1F7aMUFryIBnnnj48CFz70VZTzZs2AD9hrxnzMvLCxcvXoSjkxMqVawIg3LlcOTwYfTv3x8AcOjwYSQlJiItLQ0jR45k7lGaVUdZFqLe7jboWdcSwcHBAIAPHz7AyckJHA4HNWwr4eCUZkqxQinzesXJi2PZFrW+AZKtZgBwbqw3NDQ1i3x+FHnm5LGmif6AKS69DV+RLW6ZPr7F8J9zL5Caw1XIupv+4xuC589Hp06dsG7dOtm5/cQgOjoaM2fNgU/3BqjTrj+gDUQ9eABLS0teA2Wuz8XYb5YsWYKEhAS8efMGVatWlT6OjB4q61yeIhgSEoLIyEi0a9dOvvtRh7dNCShTDNUBdbhFRV8gj1FAcDDQaBTApj9TYZOxZqWyIO/Czz9HGe5naZueIpux4IbP4XCwbccOTJ8+HR7u9XHhwgXo6OrJ5ZYWVHbEjSGKoupLc40s8eTJEeTl5UFTS6tIt/SZj7+XKi0tLZw8eRK12/VDbEEGvJ3K4+nTpwCAWbNmQd+5KR6++YEmjSvA8Be7v7pcs6IZ3P/++y84HN7WfujQQWR7DAZbU6fUuJIljaGIa1YR9y8AzDz9tFDlk5JSlqW5mOV9D5WlyPIR0MgOu8I+KqTIDmjigqc2Nrh+/fpvBU4BEBFTPQNOzdG2W1+sXr0aqampwv0qa32WZ7/Jzxc6bGdnB4DnQl6xYgV69OgheZwiFNCUc3PxPpWLctUdUb58eSQlJeHp06fyKYYlEB6lMFQe7VjK8T/DY8gf5xfPUqFA3BKoXfknQHSeFE2oIFJOkD4RUX4BpxCXoLz8dPz6qC2m7qB69eoRABoyZAilp6fLXRO659Zw6rvjTqHAbkntpR2rM2IN6Tl5EFhs8vLyIu6va5al7rT3wrO0bt066tatG1WpUoW0yplS+Q6TqbJnFwJAy5cvlxigr46EIL687eprDO/chw8fmOB8/r9lhy6XeEJDUWNI+06UJee/e9VmnZPrWZT3mZNXLi5hg0gxbkdlJP3w5yk7J7dQX/xzxK1bgvINGzaQhoYG/fPPP8w7Jy8uXbpEzZs3JwB07RpvLzl//jwBoNDQUIX6VCbEJZ88evSI2rRpQ1paWkXv8WJqFz948KDQ+wuAunbtSh8/fpTvAv8gHsMyxVBdWclqLpgtKUOrDMIQnCdpihaHK54EmUh5GxKRcrKiv3z9Sn3HzaFylo4EgGrXrk3h4eHMfRRX2ZHWXtIxLpdLkydPJgBUztKRBk1bQhkC2dDyKkHx379T9druBIC0tHVo+fLlMvUl+FcQysx0FSUDz8/Pp8GDBzObytKlS4s1hrKvV5Jc1conPyu57eprpSL7t6gxiORXfuVVPsWdw3+e/LfdKdQX/3pFicVF5ZMmTSJra2uFlcLc3FwyNDQkFxcXOnLkCBHxlC4zMzP666+/5M5qVgUk7Xn8H2fHjx+XqZ+UlBQ6cuQI9e7dm3lnmzVrRhEREfTjxw+F55CI1K4HiKJMMZQRaqWrUSP+ZxRDFb9IovMki+VKFkuUIhvP94QEatW9P+mZV6YqTs7UokUL6tWrF40ePZq6DZlAVbuMozGLNtGVkBCKioqiiAcPaOaGw1Sz33zqM3Y2+fn7U9WqVQkAaWtrU48ePSjov/8oV+QZKI6yI46uRpyFUxKdy8pVq4j767isVlRJVpf8Ag6PGiI3TyYrjboqiVyJjqOgoCA6HB7LHHv56hXVqOtBAKhOo6bFHkNZ1UqkjaEsShxJ3620yieKWNv/F5Rlccf4797h8Fix58iirF67do0A0KZNm0gRhIeHEwC6f/8+syb7+vpSlSpV6Pbt278blqBnStqe5+LiQj169JC4H3I4HAoMDKSWLVuSpqYmAaC6devSwoUL6dq1a5Sfn6/qy1cLSg1dzYIFC7Bw4UIhWfXq1fHq1St8+PBBYhDs8ePH0bNnTwDA2bNnMWXKFLBYLKxevRodOnQAAOb8ChUqICYmBoaGhsz5derUQZcuXbBgwQLV3FgZ1F//WYb6lcUBt5gJFVyRuCPL7Fi0KbiH7+GRuJbuAjc3N5QvX55pz+Fy8f37d7x7+xYhISHYvHkzuFwuhgUEgMPh4EdiIpISE/Hq1SskJibix48f2BSUD3H53rF6enBzc0O7du0wb948tPP1RWR8PnbcikXm428KVUURlIvS1fDvUZZa0VPraWLGjBmYPXs2Igw84b/znlLi6fx28uvGxsoUT6euSiKrQ95glCNw4O4H9GnEW9/+ql4dFf2X45tpIKKu7sCa1asxZcoUhcdQVrUSaWOoOo6R/66Iq3wiT1/cIuIYS1t1E3kTdfjJJ93rVUafRvYKxSo2bWCDcePGYcyYMUhMTES3bt3g4uICWXH79m0YGBjAzfAncKgHtjzIQ3BwDnbu3Pmb8kbZa7IS95d+/fph+vTpOHnyJG7duoXGjRszx7hcLuzt7fHp0ycAgKurK86fPw8bGxtJ3f3PQy3JJzVr1kRoaOjvQTV5w9rY2ODbt29CbXfs2IGVK1cyQZ25ubn4+++/sXfvXhARBg8ejDZt2kBbW5s5Jz09HatWrSqkgP6/Ruxtycf+FKVNzSWEBBdsWTkDxW0kmRkZ6Ne/P87+9x+09csBnHzk5eYCAKysrGBiYoL07Dx8ifsEbj6vooiJiQl69eqFb46d8N3ErNAm2msbr5RcXlYWVnWyR2JiIrS0tDDnQix0yxlCQ6ccToz2ErqfpXtuyJwVHXH/Pgb6DUW+lgGueLSF5+q/UbFiRab9h7cv0Ni3MhJ//EDlypWZe5clW3rFv5Ph7OyMefPn4+Tjb3In9ygjQ1QRpV9eOb+vgEZ2QEI0AhrZFTpnJ/kh5sMtTJ06FUOHDoWRsbFaM3DlGUNZ8y7tuwV4dDUBXo4lnv1blFxZnI/SlE9JCi4ABD+Nx613yYUStGRVStetWwd9fX0sWLAACxYswPnz59G+fXsUhYyMDOzatQtNmzaFVspr/PcqH38H52CChzaGVM/gNVL2mqzk/WXq1KlISkrCv//+i+joaBgYGMDQ0BAO3I/YuGUroxQCgIeHB08pVMY++adC1abL+fPnU+3atWVuX6dOHRo8eDDz+efPn2Rra0s/fvygHz9+kJ2dHaWlpRER0fv37wkATZ06lcqVK0ffv39nzqtduzbNnz+/yPHU7kpWR4zB22u/K5/cXC98TFlBrgoWHZcbKq5+Is79IG9yhqDLJi4ujmrXrk1GRkZUs/8CqjLtHDVbcZVev35NgYGBNHv2bBo/fjxZN+5Bpi2HUa0Bi+jJkyeUn58v1fW0/847pcRcFXA4NHPDYTL7y4OqutSj/QcO0KxZswgA2VWvReWsnAgA6ejo0LBhw+hxZCRZ2joKVfPw9PSk0NBQys7OLtIdl5ObSyYmJsy7KIv7ThUuTcExZHF9F2eM7JxcJllA3Dmurq4EgG7cuKHwGKp083K4yquIIni+6Hcu6EoujptXkfdA1e7fos6RJ1aRv0a1XX1NrjhGcfK8vDw6cOAAASBDQ0OSBQMHDiQDAwN6/fo1ERE1c3OiZnYaxOVXOVFBRSpF9peiwqcmTpzIi0nW0iIA5NOkAS1spkNsFqiTdy3avn07xcXFFR5HSXtyScYW8lFqXMkAj2jSysoKurq6aNSoEZYtW4YqVaoUavfo0SNERUVh8+bNjMzIyAiDBg2CpaUlWCwWFi9eLOQyBniEmyEhIfjnn3+waVMxiZVVCXW5RgUtbTeW8OhqlG1pU7DouNwoRgF1RcCV0QomzurSulIOPDw8wGazcfvOHTzLMmbaO1W1QbVq1ZgxGkR8Zo7VcvlNVvvbYskBm8UCEeFueDgGeHvDwsIClvWPgQWei0OchdPdzhRxnz/j4aNHePboEQyfPcPey9nYQ1wUFBTgxYsX+PHjB+rXr4/IyCgMCAhg7v3r+zewsbFBBniW+p07d2Lnzp0AhNn8w8PD0apVK2hqamLKv9uRbemGptUtxLrj8l6EIjU1Fd27d5doKeFDWrUUZbkuZXF9F3eMwfseoHclYPC+BzgywqvQOXb9luHl7C4YNWoU9u/fj5WPCkoVXY26rGOnHn2BAYAbrxLQspaV0Dsoj5sXkJ96RtI5isgB+aqbKGLBBX7T1chrqe1orwEDAwOYmZlBX18fb968AcDztMXExMDR0bHQ/fCxadMm7Nu3D3v27MGtW7cwZswY3Ih8h53TeoPFushrpIo1WQX7y6VLlwAA+b9obcZ2rItN2x+jfVVN/NfyE1AjG6hcudRbP9UBlSuGHh4e2LdvH6pXr45v375h4cKFaNy4MZ49e1ZIwdu9ezecnZ3h6ekpJJ8/fz4mTJgANptd6BwAYLFYWL58OTp27IiJEydKfdBLFOpyjXqOAQq4PIJr/lh/stKmxhJC0uJ/JMULtatuhKhTwVgwez3sq/6F0MvBsK5cGbWIinRL5+TmISUlBWampkhOScHSHUdx8shBfHp6j3ebDg6IjY0FACQkJKBF8+Zo0KABpk6dCl9fX8THx2PsxlOIuBmKxMgrEu/L2dkZrq6uaNy4MW5nVMTPL+/A+UUw3bp1a/Ts2RObLz9FbnoyatVsiVFta4PFYiEvLw8aGhp4b94IQBwa/3MWPU0+YNiwYbzyUJOGAACu+M9C212zoa+vz7jj3sV9R8L+lejVqxcTzyQtpk0cIbi8bkVJcn5fyiAKL2qMDq5WQEIiOrhaiT+nSS3MvXsX/fr2RYMGDWBp64hybu2xYMZktbvXi/qxU1zyZ2mu2QN3P2CUI7A65A1auxQvhrKkSv5xpbh/5VVkJcm5v1zuXd2shGIMpSm+gutY3JcvmJSVhaysLOZ953K5ePjwISMThwMHDmDs2LGYNGkSGjVqBGdnZwC8/XjwvHnAoW6qXZOVvL907doVL1++BMAjyW8zYSPm7jyPlIQvuBJTgMb5c6Gnin1SzSFRygCLiAr//FEhUlNTYWtrizVr1mDIkCGMPDs7G5aWlpg7dy4mT54sU1/85JPIyEjUqVMHLVq0QKVKlXDkyBGZk0/4ZJ2JiYkw+kWGq1Lc3cqz4vFh1wT4cOv352azeaTUxUR+fj5CQkLQ+uk4aHFzhA8qaQwGgX2E78GuCeB/RHn9i84ZH0q4D2aeWreGlpYWuEQIfhqPsHeJ8HIyh69LJWaxHbAngtkY9g9uAAB49eoVevTogW8JP1CxYXfYNe2G4MmtmP5nnn6KiPfJ0NfWwLmxv+uSDtgTgfd3/sOTUzwLd7ly5cDlcpGVlQUtMxsYWlgh+dV9dOnSBX369MG6J4TPmYBx8muYvg/B7dtSYkgBtPXxQbZGOTx+Eo3sb7HgcAoAAHPnzsXTuFQE7d2Itr0G48i2VdDU0GAsOAfufkBAIzt0r1dZSBk49eATDBKfI9O8Jrq7V0FmRgbWrl2L1atXS70O/XKGCA+7A3s7u6LHkHKMvxEKKuryypXZl7Qx8vLyEBoailatWgnFQouew+FwcOXKFRw9dgxBZ87gxs2bcKtTR6axAaj0PpQ1h5K+V3HPlEznKEGuzDEAoOPGO3iflAn78gZC77i0Y+LWEkly/hq18bU+6tiWx7JuLkL9Z+Vx0MDeTEguuI7lvbqBXSvmoFu3blixYgUsLIRjF8UhLCwMHTt2RL9+/bB+/Xo8efIETZs2BQCEhobCveChytbkQpBxfxFdy0VRp04dZGVlYc+ePWjUqBE0NDRQtWpVJPyq2V7fSgNXA/RVcy9q2veLQlpaGszNzfHz50+p+o7aFUMAcHd3R6tWrbBs2TJGdvDgQQwZMgRfvnxBhQoVZOpHVDGMiIhAo0aN8PDhQwwaNEguxfDIkSNM3dgylKEMZShDGcpQhv8lZGVloU+fPkUqhmoviZeRkYGYmBimrikfu3fvRqdOnWRWCsWhQYMG6NatG2bMmCH3uW3atFGPxZAPFVvZ1GIxVPWvoPs7gGsCxdL5/YmO22Ie4DFcoSHEWQxlsVydP38ew4YOhbu7Ow4fPgxDIyOx1hIiYN7cOdi0aRM0NDTAZrORn58PJycntGrVClevXsPbt2+Y6/n67RsM9PVRwOEi8nMq6tmaCvWbnZOLXv0G4lZIMLp27YruPXqAW7kO7r1PEbJwirN8njl9Grdu3YJZdXfkVKpdyCIKSLZwDtl7D90rJuPUdzPsHtSQkYtaOLKysuDk5AQje1fU7zcdGjoGQhYRaWNIsqJIsrrIK1dmX9LG6LbpNoY6pGNXrCFOj2lc5DkNhy/Dy2PLUX/CDlxd2LvU35+850j6XiU9U5KsYNL6kkfOf8fPR3/FIE87NKleQeidFecxkCYnAjTYLHC4BBbrtyX30ccUuNmYQFODLbR2iJPzxxbXl+AaJVhTmn+OJOstfx3zr2+JpcM6Iz4+Hu/evYOJiQmkISAgALdv30Z4eDgsLS3x/ft3VKtWDfPmzcNkb0OVr8kM5NxfJFkMAwMDMXr0aPj7+2Pt2rV48OABQkNDceDAATg5OeHUdF8YRqwqPH4JWj9VhbS0NJnaqVwxnDJlCjp27AhbW1t8/foV8+fPh4aGBvz9/Zk27969w61btwrVYlUES5YsQc2aNRlKHFmhpaUl1vysEtxZD8SKxIPFXgHub1FejMbdrQBsocXN4SmGgsW6r839nZCiKMI38frhQ1zdx2tzAU224jEalWoAfKW21T+A9zje/5uME67/XKkGUMzvjv/9c4nQ28MOfRrZCy3OAG+xvfYyAdMXr0bksdXo0aMHDh44AC1tbbGxRESEBgPm4PGRtaji0Q77lk5GQkICoqOjsfu/G9gVeBo5P5Px4O5drF27FocOHYKBvj6mnX5eKEECACYcjcShFdOQ8uwmTp04ji5dugAAWqzi0dI8/pyGrvV+J3VtvB4rJPfz84Ofnx+vfeS3Qu25RKhrZ47Hn9MQ4OXAbEJcIvi4WAMJyfBxsRaSd6hjw8SVaWhqIjIyEomJiZi8dhquxGtjqIeN0GYmbQzRvvjyAC9H7LodW6i9PHL+2Mroq6gx/BvaAwnR8G9oL1NfQ3r6YlzgCnw+tRxLdWLQsmVLeDRsWGrvT96+JH2vkp4pfj917cwLjS2pL3nkALAr7CNiEzMRn/5WKL5R3HtTlLzXNvFJQnP+eyk2vlGSXFJf/NCBeedeCdWU5hLhvIQknUfvk9GrgS36NLLHi5cvER8fjzp16sDc3BwskdAAQTx58gRHjx7F/v37mQRRIgKXy8XXr1+hVamretbkYuwvgnv558+fMWDAAAC8JFhLS0tkZGTAwsICvr6+WNC+CszuLf59srL3SUGoY98vAjLrOKpOj+7duzdZWlqStrY2Va5cmXr37k3v3r0TajNz5kyysbGRu6wOn64mMjJSSD58+HACUDrpalRMv0JERGEbf9PVLLRQDZVMCdR/LgQVVD4hkkz/wOVyybZlfwJA1t7dhJ5XcTQTO3bu5JVUYrGE6mzq6elRzfqeZFTFmaxsHcnS0pIAUIMGDaRSX8zaHEgAyMrWkb5++8bIlVnyqyhqEX6ZN0ntT548SQDo1S9qC3H9q7qihqppaYpLVyOur/v371OrVq3JzMyModRo2bIVff/+/X+SxkbSPAn2K/rsSDv2p9DbKDK2tJrS0mhv+CX3tm3bRgAoOTm50HxmZmbSsmXLqHPnztSiRQsyMTEhBwcHofWwadOmVLlyZYqKiuIJ1ES5Ju/+Im4t53K5tHnzZurQoQP17NmTlixZQo8fP+at3eqiXBPXpyr2fRkgq75TIjGGpQn8GMOifO5KQfgm4Mrs35/F/QoCgDZLipcJ9e468o/4Ibj2DviafoRWk3G/jykzPV7VlU/UgPz8fAQHB8PX15exGB4VoJLh06YUFBRg+PDh2Lt3Lxx8h+OfubPg71GF+YUu7pxOnTrhzp07SElJwaZNm9CkSRNYWlrCzMwMYLGwaeNGjB8/HhYWFkhISEBgYCD8/PwkBvGnp2dgwvhxOHjwILp07YqTJ05IdWWJk/P7YrNYKOBw8fBjCpMtzSUpNCGPP4MdFwmutRs615VcRSUzIwPGxsYYPW8VtJxbiKWlkWRhUZYcgFhaGnWN3Wd7GHpXSsSxeHOGrkbWvo4O90B4eDgeP36MaXMXAlwubBu2w6srhxlLj7SxVX1/LVbdkMvaJU0ubp4kPYPSjikil/TORLxPRn1bU5ndvLK8Z4L9FCUHCicV5ebl4dLFi9gaY4QAL0ehKkbi1h1ROet9OPr4+yMhIUEoVIvL5cLX1xc3btxAs2bNYGxsDEdHR4wePRrW1tYAgPj4eFhZWWHPnj0YOHAg1Ao59xfRtVym/vk0MqJZzsrcJ9W178sAWfUdtccY/r+GRc3f/xd8EEXT8gXbKQKn5kDvQOBVeuEYCe/xAFtDOUqbU3PJfaj4AVcVBGkeBGlmxowZg4MHD2Lfvv0YMCBAIs1EAYeLDx8+YN3atTh37hxzPL9CddSsVUto0zCu2gBgsdCkS3+M7O2Lli1aSNzM2CwWhh15hnTb1tDUC0JuTg5zvcrk85NEB7L9ZgxGOQLbb8YwbjRx7Q0NDVGjRg2cuXITWjnVC9HScEk9FTVUSUtT1NhF0tVI6QssFry9veHp5QVOZTcsWzgHb0IDUaNGJIYOHYoRI0f+T9DYSJonSc9gUc9naaO3UURZlVb1BADOjfUGW0N4yy6K9qaAw0WzyZvg6uoKc3NzoTbr16/H5cuXcenSJbRt27bQeImJiZg9ezbYbDZThlatUPX+4tScp/SJUz6VuU+qa99XJtRivyzF+J+sfEJFs8CXgQdJ8yRY/YRfgH7Hjh0S3cw9Nt+hBgPnUHlHXlULFotFpqamjPvYrlVAobGbr7zOHA8PD6ePHz/SxIkTyaJ2MzJp3J+MbGuSoaEh1atXj/bu20eH730kfQtbMq9kTR8/fSIi9VRq4HC5dDg8loKCguhweGyR7Tt17ky1GzWT212tLLmkY6XZlSxJzuVy6UpIKPX28yMANHfuXJW7kpU559Lk4sIT/jQ3r7Rz5KluIk3OX6P67rgjU3UTQbnXqH8JAG3YsIFOnz5N3F/XFxkZSdra2jRx4kQSBZfLpXHjxpGGhgax2WxavHhxoTalEaV6z/vDKp+UuZLV6UpWI+Q2q5d2qMhtLW6eRF0x26f1x61bt7BhwwbArgHOvM4u5LoZOn0p9q6cgypVa+DT2xcAgDZt2yKDdJFYzgFzp4xDn4a2QhaWwPuf0K+RHQCgS5cuuHjxIgwNDWFu44i3L5/BqrINRg8dgFu3buHixYuIevIEw4cNAwBcu3YNBgYGTF/86wBkcxnL4xbjEiEvLx+XL11EW5920NbWktp+xowZ2Lt3Lz5//gy2hqbs7molyPn3LelYaXclS5MbGRnh77//RoxtJ5W6kpU551LlEsITVO3mlVfOH4P/bomOLUkui5u3KDmguCt5Y9AdfDmxCJ9jXjHf97lz59CiRQvUq1cPOjo6uH//PnR0dISeoQcPHqBBgwZYsGABRo4ciYoVf9eeLs3hQ/9ze54KUOZKLoP6oOrFQs0lhQTdyVwi9L3FoxcYN24cNDQ0cPfuXdSr/3vhZrNYqGGhA01NTUYpnDBhApYvXw4tbW1GOROtfvLl9nHe+Ww27ty5g1mzZmHCxIkwMCgntDlNmjQJ5ubmGDRqPKyb+OHKptmwsrLC4MGDsXbtWgCSy8kpqwRcUWXeBNsHBARg5cqVaDVkBvKc28vsrlaGXNoxVbta+WMUx5UsTd65c2fs3bsXC/f3xrGnqSpzJStzzhUJTyipKibS3MLi3LxFuX+VUXKPSzx6G4DnShalq5FUis8i4y3uLe8r1L+JiQlcXFwwefJkfPz4EQ8fPiykFAJAREQENDQ0MHPmTCGCdrWtw6VY+fz/AnZJX0AZVIzY27yXSRzCN/FewuKAv1hcmc1bHARxZz1PfqhL8cYRLSnEH0dcSSElgG/lmHgsCkfD3qBq1aoAgMmTJ6OcsSmGzPoX/0V9ZawFAI8WoaCggPm8bt06/PXXXwgNCcHEY1HwXnEds04/BZvFwrVr19CkcWNMnzaNNx6Xi2nTpmHw4MEwMjSE/4578NtxF/477oHNYkFbWxvHjx/Hs6jHCN6+DDUGLUNaWhrOnz8P4Hc5uYgPydh1O7ZQrJ2+tgaz8fPlXdwqIyEtF13cKssk7+DKq2XLV3aICKFXryJqyxhcXDoM1bif8ODhQ+zfvx8PHjyAg4MDbh9ai8zXYYXGHtrYAQ7mBiqRSzvGV/ivTWkmZJHhyxd6aCD9ySWsWb0ac+fOhUWFChjSrDq2dbIS2160H/6x7vV4yo8gB2ZRY8siX7p0KbhcLpYM64RJf2WIHbu4Yyh7zqXJA35ZywMa2cn0DA5t7AB9bQ0mvlFwbHmfZ3nkbBYLu27HIuJDMpYGvxSaP3Fy/jHR91hR+T/neD82Z/5aP/jXejTiM/y230Poi+8QBJcI7/N51iDvtp3B4XKRl5eHHz9+ICoqCtu2bcOaNWtQo0YNiKKgoABbtmxBy5YthZVCQD3rsDr2E/44qtwX/3Sow69dmqH2GENBqDDugIm3WGihWkoZdaX8qyjdX1JcSvOV18l6zCHSK2/FxAGOGzeO7NsMIgBk26o/JScn09SpU4WoaACQtWN1mjJlCjVp0oQ0NTVp5sbDVGfEGgqYtIBGjhzJa2NtTXoGRkLnubi4SIxZevnqFWlqaRcaq0qVKuTt7U3rg8IlxkYJ/lVUzo8HO3X6NLFE6HeK+leaaGlOP46jCUcj6fTjOCH5ioPnmevV0tISuv5BG84Xai+uH+bYw48UFBREpx9+lGlseeRv3r4lF3cv0tDSpkO3XhYeWwljKPIdKUJjo4xYTP4xVcatqiMmUapcTHwvkeSYRP4xw3odSd+iCiP78uULlS9fnjp37szEGopi8+bNxGKx6PHjx2KPq5x2pRj7icwxhv8jVGuKQFZ9p8yVXFJQs3tUZcW7Pcfwrp/fX+g8HkForMAvLmUUIldyQXVp4BJhiLc9xu2chuykrwCAGjVqYMrUqWgQx8FcAB+vHoCZ2UGx58fFvMaqVa/Rq1cveHp6YtlYnkvnhbY2nJyc4ObmhsjISLRq1Rpt27aBjU0VZOdkw6MBrzKDqEupc+fOOH/+PLhcrtA4Xl5e8PLywpEjR3Bw8Tjcv38fBGH6C0Xj9rwczdC4ii6Sk5IQFh6OadNnYO/uXejXty9IwFozdOhQhL2Jx8tb54WuTVtbGxxigZOfi45LTiJ4bk8AynPv8Y/J6yqX5Na88EkDWhYOyE+IBRFBR0cHe/bswc64irj2JQcfrr6V2aUqawa3IvKqTk4o13oMOI8DMGagHxpcOsFYtFkA1gZH4+PPfJVl7SrTzStPeIK8bt7iPv+iMZTyun8j3iejV33rQm5eSeeIhq8IWgy716uMSxejC1Vh4meI8y2x+Xl5uHnzJtLS0zGwYW28OskFR+tXHCQRAgICoK2tjV27dokluU5NTcW8efMwcOBAuLm5FToOQPXrsDr2E1HLJ6D8fVHd+7uSUaYYlhTU8XACvLI+fAb5P11p8x5f+PodmiudNZ7NYsEs9SWS3zxgZNeuX0dFCwv4Vyb0vbwHT59NxsrNO3H5dODvIuxNWsO0hhe6tmuF42vn4NGjR7h56xb+3X0C738SqlnoAgnvsHr1akyaNAmfnLrjoQYLESkiCQG/NkBOARcNs+/h7Nmz0NDQYI5r6JaDib0LxowZA0tLS+Tn52PTtp3w2xEOQEMmRUic/PKlS+jm6wsAWCcyJ3p6egAAbcPyWLFiGlxcXGBhYYG/nJ1/BcBPxhBve3StbQFdHR2AxcKGM2GY2L0xnHXTVBbPJy8tjehmypePaFkDXM56uHJjUMNcE61bt4ZT1aqAQHB/Uf3wrymgkR2QEM24SIsaW175mE6eyE5aji/n1sHFxQXNmzdHXl4eHj9+jNTUVLA0tFClTUfkjvOEjra2QmOog8ZGGbGYfHdubGImEjNy0arG70QJZcazqlpRlqasBj+NBxtA8NN4JkkHEFYw79y5g65duiA5OVnovZ00aRLYLBZWr1mDq1evIiQkpBBtDR9Lly5FTk4OlixZIvY4A1Wvw2XKZ4mjLCu5JLOSRR8SwXI8QLFeAqEMrftbhMdRQv9icaBL4cUiIEh5/YvOFx/KmqdfmWxEBHd3d3xNy0dGHhfpH5+hZcuWWLZsGapVq4YFl94LWakyMjLQtGlTPHvzHibtJ8PW2gpOX0MQGBiIEydOYPWjPCYQnM1mY+HChZg5axaOPYiTmp1olxCGvSvnoFevXjh+/DhzzRq6BtDT0UbGzxRG1ryzP+A9vFgZkGfPnUPnTp2E5sfd3R3t2rWDlm1duJQHMi1c4dfQTsgCIrh5s1m8UoC3b9/G8OHD8fHjR7x99w7WlStLbC9NziXpmaLijpWUnH9N8mRwF0eenpGJjRvWIyIiAppaWihvUxWN69XEh/fvMXfuXBw+fBh9+vQp8fkACmftAr+zbX3atYOOQDybvM+HpGxeeZ9/VcuLul5+aUsHcwMhUnafNdcxyjENW2OMcGnSbyWCn1GekZSA+2uGokaNGtiwYQOGH3yMhHfRqFurOk4uGoG8vDxYWVmhd+/e2LJlC8QhLy8P5cuXx7hx44pWDFWwDouFnPuJ3FnJqr4PFe7vikJWfadMMSxpuhoVPZyFXpI/UGmT2r+SXjJxi8np06fRvXt3zN58FGGZFnDOfILtiyYzrtxOA/5G9Jd01LY2QlNXB9SoUQMurq5o1aEbXj6+x/Q9ePBgbNm6FXuvPcfEvu2Rk/wNq1evxtChQ2FkZCS00QGFaWZGjRqF27dvo169ejhw4AAAYOXKlZgyZQo4XC7atG6Na9euAQCeRD+Fq0stlW3w/E28TVsfPPmaIZF+xijlNfr26YPv338HxO8JicSAlrUVduOVZKWP0kRXU5RcsCpJ1LaJyMnJQXh4OFgsVonMhzg3r+AzLms1naLkkp5lcfQ2kuT8a1JE8ZVVieXLATnpbe59gEFCtNCPMsH2Nt9u4vD6Rfj+/TtMzcyE+ulVvzL6+PsjKCgIUVFRYhNOAODSpUto164dbt68iSZNmohtA0B9yo4C+4lCdDV/+r4oJ2TVd8qykksa3uN5D6MglO0evbNe+OEHeJ9Fs76K07/oYsGHYPaaogjfJNx/q394L2+rf4THkZRlJifWrVuHZs2a4Z9RvXB9anNsmj8eCQkJqFatGgDg7P7N4Ly+jshrZzF79mz4+PigZYsWGB7ghwkTJjD9LFq0CFpaWhjWpg5ePb4LL29vTJ48Gc7OzsjLy+Nd9ovv8Nt+D0cjPjObBl8e/kMTz58/x4EDB7Bq1SpwuFw4tvTHxGNROHE/llEKAaC+hydYLBZcOgwWik9SRmYk/xgAtF13m8mu5ss3Xn2LU/feYkjvLmjVsiWjFFbtPBYAMH/xcnA5HKH2ZyK/YOPVt4X6EZXzXYiimaLSjpWUnH9MNINbXdckmLU7c9Ys3Lt3Dzt27Cix+eC7eSVl7W6/GQOAF4tZ1HMgTS7pWZ51+im8V1zHxGNRRcoVeS/4yurEY1FCLAXS5OIyifnKaq/61mIz3bvVrSz0l4/e7ryMct2Mr3B2dkb58uXBZglnmp88cQInTpxAYGCgRKVw0KBBaNeuHZo2bQpPT0+xbQCobx1W9X4iOI4q90VAPfu7ClBmMfxftxiafoQWP8YQUP4vPHXUgVRhTUtxvzK1tbWxYsUK2DXrJWShOHniBHr37o26flNQtXk3cLnAsRENcePGDWzbtg0nTp4CcTnQMTRFbnoKunXrhlOnTglZU95uH420tDS8evUKWlpaEt1HLVbdwLsvP8B+dg7z/Jth4MCBYLPZQu1HVklA7969he6njlcLPLodqnQXWFEku/OXrMC7czw31bRp0zBx0iRc+5CLyWNHIP7BJVhaWqJz587o3KULYjnlcTw6GcOb/4Ve9SsjJTkZ8d+/4+jNpwi6+xL1LNiw1s1DQkICEhMToaOrC1cXF/Tq3RuOjo4yW2bUJQcgdEwZLlJlyIcNG4bjx48jMTGR4b9T5zwV6VItwhJWWty8irh/FZFLqkEtyQLdY/NtRB5bjfdh59G9W1ec+FU7XdCyumpMLxgZGeHixYsQh/j4eFhaWmLEiBFYv369WF5DBuqoLVyM/UQui2EptnyqEjLrOyrNjf4DUKJ0NSpM/S9EV6MqKpk/PPVfHMWBnp4erV+/XiwdhDRaiXVnwshl4GLac+MVTZkyhQBQckqKUHszMzNatGhRkX3JKk9KSqLhw4eTi4sL7dq1i5KSk4XuTxZaDlkoYPh0NXl5eWJpRPz9/QkALVu2TEhORBTx4CFNmDCB7OzsCAI0MHp6esRms4VkAEhXV5dsbW3J3d2d2rVrR82bNyc9fQNiaWjS8DmrlE4BI6+cfyzkeTz13Bpe6DtSJV2NPPKjR48SAPry9WuJzFNR9DZZ2TliS+Kpo2yh4F9BuSppbBSmvZFQjvLvfzYQAGrUuhM9f/GCOUdw3apcuTLNmzePJCEsLIwA0NatWyW2EYKqKViKsZ/ITFfzh1OsFQdldDWlHeLM8qK/jELnAWwN5RQMV1XxbnUVIld1QXUB6OnpISsrC0ObF87Y5LtqxNFKjO3cCOO7eIJLhHDNLtiwYQNqODtj1KhRqJuWhlP/vkdycjJsqlRhxipudQQzMzNs3baNsRKYfchCZxOTIuP2APmrpQDAoH0RyOOwhTIwu228jjNHjwIA9PX1GblQ9uXatVizZg2ePXuGUVsuIj8nA3mZaRjZ0hkVK1aEhYUFFod+gZ6JKdha+jgxStil1XTZZTw+thY7Fk+BpUY6pk+fDj09PZVWUZEk5x9bGvySyYbt4/GblkaVdDWC8kfvvqFJFV3GhSjYvm11Uyxfvhz16tWDlaUlNh6+ofZ5KvQMiGTtFhQUYLKzsLVV2jnKqmIiKVZRXtobce8jH8qSAxBLVxMZGYkDK2fD19cX584HCVk9+RniQ7ztMWhWgnBJOxFcv34dhoaG6N+/v8Q2QlD1OqyO/URwv1PVvqju/V3JKFMMSwrqeDgBoHcgkPTif0ZpUwd0dXWRk5MjkaNMNmXLBm/fvcOE8eOxbt06kE45cA3MYdO0N/x+uX+VuQkqS7HhkmQKGABITM9DgJejkLx8fATw6zhf6eWSeHqRmrVqYfRAY+y6HYvxIu66fhq/3HUe1oVckiNaOGOn5nSY1quOpUuXYufOndi+YweGNnZRCgWMPHL+NUk6R9V0NdZfbyIi8AA+JXyA+Rwu7OzsUN/dHZUqOiEx0xCVMtPRpMkUvHnzBnfu3FH5fEiTS6O3ORAWw8yl4PulyrKF0t4VRWhv1FGK73zUZ/Su9Ht9+vjpE1q29oGmmTVGLNggNHeC/InJKSnIz89H+fLlxa5zSUlJ2LlzJ1q0aMHUXS8VKFM+Sx5qsmCWWvzPVz4pyqz+p0HJcyZunhwcHGjGjBnUc2s49d4eTj23hgudU1TFAXnczyUhl+WY4F/ReRJ1s+UXFNDr168p/vt3Ibkk91txK5kE/nKRenl5qdTtWNS1qrKihyT5smXLCAD5+fnR9u076NDhwzRp0iRq3LgxGRgYEAAyMDCgFi1aUFhYmFrmQ1E5/5kSdSXL486VV67Md0Ud72nzldep2qxzQmtUx44dSdvInKzHHi60BjVfeZ08loTShKORVFBQQObm5jR69GgSh379+pGpqSl9+PBB7HEhlMIqHqIodXteKZwzWfWdsuSTkk4+UREUSt0vLlRd/FwFwc/i5qlWrVpo2bIlPPpMVhpHmaAlozjB/FQEbUYBh4uHH1OE6GQkycX1xZVgEeUnVNzOsYFXtYrFqiIhjepFnBtb9Jwfx2YhKSkJ586dw9SLX0uMlkbSMVXR1RARbGxskJJdgKbj16Gcha1Q+55b7iDnZwJ0jCrg5N+NZbrWkpRra3DR0yKReff4749EehslVDGR9txLkstLe8O/XmUl7xwIi8EoxzT4tGuHF8+fo27duhizaBOe69Yscm2aN3cuVq1aheTkZCbMAwBevnyJGjVqYPv27Rg+fDikQh1JJ/xxirF/lMie94ehjK6mDOqFOoqfq6OIO3iu5NzcXIb2oVd9a0S8T2YWbXFyPvgUEr3dbRgZf5OSh9ZCnJzNkk4nM/FYFLxXXC9EJyNJLomaQxItCACci/4qM42IIvQz7nZm0NfWYNx/oud0dKmI8PBwDBs2DDZVqpQoLY2kc1RFV8NisXDi5EloEgcX5/vjxe6pOHnqFDgcDrhE6FqvCjK1zNCtvq3M11qS8sT0POYz/5mRRm8jL42NvPQ2yqK9Udb7DvDWk3NjvZnPgYGBKF++PFZOHVZoneG3F5QTEXR1daGpKRw19t9//8HQ0BADBw5EkVDHuquO/aMMMqMsxvD/C1RtzVNHCSA11WXmxxjylSpxiRjKqtErj5y/ocpbHk5ZZeMAwL68AQK8FI874yvW/OQdQYgeE9yE+fIPHz8ylRxkaS9qmVFmUoCkY93qVsblS9FieeeKO7aHhwe+ffmMM6dPYfOWLejZowdGjRqFTZs3q6VurzLlPetaIjg4uMi4TWnHFJEro9yfJDn//pSZvMOzrAIgwtGjR9GzZ08E7H0kU6yipqYmDA0NoS1AmwQAX79+hbGxcSG5WPwvlZBT9T74P4IyxbA0QJ0uWFUV9FaT0qaOusx6enrIzs5Wi7KlaqUKUK4ydG6sN9gamjK1l9aPuIzootyCF8Kf4saWGTA1NUWrVq3kdiPKG/wvSV7UOYP3PUDvSsDgfQ8YV7K8Y+Tl5iJgX6TY9gP3R4LNdoB1/1XY3DcSf//9N3x8fHDoawWV1+1VpjzszXc01hVOPlGWcqssuSI/PpStxB4IiwEsgPv37+Pz58/o7eeHr7qyKbeamprIz88Xus+3b99i8+bNGDNGjrX4f6F+sTr2Qf44f7jyWaYYljTU8bCq69eYGpQ2ZhzRRUOJbPKCFsOSsEgJ9l/A4SLifTITGwjIp1QpSxnijy2OrqaofpLev0Jq6BYUFBSgVq1amD59Ohaf/4oPqfmFqF4kWU7+2XEC0QcWQEtbF2GhITA2NgYApVhc5ZUX1VcHVysgIZFxJcs7xuNHj+Du7g4AsKjdDN27dMb7D5awt7Mr1L63uwdCQkLQp08fdBwwFh+MXTG2s5dMli1J16RMi5c0+ZeUDDRuAKH3Shq9jTzKrbLkiii94pRYeeWC6wPfsnry5ElYW1ujSePGAIsl07p0//59VK1alemPiDBlyhSYm5tj2bJlkAsqXndVvn+oYx9Ul/KpYpTFGJY01BG/4TmmcNmiA10K8ywpgwrgf6DEn66uLmMxVEYMoLxy/jFJsYFLg18i4kMydt2OLeS+kjWmTxE5wKOrkaefiNO7ELZxPB4+fAhnZ2ecO3cOLi4u+Hl+BRzMDQrFEg5t7CAkT0lJwZIlSxC9cwqMrBywLvAi3OrWldhempy/cYqWHZNVLghp53Svx1OABHnn5Bm7du3aqPKL9ifhyQ1sXTgRDvb2sLW1xYCAAGREX8Z/I+rBr4ENNNhsHD58GN26dcPxbf/iybohaFyZXeS4fIg7Ju+8Kiq3L28g9GxJe5YEy/0JWhiV+XxLKusnbwyjPDGJkuT8Y/477mHQvggAQFBQEK/SEYsl87rk5OSEZ8+eIScnBwCwdu1anD17Frt27RJKRpEJf3oJOXXsg2qKg1c5VJ8gXbpRonQ1fKiAIV1s6r7oOKpgYFf1GEqeK3Hz1L9/f2rcuDERSaamUSUtjSr6EvyriFwaXY0kmpnNW7YQABoydCgdvxpBuXl5NHHiRAJA5y8EExGPxiQpOZnmzZtHS5cupXv3I4jD4VBuXj5NnjyZdHR0SENDgyZNnkz5+fnFpklRaoUTSX0pqfLJtvP3SFvPgFw9GtPJU6do4sSJVK9ePWKz2WRuW40WbD1K12/coEuXLtGt27epsU9nAkA7LkbI1P/hex+p59ZwCnkeLzMVjyrpahSlDZJES1MS9DaqWANa/3uFgoKCSE9PjyIjI4lI9nWJX/0mJSWFTp48SZqamjRlyhSSG+qq4lGM/UNmupo/bI9SJsroamREqaGrUXJNRYmp+we6FHYFBATJ3b9YqLr+pArqMoubp+HDhyMqKgr37t9XqN4qvw1QmE5GklweOhl55cqIC5NEV8OHYDLO+rblsXTpUhw9ehTlrR2hW90biY8uAdmpyM3NBQD4LDgKY8sqSPv+BS/3TMWPHz/AYrGQkZGBGu0HoZxFFUTsXYgFCxZgxIgRGBf0Xik0KfLWsFXkHJ811zHKMQ1bY4xwaVJzhftpseoGnt65jMT/luPgwYPo168fAMB9wg48ObwE+YmfhK6TrakF42aDUaetX7Hq9ioyr8qgq5G3H/57KEpvI0nOP0fV9Dai8idRUfjvv//QvkNHuNevJ5Fqiv+XDy4RkpKSMGc2r8IJAHTu3Fni+iNOfvPGDbRo0YKJm+7SpQuOHz8uH52LCtZdsSjm/iEXXY0q90Gg1NVI5qOMruZPw5/ughVXAiggqLDpPnyT4mNIY5MXHKeYbPIFBQVg/VqgxdHPSJMDvBhAv+33cDTis5DbS5xcmstYGW5pRVxh8tDVAL8zorW5Oci5shaurq4ICwvDunXrkBQXgy9X96Omi6sQNUbrGpXw9NZF3Fw5BFpaWrh+/TpMTU0BAKnJSfjLlAVtbW3MmzcPFhUrKs1dKI/LU5FzuPSr8gnAVD5RtJ+hjR3g4t0Wzm4NsW//fhARuESY6O8Dr6l7MXPjYTx4+BAfPnxA9NOn2H7pMeq09SuWm1fReVUGXY28/bBZ4ultxMm5XC5yc3NRkJ8v9vnOzMjAsoMXcTLsBVaeCsffo0ejZcuWuHD+PPx33EOvbXeE3quszExUb94ddf0mwX/HPbAAnDlzBgvmz0eLSZvQde1l1Os1Hi1btEDdunWxcOFCNGnlAxNrJxhVsMI/Cxfi5s2b4HC5+C/qKyYcjRRyC3O4XAydvhRWNnY4eeo0AMDX11coJlEwBECSvHnz5ggODsbs2bNx/fp1nD59Wn6OP3Wsu+rYP/j4013i6oA6zJelGaXClUykdPN2IbO6qs3bxSh+Lvc4Kqx8wuFwyNbWloYNG0ZEJLH6iSQ5Ucm4n1Utz8nlVfNou/paIZcXEVEBh0O+vr5kaGhIu3fvZubzSkgIGRsbE4vFIgCF/tWrV4+SkpIoIyODkY0cOZK8vLyoatWqxP3lEiyNFU7UVflk3779BIDWrVtXau+5qGo24ty24lx/ynbz7r35mgYOHEj6+vrM81XNtT7ZtxlE5YxNycrKilxcXEhXV1fs8wmAHJxdicVmU/U6DWj69OnUu3dvMjQ0ZI5P+ncX/f333wSAzM3Nhc5t6+NDI0eOZD6Xq1yV3Jv5MOO1b9+eTJzcCBpapG9hSx8/fiQioi2/wjDK1WlHzReeEZonweomgpAkVwpUXcVDCfuHTK7kP8AlrkqUuZJlRKlwJavABStkVn+wXT2ugD8wTV/U/RASEoI2bdogLCwMDRs1UsiVXBz3s6glTlY5V0muZ8H+Bd3bnIICZp5YbI1Cbu9jd9+hj1c1NO/YC6H/HRVyz12MeAW8vwdPZ2u0aN4ctra2AAB94/JgcQvw+dMHmJqY4HtCAgaOmYbrwWdgoK+HXTu2oWuXLgBKrkKHIudIq3wCcJAY8xyeOp+RkZEBbW1tnH+aADPb6rCo0RCnxzUX27/1mxPYuHEjbty4gfVP2aXuniW5eoumq/kMn3btoKOtrRI37+o1azBj+nQsXLgQla2t8eLTD4ScOYLIx48BAFOnTuV9D+Uro1ubpvj29QuuXr2KnTt3Yv369bh+7zFMdNmwsbZGWFgY3r17h8qVK6NNmza4cOECHj58yMzB0qVLMWPGDOzavRsb1q/HiBEjMGbMGBARtmzZgtzcXAwZMgTXb9yAnq4uzpw5g6dPnyJXsxyiHz9AfkYKnj57hpTkZLRo0QItuvRFvsdADPWyhUFCNHx9faGhqSn3+vLHQNWVT/4Ql7gqIau+U0ZXU9IQZ0IXfVhD5/EKeyv6sKqroLeqi5+rAadPn4axsTHGjBmD9PR0NGzUCAEtWsDL0oFpI0gLIY5ORh5qGkU2Q3Vw9IkSeHN+HZt5+inuxKQUIvbeGf4FhvU74/q54zhzuhe6d+/OuKVjE3PgYO6FHQN47Tt37oyz586DKlZD1pu7uHXzJjp37oyKFhbIbTAQlRx6wsHcAF27NGPmSBk0M/LKFR1bHF3Nm7dvkRK6FTcvn0d+RgqeVaiAChUqID8/H4mp6Xh5aT+0dXTR7aYv6terBwDQ+JKO12SJyQGd0WPwcjx69Aht27ZF/ymL8bpcnVJ1z4rQ3oijq1GEAFras/306nMUFBQgMzMTAwIC0GLVDWh1dUXvrt+wdkhrWFpaAuDFW967nYtGjtWxY0dv7NixAwAQlHcD3/I4MHEsj6v//HZrcong1KI3Dh07BUdzXTR3tUfnTp1AAMzrtUOrOfVRuVoFZi5GjR6N/6K+omGHvnh15wLTz5QpU9Cpcxc0aewNMzMz5ObmokePHvD09MR/h3ZAV0cbObl5uHwpWuo6Irgmif6Q/GOg6v1DHfugOvZzdUAd5svSjBJ3JavIBVvIrF4KC3qXBojOU5cuXRi3T9++falu3bqMG9TFowk9f/GCOXfC0UjyWBJayGUsr/tZHtezJLkqsiEFj/Hn6XB4rMS+mv17jVwbNqX69etLHePjp09Uq0Fj0qtgQ0079qLklBSZrknwryrlxe2L73bPyc2l6Oho8vf3JzabTRUrVqQpU6bQ7Tt3qKCgQKifN2/e0PLly8nDw4MqVKhAFSpUICMjIwJAnp6etGnTJnr79i0NGDCAAFBAQAD9TEtT2VwoI9O3qGev7eprhbKSlR0Osevqcxo1ahQBoD179yrtHSFS7L3Vc2og5Gp+//49tZ1/hAwrVmFkNWrUoO/fvzPrhf+2O0JrlLh1hMOVnFn//wUyuZL/AJe4KiGrvlOmGJa0YkikkodV5tT9PxVKmjPReQoICGAW6C9fvhARkffC/6i870TSr2BDZmZmFBMTU+piAPnH+H+LKxeNGePPU3ZOrpBctP2IESPI1tZWYj+KyuWlelELLU0RdDWerXwJANna2tKmzZspMPytXP2cfPiJOk/fSLXqNyItLS3S0dGhwYMH08jZy0lLR4+sbB3oy9evSp8jcTQ2kuRF9aVKuhq+PDU1lY4dO04zZ85k3t2OHTsx7fPy82nIkCEEgHbt3i307Aveg7xyRd7bxv+cox7DJ5Gbmxtt2rRJqP2KIyF0JDCQMjMzhfsJjxWiipL0/ktSSIuFP8igUGr2vFI8Z2UxhjKiVMQYqgBype4rG+os8ScaryEnu7zoPI0fPx4bNmwAm81GRkYGdHR1mdgd/9rlMbVnY0yYMAELFiwokRhASXL+GNdeJoitiiKPHCgcM8afp2Px5uCy2GJjzOIeX8XdnXMBAN++fUOlSpWUFvemCNWLqmlpROXfvn3D8ePHsXTLPuz4dwGGjJuGNYtmw9/fH1paWsXq/9SQ2qhYsSLy8/NRZ+RaJHD08f3gJPw9fAjWrVsHFoul8jlSZO5USVcDAM0mbMTdXXOQl5kGU1NTZGRkMCXgqrf2h12jjuhcKRUmJibo06cPDAyNMWznjWKX9It4n4z6tqbQ1GALve/KkPPB/8yniuLPk6T1RekxhkpcZ4scRwn7RYnueX8IyuhqyiAe765LTvkP38Q7Xtz+D3XhBfmKpv/fWc+TH+pSvHFUyC5fo0YNAACXy4Wenh6A39Q0g1vWgpe3Nx48eMBsGuquiiJJzj8mqSqKPHKuBHoQAOjgaiWRTuTLuxdMu9evX0ulH6ljkIrrW2ajfPx9mehKilNxI7+gABMmTMDMmTPRyiIL9mZ6SqGl4VXv0Ed9jY9o1bo1rK2tMXXqVDjYWAEA1h0JRr/+/ZnNXNF7CGhghb79+qGgoACLFy/G1IHdUL1aNbTu3BsbNmxAw4YNEf/9u1qqlchL6aMquhoOh4NVq1bhzubJMLCqhvVB4fiRmIi8vDy8ffeO9wyGBOLyP30wevRo9OnTB/b29rDxHakU6iZlVDeRJOffM38dCX4aL1YuuL4A0im0FII6qnioY7/gj6PKfe9/DGUWw/9PFkN1/AL8wzK/ROcpIyMDhoaGAIBjx46hV69eQtYKs6j9OHToELZu3Yq9321ktqxI+kWvLLkyx+D3JWiV4M+TYAap6EYGIhgbG8PPzw87d+4U2w8AZOfkwLZKFfz48QPOzs549vy5Uq2nonIiLrQ0hfPsfH194VjbA2baXJibm6OChQUqVLCAlWUlmFewgKmpCTTYPCsOl0tI+B6P//47i5u3bqKumxsmT5mCnz/T4NvOB/fv34eHhwc6dOiAKra2MDExBXE5aOvTDtraWsW+hyFDhuDIkSM4c+YM2rRty8gLOFyEhFyBX+/eGD16NBYvWaoyi7Q0uSSrV1F9cTkFQs+UpOdFUJ7w4wdOnTyJXbt2ITIyEhMnTsTy5cuhoakp1D4mNhZJiYn48PEjHB0ccPfuXfTr3x/Br36q7B1U5vsnuI78ZaGPUY5pzBolr3W2WPiDihZItBiqy/L5B0BWfadMMSxtiqEqzep/mNIm9zgK9C9unpYtW4ZZs2ahefPmCL16VWjxbu9sgrFjxuDgwYPw+3smvtu1UXlVFElydVZL4dPVcK3dcOtdsliX281X37Gxnzs2b9mCkSNGSHTFffjwAfb29sx3cPnyZbRp0waA6uhnoqOjMX36dFy6dEmm58LJyQmurq5ISkrC3YdRyMv8yRxjsVhITk6GR8+/8Sb0CLTLmSAvI5U5rqenh8DAQBx4q4FTUzoU6x6+Rt1A2PZZ2LlzJ4YOHSq2/YABA3DhwgVU6z4ZVeo1UzulT4tVN5CVx0Ejx/JY27uO0DzK40ouyqV7PuwJjs/pi5zMDDRt2hTzFyxAooG9WquYyBLOIWt4SVFyZh0paboaVVfxUNGPfAbq2Pf+EKq2MrqaPxGCv2y4HOX/svEcw+tXMG0+9nrhl7C4afSi6f+qUAr544hevxLY5Vu3bo1Zs2bh+vXrSElJEaKHYLGA/fv3w8bGBkuXLsXDh91Rx+2360aQNkIclU3oi+9iY/rkkbNZhelk+GMrg7KG70ITpAfh09VsvxmDVwlZYulEXr99y3PB6+pK7AcAbKpUQTu/Ibh88gC4Bflo27YtAOBxZKTK6GdcXV1x8eJFFHA4WHviBvYGhaJuRU1UK6+FmJgYHDhwAFVd6qGcd394V9ZC4oswpCQnIycnR0gpBIBDhw7ByNgYvbp3wfpXj5Ae95o5Vq9ePdRq0p734cVlcKm9wvfQoVYFDBr3D4zNKmDQ4MEAxFPJLFiwAJ8/f8b1HbOQN24rJvXxVercFSUf2tgBu27Hwt3OrJDFUFpfB8JiAIvfrmRJzwubxcLKU+GIPLwMBE3ExcWhYsWKAH5b1ZRFb1MceVGKrbwxjL3qW6OPR5VfMYbR4BLhkYBccD4jxMiVBhWts0L9A6rbL1S976l63y4BlCmGpQmiMR2A+F82Cc8Vf8D+cKVNCNJKGxVjnDp16jD/371rFx4aNRbaBOiXogEAozf9B1uPvCJ5APnypcEvEZuYicSMXPTxqKKQnEu8EnRPPqeqfLPny1NSUgAATulP8CEmDlUKjLB33wf06tkTmlpaqPTxCq7vXIEKVjZo6+PDXKdoP2wWCxpsNs4f2YmC/Vvg6OiIuLg4AEBUZCT09PVxbUpvoc1NUNmWJhecf3FyANDU0MDk3i0w1a+lUF+1atXCtGnT8D00GOXNK4DFGgU2i4UVK1bg/v37zPkVK1aEn58fAGDB8B5YNLIn7kc8QEOPBrCzs0NMTAxevFiJrk0DEX7xJDIzl8GwXDkA8vFbAkBfT0fcHTIQ27Ztw7q1azF58mSx7e3t7XH23DmYly+P+rlR6FF3aJH9yzqnisr5kHTMr4ENeta1RHBwsJAiKfq8pP38ib79+uHuhQtga+lg4tItqGBhIfX5kiZXpZIsTSGVVz7r9FPGCvtvt5pi5XzrrCS50qCidVYIf7LyqY59W91QdXp0aUepoKsRhJJK9hSZui/a7/7OyroDHlRdEkjF8+Tj40MASFdXl3Zff0nNV16nrRcf05HAQPLy8iIAVMerBTVeHCwTD6Ay5fxjgn+lyeWlAckvKKBTp07Thg0baNu27bRmzRpydHSkoKAg0tPTIwCkra1NAMjX15d279lDAMjb25u+J/yQiWZkdeBlcqjfjADQ0KFDCb9oRthsttJoZmShn8nLz6fc3FyaOHEisTQ0qefW20L8cLm5uWRVuzGxWGxy6TKKUlNTmWOCfHJLliwhNptNM2fOJD09PWauQkNDxbYXhDR5r21hVK2lH2lpadGPHz+ktnfrPZnAYpFplb9o5cqVUturmgJIFtqbK9FxQjQsd2MSKb+Aw7RLTU2lWrVqkampKe3Zs5dSUlJKpCSi4F9pclW994K8mIrwLRYLf1AJOZnoalS176lrnooJWfWdMothaYM6LHqq/gUoLWZE8BeVolADu3yTJk2YeDSbgjhYRx/A2JmBKCgoQJMmTRAcHIy2Pj5CMYCCkNc6pMpqKfK6xL5++YLu3bsJXQc/Q7vtvCPQNrUAEQu9zL+gZ8+eSEpKAgDk2HpizKm3OD7SXGr/8d++YWr/DmDpGqJG37nYtGk2du3aBQCoN347zkR+kcmywj8mj7X1e3w8+rfzBsvyL5y3ccCkp8H4+PEjiAg9hk9GYkYBhjb+beHS1NLCsk17MGl4AGKvHUFm1lwYGxsLWY2GeNvDxaAj5syZg5ycHBibmTPXl56RwXxnilqlxo6fiHHXjuHEiRMYMXKkxPZTJo7DSlNrsJ9dwLx587B48WLs3LULXdwaqNS6JUm+63YsM/+talQsJE/LysEox9/fo6jVa8aMGfjw4QPu37+PBbd+4vLxlypxASvLNays91tUXlCQz1ynIlZbhaGuKh6q3i8Ex1HVvqcuT5yaUEZXUxrhPZ73cghCWWZ1cS8hH4KUBIpC3GISEMT7KziOJOoAWSCttJHgOMUobdS0aVMAQE5ODtq0bo3LoddQs/MorA8Kx/UbN9CuXTsAvBhAv+33cDTis1CslCqpafibsaz0GnwlQxYaEACwtrHBrM2BqFCrMapUrcFkaQPA5X/6IOzgKnRxq4xu3btj//79KCgoQIuufWHg0kam/qOfPgW3IB/1Rq7G7LFDGXqRkNBQTOjdRmlUKeLkOrq6yPz+ARlRl/Du3BbUrFUL27dvx5kzZ3Bs20pcm9IMfg1swAKQmJiIQQMHYn7/1kh6eReZaT/x7OlTZq79GtigZW4Yxneoh9qurjA3N8eMGTMQdPokc33Xrl4Vas/vX3RTlyYf3cEdffv2xZw5c/A+NlZq+8itE/Dg1hV8/PgR9evXx/x588S2l3feVCEPaGRX6HvU19aAu50Z3n/4gG3btmHx4sX4y9lZrudXWXJ53zNVv/d8hVRWiqxiQw3rrFr2C0D1+x6g2n1b3VCPAbP0otS5kolUZ1YP2yi+P9HxJLG2ywJ1lQRSUeUTPnJzcwkAGRgY0K1bt6jZilC1l6crKdezOPmPHz8Y9+iMGTOK5apOSEggADRt2jTKzcunRet3EgC6e/eugDubQ3djEgtdhzi5vG7DtevWkYaGBiX8EO5H0CU6e/Zsxr0t+K9WrVqUm5tLHC6XJizZSADIp+cAOnHyJCUnJ/P6+VX5RE9Pj4yNjSknJ6fYLtrvv+Zs5MiRMvez55eLf/eVx3JXJVGHPDsnV8iVLNh+z969xGKxKDEpqZCLWfC5k+TSVZa8NLzHfFdyXl6e3GX4io0/qIScRFeyOvY9cf2VMjcyUVnlE5lR6uhqVJm6/4cx2asD0tjyPT094ejoiP0HDhSbB5APcXIuqZ5rrrjZkny6Gn9/f+iXt8KSbYcxzLcBcw/yUpwsXboUs2fPBktTC1SQj5YtW+LKlStgs9liE3cAYNjWK7j3LAaGpuVxbU4n6P7KfuaPkZ+Ti/F12Ni9ezeMjIzwtVoPaOlog8sFDgx0w/nz5/Hs2TOs3LgdeqYV0GLKdonVVabWzEaHDh3g6uqK96kFKChnAUMdTcQ/usJQxxhUtANX3wyN/l4jdJ0+a65jlGMa/P39kZ2djePHj6Nnz57FqjRydXJT6OjoYM2aNTidU6vI9vt6OcDW1hYAUKPPHGRYe8CxQjmZq5KoQ84mLnpX+k1XI9jeNuY0Tp8+jZiYGLF0OPx3LyjyC4Y3cUALZwuhd1IZckX5GZVNb8N/93zatcOpyHj1UtWoA6qufKKOfU9dFG3FRBldzZ8IVcd0ODXnPfziXkLv8bx+laG0OTWX3EdxqXDUCC6XC+1fxLulKQZQEfmac48R+zUBTz47KhQ3ViDw+zEpLgZ7brzEiPYezP3KEz8HADNmzkS+hTMOnbuKjl61sXLKELB/EUpLyri+vWMuXkdFAAD0lvCuxcnJCSw9I3z9kYKcxDic/hWPxcMm6Bibw8HBCVazXiMlJQWVKlVCNeca0PYcwLgNxWWytm1gAw6Hg/HjxyNqwwYAQOavXr9//w4uEbQ5WcjN5DLXyQJw4OBBVPj4FnCsDwDQ1dXF/v370b1HD7kzaAXlz54/R35+Puzs7THUvOj2FStVQq1atfDs2TO8OLIYRlWcYdSiJfILvKGlqanyDF2Z5F62QEKi2Gfo8r0fMDc3F7ovQTocWWIYiyuXlOmr7hhG35oVpK430uR/BFS9X6h631NXLKY6oRb7ZSlGqXIlq8OsXgYhSJqnzMxM0tPTo3///ZeI5M8olcflow7XVFWXemJdo851G5Lr0BW079Ybpn1KaipNXLGD7FoPJI+WHahWrVpkZGREQUFBVNnWgZouuyLVVS2LXPRYUXIOl0u374RR8+bNydraWugenJycaPjw4bR+wwbasnVroXusW7cuzZ49m16/fi2363Pvvn1MP7a2tmRoaEhGRka0ePFiYrPZNHXqVKZ9YGAgAWCykgcOHETtfH3J09Oz2K7YO2FhBIA8PT0Zt6os/WTn5NDIkSOpXj3e9+/u7k5xcXEqdxnL8v0KukhF22/cuJHYbDYtX76cklNSxD4PJRmWoay+ZFknBNcoedeh/08osT1PXeFTSkCZK1lGlDpXcllBcdmhhLmSNE+HDx9Gv3798Pr1azhVraqWcnaCGc7FlYtWRZkzZw6WLFkicR60tbVRtYYLDHW18OjRI+Tn58Pc3Bw1atSEeRVHeNapAScnJ1hUrIhGDRuKrbpy7WVCITJuaXJ5Mz75rsaEN0/gofURR48exYcPH5h7MLOrga+vHiM5ORnbtm3DjtMhMLa0R82Ow3FqbFOmXXFcojv8nGFqagoAqFSjIRqPXQluAReZF5YxWez8yid8V/KBAwfQv39/hcfNSk3Go/XD8fXrVwBAg0HzYd+wrdz9JLx5gsjdM9GvXz9s3LhRpS5jmb5fJzOw4yKZih6iVrJpU6diy5Yt0NLRhc/4lejfo6PaqpgU5f7lv3OiLmZ55bKsE93dKuHSxYvqdyX/QSFBQAnveX/IXJW5kv9U/I+4YQGo9mVRIdt8fn4+lixZAl9fX1SrVg2AZKLeosiDRauflFRVlMWLFyPTpQcj39/bEdOmTcPRo0dhXaM+NOw9kPjtDepWrYQ+ffqgQ4cO2BDxE3djkpCirYFx47wQHByM+vXqKY28Wx76E0FXY/z1/Vj++F6h7y35wwucPHUKffv0wfwFC1Dddwh23Y5F9wb2SnOJfv32DQBgbGyMf9bvQGBUEhpofcaKX0qhnZ0dzKzsmWtq07Yt+vXrp9C4nVwrYemm3YgP2YXUpAR4eHjA2sYGF0+uhYZVTYzp6CHX9fds2QDPD2qDiFTuMpbl+335JUWIrka0/fr16zF9+nRUb9AMwYe2ItX0L7VVMZHm/hWNSVRULq5aiTg5p6AAAPDoY4rE6iZKp6tRRzWPP0SZkgn/S/s25FQMFyxYgIULFwrJqlevjlevXgEAmjVrhps3bwodHzFiBLZt2yaxz4EDB2L//v1CsrZt2wrVNL179y5GjRqF1NRUzJ07F0OGDGGOsVgs6Ojo4PXr10ywNQB06dIFJiYm2Ldvnzy3+P8Hqn4pVb2wqJBtfvny5Xjz5g0OHz7Mu3w5YwMB6dVPSqoqiqC8srU1AgMDUa9ePUydOhXst0+ho62Ng4/TcfDgQWRnZ8O9qZ9QP0X1369eRWy7cA+NDY0wZswYVK5cGZ06dy4yhq6VRRbWrl2L7t27w9rGRmx7QaU6YcBZ3L93Dy9fvsTjx4/x4MEDxMbGAgBu3byJrl26QF9fX2WVPT5//gxra2twiTCsFQscLhed3B2hq6uL2rVro4DDwaWLF7Fj50409vYGi8UCC+I3b37/HWuY4tz58xi89TK0tbXRtWtX3A4KwsvAHQB4a+Lhw4fBYrHg6uoKvbvb0GthN5muv6mNJmKyb2PRkK1gsViYPn26UuZBmlzw+5X0vQc0sgUSoqW2r2RpCS8vb1w9dxJNTVNLJhZS5DlUZQyjODm/HOU/514gNYdbZBKOUqDqah5limfphjz+6fnz51PNmjXp27dvzD8+uz4RUdOmTWnYsGFCx4vyZQ8YMIB8fHyEzklOThZq4+zsTCdPnqSwsDBydHSkT58+McfwqzpFQECA0DmdO3emAQMGFHlPpSrGUBDFpAmQGm+hjpgIdVAEKIFtXnSeoqOjSUtLi2bNmiXUznvhWarQfR7V7L+AXr9+TUTqiUNSNAZK8G9R8ishITRo0CChuLyHjx4JxY4JVl8QlH/5+pVOnz5NPu3aEYvFEhvH2MjTk/bu3Us5uXliY9Oat2hBAKhcuXIUePSoxFg2aTQtI3fdoIGT5pGuri7VrFmTfiQmqryyh0T5L7qa0w8lV1zhyz9++kRN23cnDS0dAkB16tShqlWrMnNnaG5Jdb2aM+2zsrPJu21nAkDzNh+W2n9GRgaNHTuWNDQ1SVNHl1p19afY9+/VNg/FoasRbB/35SvVr1+fADAxv6qIeZQ11lXd7zV/jTocHitXnGKxocpqHirYH4TW8j+MNk1dUFnlE01NTVSqVEnicX19fanHxUFHR0fqOZmZmahbty4sLCxgamqK9PR0oeNjxozBmjVrMHXqVNSqVUuusUsl/mBrGwNVFy7nXzO/b6DY9ABEhCFDhsDJyQnz5v2eh9t37uDhij7IycrADwDVDy7A+/fvYWdnJ7EmryRLClC6qqLs3LULOw+dwOPwG7BxrIr5s2ZgQEAANDU1hWLHDg/hZdmGhoRgysbjSI17g9TPb5GTxqt6Uq9ePbj1ngwTG0dE7F8GVydrhISE4MKFCxg7fyUGDRqE6cs348GVU7C2tgabzUav7XeRkfAR169dAwB07NgRffz9sepcFJyadCrk2ivKNelg2RwPH/aCp6cnli9bhseVOqi0sock+fabMRjlCGy/GYOu9cS7zn1rmGPt2rVYtGgRCtg6MPTqi78atcbdpX4gIrx8+RJDD0fj+fUgREX8h+ALF3D27FkcPnwYWVlZMKjVCjdSTbFQwHInej3jxo1DYGAgbNsMRn7VFuBYV4S9nZ3a5qEoFy2PrgZFtp949gPsh6xFhvlOTJs2DTk5OZg7d65Kq5hIkqv6/RW3bvAtht3rVUafRvYyWWaVAlVW81D1/qCOPU4dVs8SgtyVT96+fQsrKys4ODigb9+++PTpk9Dxw4cPw9zcHLVq1cLMmTORlZVVZJ83btyAhYUFqlevjlGjRjEltviYN28enJ2dYWxsjIYNG6JGjRpCx728vNChQwfMmDFD3tspnRB9qPms7OIeakXgOaYws/yBLoVT7osbGyHKkK/MhUVwDCWxzRcUFODJkydISkrC6NGjsXjxYkRHR+PkiRPIycrA8uXLYWNjAwB49fo1c5646if8jUW0GgGbVTJVUYjLxcpT4Tj9OA6rztzHsaNHMWfOHAwfNgyRD+6BCzY+x7zF0CFDMHDgQMaVlpCWi4bl87BgwQIAQM+ePfH98WXkcwGf7n1w/MQJxMTEIOLBA0yeMBZk8Re2n72N23fuQF9fH9179MCavSdRZ8QaZHx5A1tbW2hoaIDFYsGnugneP3vMzOOhQ4cwYsQIPDq8HDEvnhaqTlFUJY3qGVFYtnw5MjMzkZqaWmKVPfgVPQIa2Ylt38fNHA0bNuTN//Dh2Bx0C27t+2NsZ09wicBisfCXszPGdPJE7Xb9YOdUDR07dsSZM2cwffp0LDkQjAYDZmNEy5pSr+fEiRMYP348Fs2bDSfrimqfh6KqjHRwtWK++6LaJ2YUYO6CRZg1axYWLlyI+O/fVV7FpDRVNwGA4KfxYuW93XmVbXq720DpUGU1D1XuD+rY41S9T5cg5MpKvnjxIjIyMlC9enV8+/YNCxcuxJcvX/Ds2TMYGhpix44dsLW1hZWVFaKjozF9+nQ0aNAAp0+fltjn0aNHoa+vD3t7e8TExGDWrFkoV64c7t69Cw0NDaZdZmYm8vLymIxA5gZYLJw5cwbVqlWDq6srrl+/jsaNG8scY8jP0klMTCwdWcl83N0K3BDIIrVrAny49ftzs9lAo1EST8/Pz0dISAhat24tOUNLdAwZ+5YbgX2Er92uCeB/RDl9F/MeROfp8uXLOHjwIL58+YKYmBj8/PlTqL2T31wkPjyPjA9PsXbtWgQEBKDjxjt4n5QJ+/IGODfWm2krTs4lwqlHX3Dg7gcENLJD93qVGQuFKuRd3SwR/eQJevXqhR8/foCloQkWCFwOBwYGBmCxWMjMzESl+u3g08wTBfGvcfDgQTg7O8O5Rg08f/YMr1+/RsWKFbFu3TpYWVnBtXZtZixB6wT/syT594QEVKtalZHv3r0HQ4YMBgBUqVIFT58+RXZODqpVrQpzc3McOHgINWvWYPokKdmj8d++Cv1gHDlyJP5ZtAg62tpqzVrlEiE/Px9XQ0PRslUraGlpCbXncgrQo0dPPH78CBcuXEAtF5ci+8/KzsGzZ09Rw9kZ+gYGMl+Pb7t2+PnzJ86eO48K5uXVPg9FyXPz8nA1NJR594p6fn6mpWHc2LE4f/48Pnz4AINy5WR+DtX93ikqF7du8NeoXbGGeJOYVWidGbAngrGQ7h/coMg1Ty6oY49Q0v4gds9T9fUXc59WN9LS0mBubl5kVnKx6GpSU1Nha2uLNWvWCCWE8HHt2jW0bNkS7969g6Ojo0x9xsbGwtHREaGhoWjZsmWR7fmKYZcuXTB48GC8fv0aYWFhciuGR44cgb6+vkzXWIYylKEMZShDGcrwJyErKwt9+vRRLV2NiYkJqlWrhnfv3ok97uHBq4wgj2Lo4OAAc3NzvHv3TibFUBALFy5EtWrVEBQUJNd5ANCmTZvSZTHkQ8FfU6XCYqjKX1P3dwDXBFwF/L5Ex2wxD/AYLrEbWeaJiBB69SpWr16Nu+HhcHR0RNdu3bBh/Xrk5+dDV1cX9evXh6OjI3R0dKClpQULCwvUcauLJo29wSUIWUyCn8Yj7F0ivJzM4etSSaockGwRkEXeFM8wefJkNGnSBHEwh6GFNZI/vcbHexfh7u6OBw8eMOfZ1G+N87tXwe5XHJqg5eL0SA9mnrptuy+zhZQvjw45jtSb+2FtbY24uDiwWCxI+02qY1Qeo2ctxbyRvRWyvOzZuxcTJ0xA7eGrMba3j3otQQ8+wSDxOTLNa6K7exWh9gsXLULCvf/wNPoJKlasqPLruXv3Lnx8fGBkWxNr9hwvcYuYkNzDBgaJz5l3T9zzk5iYiNpN2yEj7g1MnNxw+8wBVKlSpcjnTZy8OO+RonJ533dxllZOQQFCQkLQslUrmS3gxYaS1lepUPL+UCIWQz5U6RVTItLS0mRqVyzFMCMjAzExMejfv7/Y41FRUQAAS0tLmfuMi4tDUlKSXOfwYWNjgzFjxmDWrFkyK6J8aGlplT4i6DvrgdgrwrLYK8D9LTLHX0i8rzvrgWsSajtemwuwqXgxHuGbeP3wIa5M0LW5gCZbsTiPSjUAbo5A3+N4/28yjnft/DEq1QBk+F6L+v5927XDvo8maOL5Dlr6xtiwPAAFBQXIyspCPjRwL/oNIl5+hI2JNvLy8vD161ekp6fDslYjeI5cgZN/N2b62nidR1/x+HMak5wgSc4lQoc6NkxwuYamJrMhyCL3a+CJESNGgEuEf3adwaa1q6CdlYDs7GwMHjwYt279Xsze3D6LatXOIjs7G9o6OgjwcuTRinjx+gcADU3NQnL+uNLk82+w8S07G2/fvkWVKlUwZ84c7N+/H2FhYWLn+/z5/WjWvLnQJufX0K5Q8L0k+dAhQ7B61SqYvT0Pv4Yji2zPJUJvD55cdOOVV96tfhVcvvQc3epXEXIl9/awg8+hVbC3P4UhQ4di08aNcHRyUtq4vT3sUMc4G0OH9cONmjUxYMAALFu+HNnZ2bDKT0FvDzshxUGe+ZQmb1+zPMx+vsaPmDDsi85ERkYGMjIykJmZiVYWFmB9/I41N97j0qVLyMjIgKWVFVo2aQJPawc8SeS9e+Keq9SUFPj4+CAv6RuqD1iKKQO6wt6hikzPmzh5cd4jReXyvu9AYcLw/F/9DD8UBS6LLTPxeLGg5PW1EFS4PzBruar3OD6UsE+rCzLrOPKkOk+ePJlu3LhB79+/p7CwMGrVqhWZm5tTQkICvXv3jv755x96+PAhvX//nv777z9ycHCgJk2aCPVRvXp1On36NBERpaen05QpU+ju3bv0/v17Cg0Npbp161LVqlUpJydHpmsCQGfOnGE+JyUlkbGxMenq6v7ZdDXFpAqQSlejDioZddAFKIEqQNYySqI0En369iVjY2OaMmUK6ZczIrDYQmXlCjgcmrB8OwGg0QvWqZV+RhLNxuw5cwgAtW7dhs4EBRERryzZ1q3bqFKlSgxFipeXF+Xl58tEVyMrPQmXy6Vbt+/QxUuXKC0tjThcLm0KukUNug6laat2Mu0/ff5Mrh6Nia2hSSNnLy8WXUr12jyKkwM3nhXZnkj+soeS5P7b7lBQUBD5b7sjtr3niGVkZWVFbDab3NzcqEK1umTl4kU1Ow6jhIQEhcftvOoS6ZevxFDfCP6z9+pITcZvIK4SaGl2BEfQ1WvX6MGDB3T5yhUqX9GSGYfNZpORkRFZWVlRRWtb0tLRIwCkr69PXbt2pWHDhlGdRk1JQ0uHDAwMKCgoiPbu3UvRT5/S94QflJKSQj8Sk+ja9etUs2ZNMjc3pyfRT+V+3oor5x8T/Mv/v7xyVdLVSFs3ig1VUrGoYH8QWsvVsceJ60+ZlD4qgEroauLi4uDv74+kpCRUqFAB3t7euHfvHipUqICcnByEhoZi3bp1yMzMhI2NDbp37445c+YI9fH69WsmoF9DQwPR0dHYv38/UlNTYWVlhTZt2mDRokXQ0dGR59IYmJmZYfr06Zg1a5ZC55cKqLoot0XNwn0DhekJBNvJC1UXLuePoSa2eVEaiearVuHSxYt49+4d6rjWQnZ2Nvp7OzFWAhaLhVVThyHs1C6kvI4Q6kuV9DOS5GwWC0+NPQEAX7Ss0aVzZwCAjrY2rsEVTt0mIn7LdABAWFgYuBwO+u56UIiuhp9NqVgFCTaOj2zL3Nuptxx8r9YFD8iAmTcba2uYdp0Hg/z12LZkBjR/xmHt2rXQ1NQski7l7uNnqJBph3v37mH9+vWIi4uDhmEF7L77Bf2b1izUXlJlleISI3dwtQISEtHB1Ups+78H9UGXteNx6OBBPHjwAHqffuDVp3i8CzkEO7vDmD59OqbPmCH3uK7l0vFfUnyhZ7eyfVXEv36M92HnYLR7FmxtbaGlpYVPOTrglHfAtequ8F4+BBUqVJA4P+lpaZixeDU+hJ9DXrxw6JChzV+wHDIXVR0dcGNGG7B+fZctVt1AzI8M2JYDrs30geYvq3OLVTdg5ZEA0++8jPTRo0cjOzu70HXXqlULN2/exIJbP8G+e1ftFU5E3yMAKqtuIk0uja4GUEHVEz5Uub6qen9Qxx6n6n26BCGXYnj06FGJx2xsbApVPREHEogr0tPTw+XLl+W5BKn98TFz5kzMnDmzWP2WKFT9UKtDaeOP8z9UJggQLkO3e88eEfwPmwAAjGJJREFUdO/WDVwuFwCw+9JDDPGpzyhivbbfRYqBLc6F3gJ/uVZk41IGrxyXCD41K+K8lg5eBO/BocMt0K9vX0bRWPPCkLlHCwsLNGrUCHVa98CHCp5iaUiUoUBJqowxvFk17NKYDPvWnti2eh6Sk5Nx8NChIitpRK4YjMb/JjD3oV/OCHXGb8Hw5n8VOa6o4s/fYBWRd69XGZcuRgtlmoprP3z4cAwfPpz5nPDjB9asXo1//vkHKSkpWL1mjVzjzhncGeV+roBBuXJo0rgx3rx5AxsbG9R3dwcLwNVr1/Do4UN8+/YNeXl5wJNXeB4VjOiwQFjsmQl7e3vUd3dHeRMbxCXko2KGAWbPDsHLly9x+fJlZOfkwOyvhug7bhJGdm2O7Kws5OblIYZbEXvCP2JoYwcQABYK8+uxfzFM/JYDQ3uNABKi8enTJzx/8QJnwl4g9EU8WjpXhH/Lumjg7g6wWOiS/lkpz5sySvqpqrqJNDn/3Ru09wEGN3YqUiH9Y/AnK56AepTPkoJa7JelGKXWlazKyidlYCDvPIlWGThz5gxp6BoQAGr27+/vhO/icR2ynABQWHi4kLykKqK8e/eOcfsJyomIvn77RvPmzaOhQ4eSpqYmeXl5MccF50mSy0wVrr0tW7cSALp46VKR7UeMGFHIhfr8+XOZxy2Jyifi5GPGjKEqVarQqUefVV6ZhMvl0tt3MXQkMJAmTZpEXl5eVL58edLW1iZTU1OysbGhZs2a0eLFi+njp89K/X75lU9ycnPpbkwi5RdwhJ4rDpcrVs7/v7wu3cysLEpJSVHYBVxS7y7/3as261yh6iYqq3ryB6JE9rz/0conxaKr+V8An66mqPTtPw35+fkIDg6Gr69v6UuqUTaKURNTnnni/0L/ndjBI5Q9HP4eu+/EYnizavBrYCOU0BAbE4OqVavi4KFD6Ne3LyPnWysACLVns1go4HDx8GMKGtibMe2KKwfAuMbCtk7Hg5tXcOXKFbRs1Uqs67l/QACu3riNnitOotlfleBbswIuXbwIn3btEPz8h9iKEJKC4BWVf312H3e3TkVBQQG0tbXRad11aGiwJLYvyMtHD/OvKCgoYBLinJp2g1Oznri4wK/IcVususGroGJuwNSwVkTus+Y6RjmmYWuMES5Nai53P1euXEHbtm1Re9hKpJo5F/t6JMmV/X3JK+dVPkmEr68v2q4PK2Qp41+7OAuaqKWM/9xLkgfe/4SJAV2R+uE51qxZjTFjxkhtH/E+GfVtTaGpwRZ6X8VlAMsr59+D4LvKhzh5bl4eLl28iK0xRgjwcmTWGHHrkVJcyX9ojeH/V3uegpBV35G78kkZyiA33l3nLSjiEL6Jd7w4fR/qAlyZ/Zt5no8763nyQ12KN4YARKsMsFks9PW0x43prdCrvjUi3iczCh8ArNl9FCy2BvKs6grJxVVLYbNYmHgsCt4rrmPW6adCbsLiyvmusTORX6DXdjJ8fX3h6+uLMX//jfVXXhWq8NDQwwPfPn/AyeuPZKoIwXfJyVqJQha5SeobFBQUwNraGjVr1iyyfTd3O/Tq3Rv9+vVDWno6/EbPQGzYeVxdNhCf4+KQmpoqdVx1VT4R1499eX30djXF4SNHsGjRIgBAPbP8Eq1Momo5v/IJ/xr1tTXgbmdW6NpF5XzXbcSHZCwNfin0bEqS/7vrKH68jYS2mRXGjh2LwYMH42dqqsT2s04/hfeK65h4LEpIrsrqJpLkfJwb612ouonSq56oYz1V5X5QBuVALfbLUoxS60ouJmQ2q6vaFK7q7ORiZp/J636QlBE64WgkeSwJLeTSqdl/AS/Td/5pIbk4F5AqXVKisqzsbBo9ejQBoFmbjhTqI2DAAAJAjWYdpcP3PgplJasye1rw84ePH8nR0ZFsbGzoZ1qaQi7Lrb9c0QDI29tb5dmsHC6XcZFm5+QWav/+/XsKvniJNm/ZQlOnTqWu3bpRnTp1yMjIiLnOZs2a0ekzZ1R+ncWVF9fVy3+mJIUnCH4uys0rTR799Cnp6pcj06r16NDdD7Rr924yNDSk8uXLU8f+o6jhzEC1h3VIcgGLk/PXqL477sicoa4wVJ3Nq8L9gFnLX139o9y76oRKspLLUAJQpVlfHUXAVV3MXNXF2AXAt3aIC2B3tzND5PsEdK1eDukZGTAsV45n8ejaEhMPLoC7boLaEiCAwhnOgm35LmYigr6+PoZ18MQSW1sUcLiIeJ+MBvZmmDtnDi6cP497y/zx7YgtDtVxx4iBfQv1w28PQCnZ06LyM0FBcHVxQdidO9j70UTubNMfr/OZOdHX18fmTZuwN/wjctOT8TU6HOY6HDRp0gReXl7YGf4F5k61oGtkgROjvZh+OiwLQvyLu9AzqYhbGycWOe7gfQ/QuxIweN8DHBnB6+f6tWvoN24O4l/cAwBoamrC1tYWDg4OSDO0g30rL+iXt8KBab3g5OQEQLwrVtXZuPLKJSVLyJK929jJlJGfl+MZEfd8S3ruAWDN6tWwqmSBJ49vQE/fACyPQfBt1w7Lli3DwYMHkZ6+A5tuemADh4Nhw4Zh0ODBYvtR1vsp6f2XJgeAxPQ8BHg5Fpk4Uyyoej1V9X4AAMf8eRyMqtrTgD/W3S4ryhTD0gxVK27qeEnVobiJZoGJ9q0kklFpitinG4G4PXsWbnK5+EUFi1OnT2Ns5y7Y4eyM+Cc3AfyuEKBqahrRTfz06dOYM2cOvqVkgqNnCk1NLaS/j8LEiRNhZ2uLiceicDcmCfraGrg2pRmcnJzw9u1b9J61ERFXz+HG5fMYMbAvevXqhVatWmHLyRCkcHTh2KoPbs/rBABKyZ4Wle/qxiP+5XC5CmWb7kjNQJWqNfDp7QtcucKLq2RraEBDtxzqezVF3ao2uHXrFvbs2cN8D1o6Oqi1xQkeHh6wtLREyKrVyMvlkf0eb2KN3r16SR2XT1fDjQrC5i1PoMFmY9SoUbCrVhPOvWdgeO/2GN3BA9q/6gMLxok5ONowz4Iqs26VIQfAKDJ8Vy//+ZMle/fyeC+FnxF5lNevX78i17gKhh55ysgtLS2xYcMGxDl2wYe75xH3LhrVTVkYNmwYXr58iRfGHjCqaF0khY0i76ek91+aHOC5ktkav7dsaetRsaDK9VSNP+RVtqepw6BSwihLPinNySfhm3gxHXyI40kCgDZLCr1IMgfiivYlyA4vOGZxITqOsvsHgANdhK/doTkQECT1FHmTT8Qt9PHx8bC0tIRjk66wrOWJO1umAgBq166NqsM3I+bWKUQeX48Xz5/B2dkZgGoTIPjKxraQZ9B6fATxryPx4sULtPP1BcukMh6+iEEVYy10buWNCRMmQN/AQGwQO7+ff3cfx5tD8xAYGAh/f3/k5OTAxskZXz59gJ1TdTyJuAM9PT2pfSgqD9k2HydPnsTnz59hZGwMNouFC8HBiLh/H0OHDoWNjU2Rgfxp6em4GByMM2fOoE6dOpg2fXqh9qmpqUhLT8ezp0/x5s0bvH79Grdv30ZsbCwGDBiA9PR0HD58GL6+vmjQoAE6d+4M19q1xY67d+9emJmZYejQoUhMTAQAdOnSBSdPnYIGmy3xOkuDnK/wySMXJ5Plu+3uVolJaDoVGa/0Z4cvr1+/PgwrVwWr8Qip7XvVr4x/Fi7Exo0bkZqaCtNq9dHbrw/+7tUGNZydwWazSyTxh79GnUgwRx6HXaSiqjQosJ7KDBXsB8xabvoRWoJVVZS9pxVjXy5pyKzvqMOvXZpR6mMMFWRWlyt2TnQMVbG2i177/s7K61vBeyguXQ0R0YXgYAJAHtMO0sHw94UoU/j/Nm7kxb2oI4YpMSmJTExMmLHPnz9PXAlxX4KfRWO5CjhcKigoID09PQoKCiITExNKTEqiAg6XwsLCCAB17tJF6Nz8Ag7djUks1KcscsFYtoQfiQSA1q1bx9CutBn9D3NPCxYsUIimRRF5q+HzCn2fW86GiW0/fPo/FBQURIaGRkLtO/cfQYM3Bav0Oosj5z9PPbeGU8jz+ELPmTi5NCocIulxisWtpiOr3NrammbPni3zc5+VlUW79+whDw8P5rtzd3dX+XsrSc6fp7arr8kcq1hsqGNPUPJ+ILSWq/r6/7CKJ3yUxRj+r0AdblLv8YXN+Q7NlVvn8c564f4B3uc764s/jjSrp6A7oZjgSogBehIVBRMTE9xd3hcEYLqVFb5+/YpGjRrh/v37DAG2r68v05e8riR5q6Us3nYEqampjOzmzZto07Ytzj/9rpBL2sy+FgDA3NwcuTk5qNN1FF5dPgAA8PL0RE5ODuLi4rAu/AcexmUxLmngd5a0oKtamrzX9rvg5mfj9pYZYLPZaN26NdgsFhbtOo3IrQugpW+E/Kw0dOvWrdiuamnytLQ0lCtXDhuvvkXUu6/MXLq6usLV1RWB0T/xKT2lUD8fLbwBpMHKuwf0vj1GdHQ02rZti/PHD0A7/DHeZ/+r1OtUplxeAufiuHqVU01HuvzNmzeIi4tDqoYpJh6Lkum519HVhWmdNmg00Q1/V9LCmgn98ODBA3C53GK/n1wSX92ED2mu4XNjvYVqMEtaj4oNdaynqtwPANXvaWoKXyoplNHV/AnwHs97qAWhzIdc2kuqrP5FFxo+QucVbxxxZYkCgnh/BceQRI8gB/gxPdemNGOoabKys2Fiaoq09HR0nbkZ3m064uvXr1iyZAnuhIVhb+gTaOuXg0/PAbCzt2f6UQbthTR5lFZNWA7ehNrDVmHmzJlYuXIlZs+axVDNrDwVhr179sDPzw/u9eujv28TbJ81DPM3HhDaePjUIv/uOgYAePz4Mfr27YtnZ7ejIJdXxmz//v3Q09ND1apVsWtES3w4tQINtOOQkZmJjIwMJjlHX1ujEGWKu50Z9DRZ8K9THolJSUj9+RPtnM0QdmAlfn54hkuXL6NGjRpIz8jA17OrYVTFGc6ubnB3d4eLiwuzOSqb1uXuvXswNjaGhoYGbs5oBdbXp8xzkJGRAXd3dwxv4SyVrmbe3Dl49PgxIiMj8fnzZ3Bys4XmIDU1FQUcjkquXxG5MvsuaSocvvzVq1cAgIP7duPI6XPYEPpG6D0RR70kKN/74Ae6dOkCAPiZmlrs95PNEk+Fw/3l1vbbfg+hL74zzxqXCKcefQEA3HiVICTnK5jXpjRTHoehOtZTVe4HgmOock8DVL8vlyTUZMEstSj1rmQihcziMrtIVW0SL+X0B4qw5U84GkkNFl0mE8c6jKtJQ0efAJCmrgHt3LmTcnJyiIjIod0wYmlqC9HVqMMltfTgRaraeSz5+A2hSpUqEQCqXbs29Z84n/Qr2gm5OIcNG0atewSQsV0tAkCDBg2i5ORkpi8iYWqRV69e0fz58+nYsWN04uRJatiwIQGgUaNG0aJFi6h69epC/Xt4eNCyZcto586ddPjIEbp37x5xOBxK+PGDhg0bRqampmJd79t37GBczNu2bSMWi0XXr18nNptN0xatKuR6VqYbMjcvj7y9vSWGBQCgvn37ie1HlK5m+vTpBIBat25DKSkpVMDh0oCBAwkANWjQQOkVRRSlmVHlmOLkitLVyCv/7+xZcnB2JQBkV60mLVq0iE6fPk0vX72iQ3c/SH2vxi/dSmw2mwYPHqyU91Na+Ickt3Db1dcYV7Igmq+8Th5LQmnC0UhSGko5vZg0MGv5zfXqcfOqKwRLiSirfCIjSnXyCaBwcohMSRXqCKIVzOASvVZlZXCpqfIJ8PuX/aTRQ/H9cQgjr2BlA+suUzGqazMMaVmLcVV5NG+HmK9J2HTwVKGqKPw2gPSKCFySr7JCZFQU6rq5Fbp2ExMTpKeng81mIz+fR+NiY2ODC8HB+PkzDZkZ6XgcGYmlS5YgNzcXbm5ugIEpdG1cMDigH0wyPsDX1xcamppCLriOrpXw+NEjuLi64tLLJNx8nYDKnG+oyE0Ccbk4ePAg7t5/gKyMNOZaLCwskJ+fj8w8DpyadoORlROmtnNGbm4u8vPzEfrDAG9yTaCvrYFljfXQsGFDBAQE4E22PiKC9sJr7kncmtsBgGordSQlJaH7kqOgglwkvHnCuM/58Bg0F1UatEPgsAbQ+FUPuM/2MPSulIhj8eY4MsILXC4XHTt2RMiN2/CZdxC6xhbIu7QC//33H9OPrpEZNLR0YVvRBBYWFrC0tERksiaMrexQ2a0lTo1tqtT7EldRRNVzKSo/PKQ+goODJVbT4Upw9Soi53IJt27ewKpVq3Hv3l2kpKQAABwcHNC3b19079ETtWrVZJKD+O9V69ZtEBPzDq/fvMGjTz/FVkMRVyUFUGLCzr0PMEiIRqaFK/wa2klNtFEK1EWRpuT9gFnLnwyHFjdHeAxlJ4aoK2lTyZBV3ymLMSzNEGfWF33IQ+fxCoIr8pCrowi4OoqZq7IYuxgYJz0XUgqnTJmCFf/+CzaLJcTtx2axULVyeWQnxqFnvcpCcoBX/WTHrVh0cavMlNeTlX4GkBxr5fzXX6hQrS4KcrNg7lQbM/q0gZ6eHjp16oReay/hceBKxL+4DwD4/PkzXF1cmHupVKMh3rx5g5MnT+LRo0c4G/YUKbfWIerCARzYvYMZVzS2rEGDBgB+U9bwsjB5MZUDBgxAi1U3EJOQBlsjDSxtZoxLly4hPz8fVRr3wKlXmRja2AFdBDZDzYjP+Hj1Oay/RqBp02Vo1KgRxo4bh8ZNmsLKoz1GtnFlNl5V0rSYmplheO+O2HU7FnP79oDfxX3Iy83Frdu3MWfpGkQcWIZvt09Cc9RLaGlpYf78+Wjt1RX4mYgOrlZITknB40eP0LRpUwQHByM65CRmz5iG3gcPwvjXwtx18DhExaXDxVIf9iaaiI+PR1xcHBLfvserK19gfnEvYtrfhKODg1LuCxBPM6OsOZP3WtQRT+m38x7YbD2U6zQHSRca4vv374iKisLoRVuwfOUaLFq0CGZmZnBzc4OxsTGiUzSgrV8OL0JD0KxZM2hpaorlbOS7huXhcpSXwqZb3cq4fCka3epWFpJLo7YpFlS5nqpjP+BDVXuaqvfl0gC12C9LMUq1K7kYZv1SU/mklENeV3JWVhbjSqzs1oxu375NHA7PJSeu+smNGzcIADl0myJTlQN1VUSZv+MULV++nNq1a1fIRVq7dm3GvXf43kfymneKGrZoR0FBQXTkyJFC/cfExlKtWrVow4YNtCMkWq7saUkuwdCrV8nOzo7YbDYNGzaMEhMTyc3NjaytrSkpKUliH/z/c7lcSkpOpqioKHr+4gVxixhTFvdnTOx7WrVqFV26dIkys7Jo167dQvM2YMAA0tLSIjs7OwoKCqLBg4eQvr6+WDc0393u6OhIObl5FBkZSefOX6Br16/Tx48fKS8/nwo4XHrx4gVVqlSJhgwZIvN1ylqZRNy8q1POf/ckVdNRVxZwk6WXacb6QzR//nzq3r07tWnblqxsHYmtqUWNfbtTSmqq3H0TyVfdhEhyFRP/bXcoKCiI/Lfdkan9/1eorfKJqt3tKkRKSopM+k6ZYliaFUMihRU3RWLn/j9C3nmaNWsWs7nvvvZCps3HzaslGdnWLFb8kTjlR1Y5HwUcDn2O+0Lfv38XUiqqVatWSHG5cuWKkLKRkZlFQUFBVK5cOXr67JmQEhL35QtznqamJvn4+NCBAwcpLz+fuQZJyowo3UlBQQFNnjyZp3g716XNQbcpLz+f2rdvT4aGhrTm6JVC9CgFHA5NXLiarGo3pjqezalWrVpkaGgodD81atSgvXv3UkZmpliKFSLJG23PreFUs8Ngof6MjY2FPnfr1o369OlD3bt3pxqtelFQUBBVdKpFCxcupNDQUKZdOQtrqtVpBP3999+0ePFi8vPzIy09w0Lzr6mpSQblrai8fU1isTWoQYMGMl2nqFxcHJq4eS8JubroahRRmjlcrhC9k2AbWZRgpSqv4bEUFBREh8NjZVJI/79CrXveH2hQyc3NpTZt2sik75S5kks71Owm/WOhhhJFL1++xMqVK+Hm5obIyEg0rPQ7NpCfsSyOaiIvNR6OZtqFCt0XVbauKNezPC5pFoA+/v44fvw4AMDZZwBeXNwHAOjQoQPWrFkDE5uq0DerBGudXMTExCA6OhpB7wFzh5rQMzBE70q8mMRpU6eiXKe5Qi7s9evXY/z48SgoKMCN8AhcunQJ27Ztxblz57Bt2zaceJKI6i17FnKFi7r+5i5YgNWrV8O2ZT9QvV44+bYA2vv24cKFC7h06RJWPNVCbOIXpn1ubi78/PwQFBQErQp2yK1gBb/mTWFra4t9T9KRzDKGhXY+bH+EY9CgQfj7779RqVk/cFw6Cbkbxbk5WQCOBAbCq5IdwhJ/8L4zPz9079YNYWFhKF+7JQ5fvIXXJ1bh9OnTzPfarH0PAMCaA2fh19AOkyZOBAA0bNURem0nYWhjB5ilvkTfPn0AAL69AvDJoDoGtXVHy6rG+PTxI2Lfv8elu0/x4OV7tG3ggX/nTJR4nfK6jEsLRU6HWhaMXFV0NdLkynABK+IalkfOZrHQvV5lXLoYje71Ksu03iiM//Eyb0rFH7gvb9u2DaGhobI1VpOyWmpR6i2GCqLUWQxV+QtLHS53IvL39yc7Ozsas2ANASDzSpWpX//+NGvWLBo5ciSVMzIuZPnh/3Pw7iSThYdIvEuaSD7XlKDs3bt3tGDBAqHrcfNqSUnJyUIk2PzMajabXej63Zu0pqCgIJozZw4BoBnrDxWyVoSEhlIlG3tiaWhRLXdvKleuHFlZWf3uY/IeJtu4oKCgkNWjgMP5PV81alPzlddp783XZG1tTX5+fmKtJBs2bCAANPHfnUVaX9+8eUPjxo0jAPRXr2l06O4HqRah6Oho5nrGjRtHg4cMIUdHR6H2XC6X9u3fT2PGjKHIyEjasWMHVahQgYKCgsjFxYWqVKlC2traBIBcXFxowoQJNHz4cGKxWOTj4yPkFleF+1bl1qxiyAUthuq+HmW6gOV1Dcsj53C5dPrhRwoKCqLTD4XvSZLlWyGowz2qYitbqdvzShHy8/PJzs6OunbtWuZKlgVliuEvlFLFTSYUgwJBnnnS0dGRqPgV9a/+pN1q3/QazjhMXj5dha5j9OjRBIDs7e1pzJgxjNzU1JTi4+Pp0OHDtGXLlkLXz698oqenRwBo0+bNzPh8cLhcysjMojlz5pCmpiZpa2tTzZo1qVPnzoX6a9++PRVwOEIuvn379wvFORIRrV6zhjQ0NOj169diXYIHDx4kADR48GDKyMgo5EIkEq6sUsDhUEDAAAJAFhYW1L5DB9q3bx8lJiUxG+3Bm89py9at1LNnT56S+pcLaeuXIy1tXWKz2XT69Gkq4HAkukv3hDymoKAg6tR3KE2bNo3q1KlD2tra9Fft+mRc0YYqVq5CixYtYpRjVVYyKU5lElXLFXUlC3634lzA8sjFyUqD0ix4TdLoapRW9aSU04rJgrxXVyWv5aXUvasupKamEgBatWqVTPpOGV1Naaer4UNOM79cNCyqppRRBy2OKml9AHC5XIaOZPiclbj+9BNM02NgY6YPNouF6tWrw8K9Pc68ycHQxg7oUdcS+Xl5GDZsGN69e4eIiIhCLh9BN6Ci1DSS5MuXL8ecOXPA4XCE7qNlq1Z4+SYGXz+9h4amFjgFPNoa5zoN0HbGdsYdxuVwcD8iAvvP3UDQ0YPI+P4RgYGBqFSpEgzKlcObXGPcfptYyH3GpyNJ+RyD9KtbcPfuXQwZMgQPknURfWaz0LXcv38fqx5zEHlsNd7dOAUA6Nu3LzZu3IgRx16Bk5eJC7N7or9fD+zcuVOof75LkIiwZ88ejBg9BrqGpqjWyh8Pj6wEm83j7hdXWaXnljv48eYRfryNhlnaG4SFhUFDQwNGDm6AQ0Ok3z6I/MxUmJqawsLCAuXaTsB3TjkkHBwPyk5Hfn4+RowYgTdOfmLr4PqsuY5RjmnYGmOES5OaIzs7G76+vrhx4wZMmw9BHd9+xaqzK49cGbV6VSXn09X4+vqi7+6HMvcjzn0LoJALWJDWRZxckmtYGe+fJDn/+kXfe0lyrjrpalRJwaKCPSAlJQWfPn1CWloa0l7dQfq1VdDz24OYa4fw06QG0tLSkJ6ejrTYR8j/9ox3ko0HYFCB6cPc3BzdunWDj48PWEXN3x/uam/Xrh1iYmLw9u3bMrqa/wkIKm5cjvIVt4Tnv/8vWPJI9KVNeK5Y/55jeNctmMofe73wolOc2AwVlyhKSPhddWDrP5MZapqHH1PQwN4MAEAEjBXYCLS1tGBpaYmLV0Ixeu8dtK5jL1cZOkXjp+Lj4zFz5kzmejV0DTBv5jRcvnwZV/kxJmwNQEMT+KUYpmqb4/SjT0xMGFtTE16enpgbngf93rVR10IfQBrq1q0LLS0tjPulcEiM0+vQBL0W+GPXzp2YOXMmUlNToW9hC98u3dHRuw6q2NigXv366EJxuLLiNXOtK1euhLGJCbq4Vcac+QvByc3CnLlzC/f/K44u+MIFXL5yBbaO1RD7MhqRx9bgx4bpqGhhAS7xKqs8+ZwqFHfXtV4V7MoqwGz/rvBrYIP4b99w+vRpbNl7GC8vbwaIV8LwxYsXsKhYkdmAZwVewej2vDJu+/fvx4azfyPwCQrF+gU0sgMSohHQyA6v37zByn//RVhYGADAytqmUHtxZc2UIQcgsa26aWmkxUHK24+kGEZ1lvNTVC5NIRWn7Ea8T0ZXNytcufw7xpBL0svqKQxVrqHF3AOSkpLw6NEjPH78GI8ePcKjR4/w/v17oTZ6enoI9AOW778Abe0QGJlbwRCZMMz5Ah3NX/OTkQJoGTPnhIeHY9euXahTpw4aN26MDh06oEWLFtDUFFGNVL0HqwGLFy9G/fr1ZWpbZjH8EyyGCvzakpe4WS2EnaJjKLNvPg50KVwfMyBIYnNZ5+nRo0fMS0VE4mv8irFuvHv3Dn+51IGmhSMajlqFGzNaM32Ks+ZIsgTII/+ZmooBAwciIZOL9EpumDCwJ4a0rAUWgDdv32LjscsIiYxBfety0M38hosXL+Lbt2/QNjTDoAmzsWXhRDyNjkbTpk2hqVcO5Wo2x/xZ02GWHsMQXEuyVoizeuTl5eHN27f4q3p1sDU0xFpJHjx8iHY+PujRowe2bduGxKQkODo4YNCgQVi3bh3TVtAK8/17PKwrV4aFhQWqVa8ODTYbY8aMRdduXYu05khS7JMSf+DAwYMIDw/Htq1bYV6hglAfP3+mwr1+fcTGxkJLSwsrVqzAuPEThPrOy8vH5UsX8enzZ0ycMAEVKlTAxIkTMWz4CJiaGCvdEgVALMmyqq1fxbWKcQoKmHePXwNY1FoGiCeIFn3+ABT7vVGHXNJ7L0neYtUNFBQUYLJzptAaJYmgXCmQcw2VC2L2gHwO4VudSYizbIO4uLhC/z59+oQvX3hlAQ0NDeHm5oZ69eqhXr16cHJygrGxMYyMjKD39DBu5VT7TXAtwx5GRAgJCcHu3buZxDyAl2R3+PBheHl58bwP6vB6qQGenp64e/dukfpOmWL4JyiGgNyKm9yKobgxJPRdLKh50QEg9fplnaebN2+iWbNmAAAOl8ss+n3qVkDB2zt4EhWFl3FJeP09Ha62FTHMryO6de0KsFhYsOMUFo3siX4T5mH/mgVFbhzyuJokyeXZzDkFBQgLD8fGDRvw33//waVeA0RF3AXAcz3fDQ9HjZo1MWf2bPi0awctLS2JfRenUsXTc7uxfPlyfPr8GcMmz8WFo/uw/cJdDGjhWshVzeUCe/q5oGrVqsjNzYWeTU0YVrRBeQdXBK8YBWNj40LtBV2Usir24uRcLhd79+7F8FGjwc3Pw18+AXh5cT/Tnl/5xN/fH8OHD8fy5cuhq6urUjdtaahkIq9VzLdmBVy6eFFs5RNAsmu4JJRgRd4/QDalVtJ6wJ+DA2ExGOWYBp927aCjra0aNzIfKtoDEhMTcfPmTcTExCAudBviPn9GXBoXcWmE+EzenPNhYGAAa2troX81a9ZkFEF+mIgoxFY+keP6W7ZsiWvXrhWSN27cGGvXrkXdrNtgXZ3/+8AfUu1EED4+Prh8+XKZK/l/Bip2lTJjiJr3lVkUXFphc2VbIwVfWkH3uIKoVq0aAKB9+/ZgAejsYg5n7ST4+LRAUlISatSoAX19fdhpsPDx6Xv0PLkfkydPxqpVq7BgeHesmqiHE1tXoFZ1J0wZ3hcsFkssPUVxlStFXNJsLS00a9oUpz7pokI88CU9EfPnz0ezZs3QrFkz9F60H+eWjWLOl9Z3cehLjo4cieXLl8PJ0RE/f/6EsXdfHIxMwaCWvzdVQdeivoEBIiIisH79eoSEPcSbh9fxOiQQZjtnw9raGlWrVoWNWzO8M3TFqLZ1hDZwcS5mmd2ZbDbqu7vDxMwcyd+/4tWlA7hxczCaNW2KAg4H2q+vAJXqwrtdV6xes4Yps6ZKd2xpqWQizdUrKhekq5HHNSypAklpcgFLorwR5wKWJu/tboOedS0RHBzMrBFslgqoagClrqG5ubkIDw9HSEgIrly5gsePH4OIYFxOF9Z6ebA2YsG1ogZ8q7JgbcSGddMBsG47BtbW1jA2Ni463k8a7JoAsVeE76OI605PT8e1a9fQunVrBAYGIjw8HOPGjcOHDx9w+/ZtxlvUpbELlrrEwrmCxh+nFIaEhODy5csytS2zGP4pFkM+ZLS4lTqLYSkNbJZnnt69e4eYmBj4+PgAACzs/0LC+1fw79MHI0eMgL6+PtaEvMHbHCNE/esHN5caiIiIAAA0HzQDD0LOIPPLG7i5uWH69Ok4nlARWjo6hawwykg6UJbri8PlYsa6/dg8528EBgYiJzcXPXv2VJm7LeL+fZw9exZJGmZ4Y+iGYU2rymVNjX3/Hjdv3MDbt28RFRWFK1euQENDAx06dsTAAQN4bksNDYlWHlksQt++fYOVFU8B6NixI86dO4eKFSvi8ePH6Ny5M168eIEjR46gRcuWMCxXTmI/ypSrqu+8vDx8//4d+gYGKG9mJvO1yPJ9d3erxFgMT0XGl2rXsLzvn7S24iy70iy+2hpc9LRIZNYoSUpqsVBMVykR4cWLF4wiePPmTWRlZaFChQpo3bo1WrdujVZGH2Edveb3SUq2uBXHYkhEGDhwII4fPw57e3uEh4fDxMQEHA4HFy9ehJ+fHzIzM4XOGeOuhWleOrBxa6k8r5eK8OzZ/7V33mFRHV8Dfukg2AVRFBAUsHexYI01iT2xJbHF6M/EGhNj7+UzURNLYjTGqDGWFHssUVFs2DX2rtixoCIgsLDz/bHuugu7y7YLrLnv8/Cgs8O5s3fvnDk758w5Z2ncuDEVK1Zk9+7dWds7kp2PthPsKl1NxtQBho7/CwvS1WSUvaxtltcwmVycCsGc+3To0CHh4uIieJVSxbfnXFG8ThtRqFAhTZv6x8nZRRw5elQIoZuKYswPa0SjRo0EIEoEh4r6kzZJlubCFnnw6tevL9BKV5MvXz5x7949valgtP9Wu92W6U7MTeGyZMdJ0aD7F6JUaHkBiPLly4vVa9aIP4/dMpjaZcnOU2L/gQNi586d4tLly2LN4es6fZOSkkT3Hj0EIPJ5FxMFixQVZ86eFaNGjRJubm5i+pK1JuedkzotjTntsQ8fii+/WShqv/c/UbfZu6JcuXLC2dlZ80yXqd3MZPnmpKs5cPlBpgoktnpmzH3eDbVbki7KVils1OlqklNSNOOxaaoaISzSoQqFQqxevVr06NFDk6/Uzc1NvPXWW2LGjBnixIkTmnKhkq8BQojUqDkqXT7Rx+I17NKlS8LBwUEMGzYs02vJycni6/5tRCEPBx1dv6SNu3XrYzZQrlw5UbZsWXHjxg2T7B3ZlWwvSOkqlboouHbBcikKm2dTYfZffvkFhUJB02bN2LljBx73TjLh61l0ruXP5UuXSEhIICExkbt37lCtenUqVawI6HH99O/EiRMnaNSoEWXvbKJTjbd1qpzoq34C5lVMMLSrYG576dKl2bf/AH6V6gOqb+VLly5l5MiReuP0HB0c9Mfv2cjNZ66r+tdTz4gp1oigPu+wMsKdCRMm0KVzZ9zye+MWEsH+ui1oV6Uv165dY+BnXxB74TBpzx7ofvAODjh5FeaXIsVpW78Kz54948CBAzi7e5LwIgHfKo2pUL48CoUCPz8/9jwtQNlC8SyMukb76v4Wj92cdmtl7Nixg+bNm2vect4SIXRv24wqVaqwcuVKAO48ecG8XVdMkm/K5/rbxyr33KRN53mWrJTENWyLeQBY5AI2p7qJPtewul3tStbekdV32twqzNShJ0+e5JNPPuH48eNUrFiRrl270qxZM+rXr0+ePHkyy5d6DTg4H/ZMhcqLdK9h5hoWEhJC3rx5WbZsGcWKFaNLly74+amea7ejP/Klzx6+HJ6Xm8+U1FySyuMXqfTemExUzJcsnJ2GW5Nhlo1fQqZOncr58+f5+uuvKVSokEl/oz+KUyZ3oc9w675e9VvNznGqfpZgbNJqX8Naw6351MzGa8RgVbu1x/xLNzY84esOsEkKgZkzZ9KqVSv279sHwI1/fmHXT1NwcXGhYsWKhNeuTULhsvzrUYVraYU08V/qxWbomlNsOHUPpRBUq1aNkSNHsmjRIsr3nMbIv07rLAhD15wiYsZuRq09o4nb6rroEF0WRavKh2n1NdQ+b9cV1p28y7xdVyxuX/zzz/wWfYPgTiMAcHd317izagYWIo+rU6bUIxnbla9i0R7Gp9Cuqp9OX3Pb+9QPIqiIZ6ZrmtJeKzycbdu2EX3oEA2ateLl+d0cm/M/SpQoQdmyZUm8Ek3R8nUYNO0HTp46xZUrV/hnxw76jJhOiVotKV+mFFeuXCEpKYk+ffrw7e+RlKzXjvuHN7Fu/XrCwsK4ceMGnSoXAaB7nUCbjd1Yu6UyAvI50rNOSdXzuWGDzrM+ePQUvpk5k/379xMYGMj0Fdup3Xe6yfLN+Vy71wkkj6uTJj7S0mdDX7st5oE6rjFixm6Grjml09dQuzlzVZ9+0G4fufaM5v9qOtcsSeQXjTKV2bQKE3RoUlISw4cPp2bNmqSmpnLo0CFOnz7NzJkzadGihX6jUC1byjVAe21qNNqqNaxFixY8fvyYL774go0bN6oaM6zBge9N4cS5q1QKVn1xWPavggFfjLR8DZaIJUuWMGbMGNUX+VelOU0im3Yycy124Uq2YJs/V1U+ycWYe5+SkpLEyJEjRaHAcqJo2ZqixfiVOq+bWjYrMTFRxx1RvUFzoUhLE0Jkj3vKXJe0+j4VLFhQfPfdd5p27d/a/TO2a7fZ2l1oabtCoRBbtm4TI0aMEN99951ISEwUQpjnBk9PTxcdOnQUrq6uYvr06QIQ+/YfEOvXrxcvk1Oy5T2ZK+PZ8+fCz89P8+xdvXZNPH36VMydO1c4ODiIOnXqiM1/bxHu7u6aPosW/STu3Lkj4l8kiPMXLogdO3aIn5csERMnTRKzZ88WGzZuEqkKhdExRl97rOMyVj9TahepNa5eQ+22mAfZMceM6Y2QUZsy6ShDZfVsSVxcnNi8ebMYNWqU6Nixo6hVq5Zwd3cXbm5uYurUqbmu9JytKp+8ePFCrF69Wnh5eYlixYqpbAMDa/CNGzdEtZASAhCuTuSqdXL58uXCyclJ9OvXTyhfPWum2jvy4RN7OXwiZeWT/zCW3CelmYHraenpzFy9iw3n4hjYtq6mffLkyaSnp+Pm5sbo0aOJiIhg4cKFlC1b1ugBA+38e9p99OXls0X6jg2n7nHgciz13W/TtWtX/vjjD1q9/bbZLrrICw9ZtPc67ar6ae5BbqjAYW0am7RUBbd//VJz0KjVmF/oV6Mgax4UYWW/etkydnP6rlmzhi5dumj6nD17loIFC9K2bVuOHTtG3qL+vIi9pe/Rz4SrVwHSU16SrkihVq1abN68GW9vb5NS6qjnnrJEVfZejbMq5MFYu6nPO9gmHyRYUN3EiD6RIl3N06dPOXLkCA8fPsz0Exsby8OHD7l9+zYARYsWpWLFivj5+VGlShXatGlDUFCQWdfLDmy95t2+fZuQkBCGDh3KtGnTDK7BL1++ZPqnHShXuQZdhky2+rrWkpiYyMCBA/nll1/o0aMHixcv1iTsNtXekWMM7YXSjQ1vs+fihJrZTjaVLTIlhmjr1q2sWrmSrVu3EhcXR9WqVek85bim7+gxY1h/8i6b9p2g1xcT2L5yEZUqVWLkyJFUeKcn0TfjM6Wgyam4vrtPE6hfC1xdXalTp45FsXHTtlzQpB9Rx1PZKm1Ks2Av5q5YT3UfWP7rVZo0bkxxPz+z5ViaxqbJ+xsZO2YMqQoFjZuGw7PLvFupuFXvyZx0Nab2dQDmzZsHgFex0iTcv0qFChV0nmFTjMKKFSty6PBh1p9+xE97r1E/3xOmfdaF3377jUGDB5uUUkfNwqhrXHyYJEnspbnPuy1S4ZhrvGbUG2oZ6vhF7RhD7XZz09UIIfj777/55Zdf2Lx5M6mpqYAqabRPwbz45HPDJ7gS1apVw8fHhzJlyhAREUHQgy04FK2Qayt6SEXJkiX57LPP+Oabbxg6dCjeBtZgDw8PJv2yNQdGmJmzZ8/SqVMnYmJiWLp0KT169LBMkOT7mbkcu3AlW4DZruTsQkqXdXa43F9hyJWjbm/0+Q+qk5xlyoiePXsKQBSvFKHpn56eLr7//nvhXrBophPNzs7OIn9QJVFyyO86rqWccH+lpaeLKUs3i5I1m4n169eLyZMnWyTHWLv6t6UnSRcvXqw5Ga59irZ06dJi8ODB4s6dOybJsYWbNl2pFC+TU3KtK3nnzp2a+zNt2jTNv0uVKiXKlSsnvvrqK80pdO1nMjQ0VFy8eFHsP3BAAKJjx46a5yMqKkq0btNGAGLz5s2ZwgUM3XP13Pvt4PVsD5GQcs4IYXpIiRp9+qTxN7tF+NSdYtjq4zo6St0+ZPVJYSp37twR1atXF4CoWrWqmD17trh69apISkqyKquDyWRDqJIUa96qVasEIOLi4mwmUwoePnwohgwZIlxdXUWFChXE+fPn9fYz1d6RDUPZMNQllxluZmFBSgRL7pMpC8WknzcIQPj5+QknJydRuGhxET78V03/f/75RwDCK18BUan3dDFs5hLNIrx8+XIBiLDOX1mVgkaf0ZWVEZGcnCymTpsmhg0bJjp06CBKliypk65m8uTJokePHqJevXqiaNGiwt/fX4waPVo8eaU4LTF0dpx7IN5fcFBngTY1zcqWLVsEIBq+01H0nve3+PPYLfEgNlb8+eef4tNPPxVe+QsI1zxe4pvfthqVo0bfAp3Vl4CM7V1/3C/Wr18vuv6436T+hto7zt8rwlp8KIqWrSUUr+L3rB2jOrZw6LT5Ysjqk2LQpO80qUYAUaNBM1H7/f+Jb1ZsETt37RIrV64Uffv2FYAIqFxXODu7aIzKe/fvi7AqNVWpm0oEiFWrV2vmwPsLDood5x4YvefqufcyOcWs58UcgzljXKP6t772jH20MXfe2dRIPXhdo6OMGaOGiI6O1ny+rVu3ztwhF6cTM1n+gXn6dbmVa5baMIyPj7dYhpTEx8eLCRMmCC8vL5EvXz4xadIklbFvAFPtHdmVbI+Y4i4NiLBMrpSFwh+ee/1v7RQ7GVPxPDxnmXwrC7WbSsb0Eobaqxfbwp7duwkKDqZjx/fwLlJY4ypSl2xLiH/GrbUzmLXkGf7+/kyZMoXjx1Xu5v/rWod3tU4dKi1wUe08H5s5rs+IW+z53Sv8M2WUwfe+aNEiSpQoQXBwME8LhJKa+JxpU6cybepUNm7cSOvWrTXy09PS+ePTCB35+q6b0cUMpqdf6dy5MwC30gtweu1azly/T8fZH9OxY0c6duzIad9WHFkwjFH9utGu1hFVSS0DbkellW5adfu7lYrDw8dWu5JTDq3k4vYVALTv0IF2bdvi6eWF75MnRF6MpUlYUVavUbl91W3vNahM3NOn3IqJISg4OJNsdezVSedy3Dh5l4D8ZZk6dSrbtm1jzZo1HNu7g0LNy7DlvgdffNAIgPfff59d95y4d/EEHr6lqFHGj759+zJr1iwunTmJz/sTCasRQZfOTQDDFUsy3vP0V89U76VHUTo4SlKxxJBrWF+7ufPL2Lwz5ho2JeUNoNElKampbNt6Wq+OMeZGFkLw7bffMmzY6xQqek+mSq03pdT72mtWmhIIeP2aDdYsHx9V1Zq7m2cQ1nVK5g42DE8yh4SEBH799VeGDBmCQqHg888/Z8SIERQpUsQm8mXD0N4w1Xjrus582W+C4ZYdpQNfoc/o0m0vz//NaKn3kEWtWrWIioris1m/4eDogE/CC/I8Pk/37t0pUqQIIW914bd7hfht0SGr8viZHdeXkMLExetJuhLN48ePCQoKIpaCHItTGRRXrl7NFABfPrAYG5bOJykpiTt371I67QbfTRrBy0e38fwyD9WqVaNvv360rliXX6JvZbpuxpxsQKY2Q30bN27Mxo0bubHtZwBOH1pF2jc9iXvyhH927CA8Tyov3ulDzLpvaNGiBQcOHMCnaFG98g0tusZyzOlr71jdj21bT9Oxup9J/fW1b1i/nq2rfmLy5Ml4eXnxyy+/8MknnyC0voicA+ZleCbPrYCJfV///9cVK2iUdI2l46bzwT//MHv2bMqEhBBfJJif9l7j9MwP6PXwPgB169alfON2XC1QU/e+ODkxacyITIcdAkuVAqEkoIQfnzQsbfZnp+bdSsVZfCAmW0sF6muXOq7RYiO1dCFNXjljxqg2cXFx9OrVi40bNzJgwADq1FHpnNKlS2fqC0irN6XU+9prlnYeQxutWZXzxuPiCJ+N/j9GPIij6ZDvX5frs8VmiZkIIVi7di1Dhgzh/v37hIeHM3PmTOrUqZP1H5uBfCrZXk4lqzGxdJGi6TS2PClh/gktKUvXGbqGLWWrkbJ04CtMKZE1ppqS9evXc+OpgugTp3GOv4ciKV7TNzVNSWLCC4Qynbp16zJx4kQaNmrEH8fvSVr2S/0b9J+kzNienpaW6T6p++/bv58G9evr3Jtq1arRt28/XryI15TJCgwM5KOPPqJR4ybUqVMbD3d3g6dD9Z321NfX0cGBq9euUbBgIXbu+IcuXbrg7e3No0eP9H5mrVq1YvPffxs9gZ0xubHZu0gnbuN45yTKElVpW61k1v0ztG/fto23336byrUb0HDgLBqG+eLx8Cxvt2pl1vOpxjWPF94+vty9eZVPP/2UefPnIwS8TErUlP1ycnIiPT2dyN17aNyooUknePddvMeAzq24c/s2zVu0IDQkhKbNmlGvXoRJn536mWrRshXubq5WnfjVfmYzttvqdLA17WBeiT94rUfCfPLQPzheM/cM6R01x48fp2PHjsTHx7Ns2TJat25t+sNiot60CKn0/iu5Ckd3tlRexNvPV+CiXSvZynKra2YPZ0JUChcfK8nn6U6ZsPIMfbssHzhufN3PQKlAa1EqlZw4cYI9e/Zw6dIlDhw4wIULF3j33XeZO3cupUqVMkueqfaObBjam2EIJhlvivBPLT+6n4sMN4swY/yWGoamLAptg10Y0uG1S79JkyZUqlSJokWL4uio2gMQQpA/f37Ca9emUqVKODk6GlwUbdmuL3WMvnZAJ12NdsqMDafu8cvKP7gWuZrzJw4D8Ndff/HDkacUCggBB2fNbsm///5L234juH/2IKmJ8Xh4eBAREUGTJk3Y+cybwqXKIoSD0fQr6enpvDXsB2IvHiH+/k3und6vubdhLboTc2QbL58+BKBz58507dqVxRccSHnxhKSnj+leJT/BwcG89dZbBtO7mFv3Vl97y9m76R8cz4Jr+dj2eeMs+2u3f1DwGn369NG85uDihlCkGH0WQ0JCuHz5Mq7FQwl7uzefRxSlZ8+e5M+fn+Jvf0Zi8RqU8s5H4p8jOH78ODVr1sSpSjvy+vhxfutyQgtAbGwsZ8+eVY1//G/k9S2VZdqbJjP38DzuMQ5n/ybPs2tcvnyZR48eEdqyB5XafpJl6hz13FvzoEgmV7Khaxoy3lcfuc36k3fp2yCIJmV9LHIB22J+gf6UN+qxW2Sk1gvA8+FpVZ1vZ2ejqWq2bdtGq1dfIGJiYvD39zf67Ohgz3p//xwUkdNUhqF2rWQbbWSIHWPZdyud6Nvp7H6Yn+1nHtKxrDM1iztRpVVPqn40SeN2NgelUsnTp081KYK00wVdu3aNHTt28PjxYzw9PQkJCaFatWq89957tGzZ0qK3IqereZMxZdtfobBOfsat/qDGtlMO++foygbV//fPsf1upC1LB2Ygq5Q1M2fN0mn3KFKCu6GdqKnHGJu+9zrtUu6YFAdoi3Z9cX3G4v3U6Wq0XaBzd1xkz8zXMUt9+vShXfv2JPupF64AzWJXsVIlps1ZyE9RV2lWLAXX2HPs3r2bqVOnkpCQQN6SYQwZPVlvXJ8iLY0Vv/7KxIkTiYmJwTVvIcLKV+Se1r29uH255t/Vq1dn+fLlOLu4kOirGsunbd7SueeG3JHWuLXV7d3rBMLD05rKJ1n1V7eLvT/SZ9MaQFVx5uDlWJbMyBzvWSikJqkPrpAQ/4x3332Xp0+fcvnyZVLvXSJm7Uw+X5uOp6cn0YcOcfJ5Hs01W3+2m9/XrKFPnz743LnPkyeP8fEtjjKfv8Yo9PHzJ9G5AB+9qh6S8X61CXLmp8WLiXvyBK/zt7h0+Tbh5UpRqkxNnjx5QmxsLBe2/MK9i8f5fNSkLF29YJ4r2ZD71tS4xqzapYprtCZOMTklle3bTmfSL9p6Jy0tjSpVqnDu3GvXanp6OiaTHXpTSr0fMRiu79dts9WaFTEYB6DBznE0CHDmS/GS74q5se5iGlMOQsKuhfDFQooVK0aVKlUICQlBCMHBgwepWrUqVapU4cWLF5kMv4cPH/Lo0SPS0tJ0Lufq6krRokXx8/Pjk08+oVWrVtSuXTtb8xHLO4b2uGOoxsi3L6uSfUr5zVFKV7WJbnbtbX9r7pOxpMLx96+xfdJHuLm50bNnTxITE7lQqB6PPUoa3DHyc03hy2qO1KhZk7130iV1JZvrAjOUZPeD2qpg76CgII4fP06BAgXM2hlRKBTsioxk7JgxHDt2jLZt2zJ48GACAgN59vQp0dHR/Pjjj5w9e5b33n+fIUOGUrdObZQCzc7Ng9hYFi9ezNgxY/j6m28YNmyYzXdYHR1MTyCemqpg+7attGjZCldXF5OuOXfOd3z++ecAlChRgr/WrqVJ48YkJibi7OzMkCFDGDlqFJ6eXri5uuiVsX7dWqIPHSKPhwfdunUjJDQ00zVTkpMpUKAA3t7e3L17l4EDB/LViBH4FS9udIxXrlxl8qSJmrrJ+fPnp1ChQuTPn5+TJ0/qnR9NmzVjxz+vXXoZP3/13NN+pixx96qf0dw0X4y5hk3deVbrF0ehpLPvY42Oyqh3Tp8+TeXKlXXufbt27Vi5ciUeHh56PxsdLNCbZiN1iJKUO4Zq9Ky3yg/Xcv36dU6dOsWpU6c4efIk169fR6FQEBoaytWrVwkICODYsWP4+Pjg4+ND0aJFjf47X758r+MYbYzsSjYRuzUMszDeLDZ4cpnhZhbaB3MyjtNAoLBUruQ5f+3hyDc9mDx5MqNHj0YAM9fs4qdV62lUviS1ggoTc/Mm/54+zeZNm3RkT5w4kTFjx2YZB2jKImorw0gdD+Zdrja1S6tcJunpSrp07sTVq1f5e8sWSvj5WeS+23DqHlEXY3G4cZB1i2YSExOjeV8Ojk5UrtOQBbOmUTs83KAx3mbGZjaNaE147wkc+nm8pj0nKqJ0W3iAzr6PTap88vLlS8K7DePM+gUAvPXWWzRv3pzJkyeTrFDy9uQ1uOUtbFGFE0PtK1as4KOPPsLZ3ZO05EQAJkyYwPjx4zX909OUrOobDsC6devo0u1D3LzyUbZlT/b9PJE8efLQ6cdoHFDye//MGRDK1G7GlO9+5L1apQx+/upnytTKJ2CbyiTmthubX2BaXKNVRqceV/IP205y7eehPLh9Q+e+T5kyhUGDBpE3b95Mn4lBLNCbZiG13pcyxjDDNTJhgmylUqkJHcppZFfym4wp2/7hn5ovN0OhcL0TeOc4cHSybAJrFzDXnlAZXeMmFDrXi7pQu75UPhGDVeO2UWoBRwfDaSNUrp7ujHC6wNixY/nzzz8pFRTExg0bcHFx4edNqSwWgmLFimX6ZlioUCEGDhwIZD71DOYdhLCl63n42jPUd4dJm86z7XOVq67L4iO4tPiC0GZQws/6ihVBRSpw5coVrl69SkxMDGO23iSWAhQs6Uvt8HCjLuAujSqxNU8+PB5dyNIdaUm7ORVRskpX07teIAcOHmT9unWsWLGC2IcPcS9UjOS4++zatYtdu3bRo2dPwt7qwj8P3Oj8yq1r7JrmtHf74AMmff0tV86c0Dx3EyZM4O7du5QsE86fW3dxa/cq/vxM95mvOXwF/ZqUxd3DQ0f2b4diOL9pEVOnTtX0jStSiR/33aJTeJDBz1/t6DSn8oktKpOY225qXKOhvhldw8aqJakRQvBO2YLU8y1F7MNH3Ad+/PFHJk+ebPBglcV7PFLrTSn1fsY1C6DrSjj8g23WLLDazZ5bjEKzsDrDop1jdwmuTUxGmrpvvvkJrt+QDPjmYGkicFOTJO/atUu8//77olatWqLye4PFe99HiY7z94q0tDRNcmZAeBYuJvxrNRePHz/WyNFXJcGcigo2rRLxKsnubwev27RKhCXt6t8Zkwrny5dPTJgwIdPnpP3blu2G+ianqCqfJKek6LQ/iYsTvXr1EkWLqqrd+Pj4iP79+4tLly+LM2fOaJ4F9c/xEydMvqa57f+ePq1zLUdHx0zVTjL+xMXFGR1Lenq6GDdunHjng36iwdQtWX6e5lY+yalnTgjr56IQhpOQd5i7RzQe9r0o26qnqFOnjvD19RWurq6a+65OLl+gQAG9n0v+/PnFTz/9JHI1Uul9rTUrNWqOri7PoaIJuRlT7R3ZlWxvrmQTt/0VXdex5eIL813J2VRrOLdg63Q15riMvnuVfHbyL5uIfOwlScoLc1zPhtqVRuLmzIm9s1W7oZPT/d9rTmhoKLv//ssk97U17eprmpqu5tjx43T6sCf3bsfwTqfuDOrZiYh69XBwdNTICff3IuXKQRb88AOhYWG0HjCJA9firEudY8x9f+khtQPyUsfPlbV//cXuPXvYf/AwTx/HUsy/FPdvvXZTOjq7sGL7YTo3rpKlbFM/T0PpasA2LmNLnnPQ7xq2ZVxj73qBlPd4RuSuXezYuZM9e6JISX6JV/6CtGzelArly1OoUCEuPhXsv51M53plKV9QSbNmzWj+9XYuHj+An08Rlg1sSYkSJWyW1NhuebVmKWr2y6zLrV2zpHazZzNyjKGJ2J1hCCYZb4qACMsPn7zBdOvWjVWrVuHu7s7Zs2fx9/e3aYyh+rWs4gPjnjwhKCiIRo0bs3btOpydHC0+CGHMSLNVJQdDcXPmxN7Zqj2jQa5UKinXeTiX1s4hoElXbu5YbrCvLdsN9W3+zQ4+C0li8qZzhCSd5cSJE1y6dAn3gkUp2HY0YeUr2vyatmpv/M1ujq/5loSTfzPiq69o2LAhQ5bs5nneAMLCypp1aCI1KYlZbYMIDAzEwSFzkndj6WqazNyTyWWsLdvS1DbGDH1DKW8sNVIVaencuhXD5UuXOH/+PBcuXODSpUucO3eOp0+f4u7uTv0GDWj61ls0a9aMipUq4ezkpBk7qGSqKp9spWWrVvx18oHBVDX/daw6cGmMN2izxFR7xw6d3zKUbmw4XqLuALt5SCXj6m7VhNXD7XOqfHvJycmZ0gSYgzrGMPKLRnSqUYIjN+I0yhxU8YFdFh5i9ZHbKIXQLEZD15xiw6l7HD12jBcvXtD9o4/o9tNhuiyK5v35e7h75w5paWk4OjjQddEhuiyKpuuiQ5oFwNHBgaFrThExYzej1p4x2q6O01p38i7zdl3R6WtOu1IdNweauDl1e83AQuRxddLEsClfxZ49jE+hnVZsnC3b+9QPIqiIJ22DXRg/fjxBQUFc+nMW3uXrMnHcWL19tWMDzW2/dPkyIfEncLmwlbCEU+zctYuz585Rz+sRHjf3UfDSRnr07EmjRo0IDAzk4KQOAJxfOYVVq1Zx6dIlAFq0fZ+w8hUtHout3o+x9k8aBFPt/cEIpZLp06fTsmVLLv4+g3zPrxuU8XFEKfYfOEDPnj1xcHDg6Hd9ObXjLzaPeIegoCDeffdd0tLT9X6e6mdKu10tO4+rk6YyiSXPijnPuaODA4v3XefIzTimbbmg03fU2jNEzNjN0DWndNrV87PT/CjOnjnDmjVrmDxpEqXCm1PQPwSPPJ6UDg7m7bffZuzYsfy1fS+3UjwoHvEekZGRPImLo+eknziWrz5P3IprYtHUXzq7LDzEzvOxOnqnc02VzumsVSrTKozoSg7OV73+X+e/uN5K7dMeP358ppiI0NDQTP2USqVo2bKlAMS6det0XtuwYYMoU6aMCAkJEZs2bdK037hxQwDC29s7U5HrypUri/Hjx2c5PruLMTQRS2Pnso1siDkxFCcZM8RLI9/a+zRk9UkRPnWnSXFG2m0KhUI0adJEFCxYULTo1Et4+pbSiRmKe/rU6vgoW8ZYGYqbMyf2zlDftHTV/9PSlTp9DLVfu35Doyu8vLzExx9/LPbtPyCUr/7GFBmmtCenpIhu3brpxHpl1GWA8PX1FbVr1xZdunQRI0aMED/+uFCsX79eXLh4SdOnRo0a4tbtOxaPRf15WvN+zGn/cvhw4e/vr/M+jxw9Jk6cPCnWrFkjvv76azFw4EDRrl07UbJkSQGIEiVKZLo3w4YNE4CYP39+pmuq5576mbJ1LKUUcyUlNVUcPnxY/N///Z+oWu8t4V6omHBwcNC8X29vbxFapZYoFv6O+HDwWLH577/FjRs3hCItTa98U+IUtXWUoThFi3jDYspz/ZqXCzDV3smWU8nly5dn586dmv87O2e+7Hfffac3d09KSgqfffYZv/zyC0IIevfuTfPmzXF1ddX0efHiBTNnzmTixInSvIH/IlJun5ta79mSuA0T6j3753e0vN6zFuodM+3Tqmp3UlaJkh2dnFi7di1Nmzblnz+W0rVrV95qOpZ/T51i7ty5dOncmaXLltEtvFGm+ChTTzdqn5xOS1dy5EacxsVszglJpRBsOfMAR2DLmQdGy7yBbU9OOzgInt+LYeXgFty+fZs9e/bw5aixuHh4UrP7GHb/MAJPT086/RjN/HOHbHIqNTUhjpN/zEN57xx37txh0aJFbH1ZGld3d1KTk/n67ZI8ePCAKTtv41WkGI7ObpnT1fCYSXtenx5dsGABwzbfwtHxllVjlDrxuaa9VDtiYmaQkJDAu+OWcXLNLOrUiyA9VZUfzsvLC8e83uQpVJS8IXXZsaQPTZo04c6dO7T+/BtO/6XahZo1axZFixZl6NChLNj+L6UbtsPVsyC//6+O5lTyljMPdNLVmPsMGQuByPg8q13DnWqU0MkooBRC77OvUCgISL9L87RDLB8/g34H9pOQkECePHmoU6cuAz/+kJCQUMLKhlE2LIzChQsbjF/MmMVAn54wpj/UO6MZ+1uMCbpS088SXSmlnlfLf0NcvLmNbDEMnZ2d8fX1Nfj6qVOnmDVrFseOHaNYsWI6r6WkpODk5ESVKlU0slJSUnQMw4EDBzJ79mw+++wzi8rS2C3GJsbhRUAJy+VKOaGlVEhSFmzXg7VG2r79+1mx5wznnjlTMMSbb3v2pG7duvTu3ZueA76k3PvDrE5BozcO0EwZC6Ou0T9YlVqkffXXlVL0pRQxJ12NocVOKQTvVvDmy4H9eHgqksKTVPfVycmJ8LfexbHex/RvURmPPHmMyjCn/fiJE2wf1Yb4p481n2Pbtm35uE8fPDXxpMEEBZckKDiY7i7qNt0qIdrpat757Tc+/OADtm3bRrtm3a0aI9gmXY057Xk8PenbuTVzXfJzddlXPIlV1ZuJPnSI0wl5NX2bvIp3K1GyJF99+SULq7cmYcs3nNi/kydPnjB48GC++24O5//+mXLV6jA3tRMNGjQA9KersTTlkSmVTIylvElPTeLp7as0LfiEqKgoDhw4oEoy7paHwsEVGT16NA0bNqRGjRp88PMxbjnCzTToU8/81Dam6o+0NEWW/S1Cal0ppZ43tkZFL4DIsap/W3Mo5D9seGaLYXjlyhWKFy+Ou7s7derUYfr06ZoajklJSXTr1o3vv/9er/GYL18+evXqpcn5NmXKlEzJO7t27cqOHTuYNGkS8+cbiJd408jKeIucBpUXwfV9ENrEPNlSf5OUWiGZUjLQBtjCSEtMSGDumh08iH/JkXKVaV+1I506dWL48OGcj33JpZN3TTakbJGDz1C7qWXewLwSctoGs/YOSGJCAssn/I9n5/czb948XF1dKVeuHFWqVCGPp2emHRltGdqfjz7Zhtp3R0ZqjEJHR0ccHBwYN071DOlbkA0t0h2q+bF922k6VPPDtXZXWr/7Li6urri6upm8S2vuNc1pN/T+M95H3Xv7IU9HvsvGDRtITk4mpEwZyrm4GLy3bcoXIu8olZdo/PjxhIaGErk7koYNGnDu2AEGHzuAh4cHq1atoqbbfVKLFLeqDKGhdn3Gde96gfyw+RBusfeZMyeKy5cvc+nSJU6cPsfTRw8AOJY3LxEREYwdO5aUImFEPvakb6OQLMsqGjJSDZXsM0V//PZxDU1710WH9B6+sRgpdaWUel7fGqXO3btnqm6/3LjbmcuR/FTy1q1bSUhIIDQ0lPv37zNx4kTu3r3L2bNnyZs3L/369SM9PZ3FixerBuTgwLp162jXrp2OnOfPn+Po6KhjFN68eZNSpUpx8uRJYmNjad26NRcuXCA4OJgqVarQrl07JkyYYHR8dnkqGbLMJq/JAl/4Di4RnxmWYwipSxjpu4YtZYNJBdutOcmmFNaV0Eo4vZ3/9eunSUzr4OhIq1at8C5ShGXLljHmhzUcSPSRLAWNvlPMwsBJS3PLvJkjW58b8K+1a3mvY0fqfziUoZ8PM8mduOt8LAt2nKNhgDt1Srji4+PDyThng67Hvw+eIeXyXlJjr+Pj7c2OHTu4dOkSzm55KOAfwqVDuyhUSDV2fadebVH5xNx2W8gwdVfLEvetUggWbjrIp20zV0Px8PTiZWICAEOHDuXHH39k1apVdO3alW7duvH1N99QqGBBo8+Vqe3wOuWNIjWF/QcOsDsyksjISE6fPs3Lly8BVV3akJAQQkNDCQ0NJSwsjLCyZalapYom5MnceWXrkn0dq/pKfyrZBF1pMVLp+QxyFUHN2ZL/w9cl8XJzla4cItdUPmnVqpXm35UqVSI8PJyAgAB+//13vL29iYyMNFhrU5v8+fMbfb1Fixaab3jqep7moFAoUCgUWXfMLdTsB2nK19+OIqepiojf3AuO7igc3QFQVOsFlryv8E9B6fBa/s1oeCWTRqMhvL9lcjNeQz1mNYENVO3Wyo5eoDtmUP1/71yo01/TpP7MLf3sO1b15f1qxVAKVdm4dCPtSiHoUEXVnq4UHFWUJW/evLR6+22+njGDLVu3svTXlVy7epUxY8YwqldbTRobhUKhWTS2nHnAgauPqVe6CG9X9LWoHWD42jMcuRFHHlcnNg1ULeQ9lhzRGBLLetfSvJ++yw7Tsajq98+9amvaDfU3R/bC3Ze58SSRC3ef8m4FH9KBli1a4F26Iif+XsGIh4+4824tUlJS6NChAwt3x3DjSSInz13G8ZYbW7duZffu3dy9/wBlmoJtr+Q6OzsT9sE4nhcM1ZENMGby/3FjxzJI1z2Z7uHhQYl6bfli2Bd4enmhUChUu0OVfVkefZPudQJJSU0FyNT22gVeFB4/5t0KRXXaDfU3td3YNc1p13fPAZYfuMaNJ4nM3JZMwzKFjH4+xtrXXnhBPr8yKOLuAFC9enWOHz8OynQ8PDzwrdGCVq1aERgYqLnnK1euJDIyktmzZ/POO+8YfFZMaf+5ezVOnTpF7+nLiLt6iqS7F0lXpOLt7U2DBg0QpeqQt2hJPL39WTPsXZycnHTmiZdDESoplYhXn72p8wrgeMxT2lcuqjP3M8577bmsr12f/gBIUygM6hurMFFXWoxUej7DGqW4dQQqfqha+6xdo7JYXwHVNWr2s36tykZMXedyJI9hzZo1adq0KS9fvmTu3Lk6JWPS09NxdHSkfv367Nmzx6gc7R3DKlWqcOTIEerUqcOxY8fo1auXWTuGK1euJE+ePDZ4dzIyMjIyMjIyuQt16F6O7xhmJCEhgWvXrvHRRx/RqVMn+vTpo/N6xYoV+fbbb2ndurXZsmvVqkWHDh0YMWKE2X/bvHlz+3Ila7OqW6ZvY4r3lrFjxw6aNWtmebLP6AW68RpqGo22/ptkRtmBDXTfg6XXOLwIIidllpPxek3GQXhfFAqFVfep9bz93HiSSKnCnpqdMUPtSiH46/hdzQ5Ox+p+ODo4MHr0aL7/4QfKfzieIT3eo2N1VVySvr6GZJjbrh6PIRezOh5L3Z6amsrOnTtp2rSpzsEvQzKycjGfvP2M6gEFAdMrpaxetYr+/VXPhL+/P2PGjCW8djgB/v6qXCEZ+ickJDBv3jy+njGD//XvTx4PDxYtWoSPjw+lSgWxd2+Uzjfo3Xv2cM/J1+TdIYM7Sf/exfHeaZTFK/F2ZT+b7fQavaaZ7fpcr9a6b/W1p6en8dXw4SxZskRzn995913+3rwZDw8PlixZQu/evZk3bz4dOnZg/bp1fPnll6SnpzN69GjatmuPt3cRvc+nAxAdHc2CBQvYvHmz6mBSeDgNGzakYcOGVK1aFVdXV5NdwNbOK1vN2Yz6Y+3/wtmxYweLr+fl8uOkTLrGYszUlRYhlZ7XI1/h6M6OinNpdmYQLg2G2Wa3E/Sur3Q13yuZG4iPjzepn+SG4RdffEHr1q0JCAjg3r17jB8/HicnJ7p27Yq3t7feAyf+/v6UKlXKoutNnTqV8uXL602JYwwXFxf7rBCyfw5c/0e37fo/cGwxEGD5+9o/ByINxBhGjgVHYV38hvrUGOiP34gcC86O5sdv+JYDZbKW3EGqfzcYpBqzWr5vOdC6L5bcJ6UQdK8XzOJ91+leLwgnZ+fXhzX0tAN0qR1ItzqldBal6dOnc/7CBXb8MobYUGccawzA2dmZpqW96Fq7EelKoYnVcwI6h6tkZFyQzW3fbCBGTF/JuX9O38cR+OfiE510NfpkgOGYN2sqpfTo0YPKlSszZPkBvIMrsDHBlQ9LlzbY361QIU56huOcz5tvZ88mT548dOnShS1btnDunCp43T+8BSk3T1GjRg3Ca9XSVPM4cTtec/oaYN7u65na9bUBLNx3U3WCe99N2tcsZVSGue22kGHonktVsearr75i84VnxET+BsCff/wBoPHmvHz5kiWnk+jWzY0uXbrQtGlTarTuwcCBgxg4eAj169XF398fT09PYl86cDdegWfac66cjObu3buEhYXxwaDROIc2pGmlgCxT3hh67k2dPwDHb8TRqVaAzlw2JAP0z3tD7fr0B0DX2qVYfCAmk06xGAt1pclIqech8xoVqDrl7qJMxsXaNUr7GvrW18M/2OwQY3Zi6honuWF4584dunbtypMnT/D29iYiIoJDhw7h7e0tyfVCQkLo3bs3ixYtkkR+rsLYAZE9U1Wnki3h4Hxdufom9M5x4Ohk2YT2KZ9ZNmQ+Iafdz1RKN1adFNOXZiBisGrMNkozoH26U1+ewJqFUli7bjVDfrtDiRIl+KRvX/bcSMy0KDk4OtJ9zBzupo/l888/Z9asWbi7u3Pt2jXcPPOSJ7AyeQp4837NAJycnNh8JhZnF1c8CvmyZsLHBAUFmXzCUbvdUIqPaVsuaE5QdgtXGRPmpKsxdGJTKaw/IV2pcmX6di6kN02MvhOoneuXI14spUctP7rUDsDdzY10pZLfom8wddY8Lq+bA8Dw4cPNOvUKhk/OmnqC29x2Y9e0xWldKVLepKWnc+ToUdJuHNGZOzExMRT1VR2qqD9pIz3qh2hkFCpcmCmzv+eHzYcpk3yRpNvnuHXrFi9evODi7Ye8fHxXI6d06dKcO3eOprP3cv3CUy48SrU45Y0588dYyhtrS/NlzLOojjHsUM1PY3TaBKl1pZR6Xt8aFf4pbNnyus2aNQqMr6/a2TreQORayfKp5MxkR+HwXJIjyhb1NTPugqWnp1OuxYdcjlyDk6s7ocGluHHjBoUKFcKrblde+tcj2LeA3rqzPmkPqZF0lCtXrlC7dm1Wr/+bq2eNH87y9/fn5yVLeOhVxuKTj8baAVYfuonnw9Mk+lSiS+1AozLAshPSpvQ1JsMS9+i16zeIiblJ40aN9Lqjs3Kbql3j2qevzT3BbWq7Ke8nu2Sb+vnUj4jg4MGDmtfy5s3LoEGDmDRpkiZNTI8ePfDx8SF//vw8ffqUuLg4nj17hoODA281bcryZcvw9fVFKQRjvl/F9IEf6Dz/nTp35nGqM1efKWlYJZTJQ3rh5+fHykM3WRp9W5r5YIYM7fltaj1sbaPTWE3pXI9Uel7PGqXR5QVjVDuGYPka9R8/lSwbhvZqGGZhvCkip6kMw7C8uJibx1AtPxcYblJjrWGobzGYNXMmX331FUFv92PMl0Pp3qAMd27fZtgXX/DnH3/g5OZBpRp1aVK7CqGhoRTx9mbP+fv8ffA0/s7PcVc8ByHYtm0bQghcXV0JDAzExcUFIQQuLi64urpy9+5d7t27pxlLw4YNee+99+jQsSPFXyWKt5XRYK6xY84Oia2qXGSne1SNPtd4bk9XYwvZpn4+HarpJtkvGRxKfg8Xrl27BsCqVauYMG8pxQrnxy+fC4ULF6ZAgQLEJDhw6f5z/t20BC8PN/7880/8SpRg+7btfPbZp5p0M+3at+f2g8coUxKJf/5cI7dQoUJ4e3tz8eJFq41oIJMBbO6XFmuMzvS0NLZs2UKiTyUWH4ixfaoaeyXDGqWjy48utF11Lqk2R3KAXJOuRkYisnID4AxPgKD6lss39MDbyzckA8atEAKH6O9ViiPA+iBu7STCz58/Z9KkSQwYMIC5c+dquZhLsmbNGi5MmMi6tX+xb99+1q1bx82bN1EqlQB4enqSLySEEiVLkpqSwtdff03eshGcj3ehUZiv3jjAZqW9+G3KQKKiojh0+AhRUVGs/WcvOzeswdHBskop+tp7Lz1KZ1/Vb7WxY4mb2tpKKea4r0H6SiH6XOPalU9seU0p34+5sk39fGJjY/nyyy9Zvnw5ALevXaJs8+b07t2b0NBQFAoFSeEfk1zQi58y7qSlJRLatypJm6dSq1YtzWvObh4Ur1yfxdNH0qpVK80OW+vgwsRu+JpVq1YRFxdHXFwcXRZFW/zcqw23jBVLLPnSYm1pPpDAlWzvX/6lXKOyMSQpNyLvGNrrjmEW2MJFmi3YWDklJSWxZMkSLh7eSfzpLbg7w4W0kvT9YgKHDx/m+++/1/St4OPI4V1b2XHtpVX3SXtHJTzxICNHjiQmJoav98ZmedAiJSWF58+f03fFKdw8vRDCQWe3xhQX1IZ+1WnWrBnHz1zAqVAJxLP7PI29Q55X5eKy25UspVvOXPe1dhuY5wa1tD0lNVWTkNgtixPc5rbbSob6XmTXLthPO04z7qvPib98mNTkl3Tt2pVBgwdz/949FlzLR/d6wQZldKhSlJ07dvDbypXcfpqMc8P/0bdxmN7np4zTQ2rVrAlA+96DeFa2vVXPlTkuYGPt+nZdM7qMDfWVxJUs9Y5YDhiddrPm5SAm2zviP87z588FIJ4/f57TQ7EpqampYv369SI1NdU6QVcihTgwT/9rB+apXrdG9vh8qp993+m+tu+7169pXUOpVIopU6aIJk2aiIIFCwpQhYY5OjqKn3/+WdSqVUvTpv0T6u8jmjdvnqk9dsvXVt2ndKVS/HYoRjT+Zrf4aecZ4efnJz766COd9t8OxYh0pVJvW0YZlrY/fvJE+Jcpp3lf/fr1E0qt/tq/tcduantySopYv369SHqZLKKvPda8n7R0VZ+0dKXO35nbLoQQirR0q2SvPXFHDFl9Uqw9cUenfce5B+L9BQd1PgdDfa1tT1cqxdpjMWL9+vVi7bEY28u2gYzfDsWI9xccFDvOPbBqfBk/h3SlUkRfeywUaemZniN134TEJDFv3jzh5+cnPDw8xPr168XAgQPF/82YIc5fuCAUCoWm/9GjR0W7du3FiBEjxLnz58Wu0zeNyk5LV4pZs2aJ/PnzC0D07t3b5OdbPf6Mr0s1X83pq9blvx28nqm/xRyY91q/autfbb07Pp9h3W8MC/S6RdfIMDbNmrdvvnWy32BMtXfkHUN5x9AwUn+rfBXgm5Im2HMznWtF3+aBZ1nSbx7CKSYKpYD4FHAIqMPcP6Lw8vJi0qRJfP7553rFlShRgnbt2rFmzRoiIiJIfXCJcg5XeaeMM9WLO+EV1oTky7uIewmPEgXJVT8hqMMoDh48qHOfFi1axPjx43nw4AGdO3dm1apVOBiJ51HvpHz88cf8/vvvnDlzhsDAwCx3fCDzDpapZeT0tT+IjaVxo0ZcvHgRgBo1atC+fXsKlG/AlWQvq2L41M/TrAueODs7m51qJqt2a9LYqDE3wN8WO0H62lvO3k3/4HgWXMvHts8b21R2bpFh6HMwdRcsJSWF6Ohonj9/zsf9B/Ms7iHpKa/L1AknVxQvE8hIiaY9ea/PYL2yk54+Ydekzjg4ODB58mTcS4dzOSmPSXGuhsoB2iI+V12az9nJ0aIdWvXcU+9AZ+xvMVKVPZX64IaBdUmz5qlL4tlZ/F92IMcY/pcwtm1/eJEqD5UlE0RfoXJ9E9yEQuVCCBITE3n8+DFxcXH4+vpSrM5nPHgYR/H241/1+lPv39ZxUpUDS0hIYPny5dSoUYMrV67QqlUrZs+eTVpaGrdv3+b58+c8ffqU4OBgHj16xKMiRbhy0YlRked5lCh4lLSBZ8nakr/DY+hCVq1aRb9+/fj5559JTU2lX79+mh5r1qxh6dKluLu7ow/1QrN60w5+X7KEHxcu1BiFhuKNdp6P1ckTqI7Z0mscmREH6Fu0KPWHzOPezBHEXz3OsWPHOHbsGAD563bh3/b9rIrhA8jj6kT3eraPebM2jY1S5EzaF32yc3O6Glu+d31xiuq+NQMLGe3r5uZGnbp12bZ1KzW++IUu1f3wTrzKo4cPefHiBQcv3WfNj98gMpQsvLNzKQeVD9mY53+0ad1aR7b3zb04Ojpy69YtChYs+MqgfWrS871433VNiqam5YoanWfmtutLbWPOFzP1ve31y1F61y+tiXW0moxpY2xhFIJqHVKm66Y2u747s3xL4wANrUvRC4AA3X524gbPbcg7hva+Yyj1tycrvlW2atWKbdu26X0NoFy5chw6dEhz38t7O+LuDIp0iE8VPEtzJzlNkJysY83h6OhIixYtSE5O5ubNm9y+fZu0tNcLiIeHB97e3vj4+ODt7Y13/Fm8lbH4eDrgnccB7+DK+HSZi4uLCxcvXiRPnjx89NFHPHv2DEdHR7Zs2UJsbCz16tUjJCQky1vUZOYejv/5PckX9pD87KFmd9GcHRilDePvfoq6Spnnx9j263yePXtGUIXqXLp0mZ+3RFsca6W9A62dXDerHdCsdkPUbfp2WqzdrTG2iyOlbGvT1YD+XSZ7eO/myDD0TKmfibFjxjBt2jQAwsLCmDBxIvfv3ePXX3/lxIkTVKtWjTJlyhAWFkZ4eDjHjx9nwoQJREdHU71GjWyPczVHtjn6QX2fhh9xwq+gl05fm7C8na5OD2oM3ddbLzfj2qHGGqPTkOygxihuRqsycfzbF5cmoyy7xht6GlmNnK7GROzeMDSwba/YO5ctTwNeG4bW5FuycILrc7/6+fkRHBxMYmIi48aNo02bNgCcntKYoUv2EXkjHU8XqF2mCDXb9MHHx4eCBQtqfgoUKMC2bduIiooib968BAYGEhgYSEBAAIGBgZQsWRIvLy+Tx65Wur169SIqKoqKFSuadWvUi8G4ydO4tXMpVy5fJsDf3+xFRS3LlAMBpi7WN25cJzQkBCcnJ5RKJbEPH3HlmdIiN7X6QMW+5JLUCymaZaWU3J5qRkrZtkhXY6pLNre9d3P6GnumjKU1ql+mCA63jrH2r7+4e/cux06e4sWzp+TxyktSwgvy5ctH+fLlGTRoMF26dM4WgzvjlyNT5rCp+kGhULBt69ZMh3RsgpTGG0hndEKmsWty9xaMwaXBIMtkvqH5C9XIruT/Coa27W9Gv658Ys22PagmRkZXQFBjiBhMUlIS586d4+zZszx9+pTatWtTt25dQOU+zoqnT58ypFsLlm87SlBBBzZ19aBlaWecHVOhqa9e5VSpUiWGDx+e9bhNyVwf/ikAwcHBtGrVij///JPatWtnEpWcnMzu3buJjY3FxcWFpk2bUrRoURwdVNVP3vltBmXKrOXLsVNYvXQhkDlFhRp97VkthtqGlDmurDlz5jBw4EAAytRvQ/nOw9k3TlWH3Fw3GcCm0/dU5dWyqJSSm1PNSC3b2nQ1YLpLNre9d3NT3hh6prJKaxT5RTvat2sHQONvdnP58iVSDiwn6UI08fHxREdHc0+Zn/Xx/hZXLDGnXW3QmZraRp8eUAr9KWzUbBoYgaOTDZdsqSt77J+ju2aA6v/759jG6NS3LoF1NZKldoPbCfKOob3vGKqR4tuTAdkADxKUtNtamMPnYzJ1T09Px9HR0ajIlJQURo0axaIF3+OkTGFmc3e6V3bBtUyTbA2AVjSdxpYnJahatSpdunTh6NGj9OzZk9GjRxMYGAhAYmIi4eHhmvq6AAULFmTr1q2Eh4cDqsMTfyyazb2o1RzYF0WdOnVscnjCVLezsd3IP/78k07vv6+5Tt26dZk0eTL33AL5JfqWSTLUuztZpRaxtDJEVrulYNtUM9a6uqV2JdtiB8sW792YDPVnYqlsWz9TPWuXYOm4vuzevZtKlSrx6TfLWXP6aba4jB0dzE9tY+pOsdqr8cfDIqSmO9qm6onUO2NSHWwxcg2p1zzAdjupOYTsSjaRN8YwBJ1te80kseJU8p07dyh4YSWeB6a+bnw1wa88SSdkfqJOfw8PDxYvXky3bt2ylL1q1Sq6detG9WKOrO2cB//8jrZVTibGiii6rmPLxRe8/fbbAHz99deMGTMGgAsXLhAWFsbKlSv54IMPOHjwIDVq1ODZs2e0a9eOU6dO0blzZ+o3aMC1JA923HjJ47+/JTEulrPnzvHP1UTJ4pPMXdz3HzjAO2+/TXx8vM5tKlmypCqO0s2LymHBdOjYkfDw2jg7Oep1Jb/99ts4Ojnb3LAx5Hq21k1tzk6sua5uQ+22cCXbwq1ri/duSIahU7zmyNb3TIF17t74+Od8NXw4FSJa8OkH7U2SYe580mcU28K4NDTfDRnQViFlLF12uGOlijHURko3eA4hu5L/a+jbtgfVSS0Lvj3dv3+fkiVLav5/pI8nNXtM1UzwMjvHsbmrBz8eV/DM3Z/9p6/zv//9zySjEKB+/fpUCCnF8cs36L7uJXtWzLJtkXVTM9cHRMBFVeF1FxcXhg8fzqlTp/jzzz+pV68e9+/f559//qFMmTJUrlwZR0dHvL292b59O9OnT2fdunX88ssvmS7fs0cPNm7apHEJaWOqi1ntpu4W7q9VQUUVH2ju4v4oTyC9Fu6hjHsCJRyfMmjgQG7dusXt27e5ffs2ALu2wOzZsynbqieV2vXV60rutfSIzq6FIXebua45Q65na93UprglLXF1S+1KtoW71xbv3VC7oVO85srW90xZ6+5dtGgRTWbuYeWM3SbJsNYoNndeG3MZq+e7dpvG5T4wItMhHYuRsrKHtr7WNjptoddBZXhqG4Xqa+ydC095fQ1HJ+sMTynd4Lkc2TB8E9C3bX8zWvXvPVPBUeg8zDdu3ODixYvcv3+f+Ph4Xrx4wYsXL6hQoQIffvghjo6OeHp6UqdqOaJPngcgvnyPTBP8HcbxTogLfLgYEdzIaK4/bdatW8eUKVMoWLQEXL7B1aS8mSebLcoOmVIySaHQNJ08eZKzZ89StWpVoqOjSUtLw9nZmU2bNhEXF4enpydlypTh999/p0qVKkydOpWpU6eSmJTE0n+O8/u6jexdPhOAI0eOAPpT05hrNOlLY2Pp4q5yZ7WhbZs23Lt/nxm/rGfzgVMEuiZA/AMiIyMpV6IQD+NTMhkfAI9fpNK9XrDNDZvcnLJF20DPuGDra+9Y3Y9tW0/Tsbpfln0NtZvT11B7brl/xtozPlPasi2NrzRXhq2MYilT2ACMXHtG55CO1UhVUk7qcnKGDM86/WHLFv39zEHq2Et7QMIk23aB3Vc+OTBPKMflFYqxeUXSqLyaTPOpUXNUWeAn+ggxPp9Q7p8r5s+fL0qVKiXQUxlE/XPlyhVd+VcihXL/XIPXNjfDfHJysvD39xdly5YVXbt2FR06dBAnT5604I3bBnW2/KioKM098PT0FJ06dRLnzp0TQgixceNGMXHiRLF48WLh7+8vOnbsmElO3bF/6dzHPXv2CCGEaPzNbhHw1WbR+JvdQojsr5pgrF39mvZvQ23alXRMrSQhhPXVTPRV18iJaivZWfnElpVZpLh/tpJx4PKDTM+UIRmGnjND7eb2zc65Z2w+ZtQXQryeeyGjNum0/6eRqvKJlBVhcgFy5RMTyW0xhkIInj9/zq1bt3j27Bl169alV69e/PnnnwQEBJCamkp8fDzx8fEotHa7tJk0aRIjRozQyWN4r9li/CI66fRzcnKiatWqVK1alWrVqhEaGkqjRsZ3/hITE4mNjeXBgwc6PwDFixfX/FSsWBE3N7dMfz99+nTGjx/PyZMnKV/ewm90NkQd2F2/fn18fX156623+PLLL3F0dKRevXo68ZmJiYmEhITw3nvvMWfOHE27Ugi6fjaC3xd8TZW6jYn8ey0FCxQwGl+kvRNgLM5JaSS2ytHBukop+nYoQL+b2pzUIuqx26KaiZTpVswZX3ZXPpGyOklu+QzU92lfcklmdq5mtK+x3TRbxFFmnB9gu9Q2YF0KG0liDN9A7KLaVw4jxxjaAUII7t69y6+//kpUVBTbt2/P1Gfz5s2sWLECgEuXLpkk999//339nybjwLccvkENKV68OC4uLkycOJGqVatStmzZTBNICEFsbCxnzpzhzJkznD17lsuXL2sMwIQE3TJVzs7O+Pr6IoTgwYMHpKenA5A3b17mzJlDr169dPpv2bKF9u3bZ59RmFUW+8LlANV4ly5dygcffKBJyl21alXmzp1LREQEQgiGDh1KXFwcAwZkliUe3wRg/JeDyZ8/v6bdUNxRRhczmJ+axtpKKebGoIHpqUWUwjbVTKRMt2LO+Mxxs1pb+QSkrU6SGz4D7ftUzb+gzd295oZamOruNcdlrDb0rElho43N0tXIlT0MI7Ub3E6QdwyzYccwJSWFbdu2cfDgQZ4+fcrNmzeJiYnh8uXLRv9u/vz59O/fn5EjR7Js2TK8vLxo1qwZtWrVIm/evJp+jo6O+Pv7ExwcjKOjI1u3buX06dOEh4fzzz//kJCQgEKhICUlhbS0NNLT01EoFJqfwoUL06ZNG9avX090dDSPHz8GwN3dnfLlyxMWFkbx4sXx9fXV+SlatCgFCxbUpKZRKpU8evSImJgYZs2axd9//829e/d07mt4eDiVKlXip59+ev1GpVJUJnz7y3h6++zZsyiVSuLj4xk8eDDnzp2jd+/eXLp0icjISBYvXszHH3+c6VJlmnTi6u4/qPL+EE7+/q2mPTtT04D16TgMyTAntUhWuyIZ28xt17fjaslOjr5dIlvIyC3paqSWbe3nqH6m1DWAs/rMc8M8MEeGo4NtUtjYNF1NduyI5ZDhaZMdwzccOV2NiUhpGN64cYOPPvqII0eOoFAocHNzIyUlRW/fypUrkzdvXhYsWEDp0qUN1ubNiBCCEydOsHDhQs6ePcuxY8dQKBSEhoYyY8YMZsyYgYODA66urri4uOj9OX36NKdOnaJKlSq0a9eOihUrUrFiRYKCgnByclJdyMzJfvfuXQICApg7dy6ffvqppr127dpUqFCBxYsXv5abg2kTjKX1SU5O5osvvmDXrl34+voydOhQTaUWbZRC8P4nn7P25+9o1KYLu9avtNlCpJaf3UZTRje1uvqCKelqbOniMzWNjaODQ65xkeaWdDVSybbk89L7LJy4jeOdkyhLVKVttZJZytBn5Frr7jUkw9x5p6/dXCMyW1zJUqeSkdrwNLIOKfZ/z5YnJWTD0AiyKzkX8O2333LgwAHN/9VGYYUKFXjrrbc08X0VK1Y0+URveno6p0+fZt++fezdu5e9e/fy6NEjSpYsSYOqoXQb0JY2Q2ZRrFgxtmzZQlRUlGqSGPmmJoTg0aNHeHt76x+H9mRXpps02f38/GjTpg3Tpk2jVatWlCpVCgBfX18OHz5MTEwMAQEBhguiZ1RUlhRENyWLfaPRr1McZMDd3Z358+dneRlHBwcG9+jA2p+/I+nBDZ20MtmdmgakOQmtlm1quhpbufhMTWNjiStZKhdpbklXI6VsW3yOC6Ou0T9Y9bt9df8sZUjh7jUkw1bGrznVj/S1K4XgeIxKQdkkXY3UlT2k1OdZrUOR01TVvq7vg9Am5o9ddrFrkHcMbbhjmJSURHp6Ol5eXjg4OHD+/Hn69OnDixcvqFWrFm+99RZNmjTB19fXLLlCCA4ePMjXX3/Nnj17iI+Px9XVlfDwcBo0aEDDhg1pXNIB59UdVX/QdBKK8E9fb6sf/iFHEpYePXqUFi1aEB8fz6BBg2jevLnGHZ6cnIyLi4tqJ9NBiatIxsURnB0h5vnrR7JLBWemjh1BUKfJ5o1ZGyNZ7HXuk5XfMpt8PIpYJx+KhlSx+LCFvrhBaw8i2GLnwlC91pxwzxlrt6WbOjtcpFKOQyrZtvi8AFYfuonnw9Mk+lSiS+3AHHmeDMmwxbwDaQ/pWIWUlT2kqnqSxTqk8f4UvoNLxGfmyX7DD52okV3JJmKtYahQKNiwYQPz5s1j7969mvZdu3bRpIkF31peERsby9ixY4mKiuLOnTskJSVRsWJFOnXqRIMGDahVq5auuznDpFE0mcyWpwGq8kCRY1/3y+YSR4mJiUyaNIklS5bw+PFjvLy8cHZ25tmzZ4BqV1WhUJB6ZQ+pl3YyaW+q3svfvHlTtcNoKQay2NsqLiW3xzpZawxo36eMuxa5xeDJLfwXYp3eJAPaUFtumLug34C2CVJW9pDK8DSyDllV7Ss7qrXkAmTD0EQsNQyFEGzYsIFhw4Zx/fp1nddKly7Npk2bCAsLM3s8iYmJ7Ny5kwEDBpCSkkLXrl0pVaoUoaGhtGjRwngNYn2xc6/S1QC2n5RqTJArhODcuXOsW7eOa9eusWzZMjw9PYmPj9e8p+c/vkOB/lt4u4wzf3fLw7OiESx4FsGmTZtYvXo1/v7+Nh+3LXcMs3PhsqTdGv4Lxo6tkO+VaeT2+5Rb5q4hA9oqsqMWsFSGp4GxazZDLH2esqO+cw4jxxhKSGxsLB999BE7duzQtH3yySdMnjyZokWLWiTz5cuXTJw4kTlz5pCcnExERASrV6/Gz8/PdCEZSw5pY4uHOmJw5niUoMYmyXVwcKBChQpUqFABUMUazpgxgyJFilC2bFlCC6Rx4/xxHB1gVIRK+RWI3c/Ips0ZOfKg5WPOKou90gGwYidSC7Ui11b0+trMbbeFDBkZGfPILXPX5vM5Oyp7SFlSztA6lLHyiSVy4fU9eMOMQnMwsv0ko4/09HR8fX01RmHVqlU5evQoixYtstgovHPnDjVq1OC7777jiy++4Ny5c+zdu9c8o1BNxGDVJNHGROMtS4xNdjOZNm0a+/fvZ9iwYQTnTeXsqWOkpMGWbnmo16jZ6447x1kkH9BfU7P7etVvNXumWiZbRkZGxt4wRSfuHKfqZyn6DE9t2Zbqc235+tah6AXWyQVp1087QjYMzSAhIYFly5ZpEhj369ePffv2UaNGDYtlCiHo06cPz5494/jx40yePJly5cqZfEo5EzY03jLJteFkV1cWGd04P8trX+bIJ14c/NiTFv+bZjtFZayYu7Z8GRkZmf8CpupES+sMS214GluHbPElX6r1086QXckmkpiYSMuWLTXpZ0aPHs2UKVOslnvkyBG2b9/O2rVrra8GYihuBKxzEeib7BkDc3eOU2WFNzcw15ii0h63JYrKlCz2hcvBxRfmy5aRkZGxN6Su7CGlPjdlHQI4vAjMPZUM2eNitxPkHUMTSElJoXfv3pw6dYq9e/cSHBzMjBkzSEtLs1r2woULCQgI0Js42SwyTppGo3V/g/Q7b9YYb82nZp50EYNV7dakCCjd2LCxWncABNW3TK5MznF1t+Hn+OB81euybPuQLZP9ZKUTrUnHIqU+N3Ud8i5rvuzscLHbEfKOYRasWrWKr776irt377J69Wrq16/PZ599xueff87Fixc1hyks4fLly6xevZpRo0a9rjBiKRknTfirQNw6/cFRSL/zZs23zNKNDf+tHacGyPVImdA1O8ocmphsXZadS2WrMbTDk1ufQRnjSKXPs1qHcIYnWPZlX8qdTjtENgyNMH/+fAYOHEjHjh2ZOnUqoaGhAHz++ecAFu0YXrx4kSVLlnDp0iWioqIICAhg4MCB1g8246RRKF6/Jhtv0iIbWNknW8rKCrLs7JV9fZ/qd+QkIM1+nkG1fHub828Cxtah8L6Wn0qWevPDzpANQwOcOnWKoUOHMmTIEGbPnq05DLJ27VpcXFxo2bIllStXNlne8+fP+frrr/nmm28oVKgQ1apVY8CAAQwYMEBzmMVqZOPNMMaULVhXRkk2sLJPtpQlvWTZ2Sv70QWgxGu5YB/PYE7N+egFoC5WkBsNWntHXj81yIahARYuXEiJEiWYMWMGDg4OnDx5kqFDhxIVFcV7773HihUrTDo5nJKSwoIFC5gyZQpJSUmMGDGC0aNH4+bmlg3vws7Iid2x6AVAAKzpCt1W564FSDaw9CNlvjFZdvbJzrjDYy/PYHbP+fBPVb+1T93mNoNWLV82Ot8I5MMnBnBxcSEuLo7ly5ezYsUKmjZtypMnT9iwYQO///57loadUqnkt99+IywsjGHDhtG+fXuuXLnCpEmT7NsolCoQXa20/hmdOTXA/jmq9hXtLJOfUdmq5e+fk1nZmkvdAZkDlJe3yxzIbOniJpVsyBy0bWtjQirZavlS5RuTZWevbO0DcvbwDGb3nF/VTbePrQxabT2Y0aA1Fyn1t1q+fAAq25ANQwNMnjyZatWq0a9fPz766CNKlizJX3/9RZs2bbLcKfznn3+oXr06H374IZUrV+bs2bP89NNPliWszk3klPFmrdIyRZE3Gi0bWGrswZiQMt+YLDt7Zdfpb3/PYHbO+Zt7bSNbSoM2Nxid6phVGauRDUMD5M+fHz8/P5RKJQD//vsvrVq1ytQvLS2NkydPsnHjRmbOnEmDBg1o0aIFefLkYf/+/axfv56yZS04Pp8byWnjTardMVAtTtYgG1jZJ1vKygqy7OyVDapwDnt7BiH753xgg9xr0OYGo/PRBfNly+hFNgwNkJCQwOrVqxk+fDhRUVEAODs7k5ycDKhiB5cuXUqZMmWoVq0abdu2ZezYsXh4eLBu3Tr2799PvXr1cvIt2B57dm2q5etTtrZANrCyR7aU+cZk2dkrW412OIc9PIPa8rNzzt/cm7sN2pw2OsP7WiZfJhOyYWiAa9eukZ6eTvv27WnQoAFjxozh8uXLeHl54ePjQ/78+enVqxfVqlVj7969PHjwgBcvXrB9+3batWtneUm73I49uzYNKVtbyJUNrOyRLWWydVl29so+vEj3//byDEL2znntL6+53aC1N6NTRi/yqWQDnDp1CgcHB4KDgwFVzOF7773HkSNHiI2NJV++fDRr1uzNcRObQ8TgzCf8ssO1aY18Y+WOQOXOajDIfLlSlgu011KEOV3m0NLTj7Ls7JXtXRaevCpHaU/PYHbP+fBPM5/etkQ2SF/2TSr9rR6XVOuOjA6yYagHhULBd999R0REBN7e3pr2ypUrm5W78I0lJ4w3a5SWMUUeOU3VtmcqODvKBpZafm41JtTypco3JsvOPtlB9eHiFmgyjkyVT3LzM5jdc15drKDR6Nd5DHObQQv2bXTK6CAbhnr49ttvOX36NEeOHMnpoeQ+csJ4k3J3TOkAT/X0MxXZwMp+2TJvFoZiw3LrM5hTc75Of9WX19xo0OYGo1Od71HGamTDMAOxsbFMmDCBIUOGUL169ZweTu7CXl2bWSnbLVug8yrZwJKRkTENe5zz9rqLauq6gzOaSjoyViEbhhnYsGEDqampjBo1KqeHkvuwV9emWr6xv7Wk8LpMzmCvdWpl2TI5zZtsdGrHrMpYhWwYZmDXrl1Uq1aNwoUL5/RQch/27NqU0Y+UZQizu7xhbq5NLcvOjLGExLIxa3/ktNEZEKGKWZWxGjldjRYvXrxg06ZNdOjQIaeHknsp3djwJK874L+rbKUs2WRvZQjttUKOLDv7ZF/drapPDq/qlWth7TNiz+XZjMk+vOi/W/pNXneyFXnHUIudO3fy8uVLOnfunNNDkZECY7sIhxeBb7n/1i5WxkUfMsfuqPsFREgj19wx1x2gug/asUUZU1hYU6dWlp09srWfkT1TwVHY7hmR8vnLqbkOEDkJlMmWyVbLl3dRZUxANgy1OHToECVKlKBUqVI5PZT/LjnhggTrlK6UC1FuMbLUKTNsLdcSMsYW2bpOrSxbetl1B0Ca8nVGAHswZiFn5nr0AiDAOtlSG7SywflGYZYrecKECTg4OOj8hIWFARAXF8fAgQMJDQ3Fw8MDf39/Bg0axPPnz43K7NmzZyaZLVu21OkTHR1NlSpVCAwM5Oeff9Z5zcHBAXd3d2JiYnTa27VrR8+ePc15exw6dIjatWub9Tf/WaRwp+SECzKjGyu31Xm21zKE9lwhR5adPbIz1ie3tTFrbzWBDcnWLhkodb1hS0IC7NVtL2MQs2MMy5cvz/379zU/+/fvB+DevXvcu3ePmTNncvbsWZYuXcq2bdv4+OOPs5TZsmVLHZmrVq3Sef3jjz9m7NixrFy5kunTp3P79m2d1x0cHBg3bhzWkJaWxtGjR2XD0BSkUgZSxkpJqXRBWkPIXo0sKQ0Ve6xNLcvWT8Z65bnZmFXLzYm53mh07jNopdTZUhudMgYx2zB0dnbG19dX81OkSBEAKlSowF9//UXr1q0JDg6mSZMmTJ06lU2bNpGWlmZUppubm47MggUL6ryemJhItWrVqFy5MgULFuTFC90j6QMGDGDFihWcPXvW3Lej4dy5c7x8+VI2DE1BKmWQk7tj1ihdbfn2tGOjRqpFX0q5GZPdqrG2lqwsO3tlQ+Z65fZgzGb3XIfMO6yWyLW1QSulzpbS6JQxitmG4ZUrVyhevDhBQUF88MEH3Lp1y2Df58+fky9fPpydjYcy7tmzBx8fH0JDQ+nfvz9PnjzReX3cuHGULVuW/PnzU7t2bcqVK6fzer169Xj33XcZMWKEuW9Hw9GjR3F2dqZatWoWy/jPIKUyyIndMbBe6YJ97thItehLJVdfstvu6zM/j4bcT7Ls3CM7YxiHPRmz2T3XIfP9sgQpDFp7dNvLGMUswzA8PFzjIl6wYAE3btygfv36mXbwAB4/fszkyZPp29dAuaNXtGzZkuXLl7Nr1y5mzJhBVFQUrVq1Ij09XdPn448/5smTJzx69Ih58+bplTN9+nS2bdvGvn1GcmMZ4dixY1SuXBkPDw+L/v4/h9TulOzcHQPrla497thItehLaUwYS3arLd/Wtall2baVfXB+5jAOezBmIefm+p6pudegtUe3vYxBzDqV3KpVK82/K1WqRHh4OAEBAfz+++86sYTx8fG88847lCtXjgkTJhiV2aVLF82/K1asSKVKlQgODmbPnj289dZbmtc8PT3x9PQ0KKdcuXJ0796dESNGcODAAXPeFgB79+7l/fffR2HO6ctcjPp9SPp+wj+F6/t1XUGBDVTt1lw3egHcjAZH99dtN6Nh71zrdvaiF8Ceaa/lBjZAcUtVD1uxd5aqzRL5hxdBpJbcRqNVcqIXvF78IqcBzobrwuaE7MLldOWG91d9buGfqmpIq+UXLmfe82SGXLOfk4AI6LoOHl1QvV/tvw//FHBWVUAIiMgx2XrvlR2MO1tlFy6H4tUzomg4BsL/Z7tnRMrnLwfmuuLgj/AM1f2yVDbo1X8a3R05TXVvLNWvUulsMHmdyZY1z84x9d44CCGENReqWbMmTZs2Zfr06YAqSXSLFi3IkycPmzdvxt3dPQsJmfH29mbKlCn069cvy74ODg6sW7eOdu3acfv2bUJCQli1ahVLly6lQIECLF261Ojfx8fHkz9/flauXEmePHnMHquMjIyMjIyMTG4nKSmJbt26acL8DGFVHsOEhASuXbvGRx99BKiMrBYtWuDm5sbGjRstMgrv3LnDkydPKFasmNl/W7JkSQYMGMCoUaMIDg4262+3bNnCkiVLzL5mbkWhULBjxw6aNWuGi4uL7S+g/Q0ZdL99wutv0uZweJEqn2BGGRmv1WSc+d+Yr+97XWlBa2ya+3RmEC7KZOi8yrK6ydf3vd5VycjhRapdFUvrMUsp20Qkf57eIOR7ZRp2e5+yea5r7lPBu7gUDbNcP+nRf4CufjVX/0mpszOODYyuM3b7PGUj8fHxJvUzyzD84osvaN26NQEBAdy7d4/x48fj5ORE165diY+Pp3nz5iQlJbFixQri4+M1g/D29sbJyQmAsLAwpk+fTvv27UlISGDixIl07NgRX19frl27xvDhwyldujQtWrQw8y2rGDlyJD/99BM3btwwq4JJ9+7d38iHycXFxfbv6+B8iBz7+v/qWA/t+JjIseDsaF5gsG85VZJpjcxBqn83GKSqjKCW7VsOzH1PoU2g22qDiVhdGg3HxdLKJ2r5oU30vxbxmWUys0O2mUjyPL2hyPfKNOzuPuXQXHep84nl98mY/mswSKWrLUlELaXOtnCdsbvnKRsx9b6YZRjeuXOHrl278uTJE7y9vYmIiODQoUN4e3uzZ88eDh8+DEDp0qV1/u7GjRsEBgYCcOnSJU3SaycnJ06fPs2yZct49uwZxYsXp3nz5kyePBk3NzdzhqahUKFCfPXVV4waNcqsvwsPD7foev9JjAWkw+tJa25AuqnF0i013owVeQ/va77ikjGOlBURpKyQY29jllK2lGO+buSgoFwxw/YY03+WnuyVUmdLtc7IZIlZhuHq1asNvtaoUSNMCVfU7uPh4cH27dvNGYJReWpGjhzJyJEjrZIrYwQplYEUyutNwN4WfnusH22PY5ZSttRjXtMVKi9SuQsbDLKtbHszwO0ZqXS21BsFMgaRayXLWIZswGXG3owsc+QGRJg3ZnusH22PY5ZSdnaNec9UldvRFrLt0QDXlm9If4Bqh9WQC/tNRV5ncgTZMJT57yGFASflopEbFn5zDcO6A1T3QS1n5zhVDrKMecgsrSkrhWx7HLOUsqUec5oSnmJb2fZogINx/RG9AAhQ7bB2W527dlBl3khkw1Amd5Jdu2/hn75+zRoDTspFIzcs/JbkBssYC2TrJOhSyLbHMUspW8ox1+kPW7a8/r+tSrTZmwEOxvXHnmkql7u6X27ZQVXLl43ONw6zS+LJyEiOlMXTMypgdcWT6AX2W+fZ2oU/u0sQ5vb60fY4ZillSzlmUKUgsaVsqasy5VT5t0ajrd9BtWW9YSn1tEyOIhuGMrkPKZVZRgWszpGVsTyXvdR5zu0Lv73Wj7a3MUspW8oxg25eOlvJtlcD3JD+AMsqiEj5hVVKPS2To8iGoUzuI7t337SxtzrPuXnht8f60fY4ZillSznmjPXJbX0/7M0AB/36I+OOqiUyc2qX0xo9LZNjyIahTO7EXnff7M3IkkruwfmZF4ju6zMvJAfn5x7Z9jhmKWVLPeaMu/S2km2PBri2/Iz6I+OOqiXkxC6nLfS0TI4gG4YyuRd7232zNyNLyoXfWHJabfmWJKeVSrY9jllK2dk15kajbSfbHg1wNcb0B2TeYTVXdnbuctoyBlUm25FPJcvkXowpM2uUTkYFrI32iUBz0LdoZDyVvHOcKimrua4VqSoASFlZQOok6FLItscxSylb6jF3XgUXX2SOnbNGtpTPtJSyjemPyGmqtj1TzS8zCvoNTrVetVTfZZQvhZ6WyTFkw1AmdyKVMsuogBuNVuVSazT6dV1OSww4ezSyzJFrSboaKZPTSlltwd7GLKVsKcccVB8ubtH/Wm4s0ZZT5d+UDq/zPdpiB9VWX1hBeqNTJkeQDUOZ3Ed27r6Fv8qlVqe/btF3e6rznFsXfhmZnMBeDXBD+kOd77Hzqty1gyq10SmTY8iGoUzuIzt337R3wmyxoyAbWTIyMpZgTH+AaofVEpk5scsJ1ulpmRxFNgxlch/2uvsmkxkpK9jYk1wpZdvrmB+cB0rYVq5MZnJil9MWelomx5ANQ5nciWy8ZSYnDQtzayWr5UpRjsve5Mpj1i/X0f11qTdbjVkt394McHtF1tNvJHK6GhkZW3J1t+F0FQfnW14eSqryU6bKvb7P/DFLVRnB3uRKKdvexwyv07Dk5jJtUpd/M6Y3Di/S3y4jIxHyjqGMjK2Qcvcm4yINmQO91f3MkW2q3EcX0Ov2M0bdAar7oB2Ifn135iS45u4s2Jtcecz65WqnYbkZaZsx5/Q8MVcuZK03Iqepdlav74PQJubJlpGxANkwlJGxFVIuHjltWIT3VZ2MNJeMgei2qoxgb3KllG2vY9ZOw2LLMm32ZoBnpTcc3VVtjy7IhqFMtiC7kmVkbEV213i25SJtj+UH7U2ulLLtccwZE1vbSq69zRNT9AaovpzJyGQDsmEoI2NLZCMrM1LWj7YnuVLKtscx6yvxltvLtOVEzeFGo62TLSNjJrJhKCNja2QjS1euFPWj7U2ulLLtdcx7ptperrZ8e5onYFhv6NtZlZGRENkwlJGxNbKRpUJfZYTu6zO7zQydxnxT5MpjNi630WjbjRnsb55oy9enN/TtrMrISIhsGMrI2JI31ciyJGWGscoI2vLNrYxgb3KllG3vY4bXO2K2GHNOzxMpjFntnVUZmWxAPpUsI2MrsrPGs63KT5kq17ssPHlhnmypKiPYm1x5zPrlPjgPTzK8llvLtOV0zWFQfTmL+Mx8+TIyZiIbhjIytiI7azxrkx2GRUAEXLQgXY2U5bjsSa6Usu11zAER+lMg5cYybTlZc1id79G7rPmyZWQsQDYMZWRshb3WeDZFrkJhuXwZmezE3gzwrPQGzqqd1aD6ll9DRsYMZMNQRsaWyLVDZWRkzMWY3rA0ubyMjIXIh09kZGRkZGRkZGQA2TCUkZGRkZGRkZF5hWwYysjIyMjIyMjIALJhKCMjIyMjIyMj8wrZMJSRkZGRkZGRkQFkw1BGRkZGRkZGRuYVcrqaV8THx+f0EGyKQqEgKSmJ+Ph4XFxccno4uRb5PpmGfJ9MR75XpiHfJ9OQ75NpyPcpa0y1c/7zhqGrqyu+vr6ULFkyp4ciIyMjIyMjIyMZvr6+uLq6Gu3jIIQQ2TSeXEtycjKpqak5PQwZGRkZGRkZGclwdXXF3d3daB/ZMJSRkZGRkZGRkQHkwycyMjIyMjIyMjKvkA1DGRkZGRkZGRkZQDYMZWRkZGRkZGRkXiEbhjIyMjIyMjIyMoBsGMrIyMjIyMjIyLxCNgxlZGRkZGRkZGQA2TCUkZGRkZGRkZF5xf8DkjX80Zixi9oAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 1000x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(subplot_kw=dict(projection=ccrs.PlateCarree()), figsize=(10, 4))\n",
+    "xr.plot.scatter(ax=ax, ds=ds_reference, x=\"lon\", y=\"lat\", label=\"ds_ref\", marker=\".\", transform=ccrs.PlateCarree())\n",
+    "xr.plot.scatter(ax=ax, ds=ds_global_margin_crop_without_interior, x=\"lon\", y=\"lat\", marker=\"x\", label=f\"ds (margin crop, {max_dist}deg)\", transform=ccrs.PlateCarree())\n",
+    "ax.coastlines()\n",
+    "ax.legend()\n",
+    "ax.gridlines(draw_labels=[\"top\", \"left\"])"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
diff --git a/mllam_data_prep/create_dataset.py b/mllam_data_prep/create_dataset.py
index 78f443d..d8b46eb 100644
--- a/mllam_data_prep/create_dataset.py
+++ b/mllam_data_prep/create_dataset.py
@@ -10,7 +10,7 @@
 
 from . import __version__
 from .config import Config, InvalidConfigException
-from .ops.cropping import crop_to_within_convex_hull_margin
+from .ops.cropping import crop_with_convex_hull
 from .ops.loading import load_and_subset_dataset
 from .ops.mapping import map_dims_and_variables
 from .ops.selection import select_by_kwargs
@@ -253,10 +253,11 @@ def create_dataset(config: Config):
         logger.info(
             f"Cropping dataset to within convex hull margin of {ds_interior_domain} with a margin of {domain_cropping.margin_width_degrees} degrees"
         )
-        ds = crop_to_within_convex_hull_margin(
+        ds = crop_with_convex_hull(
             ds=ds,
             ds_reference=ds_interior_domain,
-            max_dist=domain_cropping.margin_width_degrees,
+            margin_thickness=domain_cropping.margin_width_degrees,
+            include_interior=False,
         )
 
     ds.attrs = {}
diff --git a/mllam_data_prep/ops/cropping.py b/mllam_data_prep/ops/cropping.py
index 0ce3e62..8ab6ca7 100644
--- a/mllam_data_prep/ops/cropping.py
+++ b/mllam_data_prep/ops/cropping.py
@@ -1,3 +1,5 @@
+from typing import Tuple, Union
+
 import numpy as np
 import spherical_geometry as sg
 import xarray as xr
@@ -93,8 +95,11 @@ def _latlon_to_unit_sphere_xyz(
 
 
 def distance_to_convex_hull_boundary(
-    ds: xr.Dataset, ds_reference: xr.Dataset, grid_index_dim: str = "grid_index"
-) -> xr.DataArray:
+    ds: xr.Dataset,
+    ds_reference: xr.Dataset,
+    grid_index_dim: str = "grid_index",
+    include_convex_hull_mask: bool = False,
+) -> Union[xr.DataArray, Tuple[xr.DataArray, xr.DataArray]]:
     """
     For all points in `ds` that are external to the convex hull of the points in
     `ds_reference`, calculate the minimum distance to the convex hull boundary.
@@ -107,11 +112,16 @@ def distance_to_convex_hull_boundary(
         The reference dataset from which to calculate the convex hull boundary.
     grid_index_dim : str, optional
         The name of the grid index dimension in `ds` and `ds_reference`.
+    include_convex_hull_mask : bool, optional
+        Whether to include the convex hull mask in the output.
 
     Returns
     -------
-    xarray.DataArray
+    da_mindist_to_ref : xarray.DataArray
         The minimum distance to the convex hull boundary.
+    da_ch_mask : xarray.DataArray
+        The convex hull mask (only if `include_convex_hull_mask` is True).
+
     """
     # rename the grid index dimension in ds_reference to avoid conflicts (since
     # the grid index dimension otherwise has the same name in both datasets,
@@ -151,14 +161,19 @@ def calc_dist(da_pt_xyz):
     ] = "minimum distance to convex hull boundary of reference dataset"
     da_mindist_to_ref.attrs["units"] = "radians"
 
+    if include_convex_hull_mask:
+        return da_mindist_to_ref, da_ch_mask
+
     return da_mindist_to_ref
 
 
-def crop_to_within_convex_hull_margin(
+def crop_with_convex_hull(
     ds: xr.Dataset,
     ds_reference: xr.Dataset,
     grid_index_dim: str = "grid_index",
-    max_dist: float = 2.0,
+    margin_thickness: float = 2.0,
+    include_interior: bool = True,
+    return_mask=False,
 ) -> xr.Dataset:
     """
     Crop grid points (with coordinates given in lat/lon) in `ds` that are
@@ -166,17 +181,17 @@ def crop_to_within_convex_hull_margin(
     convex hull boundary of the points in `ds_reference`. The margin is
     measured in degrees.
 
-    ┌────── Margin ───────────┐
-    │                         │
-    │    ┌─ Convex hull ─┐    │
-    │    │               │    │
-    │    │               │    │
-    │    │               │    │
-    │    │               │    │
-    │    │               │    │
-    │    └───────────────┘    │
-    │    included in crop     │
-    └─────────────────────────┘
+    ┌──────────────────────────────────────┐
+    │ Margin                               │
+    │     ┌────── Convex hull ───────┐     │
+    │     │                          │     │
+    │     │ included if              │     │
+    │     │ include_interior == True │     │
+    │     │                          │     │
+    │<--->│                          │     │
+    │  :  └──────────────────────────┘     │
+    │  :... margin width                   │
+    └──────────────────────────────────────┘
 
     Parameters
     ----------
@@ -186,13 +201,38 @@ def crop_to_within_convex_hull_margin(
         The reference dataset from which to calculate the convex hull boundary.
     grid_index_dim : str, optional
         The name of the grid index dimension in `ds` and `ds_reference`.
+    margin_thickness : float, optional
+        The thickness of the margin to apply to the convex hull boundary in
+        degrees. Points within this margin will be included in the output.
     """
-    da_min_dist_to_ref = distance_to_convex_hull_boundary(
-        ds, ds_reference, grid_index_dim=grid_index_dim
-    )
-
-    max_dist_radians = max_dist * np.pi / 180.0
-
-    da_boundary_mask = da_min_dist_to_ref < max_dist_radians
-
-    return ds.where(da_boundary_mask, drop=True)
+    if margin_thickness == 0.0:
+        if not include_interior:
+            raise Exception(
+                "With no margin and exclude_interior=False, all points would be excluded."
+            )
+        da_mask = create_convex_hull_mask(ds=ds, ds_reference=ds_reference)
+    else:
+        da_min_dist_to_ref, da_ch_mask = distance_to_convex_hull_boundary(
+            ds,
+            ds_reference,
+            grid_index_dim=grid_index_dim,
+            include_convex_hull_mask=True,
+        )
+
+        max_dist_radians = margin_thickness * np.pi / 180.0
+        da_boundary_mask = da_min_dist_to_ref < max_dist_radians
+
+        if not include_interior:
+            da_mask = da_boundary_mask
+        else:
+            da_interior_points = da_ch_mask.where(da_ch_mask, drop=True)
+            da_mask = xr.concat(
+                [da_interior_points, da_boundary_mask], dim="grid_index"
+            )
+
+    ds_cropped = ds.where(da_mask, drop=True)
+
+    if return_mask:
+        return ds_cropped, da_mask
+
+    return ds_cropped

From 210ff46020dfa160b894f32a92a09fa1396ccda1 Mon Sep 17 00:00:00 2001
From: Leif Denby <leif@denby.eu>
Date: Thu, 9 Jan 2025 21:15:32 +0000
Subject: [PATCH 09/28] cleanup

---
 docs/domain-cropping.ipynb         | 80 +++++++++++++++---------------
 mllam_data_prep/config.py          | 22 ++++++--
 mllam_data_prep/create_dataset.py  |  7 ++-
 mllam_data_prep/ops/cropping.py    |  6 +--
 pdm.lock                           | 16 +++---
 pyproject.toml                     |  2 +-
 tests/test_convex_hull_cropping.py | 50 ++++++++++++++++++-
 7 files changed, 122 insertions(+), 61 deletions(-)

diff --git a/docs/domain-cropping.ipynb b/docs/domain-cropping.ipynb
index f358d62..f1a76e0 100644
--- a/docs/domain-cropping.ipynb
+++ b/docs/domain-cropping.ipynb
@@ -79,14 +79,14 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "\u001b[32m2025-01-08 17:28:54.812\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmllam_data_prep.create_dataset\u001b[0m:\u001b[36mcreate_dataset\u001b[0m:\u001b[36m144\u001b[0m - \u001b[1mLoading dataset lam_surface_analysis from /var/folders/n8/kcprfkd918967drydbc_bphw0000gr/T/tmpu4sstlah/lam/surface_analysis_8633b219-3a8a-4986-9d6a-c5fa9d06a2ea.zarr\u001b[0m\n",
-      "\u001b[32m2025-01-08 17:28:54.858\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmllam_data_prep.create_dataset\u001b[0m:\u001b[36mcreate_dataset\u001b[0m:\u001b[36m169\u001b[0m - \u001b[1mMapping dimensions and variables for dataset lam_surface_analysis to state\u001b[0m\n",
-      "\u001b[32m2025-01-08 17:28:54.861\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmllam_data_prep.create_dataset\u001b[0m:\u001b[36m_merge_dataarrays_by_target\u001b[0m:\u001b[36m49\u001b[0m - \u001b[1mMerging dataarrays for target variable `state`\u001b[0m\n",
-      "\u001b[32m2025-01-08 17:28:54.862\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmllam_data_prep.create_dataset\u001b[0m:\u001b[36mcreate_dataset\u001b[0m:\u001b[36m204\u001b[0m - \u001b[1mChunking dataset with {}\u001b[0m\n",
-      "\u001b[32m2025-01-08 17:28:54.863\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmllam_data_prep.create_dataset\u001b[0m:\u001b[36mcreate_dataset\u001b[0m:\u001b[36m144\u001b[0m - \u001b[1mLoading dataset global_surface_analysis from /var/folders/n8/kcprfkd918967drydbc_bphw0000gr/T/tmpu4sstlah/global/surface_analysis_31c04db2-76af-4bcb-b2d1-b109001fb04a.zarr\u001b[0m\n",
-      "\u001b[32m2025-01-08 17:28:54.866\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmllam_data_prep.create_dataset\u001b[0m:\u001b[36mcreate_dataset\u001b[0m:\u001b[36m169\u001b[0m - \u001b[1mMapping dimensions and variables for dataset global_surface_analysis to state\u001b[0m\n",
-      "\u001b[32m2025-01-08 17:28:54.868\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmllam_data_prep.create_dataset\u001b[0m:\u001b[36m_merge_dataarrays_by_target\u001b[0m:\u001b[36m49\u001b[0m - \u001b[1mMerging dataarrays for target variable `state`\u001b[0m\n",
-      "\u001b[32m2025-01-08 17:28:54.869\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmllam_data_prep.create_dataset\u001b[0m:\u001b[36mcreate_dataset\u001b[0m:\u001b[36m204\u001b[0m - \u001b[1mChunking dataset with {}\u001b[0m\n"
+      "\u001b[32m2025-01-08 17:48:34.313\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmllam_data_prep.create_dataset\u001b[0m:\u001b[36mcreate_dataset\u001b[0m:\u001b[36m144\u001b[0m - \u001b[1mLoading dataset lam_surface_analysis from /var/folders/n8/kcprfkd918967drydbc_bphw0000gr/T/tmp0tflm74d/lam/surface_analysis_c6f5cd46-48fd-4f02-8a6a-fde52bf31d69.zarr\u001b[0m\n",
+      "\u001b[32m2025-01-08 17:48:34.359\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmllam_data_prep.create_dataset\u001b[0m:\u001b[36mcreate_dataset\u001b[0m:\u001b[36m169\u001b[0m - \u001b[1mMapping dimensions and variables for dataset lam_surface_analysis to state\u001b[0m\n",
+      "\u001b[32m2025-01-08 17:48:34.362\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmllam_data_prep.create_dataset\u001b[0m:\u001b[36m_merge_dataarrays_by_target\u001b[0m:\u001b[36m49\u001b[0m - \u001b[1mMerging dataarrays for target variable `state`\u001b[0m\n",
+      "\u001b[32m2025-01-08 17:48:34.363\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmllam_data_prep.create_dataset\u001b[0m:\u001b[36mcreate_dataset\u001b[0m:\u001b[36m204\u001b[0m - \u001b[1mChunking dataset with {}\u001b[0m\n",
+      "\u001b[32m2025-01-08 17:48:34.364\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmllam_data_prep.create_dataset\u001b[0m:\u001b[36mcreate_dataset\u001b[0m:\u001b[36m144\u001b[0m - \u001b[1mLoading dataset global_surface_analysis from /var/folders/n8/kcprfkd918967drydbc_bphw0000gr/T/tmp0tflm74d/global/surface_analysis_8c2f2bb4-5767-4180-9b5a-374afa4ce05d.zarr\u001b[0m\n",
+      "\u001b[32m2025-01-08 17:48:34.367\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmllam_data_prep.create_dataset\u001b[0m:\u001b[36mcreate_dataset\u001b[0m:\u001b[36m169\u001b[0m - \u001b[1mMapping dimensions and variables for dataset global_surface_analysis to state\u001b[0m\n",
+      "\u001b[32m2025-01-08 17:48:34.369\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmllam_data_prep.create_dataset\u001b[0m:\u001b[36m_merge_dataarrays_by_target\u001b[0m:\u001b[36m49\u001b[0m - \u001b[1mMerging dataarrays for target variable `state`\u001b[0m\n",
+      "\u001b[32m2025-01-08 17:48:34.370\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmllam_data_prep.create_dataset\u001b[0m:\u001b[36mcreate_dataset\u001b[0m:\u001b[36m204\u001b[0m - \u001b[1mChunking dataset with {}\u001b[0m\n"
      ]
     }
    ],
@@ -484,11 +484,11 @@
        "Attributes:\n",
        "    schema_version:   v0.5.0\n",
        "    dataset_version:  v0.1.0\n",
-       "    created_on:       2025-01-08T17:28:54\n",
+       "    created_on:       2025-01-08T17:48:34\n",
        "    created_with:     mllam-data-prep (https://github.com/mllam/mllam-data-prep)\n",
-       "    mdp_version:      v0.5.0</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-0332cd33-acf2-4a76-b352-d6ea924e7127' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-0332cd33-acf2-4a76-b352-d6ea924e7127' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 5</li><li><span class='xr-has-index'>grid_index</span>: 2500</li><li><span class='xr-has-index'>state_feature</span>: 1</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-de7b73b6-58e1-401b-b433-19d6ab115769' class='xr-section-summary-in' type='checkbox'  checked><label for='section-de7b73b6-58e1-401b-b433-19d6ab115769' class='xr-section-summary' >Coordinates: <span>(10)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2000-01-01 ... 2000-01-02</div><input id='attrs-c2d6e351-cb01-4105-aab1-a0df605e76b9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c2d6e351-cb01-4105-aab1-a0df605e76b9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ee2a169a-5a18-415a-9816-187fdcc73bdb' class='xr-var-data-in' type='checkbox'><label for='data-ee2a169a-5a18-415a-9816-187fdcc73bdb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;2000-01-01T00:00:00.000000000&#x27;, &#x27;2000-01-01T06:00:00.000000000&#x27;,\n",
+       "    mdp_version:      v0.5.0</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-0a9b7847-79ff-42d7-89f4-9e95d648a0c6' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-0a9b7847-79ff-42d7-89f4-9e95d648a0c6' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 5</li><li><span class='xr-has-index'>grid_index</span>: 2500</li><li><span class='xr-has-index'>state_feature</span>: 1</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-51023d5e-36f4-4db6-842d-97622a318f9b' class='xr-section-summary-in' type='checkbox'  checked><label for='section-51023d5e-36f4-4db6-842d-97622a318f9b' class='xr-section-summary' >Coordinates: <span>(10)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2000-01-01 ... 2000-01-02</div><input id='attrs-df9088d6-dfff-4838-93b1-30c96722b589' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-df9088d6-dfff-4838-93b1-30c96722b589' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-40be614c-12f2-479c-b038-8104fb9da3ba' class='xr-var-data-in' type='checkbox'><label for='data-40be614c-12f2-479c-b038-8104fb9da3ba' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;2000-01-01T00:00:00.000000000&#x27;, &#x27;2000-01-01T06:00:00.000000000&#x27;,\n",
        "       &#x27;2000-01-01T12:00:00.000000000&#x27;, &#x27;2000-01-01T18:00:00.000000000&#x27;,\n",
-       "       &#x27;2000-01-02T00:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lat</span></div><div class='xr-var-dims'>(grid_index)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(2500,), meta=np.ndarray&gt;</div><input id='attrs-805343c9-dd5f-4a4a-9893-a15a76a9b530' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-805343c9-dd5f-4a4a-9893-a15a76a9b530' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cb75b819-d292-414b-8045-e1d82c944b98' class='xr-var-data-in' type='checkbox'><label for='data-cb75b819-d292-414b-8045-e1d82c944b98' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
+       "       &#x27;2000-01-02T00:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lat</span></div><div class='xr-var-dims'>(grid_index)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(2500,), meta=np.ndarray&gt;</div><input id='attrs-f4684728-b371-464d-be26-93648e335fd7' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f4684728-b371-464d-be26-93648e335fd7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-946960ac-4912-4c7d-a356-05e9c8ababa2' class='xr-var-data-in' type='checkbox'><label for='data-946960ac-4912-4c7d-a356-05e9c8ababa2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -543,7 +543,7 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon</span></div><div class='xr-var-dims'>(grid_index)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(2500,), meta=np.ndarray&gt;</div><input id='attrs-3928647a-7e8b-4f72-af1d-dcab6c46bd80' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3928647a-7e8b-4f72-af1d-dcab6c46bd80' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c60ab730-19f3-4d1b-8b0d-0222b412fe6e' class='xr-var-data-in' type='checkbox'><label for='data-c60ab730-19f3-4d1b-8b0d-0222b412fe6e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon</span></div><div class='xr-var-dims'>(grid_index)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(2500,), meta=np.ndarray&gt;</div><input id='attrs-dadb082e-4ec7-4844-b5a2-7df5a28ceee0' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-dadb082e-4ec7-4844-b5a2-7df5a28ceee0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-411747c4-f650-4da7-a4ef-4ba98adb63e5' class='xr-var-data-in' type='checkbox'><label for='data-411747c4-f650-4da7-a4ef-4ba98adb63e5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -598,7 +598,7 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>x</span></div><div class='xr-var-dims'>(grid_index)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(2500,), meta=np.ndarray&gt;</div><input id='attrs-5921fc2c-799d-4b0e-9166-311e8d129723' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5921fc2c-799d-4b0e-9166-311e8d129723' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ac1ca605-e35a-4692-94f3-69e73615d15c' class='xr-var-data-in' type='checkbox'><label for='data-ac1ca605-e35a-4692-94f3-69e73615d15c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>x</span></div><div class='xr-var-dims'>(grid_index)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(2500,), meta=np.ndarray&gt;</div><input id='attrs-6abb7f7d-bdc9-4d4f-8d5c-b3583967350d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6abb7f7d-bdc9-4d4f-8d5c-b3583967350d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-876824a2-af04-4499-b105-dbec48fdd4a4' class='xr-var-data-in' type='checkbox'><label for='data-876824a2-af04-4499-b105-dbec48fdd4a4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -653,7 +653,7 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>y</span></div><div class='xr-var-dims'>(grid_index)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(2500,), meta=np.ndarray&gt;</div><input id='attrs-b2d3eb55-3ecf-4e69-9608-19bbf7beb16c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b2d3eb55-3ecf-4e69-9608-19bbf7beb16c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-142bc523-5fb1-4600-b2fa-7455a597f5aa' class='xr-var-data-in' type='checkbox'><label for='data-142bc523-5fb1-4600-b2fa-7455a597f5aa' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>y</span></div><div class='xr-var-dims'>(grid_index)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(2500,), meta=np.ndarray&gt;</div><input id='attrs-85719a5c-38f2-4cb5-bfce-316d135b8c92' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-85719a5c-38f2-4cb5-bfce-316d135b8c92' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2e6cd5fa-e8f5-4a42-b070-73df5fca764c' class='xr-var-data-in' type='checkbox'><label for='data-2e6cd5fa-e8f5-4a42-b070-73df5fca764c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -708,7 +708,7 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>state_feature</span></div><div class='xr-var-dims'>(state_feature)</div><div class='xr-var-dtype'>&lt;U15</div><div class='xr-var-preview xr-preview'>&#x27;pres_seasurface&#x27;</div><input id='attrs-909eabcc-1739-4245-a6b8-19131f3dc2d9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-909eabcc-1739-4245-a6b8-19131f3dc2d9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1a78fc8b-a065-414f-8ad3-1dd97a927144' class='xr-var-data-in' type='checkbox'><label for='data-1a78fc8b-a065-414f-8ad3-1dd97a927144' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;pres_seasurface&#x27;], dtype=&#x27;&lt;U15&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>state_feature_units</span></div><div class='xr-var-dims'>(state_feature)</div><div class='xr-var-dtype'>&lt;U1</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;</div><input id='attrs-7a0e33f7-e0ad-45d3-bbb0-3aba4b3e3bcb' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7a0e33f7-e0ad-45d3-bbb0-3aba4b3e3bcb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cb4003ae-0ee9-4967-8277-914fa7f19a8b' class='xr-var-data-in' type='checkbox'><label for='data-cb4003ae-0ee9-4967-8277-914fa7f19a8b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>state_feature</span></div><div class='xr-var-dims'>(state_feature)</div><div class='xr-var-dtype'>&lt;U15</div><div class='xr-var-preview xr-preview'>&#x27;pres_seasurface&#x27;</div><input id='attrs-47b3b0ab-77a3-4cc8-8279-2b0bcff7a96a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-47b3b0ab-77a3-4cc8-8279-2b0bcff7a96a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8057a714-5090-4d4f-b6d2-3339df8472e8' class='xr-var-data-in' type='checkbox'><label for='data-8057a714-5090-4d4f-b6d2-3339df8472e8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;pres_seasurface&#x27;], dtype=&#x27;&lt;U15&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>state_feature_units</span></div><div class='xr-var-dims'>(state_feature)</div><div class='xr-var-dtype'>&lt;U1</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;</div><input id='attrs-e3b7f5f5-ba1e-4b84-86a5-fe43477e0d05' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e3b7f5f5-ba1e-4b84-86a5-fe43477e0d05' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f1356a48-66d6-41b1-a813-3382d1077dda' class='xr-var-data-in' type='checkbox'><label for='data-f1356a48-66d6-41b1-a813-3382d1077dda' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -763,7 +763,7 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>state_feature_long_name</span></div><div class='xr-var-dims'>(state_feature)</div><div class='xr-var-dtype'>&lt;U1</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;</div><input id='attrs-5a1a104b-00f7-4109-8bbc-437e02911a0d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5a1a104b-00f7-4109-8bbc-437e02911a0d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d292a00f-da13-4192-93b8-a90281f57e56' class='xr-var-data-in' type='checkbox'><label for='data-d292a00f-da13-4192-93b8-a90281f57e56' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>state_feature_long_name</span></div><div class='xr-var-dims'>(state_feature)</div><div class='xr-var-dtype'>&lt;U1</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;</div><input id='attrs-93404203-a0dc-4090-a2f2-3afb07df9ee5' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-93404203-a0dc-4090-a2f2-3afb07df9ee5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b0b40428-a023-4bc8-80dc-b058e1b20b0e' class='xr-var-data-in' type='checkbox'><label for='data-b0b40428-a023-4bc8-80dc-b058e1b20b0e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -818,7 +818,7 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>state_feature_source_dataset</span></div><div class='xr-var-dims'>(state_feature)</div><div class='xr-var-dtype'>&lt;U20</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;</div><input id='attrs-58ac850e-85cd-442c-9555-3e3b72214494' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-58ac850e-85cd-442c-9555-3e3b72214494' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-02a01a5c-25ab-458b-81be-2a8b7a45937e' class='xr-var-data-in' type='checkbox'><label for='data-02a01a5c-25ab-458b-81be-2a8b7a45937e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>state_feature_source_dataset</span></div><div class='xr-var-dims'>(state_feature)</div><div class='xr-var-dtype'>&lt;U20</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;</div><input id='attrs-808fcccf-507d-4459-8c5f-b66f0c6f51bb' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-808fcccf-507d-4459-8c5f-b66f0c6f51bb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ce74496f-ec75-47d9-b544-701f9836a628' class='xr-var-data-in' type='checkbox'><label for='data-ce74496f-ec75-47d9-b544-701f9836a628' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -873,7 +873,7 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>grid_index</span></div><div class='xr-var-dims'>(grid_index)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 ... 2496 2497 2498 2499</div><input id='attrs-c7dff998-0817-4743-a984-e73885c29e6b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c7dff998-0817-4743-a984-e73885c29e6b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b51764f2-987f-44bf-901f-b1b6438e1c76' class='xr-var-data-in' type='checkbox'><label for='data-b51764f2-987f-44bf-901f-b1b6438e1c76' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([   0,    1,    2, ..., 2497, 2498, 2499])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e13f402f-3234-49df-8a35-101d0ff5cf08' class='xr-section-summary-in' type='checkbox'  checked><label for='section-e13f402f-3234-49df-8a35-101d0ff5cf08' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>state</span></div><div class='xr-var-dims'>(state_feature, time, grid_index)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 5, 2500), meta=np.ndarray&gt;</div><input id='attrs-fc10491f-b722-4d4a-9f17-af9eefeac541' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fc10491f-b722-4d4a-9f17-af9eefeac541' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dbe154b5-b3b3-4646-a037-3140ad7014fa' class='xr-var-data-in' type='checkbox'><label for='data-dbe154b5-b3b3-4646-a037-3140ad7014fa' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>variables_mapping_dim :</span></dt><dd>state_feature</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>grid_index</span></div><div class='xr-var-dims'>(grid_index)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 ... 2496 2497 2498 2499</div><input id='attrs-14c3b76c-3be4-489f-9720-6f85a9dad28f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-14c3b76c-3be4-489f-9720-6f85a9dad28f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-db640cb7-c12a-4d57-aebb-219c0b2ca4a3' class='xr-var-data-in' type='checkbox'><label for='data-db640cb7-c12a-4d57-aebb-219c0b2ca4a3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([   0,    1,    2, ..., 2497, 2498, 2499])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-c73a93cb-30be-4b3e-a622-5d8f0b2ddb78' class='xr-section-summary-in' type='checkbox'  checked><label for='section-c73a93cb-30be-4b3e-a622-5d8f0b2ddb78' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>state</span></div><div class='xr-var-dims'>(state_feature, time, grid_index)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 5, 2500), meta=np.ndarray&gt;</div><input id='attrs-221a8b74-8bb8-410a-af3d-c79607fca35a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-221a8b74-8bb8-410a-af3d-c79607fca35a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-330eda23-55d7-4689-8267-19e0e5ee9f1d' class='xr-var-data-in' type='checkbox'><label for='data-330eda23-55d7-4689-8267-19e0e5ee9f1d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>variables_mapping_dim :</span></dt><dd>state_feature</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -951,13 +951,13 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-fe8e63dc-901f-4332-9041-e947a227dbdb' class='xr-section-summary-in' type='checkbox'  ><label for='section-fe8e63dc-901f-4332-9041-e947a227dbdb' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-d9b27e40-908d-4c40-9d5e-7635f4472d6d' class='xr-index-data-in' type='checkbox'/><label for='index-d9b27e40-908d-4c40-9d5e-7635f4472d6d' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2000-01-01 00:00:00&#x27;, &#x27;2000-01-01 06:00:00&#x27;,\n",
+       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-eb7c8fce-ba14-41f9-aff3-b3a59b5d4bb4' class='xr-section-summary-in' type='checkbox'  ><label for='section-eb7c8fce-ba14-41f9-aff3-b3a59b5d4bb4' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-e4d99767-0003-4d74-810e-02aec372313b' class='xr-index-data-in' type='checkbox'/><label for='index-e4d99767-0003-4d74-810e-02aec372313b' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2000-01-01 00:00:00&#x27;, &#x27;2000-01-01 06:00:00&#x27;,\n",
        "               &#x27;2000-01-01 12:00:00&#x27;, &#x27;2000-01-01 18:00:00&#x27;,\n",
        "               &#x27;2000-01-02 00:00:00&#x27;],\n",
-       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>state_feature</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-89ec57b5-201f-466e-b79c-c6e0f5ecbdbb' class='xr-index-data-in' type='checkbox'/><label for='index-89ec57b5-201f-466e-b79c-c6e0f5ecbdbb' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;pres_seasurface&#x27;], dtype=&#x27;object&#x27;, name=&#x27;state_feature&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>grid_index</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-2dd2ede7-978b-430d-9b2f-a35a2d8b3867' class='xr-index-data-in' type='checkbox'/><label for='index-2dd2ede7-978b-430d-9b2f-a35a2d8b3867' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([   0,    1,    2,    3,    4,    5,    6,    7,    8,    9,\n",
+       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>state_feature</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-e4b8e693-58d9-4d95-9c69-55fa91cab792' class='xr-index-data-in' type='checkbox'/><label for='index-e4b8e693-58d9-4d95-9c69-55fa91cab792' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;pres_seasurface&#x27;], dtype=&#x27;object&#x27;, name=&#x27;state_feature&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>grid_index</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-2c7564cc-59e0-42a4-9a01-8da00664d50f' class='xr-index-data-in' type='checkbox'/><label for='index-2c7564cc-59e0-42a4-9a01-8da00664d50f' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([   0,    1,    2,    3,    4,    5,    6,    7,    8,    9,\n",
        "       ...\n",
        "       2490, 2491, 2492, 2493, 2494, 2495, 2496, 2497, 2498, 2499],\n",
-       "      dtype=&#x27;int64&#x27;, name=&#x27;grid_index&#x27;, length=2500))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-4541135c-155b-483f-912a-83592977d087' class='xr-section-summary-in' type='checkbox'  checked><label for='section-4541135c-155b-483f-912a-83592977d087' class='xr-section-summary' >Attributes: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>schema_version :</span></dt><dd>v0.5.0</dd><dt><span>dataset_version :</span></dt><dd>v0.1.0</dd><dt><span>created_on :</span></dt><dd>2025-01-08T17:28:54</dd><dt><span>created_with :</span></dt><dd>mllam-data-prep (https://github.com/mllam/mllam-data-prep)</dd><dt><span>mdp_version :</span></dt><dd>v0.5.0</dd></dl></div></li></ul></div></div>"
+       "      dtype=&#x27;int64&#x27;, name=&#x27;grid_index&#x27;, length=2500))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-dacf8850-451f-41e7-b5ee-6e26ef6fb0e4' class='xr-section-summary-in' type='checkbox'  checked><label for='section-dacf8850-451f-41e7-b5ee-6e26ef6fb0e4' class='xr-section-summary' >Attributes: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>schema_version :</span></dt><dd>v0.5.0</dd><dt><span>dataset_version :</span></dt><dd>v0.1.0</dd><dt><span>created_on :</span></dt><dd>2025-01-08T17:48:34</dd><dt><span>created_with :</span></dt><dd>mllam-data-prep (https://github.com/mllam/mllam-data-prep)</dd><dt><span>mdp_version :</span></dt><dd>v0.5.0</dd></dl></div></li></ul></div></div>"
       ],
       "text/plain": [
        "<xarray.Dataset> Size: 200kB\n",
@@ -978,7 +978,7 @@
        "Attributes:\n",
        "    schema_version:   v0.5.0\n",
        "    dataset_version:  v0.1.0\n",
-       "    created_on:       2025-01-08T17:28:54\n",
+       "    created_on:       2025-01-08T17:48:34\n",
        "    created_with:     mllam-data-prep (https://github.com/mllam/mllam-data-prep)\n",
        "    mdp_version:      v0.5.0"
       ]
@@ -1381,11 +1381,11 @@
        "Attributes:\n",
        "    schema_version:   v0.5.0\n",
        "    dataset_version:  v0.1.0\n",
-       "    created_on:       2025-01-08T17:28:54\n",
+       "    created_on:       2025-01-08T17:48:34\n",
        "    created_with:     mllam-data-prep (https://github.com/mllam/mllam-data-prep)\n",
-       "    mdp_version:      v0.5.0</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-736b5ced-9462-486a-bdd7-34c9027c5981' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-736b5ced-9462-486a-bdd7-34c9027c5981' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 5</li><li><span class='xr-has-index'>grid_index</span>: 625</li><li><span class='xr-has-index'>state_feature</span>: 1</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-e4a4d801-ce30-4b33-a7ac-c6c23a92e49c' class='xr-section-summary-in' type='checkbox'  checked><label for='section-e4a4d801-ce30-4b33-a7ac-c6c23a92e49c' class='xr-section-summary' >Coordinates: <span>(10)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2000-01-01 ... 2000-01-02</div><input id='attrs-98e7bc32-b1f2-4f53-8221-f26b840906f8' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-98e7bc32-b1f2-4f53-8221-f26b840906f8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8420408c-efab-4edb-8ca3-f26ca212b93b' class='xr-var-data-in' type='checkbox'><label for='data-8420408c-efab-4edb-8ca3-f26ca212b93b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;2000-01-01T00:00:00.000000000&#x27;, &#x27;2000-01-01T06:00:00.000000000&#x27;,\n",
+       "    mdp_version:      v0.5.0</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-9d7c4eb9-348a-470b-9834-3bd6abf6e15a' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-9d7c4eb9-348a-470b-9834-3bd6abf6e15a' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 5</li><li><span class='xr-has-index'>grid_index</span>: 625</li><li><span class='xr-has-index'>state_feature</span>: 1</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-c9f536c3-ad5e-4f81-a985-dca62b32e0f9' class='xr-section-summary-in' type='checkbox'  checked><label for='section-c9f536c3-ad5e-4f81-a985-dca62b32e0f9' class='xr-section-summary' >Coordinates: <span>(10)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2000-01-01 ... 2000-01-02</div><input id='attrs-168b2970-43b4-4c13-bd15-082a7145e249' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-168b2970-43b4-4c13-bd15-082a7145e249' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-47f23b8a-5d3f-4ff2-a159-13ff3811de1c' class='xr-var-data-in' type='checkbox'><label for='data-47f23b8a-5d3f-4ff2-a159-13ff3811de1c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;2000-01-01T00:00:00.000000000&#x27;, &#x27;2000-01-01T06:00:00.000000000&#x27;,\n",
        "       &#x27;2000-01-01T12:00:00.000000000&#x27;, &#x27;2000-01-01T18:00:00.000000000&#x27;,\n",
-       "       &#x27;2000-01-02T00:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lat</span></div><div class='xr-var-dims'>(grid_index)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(625,), meta=np.ndarray&gt;</div><input id='attrs-cf5cb4b1-e2a2-4d7d-8a31-dcafb9752eeb' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-cf5cb4b1-e2a2-4d7d-8a31-dcafb9752eeb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bb22c341-979e-40e8-8c72-06c8d488878f' class='xr-var-data-in' type='checkbox'><label for='data-bb22c341-979e-40e8-8c72-06c8d488878f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
+       "       &#x27;2000-01-02T00:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lat</span></div><div class='xr-var-dims'>(grid_index)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(625,), meta=np.ndarray&gt;</div><input id='attrs-e520eddb-5b88-45ef-9bc4-15f7df9af498' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e520eddb-5b88-45ef-9bc4-15f7df9af498' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fe34eed4-e850-4d20-b7e7-8d0bc72e2d5f' class='xr-var-data-in' type='checkbox'><label for='data-fe34eed4-e850-4d20-b7e7-8d0bc72e2d5f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -1440,7 +1440,7 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon</span></div><div class='xr-var-dims'>(grid_index)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(625,), meta=np.ndarray&gt;</div><input id='attrs-59b9ba76-72d9-42ae-aa58-96b0b55f46fd' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-59b9ba76-72d9-42ae-aa58-96b0b55f46fd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fa61f999-e88a-41c3-b363-d3a09490b06f' class='xr-var-data-in' type='checkbox'><label for='data-fa61f999-e88a-41c3-b363-d3a09490b06f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon</span></div><div class='xr-var-dims'>(grid_index)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(625,), meta=np.ndarray&gt;</div><input id='attrs-71a12dc7-2c15-4752-b8c0-f9a2cb9d4acc' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-71a12dc7-2c15-4752-b8c0-f9a2cb9d4acc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3dfa0318-063c-4530-b3c4-1d9861db45e9' class='xr-var-data-in' type='checkbox'><label for='data-3dfa0318-063c-4530-b3c4-1d9861db45e9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -1495,7 +1495,7 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>x</span></div><div class='xr-var-dims'>(grid_index)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(625,), meta=np.ndarray&gt;</div><input id='attrs-93619d03-1079-43cd-820b-b41ffb5bda15' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-93619d03-1079-43cd-820b-b41ffb5bda15' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-034a4251-4751-4bcf-95c7-3abce9d1370a' class='xr-var-data-in' type='checkbox'><label for='data-034a4251-4751-4bcf-95c7-3abce9d1370a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>x</span></div><div class='xr-var-dims'>(grid_index)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(625,), meta=np.ndarray&gt;</div><input id='attrs-dff0d4ea-6c18-4a1a-acb5-eb0783c7d37e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-dff0d4ea-6c18-4a1a-acb5-eb0783c7d37e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-18535fd0-9ec7-45ac-9cfa-18199fa8c4ab' class='xr-var-data-in' type='checkbox'><label for='data-18535fd0-9ec7-45ac-9cfa-18199fa8c4ab' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -1550,7 +1550,7 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>y</span></div><div class='xr-var-dims'>(grid_index)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(625,), meta=np.ndarray&gt;</div><input id='attrs-4e93df29-39a8-4224-853f-80cfa213122b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4e93df29-39a8-4224-853f-80cfa213122b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-99c4e516-743d-41fd-973d-76565af3ebe0' class='xr-var-data-in' type='checkbox'><label for='data-99c4e516-743d-41fd-973d-76565af3ebe0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>y</span></div><div class='xr-var-dims'>(grid_index)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(625,), meta=np.ndarray&gt;</div><input id='attrs-ac5c0f48-2b98-403f-a7d4-fc18633d08f5' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ac5c0f48-2b98-403f-a7d4-fc18633d08f5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d191ec81-eb23-49b1-b30e-a28a8dd62441' class='xr-var-data-in' type='checkbox'><label for='data-d191ec81-eb23-49b1-b30e-a28a8dd62441' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -1605,7 +1605,7 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>state_feature</span></div><div class='xr-var-dims'>(state_feature)</div><div class='xr-var-dtype'>&lt;U15</div><div class='xr-var-preview xr-preview'>&#x27;pres_seasurface&#x27;</div><input id='attrs-ecb21910-9bf6-45f0-8a7d-c4fb00506281' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ecb21910-9bf6-45f0-8a7d-c4fb00506281' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-47a3e2bc-c183-4ecb-a418-9f3473b10d9f' class='xr-var-data-in' type='checkbox'><label for='data-47a3e2bc-c183-4ecb-a418-9f3473b10d9f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;pres_seasurface&#x27;], dtype=&#x27;&lt;U15&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>state_feature_units</span></div><div class='xr-var-dims'>(state_feature)</div><div class='xr-var-dtype'>&lt;U1</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;</div><input id='attrs-aaebe91c-6d90-47b0-beb0-819ee88359d5' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-aaebe91c-6d90-47b0-beb0-819ee88359d5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8bdce67b-2004-468c-9ca4-0bf94c25fd54' class='xr-var-data-in' type='checkbox'><label for='data-8bdce67b-2004-468c-9ca4-0bf94c25fd54' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>state_feature</span></div><div class='xr-var-dims'>(state_feature)</div><div class='xr-var-dtype'>&lt;U15</div><div class='xr-var-preview xr-preview'>&#x27;pres_seasurface&#x27;</div><input id='attrs-60316e24-9a9d-4323-84ca-f3d7064f553e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-60316e24-9a9d-4323-84ca-f3d7064f553e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ce9fd0f4-d8ce-4b8c-9b60-12c7a8d3796c' class='xr-var-data-in' type='checkbox'><label for='data-ce9fd0f4-d8ce-4b8c-9b60-12c7a8d3796c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;pres_seasurface&#x27;], dtype=&#x27;&lt;U15&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>state_feature_units</span></div><div class='xr-var-dims'>(state_feature)</div><div class='xr-var-dtype'>&lt;U1</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;</div><input id='attrs-fbbdc307-16ee-48a5-bd13-bcf726b2b5ae' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-fbbdc307-16ee-48a5-bd13-bcf726b2b5ae' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b52e986d-c140-4426-9afc-15a47c6c7fe0' class='xr-var-data-in' type='checkbox'><label for='data-b52e986d-c140-4426-9afc-15a47c6c7fe0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -1660,7 +1660,7 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>state_feature_long_name</span></div><div class='xr-var-dims'>(state_feature)</div><div class='xr-var-dtype'>&lt;U1</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;</div><input id='attrs-d7516ad1-f334-4a3b-8feb-b8e50baa0a64' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d7516ad1-f334-4a3b-8feb-b8e50baa0a64' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6dd4c213-aa2c-46e3-84ad-379e89d016ba' class='xr-var-data-in' type='checkbox'><label for='data-6dd4c213-aa2c-46e3-84ad-379e89d016ba' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>state_feature_long_name</span></div><div class='xr-var-dims'>(state_feature)</div><div class='xr-var-dtype'>&lt;U1</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;</div><input id='attrs-a774260b-8f93-4609-afbe-547378de0848' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a774260b-8f93-4609-afbe-547378de0848' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c6368c46-236a-453f-9f5b-8a85faa29d6a' class='xr-var-data-in' type='checkbox'><label for='data-c6368c46-236a-453f-9f5b-8a85faa29d6a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -1715,7 +1715,7 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>state_feature_source_dataset</span></div><div class='xr-var-dims'>(state_feature)</div><div class='xr-var-dtype'>&lt;U23</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;</div><input id='attrs-eddb0aba-0f4a-4482-9d6e-4fb0a1c9e344' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-eddb0aba-0f4a-4482-9d6e-4fb0a1c9e344' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cd46ffb0-72dc-41b3-8bdd-476e4084dd08' class='xr-var-data-in' type='checkbox'><label for='data-cd46ffb0-72dc-41b3-8bdd-476e4084dd08' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>state_feature_source_dataset</span></div><div class='xr-var-dims'>(state_feature)</div><div class='xr-var-dtype'>&lt;U23</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;</div><input id='attrs-25cc5d88-a251-46a0-8dc6-180f5afdcbfd' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-25cc5d88-a251-46a0-8dc6-180f5afdcbfd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c97c57fc-0480-4b62-af4f-401816eecf4f' class='xr-var-data-in' type='checkbox'><label for='data-c97c57fc-0480-4b62-af4f-401816eecf4f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -1770,7 +1770,7 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>grid_index</span></div><div class='xr-var-dims'>(grid_index)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 620 621 622 623 624</div><input id='attrs-11ea847f-6efa-4b1e-8b57-35792da61003' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-11ea847f-6efa-4b1e-8b57-35792da61003' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-15219975-d0e0-43e5-8a59-26703f74a65a' class='xr-var-data-in' type='checkbox'><label for='data-15219975-d0e0-43e5-8a59-26703f74a65a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  0,   1,   2, ..., 622, 623, 624])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e2279082-05fe-4e29-a4d1-b40df41acaf1' class='xr-section-summary-in' type='checkbox'  checked><label for='section-e2279082-05fe-4e29-a4d1-b40df41acaf1' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>state</span></div><div class='xr-var-dims'>(state_feature, time, grid_index)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 5, 625), meta=np.ndarray&gt;</div><input id='attrs-88c560be-e7b6-4253-8525-cc5f18ef32b2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-88c560be-e7b6-4253-8525-cc5f18ef32b2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cb0535dd-b8cf-4b0c-b905-a130a21e515f' class='xr-var-data-in' type='checkbox'><label for='data-cb0535dd-b8cf-4b0c-b905-a130a21e515f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>variables_mapping_dim :</span></dt><dd>state_feature</dd></dl></div><div class='xr-var-data'><table>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>grid_index</span></div><div class='xr-var-dims'>(grid_index)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 620 621 622 623 624</div><input id='attrs-b927b3c2-6627-4d85-9001-34cd31b05b17' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b927b3c2-6627-4d85-9001-34cd31b05b17' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-52be8376-5da1-4691-9c12-a0cc766e8a81' class='xr-var-data-in' type='checkbox'><label for='data-52be8376-5da1-4691-9c12-a0cc766e8a81' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  0,   1,   2, ..., 622, 623, 624])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-8f7dbca4-6eea-4afe-bfba-7fa7b1026a0f' class='xr-section-summary-in' type='checkbox'  checked><label for='section-8f7dbca4-6eea-4afe-bfba-7fa7b1026a0f' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>state</span></div><div class='xr-var-dims'>(state_feature, time, grid_index)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 5, 625), meta=np.ndarray&gt;</div><input id='attrs-6c9f3f0e-39ad-4dc7-b6be-4631196a48ed' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6c9f3f0e-39ad-4dc7-b6be-4631196a48ed' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5f427456-fa2e-4410-8ae5-0d5f6b85e3c8' class='xr-var-data-in' type='checkbox'><label for='data-5f427456-fa2e-4410-8ae5-0d5f6b85e3c8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>variables_mapping_dim :</span></dt><dd>state_feature</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -1848,13 +1848,13 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-7f97a916-d161-4706-bdd5-1d00896642fc' class='xr-section-summary-in' type='checkbox'  ><label for='section-7f97a916-d161-4706-bdd5-1d00896642fc' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-0db060b1-4c58-4c4e-8f48-44df4254c60a' class='xr-index-data-in' type='checkbox'/><label for='index-0db060b1-4c58-4c4e-8f48-44df4254c60a' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2000-01-01 00:00:00&#x27;, &#x27;2000-01-01 06:00:00&#x27;,\n",
+       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-f25ac1a5-2266-415f-a048-fd801872dfed' class='xr-section-summary-in' type='checkbox'  ><label for='section-f25ac1a5-2266-415f-a048-fd801872dfed' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-1e8b54dc-ed6a-4fc0-ba2d-97b091f346da' class='xr-index-data-in' type='checkbox'/><label for='index-1e8b54dc-ed6a-4fc0-ba2d-97b091f346da' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2000-01-01 00:00:00&#x27;, &#x27;2000-01-01 06:00:00&#x27;,\n",
        "               &#x27;2000-01-01 12:00:00&#x27;, &#x27;2000-01-01 18:00:00&#x27;,\n",
        "               &#x27;2000-01-02 00:00:00&#x27;],\n",
-       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>state_feature</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-2d59fd7b-2b00-4f95-bf81-14d9b5e01d7a' class='xr-index-data-in' type='checkbox'/><label for='index-2d59fd7b-2b00-4f95-bf81-14d9b5e01d7a' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;pres_seasurface&#x27;], dtype=&#x27;object&#x27;, name=&#x27;state_feature&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>grid_index</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-81e22433-16ea-4b6e-8d93-cb4a81e82594' class='xr-index-data-in' type='checkbox'/><label for='index-81e22433-16ea-4b6e-8d93-cb4a81e82594' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,\n",
+       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>state_feature</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-cf1143d0-595d-4554-aa39-eae722c57de2' class='xr-index-data-in' type='checkbox'/><label for='index-cf1143d0-595d-4554-aa39-eae722c57de2' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;pres_seasurface&#x27;], dtype=&#x27;object&#x27;, name=&#x27;state_feature&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>grid_index</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-7338622f-e91b-4bd8-b871-445ab906b5e4' class='xr-index-data-in' type='checkbox'/><label for='index-7338622f-e91b-4bd8-b871-445ab906b5e4' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,\n",
        "       ...\n",
        "       615, 616, 617, 618, 619, 620, 621, 622, 623, 624],\n",
-       "      dtype=&#x27;int64&#x27;, name=&#x27;grid_index&#x27;, length=625))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-fc60bcdd-a3ce-40c7-b9bb-07a6030fe4b0' class='xr-section-summary-in' type='checkbox'  checked><label for='section-fc60bcdd-a3ce-40c7-b9bb-07a6030fe4b0' class='xr-section-summary' >Attributes: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>schema_version :</span></dt><dd>v0.5.0</dd><dt><span>dataset_version :</span></dt><dd>v0.1.0</dd><dt><span>created_on :</span></dt><dd>2025-01-08T17:28:54</dd><dt><span>created_with :</span></dt><dd>mllam-data-prep (https://github.com/mllam/mllam-data-prep)</dd><dt><span>mdp_version :</span></dt><dd>v0.5.0</dd></dl></div></li></ul></div></div>"
+       "      dtype=&#x27;int64&#x27;, name=&#x27;grid_index&#x27;, length=625))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-7f05fac1-1643-4991-92ac-1c193752e0eb' class='xr-section-summary-in' type='checkbox'  checked><label for='section-7f05fac1-1643-4991-92ac-1c193752e0eb' class='xr-section-summary' >Attributes: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>schema_version :</span></dt><dd>v0.5.0</dd><dt><span>dataset_version :</span></dt><dd>v0.1.0</dd><dt><span>created_on :</span></dt><dd>2025-01-08T17:48:34</dd><dt><span>created_with :</span></dt><dd>mllam-data-prep (https://github.com/mllam/mllam-data-prep)</dd><dt><span>mdp_version :</span></dt><dd>v0.5.0</dd></dl></div></li></ul></div></div>"
       ],
       "text/plain": [
        "<xarray.Dataset> Size: 50kB\n",
@@ -1875,7 +1875,7 @@
        "Attributes:\n",
        "    schema_version:   v0.5.0\n",
        "    dataset_version:  v0.1.0\n",
-       "    created_on:       2025-01-08T17:28:54\n",
+       "    created_on:       2025-01-08T17:48:34\n",
        "    created_with:     mllam-data-prep (https://github.com/mllam/mllam-data-prep)\n",
        "    mdp_version:      v0.5.0"
       ]
@@ -1969,7 +1969,7 @@
     {
      "data": {
       "text/plain": [
-       "<cartopy.mpl.gridliner.Gridliner at 0x165c4e300>"
+       "<cartopy.mpl.gridliner.Gridliner at 0x13f02f3b0>"
       ]
      },
      "execution_count": 10,
@@ -2022,7 +2022,7 @@
     {
      "data": {
       "text/plain": [
-       "<cartopy.mpl.gridliner.Gridliner at 0x165d07ad0>"
+       "<cartopy.mpl.gridliner.Gridliner at 0x13f19bcb0>"
       ]
      },
      "execution_count": 12,
@@ -2057,7 +2057,7 @@
    "source": [
     "max_dist = 1.5 # in degrees\n",
     "ds_global_margin_crop_without_interior = cropping.crop_with_convex_hull(\n",
-    "    ds=ds_global, ds_reference=ds_lam, margin_thickness=max_dist, include_interior=False\n",
+    "    ds=ds_global, ds_reference=ds_lam, margin_thickness=max_dist, include_interior_points=False\n",
     ")"
    ]
   },
@@ -2069,7 +2069,7 @@
     {
      "data": {
       "text/plain": [
-       "<cartopy.mpl.gridliner.Gridliner at 0x165db85c0>"
+       "<cartopy.mpl.gridliner.Gridliner at 0x13f1b7da0>"
       ]
      },
      "execution_count": 14,
diff --git a/mllam_data_prep/config.py b/mllam_data_prep/config.py
index 07ec9ce..27ad56a 100644
--- a/mllam_data_prep/config.py
+++ b/mllam_data_prep/config.py
@@ -218,10 +218,20 @@ class Splitting:
 
 
 @dataclass
-class ConvexHullPaddingCrop:
+class ConvexHullCropping:
     """
-    Define the method for cropping the domain of the output dataset, this includes defining
-    the method to use for cropping the domain and the parameters for the method.
+    Define the method applied for cropping the spatial domain before writing
+    the transformed output dataset. This is typically used when you want to
+    create a dataset to provide data in a boundary around a limited-area
+    domain.
+
+    The cropping is done by creating a convex hull around the spatial
+    coordinates of an *interior* dataset (this will typically be the
+    "limited-area" domain when doing Limited Area Modelling) and then including
+    all points that are within a margin of the convex hull boundary. In addition
+    to including the points inside the convex hull, you can also include the
+    points inside the convex hull of the interior dataset by setting the
+    `include_interior` attribute to `True`.
 
     Attributes
     ----------
@@ -230,10 +240,13 @@ class ConvexHullPaddingCrop:
         boundary of the interior dataset used to define the cropping domain.
     interior_dataset_config_path: str
         The path to the configuration file for the dataset defining the interior domain
+    include_interior_points: bool
+        Whether to include the points inside the convex hull of the interior dataset
     """
 
     margin_width_degrees: float
     interior_dataset_config_path: str
+    include_interior_points: bool = False
 
 
 @dataclass
@@ -279,7 +292,7 @@ class Output:
     coord_ranges: Dict[str, Range] = None
     chunking: Dict[str, int] = None
     splitting: Splitting = None
-    domain_cropping: ConvexHullPaddingCrop = None
+    domain_cropping: ConvexHullCropping = None
 
 
 @dataclass
@@ -320,6 +333,7 @@ class Config(dataclass_wizard.JSONWizard, dataclass_wizard.YAMLWizard):
 
     class _(JSONWizard.Meta):
         raise_on_unknown_json_key = True
+        skip_defaults_if = dataclass_wizard.IS(None)
 
 
 if __name__ == "__main__":
diff --git a/mllam_data_prep/create_dataset.py b/mllam_data_prep/create_dataset.py
index d8b46eb..a84d2f1 100644
--- a/mllam_data_prep/create_dataset.py
+++ b/mllam_data_prep/create_dataset.py
@@ -251,13 +251,16 @@ def create_dataset(config: Config):
         domain_cropping = config.output.domain_cropping
         ds_interior_domain = create_dataset(config=config_interior_domain)
         logger.info(
-            f"Cropping dataset to within convex hull margin of {ds_interior_domain} with a margin of {domain_cropping.margin_width_degrees} degrees"
+            f"Cropping dataset using convex of {config.output.domain_cropping.interior_dataset_config_path} dataset "
+            f"applying margin of {domain_cropping.margin_width_degrees} degrees "
+            f"{'including' if domain_cropping.include_interior_points else 'excluding'} "
+            f"interior points"
         )
         ds = crop_with_convex_hull(
             ds=ds,
             ds_reference=ds_interior_domain,
             margin_thickness=domain_cropping.margin_width_degrees,
-            include_interior=False,
+            include_interior_points=domain_cropping.include_interior_points,
         )
 
     ds.attrs = {}
diff --git a/mllam_data_prep/ops/cropping.py b/mllam_data_prep/ops/cropping.py
index 8ab6ca7..2a2ae94 100644
--- a/mllam_data_prep/ops/cropping.py
+++ b/mllam_data_prep/ops/cropping.py
@@ -172,7 +172,7 @@ def crop_with_convex_hull(
     ds_reference: xr.Dataset,
     grid_index_dim: str = "grid_index",
     margin_thickness: float = 2.0,
-    include_interior: bool = True,
+    include_interior_points: bool = True,
     return_mask=False,
 ) -> xr.Dataset:
     """
@@ -206,7 +206,7 @@ def crop_with_convex_hull(
         degrees. Points within this margin will be included in the output.
     """
     if margin_thickness == 0.0:
-        if not include_interior:
+        if not include_interior_points:
             raise Exception(
                 "With no margin and exclude_interior=False, all points would be excluded."
             )
@@ -222,7 +222,7 @@ def crop_with_convex_hull(
         max_dist_radians = margin_thickness * np.pi / 180.0
         da_boundary_mask = da_min_dist_to_ref < max_dist_radians
 
-        if not include_interior:
+        if not include_interior_points:
             da_mask = da_boundary_mask
         else:
             da_interior_points = da_ch_mask.where(da_ch_mask, drop=True)
diff --git a/pdm.lock b/pdm.lock
index e89b9b0..f94eb9c 100644
--- a/pdm.lock
+++ b/pdm.lock
@@ -5,7 +5,7 @@
 groups = ["default", "dev", "latlon-domain-crop"]
 strategy = ["inherit_metadata"]
 lock_version = "4.5.0"
-content_hash = "sha256:5433ccdea7b0ccc8b1c0e5c0d3b0b14ade0af67f891744fc473ee9c72e779963"
+content_hash = "sha256:90cfb21ae7df28b6660e27c9662b27201948f80b4a22df0dfc5596dac716cda7"
 
 [[metadata.targets]]
 requires_python = ">=3.9"
@@ -565,17 +565,15 @@ files = [
 
 [[package]]
 name = "dataclass-wizard"
-version = "0.22.3"
-summary = "Marshal dataclasses to/from JSON. Use field properties with initial values. Construct a dataclass schema with JSON input."
+version = "0.30.0"
+summary = "Effortlessly marshal dataclasses to/from JSON. Leverage field properties with default values. Generate dataclass schemas from JSON input."
 groups = ["default"]
 dependencies = [
-    "backports-datetime-fromisoformat==1.0.0; python_version == \"3.6\"",
-    "dataclasses; python_version == \"3.6\"",
-    "typing-extensions>=3.7.4.2; python_version <= \"3.9\"",
+    "typing-extensions>=4; python_version == \"3.9\" or python_version == \"3.10\"",
 ]
 files = [
-    {file = "dataclass-wizard-0.22.3.tar.gz", hash = "sha256:4c46591782265058f1148cfd1f54a3a91221e63986fdd04c9d59f4ced61f4424"},
-    {file = "dataclass_wizard-0.22.3-py2.py3-none-any.whl", hash = "sha256:63751203e54b9b9349212cc185331da73c1adc99c51312575eb73bb5c00c1962"},
+    {file = "dataclass-wizard-0.30.0.tar.gz", hash = "sha256:959925974ae6767bd61baa137462e36be74883d37632b858f1edf79ce5204b44"},
+    {file = "dataclass_wizard-0.30.0-py2.py3-none-any.whl", hash = "sha256:fcf44dcd9ae4d1cc53419cfd1434bc9bb14d13d52e8d952f1fb8315047485830"},
 ]
 
 [[package]]
@@ -2507,7 +2505,7 @@ version = "4.12.2"
 requires_python = ">=3.8"
 summary = "Backported and Experimental Type Hints for Python 3.8+"
 groups = ["default", "dev"]
-marker = "python_version < \"3.10\""
+marker = "python_version <= \"3.10\""
 files = [
     {file = "typing_extensions-4.12.2-py3-none-any.whl", hash = "sha256:04e5ca0351e0f3f85c6853954072df659d0d13fac324d0072316b67d7794700d"},
     {file = "typing_extensions-4.12.2.tar.gz", hash = "sha256:1a7ead55c7e559dd4dee8856e3a88b41225abfe1ce8df57b7c13915fe121ffb8"},
diff --git a/pyproject.toml b/pyproject.toml
index 4891fa5..6b95f71 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -17,7 +17,7 @@ dependencies = [
     "isodate>=0.6.1",
     "requests>=2.31.0",
     "aiohttp>=3.9.3",
-    "dataclass-wizard>=0.22.3",
+    "dataclass-wizard==0.30.0",
     "semver>=3.0.2",
     "rich>=13.7.1",
     "dask>=2024.2.1",
diff --git a/tests/test_convex_hull_cropping.py b/tests/test_convex_hull_cropping.py
index 4810fa7..53e0628 100644
--- a/tests/test_convex_hull_cropping.py
+++ b/tests/test_convex_hull_cropping.py
@@ -1,6 +1,8 @@
 import tempfile
+from pathlib import Path
 
 import numpy as np
+import pytest
 
 import mllam_data_prep as mdp
 import tests.data as testdata
@@ -64,10 +66,54 @@ def test_create_convex_hull_mask():
     da_dist_y = da_dist_y.sel(y=da_dist_change.y)
     np.testing.assert_array_equal(np.sign(da_dist_change), np.sign(da_dist_y.y))
 
-    da_convex_hull_margin_crop = cropping.crop_to_within_convex_hull_margin(
-        ds=ds_global, ds_reference=ds_lam, max_dist=2.0
+    da_convex_hull_margin_crop = cropping.crop_with_convex_hull(
+        ds=ds_global,
+        ds_reference=ds_lam,
+        margin_thickness=2.0,
+        include_interior_points=False,
     )
 
     # check that there are fewer points in this margin region
     n_points_margin_region = da_convex_hull_margin_crop.count()
     assert n_points_margin_region < n_outside
+
+
+@pytest.mark.parametrize("include_interior_points", [True, False])
+def test_create_cropped_dataset(include_interior_points):
+
+    tmpdir = tempfile.TemporaryDirectory()
+    d_len = 500 * 1.0e3  # length and width of domain in meters
+    N = 50  # number of grid points in each direction
+    config_lam = testdata.create_input_datasets_and_config(
+        identifier="lam",
+        data_categories=["state"],
+        tmpdir=tmpdir,
+        xlim=[-d_len / 2.0, d_len / 2.0],
+        ylim=[-d_len / 2.0, d_len / 2.0],
+        nx=N,
+        ny=N,
+        add_latlon=True,
+    )
+    # make the global domain twice as large as the LAM domain so that the lam
+    # domain is contained within the global domain, but half the number of grid
+    # points in each direction
+    config_global = testdata.create_input_datasets_and_config(
+        identifier="global",
+        data_categories=["state"],
+        tmpdir=tmpdir,
+        xlim=[-d_len, d_len],
+        ylim=[-d_len, d_len],
+        nx=N // 2,
+        ny=N // 2,
+        add_latlon=True,
+    )
+
+    fp_lam_config = str(Path(tmpdir.name) / "lam_config.yml")
+    config_lam.to_yaml_file(fp_lam_config)
+
+    config_global.output.domain_cropping = mdp.config.ConvexHullCropping(
+        margin_width_degrees=2.0,
+        include_interior_points=include_interior_points,
+        interior_dataset_config_path=fp_lam_config,
+    )
+    mdp.create_dataset(config=config_global)

From 7be2215af923774a6d95c0ee57a45501b5ae338a Mon Sep 17 00:00:00 2001
From: joeloskarsson <joel.oskarsson@liu.se>
Date: Fri, 10 Jan 2025 13:50:22 +0100
Subject: [PATCH 10/28] Measure distance to convex hull based on distance to
 arcs making up polygon

---
 example.era5_cropped.yaml       |   2 +-
 mllam_data_prep/ops/cropping.py | 115 ++++++++++++++++++++++++++------
 2 files changed, 97 insertions(+), 20 deletions(-)

diff --git a/example.era5_cropped.yaml b/example.era5_cropped.yaml
index 71a9284..ea82492 100644
--- a/example.era5_cropped.yaml
+++ b/example.era5_cropped.yaml
@@ -28,7 +28,7 @@ output:
         start: 1990-09-07T00:00
         end: 1990-09-09T00:00
   domain_cropping:
-    margin_width_degrees: 0.2
+    margin_width_degrees: 10
     interior_dataset_config_path: example.danra.yaml
 
 inputs:
diff --git a/mllam_data_prep/ops/cropping.py b/mllam_data_prep/ops/cropping.py
index 0ce3e62..22ed487 100644
--- a/mllam_data_prep/ops/cropping.py
+++ b/mllam_data_prep/ops/cropping.py
@@ -43,6 +43,8 @@ def create_convex_hull_mask(ds: xr.Dataset, ds_reference: xr.Dataset) -> xr.Data
     -------
     xarray.DataArray
         A boolean mask indicating which points are interior to the convex hull.
+    xarray.DataSet
+        A dataset containing lat lon coordinates for points making up the convex hull.
     """
     da_lat, da_lon = _get_latlon_coords(ds)
     da_lat_ref, da_lon_ref = _get_latlon_coords(ds_reference)
@@ -63,7 +65,16 @@ def create_convex_hull_mask(ds: xr.Dataset, ds_reference: xr.Dataset) -> xr.Data
         "long_name"
     ] = "contained in convex hull of source dataset (da_ref)"
 
-    return da_interior_mask
+    # Get points at edge of convex hull
+    chull_lam_lon, chull_lam_lat = list(chull_lam.to_lonlat())[0]
+    chull_lat_lons = xr.Dataset(
+        coords={
+            "lon": (["grid_index_ref"], chull_lam_lon),
+            "lat": (["grid_index_ref"], chull_lam_lat),
+        }
+    )
+
+    return da_interior_mask, chull_lat_lons
 
 
 def _latlon_to_unit_sphere_xyz(
@@ -92,6 +103,69 @@ def _latlon_to_unit_sphere_xyz(
     return da_xyz
 
 
+def shortest_distance_to_arc(point_cartesian, arc_start_cartesian, arc_end_cartesian):
+    """
+    Compute shortest haversine distance from a set of points to an arc on the
+    surface of the sphere.
+
+    Parameters
+    ----------
+    point_cartesian : np.ndarray, shape (num_points, 3)
+        Points to measure distance from
+    arc_start_cartesian : np.ndarray, shape (3,)
+        Start point of arc
+    arc_end_cartesian : np.ndarray, shape (3,)
+        End point of arc
+
+    Returns
+    -------
+    np.ndarray, shape (num_points,)
+        The distances in radians
+    """
+    # Calculate normal vector to the plane of the great circle
+    normal_vector = np.cross(arc_start_cartesian, arc_end_cartesian)
+    normal_vector = normal_vector / np.linalg.norm(normal_vector)  # Normalize
+
+    # Project point onto the plane
+    point_projection = (
+        point_cartesian
+        - np.dot(point_cartesian, normal_vector)[:, np.newaxis] * normal_vector
+    )
+
+    # Normalize to get the projected point on the sphere's surface
+    projected_point = (
+        point_projection / np.linalg.norm(point_projection, axis=1)[:, np.newaxis]
+    )
+
+    # Calculate the angle between the original point and the projected point
+    angle_point_to_projection = np.arccos(
+        np.clip(np.sum(point_cartesian * projected_point, axis=1), -1, 1)
+    )
+
+    # Check if the projected point is between the start and end points of the arc
+    is_between_arc = (
+        np.dot(np.cross(arc_start_cartesian, projected_point), normal_vector) >= 0
+    ) & (np.dot(np.cross(projected_point, arc_end_cartesian), normal_vector) >= 0)
+
+    # Calculate distances from the point to the start and end points of the arc
+    distance_to_start = np.arccos(
+        np.clip(np.dot(point_cartesian, arc_start_cartesian), -1, 1)
+    )
+    distance_to_end = np.arccos(
+        np.clip(np.dot(point_cartesian, arc_end_cartesian), -1, 1)
+    )
+
+    # Choose the appropriate distance
+    distances = np.where(
+        is_between_arc,
+        angle_point_to_projection,
+        np.minimum(distance_to_start, distance_to_end),
+    )
+
+    # Distance returned in radians
+    return distances
+
+
 def distance_to_convex_hull_boundary(
     ds: xr.Dataset, ds_reference: xr.Dataset, grid_index_dim: str = "grid_index"
 ) -> xr.DataArray:
@@ -122,29 +196,32 @@ def distance_to_convex_hull_boundary(
     )
 
     # create a mask from the convex hull of ds_reference for the grid points in ds
-    da_ch_mask = create_convex_hull_mask(
+    da_ch_mask, chull_lat_lons = create_convex_hull_mask(
         ds=ds, ds_reference=ds_reference_separate_gridindex
     )
 
     # only consider points that are external to the convex hull
     ds_exterior = ds.where(~da_ch_mask, drop=True)
-
-    da_xyz = _latlon_to_unit_sphere_xyz(*_get_latlon_coords(ds_exterior))
-    da_xyz_ref = _latlon_to_unit_sphere_xyz(
-        *_get_latlon_coords(ds_reference_separate_gridindex)
-    )
-
-    def calc_dist(da_pt_xyz):
-        dotproduct = np.dot(da_xyz_ref, da_pt_xyz.T)
-        val = np.min(np.arccos(dotproduct))
-        return val
-
-    da_mindist_to_ref = xr.apply_ufunc(
-        calc_dist,
-        da_xyz,
-        input_core_dims=[["xyz"]],
-        output_core_dims=[[]],
-        vectorize=True,
+    ds_exterior_lon, ds_exterior_lat = _get_latlon_coords(ds_exterior)
+
+    da_xyz = _latlon_to_unit_sphere_xyz(ds_exterior_lon, ds_exterior_lat)
+
+    da_xyz_chull = _latlon_to_unit_sphere_xyz(*_get_latlon_coords(chull_lat_lons))
+    # Collect arcs making up chull
+    chull_arcs = list(zip(da_xyz_chull[:-1], da_xyz_chull[1:])) + [
+        (da_xyz_chull[-1], da_xyz_chull[0])
+    ]  # Add arc from last to first point
+
+    mindist_to_ref = np.stack(
+        [
+            shortest_distance_to_arc(da_xyz, arc_start, arc_end)
+            for arc_start, arc_end in chull_arcs
+        ],
+        axis=0,
+    ).min(axis=0)
+
+    da_mindist_to_ref = xr.DataArray(
+        mindist_to_ref, coords=ds_exterior_lat.coords, dims=ds_exterior_lat.dims
     )
     da_mindist_to_ref.attrs[
         "long_name"

From 3fb140e42e3ad61f928e5e0a9177444c84e5bf93 Mon Sep 17 00:00:00 2001
From: Leif Denby <leif@denby.eu>
Date: Mon, 27 Jan 2025 17:34:20 +0100
Subject: [PATCH 11/28] tweaks to docstring and fix test

---
 mllam_data_prep/ops/cropping.py    | 35 ++++++++++++++++++++++++------
 tests/test_convex_hull_cropping.py |  8 ++++++-
 2 files changed, 35 insertions(+), 8 deletions(-)

diff --git a/mllam_data_prep/ops/cropping.py b/mllam_data_prep/ops/cropping.py
index 34a1d06..3bc5d6f 100644
--- a/mllam_data_prep/ops/cropping.py
+++ b/mllam_data_prep/ops/cropping.py
@@ -44,9 +44,11 @@ def create_convex_hull_mask(ds: xr.Dataset, ds_reference: xr.Dataset) -> xr.Data
     Returns
     -------
     xarray.DataArray
-        A boolean mask indicating which points are interior to the convex hull.
-    xarray.DataSet
-        A dataset containing lat lon coordinates for points making up the convex hull.
+        A boolean mask indicating which points in `ds_reference` are interior
+        to the convex hull.
+    xarray.Dataset
+        A dataset containing lat lon coordinates for points in `ds` making up
+        the convex hull.
     """
     da_lat, da_lon = _get_latlon_coords(ds)
     da_lat_ref, da_lon_ref = _get_latlon_coords(ds_reference)
@@ -105,10 +107,16 @@ def _latlon_to_unit_sphere_xyz(
     return da_xyz
 
 
-def shortest_distance_to_arc(point_cartesian, arc_start_cartesian, arc_end_cartesian):
+def shortest_distance_to_arc(
+    point_cartesian: np.ndarray,
+    arc_start_cartesian: np.ndarray,
+    arc_end_cartesian: np.ndarray,
+) -> np.ndarray:
     """
     Compute shortest haversine distance from a set of points to an arc on the
-    surface of the sphere.
+    surface of the sphere. All points are assumed to be on the surface of a
+    sphere of the same radius (e.g. the unit sphere) given in Cartesian (x, y,
+    z) coordinates.
 
     Parameters
     ----------
@@ -178,6 +186,16 @@ def distance_to_convex_hull_boundary(
     For all points in `ds` that are external to the convex hull of the points in
     `ds_reference`, calculate the minimum distance to the convex hull boundary.
 
+    The method goes through the following steps:
+    1. Create a mask for the points in `ds` from the convex hull of the points
+       in `ds_reference`.
+    2. Find the points in `ds` that are external to the convex hull.
+    3. For each point in `ds` external to the convex hull, calculate the
+       minimum distance to the convex hull boundary. The distance is calculated
+       as the shortest distance to any of the arcs making up the convex hull
+       boundary.
+
+
     Parameters
     ----------
     ds : xarray.Dataset
@@ -206,7 +224,7 @@ def distance_to_convex_hull_boundary(
     )
 
     # create a mask from the convex hull of ds_reference for the grid points in ds
-    da_ch_mask, chull_lat_lons = create_convex_hull_mask(
+    da_ch_mask, ds_chull_lat_lons = create_convex_hull_mask(
         ds=ds, ds_reference=ds_reference_separate_gridindex
     )
 
@@ -216,12 +234,15 @@ def distance_to_convex_hull_boundary(
 
     da_xyz = _latlon_to_unit_sphere_xyz(ds_exterior_lon, ds_exterior_lat)
 
-    da_xyz_chull = _latlon_to_unit_sphere_xyz(*_get_latlon_coords(chull_lat_lons))
+    da_xyz_chull = _latlon_to_unit_sphere_xyz(*_get_latlon_coords(ds_chull_lat_lons))
+
     # Collect arcs making up chull
     chull_arcs = list(zip(da_xyz_chull[:-1], da_xyz_chull[1:])) + [
         (da_xyz_chull[-1], da_xyz_chull[0])
     ]  # Add arc from last to first point
 
+    # Calculate minimum distance to each arc and take the minimum
+    # distance over all arcs
     mindist_to_ref = np.stack(
         [
             shortest_distance_to_arc(da_xyz, arc_start, arc_end)
diff --git a/tests/test_convex_hull_cropping.py b/tests/test_convex_hull_cropping.py
index 53e0628..bbcd584 100644
--- a/tests/test_convex_hull_cropping.py
+++ b/tests/test_convex_hull_cropping.py
@@ -39,7 +39,13 @@ def test_create_convex_hull_mask():
     ds_lam = mdp.create_dataset(config=config_lam)
     ds_global = mdp.create_dataset(config=config_global)
 
-    da_ch_mask = cropping.create_convex_hull_mask(ds=ds_global, ds_reference=ds_lam)
+    da_ch_mask, ds_ch_latlons = cropping.create_convex_hull_mask(
+        ds=ds_global, ds_reference=ds_lam
+    )
+
+    # just check that some of the points make up the convex hull for now
+    # (this doesn't check that the convex hull is correct of course...)
+    assert 0 < ds_ch_latlons.grid_index_ref.size < ds_lam.grid_index.size
 
     # Given that the outer domain is 4x larger than the inner domain and the
     # inner domain sits completely within the outer domain, then approximately

From c2406d51787e326e4ca15b70602dd416e5486a07 Mon Sep 17 00:00:00 2001
From: Leif Denby <leif@denby.eu>
Date: Mon, 27 Jan 2025 17:36:44 +0100
Subject: [PATCH 12/28] tweak print statement

---
 mllam_data_prep/create_dataset.py | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/mllam_data_prep/create_dataset.py b/mllam_data_prep/create_dataset.py
index a84d2f1..c9efcc4 100644
--- a/mllam_data_prep/create_dataset.py
+++ b/mllam_data_prep/create_dataset.py
@@ -251,10 +251,10 @@ def create_dataset(config: Config):
         domain_cropping = config.output.domain_cropping
         ds_interior_domain = create_dataset(config=config_interior_domain)
         logger.info(
-            f"Cropping dataset using convex of {config.output.domain_cropping.interior_dataset_config_path} dataset "
-            f"applying margin of {domain_cropping.margin_width_degrees} degrees "
-            f"{'including' if domain_cropping.include_interior_points else 'excluding'} "
-            f"interior points"
+            f"Cropping dataset using convex hull "
+            f"({'including' if domain_cropping.include_interior_points else 'excluding'} interior points "
+            f"and includign margin of {domain_cropping.margin_width_degrees} degrees) "
+            f"of {config.output.domain_cropping.interior_dataset_config_path} dataset "
         )
         ds = crop_with_convex_hull(
             ds=ds,

From 8019e07ff3efe4356e3ba692732a696f99f8500c Mon Sep 17 00:00:00 2001
From: Leif Denby <leif@denby.eu>
Date: Mon, 27 Jan 2025 18:03:48 +0100
Subject: [PATCH 13/28] only mask variables which share mask dimensions

---
 mllam_data_prep/create_dataset.py |  2 +-
 mllam_data_prep/ops/cropping.py   | 36 ++++++++++++++++++++++++++++++-
 2 files changed, 36 insertions(+), 2 deletions(-)

diff --git a/mllam_data_prep/create_dataset.py b/mllam_data_prep/create_dataset.py
index c9efcc4..e8536b7 100644
--- a/mllam_data_prep/create_dataset.py
+++ b/mllam_data_prep/create_dataset.py
@@ -253,7 +253,7 @@ def create_dataset(config: Config):
         logger.info(
             f"Cropping dataset using convex hull "
             f"({'including' if domain_cropping.include_interior_points else 'excluding'} interior points "
-            f"and includign margin of {domain_cropping.margin_width_degrees} degrees) "
+            f"and including margin of {domain_cropping.margin_width_degrees} degrees) "
             f"of {config.output.domain_cropping.interior_dataset_config_path} dataset "
         )
         ds = crop_with_convex_hull(
diff --git a/mllam_data_prep/ops/cropping.py b/mllam_data_prep/ops/cropping.py
index 3bc5d6f..1141e40 100644
--- a/mllam_data_prep/ops/cropping.py
+++ b/mllam_data_prep/ops/cropping.py
@@ -265,6 +265,34 @@ def distance_to_convex_hull_boundary(
     return da_mindist_to_ref
 
 
+def _mask_with_common_dim(da_mask, ds):
+    """
+    Apply mask to all variables in `ds` that share dimension(s) with `da_mask`.
+
+    Parameters
+    ----------
+    da_mask : xarray.DataArray
+        The mask.
+    ds : xarray.Dataset
+        The dataset to mask.
+
+    Returns
+    -------
+    xarray.Dataset
+        The masked dataset including the variables that don't share
+        dimension(s) with the mask (these are simply copied over).
+    """
+    mask_dims = da_mask.dims
+    vars_with_dims = [
+        v for v in ds.data_vars if all(d in ds[v].dims for d in mask_dims)
+    ]
+    vars_without_dims = [v for v in ds.data_vars if v not in vars_with_dims]
+
+    ds_masked = ds.drop_vars(vars_without_dims).where(da_mask, drop=True)
+    ds_masked = xr.merge([ds_masked, ds[vars_without_dims]])
+    return ds_masked
+
+
 def crop_with_convex_hull(
     ds: xr.Dataset,
     ds_reference: xr.Dataset,
@@ -328,7 +356,13 @@ def crop_with_convex_hull(
                 [da_interior_points, da_boundary_mask], dim="grid_index"
             )
 
-    ds_cropped = ds.where(da_mask, drop=True)
+    # it is unclear if there is a bug in xr.Dataset.where(), but its default
+    # behaviour seems to be broadcast (i.e. add) the dimensions of the mask to
+    # any data variables that don't have those dimensions already. We only want
+    # to mask the variables that share dimension(s) with the mask (i.e. have
+    # the `grid_index` dimension), so we drop the other variables before
+    # applying the mask.
+    ds_cropped = _mask_with_common_dim(da_mask=da_mask, ds=ds)
 
     if return_mask:
         return ds_cropped, da_mask

From 784e0cfd44fefea8769268cb399298c37ecbb397 Mon Sep 17 00:00:00 2001
From: Leif Denby <leif@denby.eu>
Date: Thu, 30 Jan 2025 16:28:40 +0100
Subject: [PATCH 14/28] include deriving variables in cropping example

---
 example.era5_cropped.yaml | 24 +++++++++++++++++++++---
 1 file changed, 21 insertions(+), 3 deletions(-)

diff --git a/example.era5_cropped.yaml b/example.era5_cropped.yaml
index ea82492..a73a416 100644
--- a/example.era5_cropped.yaml
+++ b/example.era5_cropped.yaml
@@ -33,7 +33,7 @@ output:
 
 inputs:
   era_height_levels:
-    path: 'gs://weatherbench2/datasets/era5/1959-2023_01_10-6h-64x32_equiangular_conservative.zarr'
+    path: 'simplecache::gs://weatherbench2/datasets/era5/1959-2023_01_10-6h-64x32_equiangular_conservative.zarr'
     dims: [time, longitude, latitude, level]
     variables:
       u_component_of_wind:
@@ -54,10 +54,28 @@ inputs:
     target_output_variable: forcing
 
   era5_surface:
-    path: 'gs://weatherbench2/datasets/era5/1959-2023_01_10-6h-64x32_equiangular_conservative.zarr'
+    path: 'simplecache::gs://weatherbench2/datasets/era5/1959-2023_01_10-6h-64x32_equiangular_conservative.zarr'
     dims: [time, longitude, latitude, level]
     variables:
       - mean_sea_level_pressure
+    derived_variables:
+      # derive variables to be used as forcings
+      toa_radiation:
+        kwargs:
+          time: ds_input.time
+          lat: ds_input.latitude
+          lon: ds_input.longitude
+        function: mllam_data_prep.ops.derive_variable.physical_field.calculate_toa_radiation
+      hour_of_day_sin:
+        kwargs:
+          time: ds_input.time
+          component: sin
+        function: mllam_data_prep.ops.derive_variable.time_components.calculate_hour_of_day
+      hour_of_day_cos:
+        kwargs:
+          time: ds_input.time
+          component: cos
+        function: mllam_data_prep.ops.derive_variable.time_components.calculate_hour_of_day
     dim_mapping:
       time:
         method: rename
@@ -71,7 +89,7 @@ inputs:
     target_output_variable: forcing
 
   era5_static:
-    path: 'gs://weatherbench2/datasets/era5/1959-2023_01_10-6h-64x32_equiangular_conservative.zarr'
+    path: 'simplecache::gs://weatherbench2/datasets/era5/1959-2023_01_10-6h-64x32_equiangular_conservative.zarr'
     dims: [time, longitude, latitude, level]
     variables:
       - land_sea_mask

From a88df81cd8f0ee6c0d2e69c8e03eb08bee9a7136 Mon Sep 17 00:00:00 2001
From: Leif Denby <leif@denby.eu>
Date: Thu, 27 Mar 2025 11:50:33 +0100
Subject: [PATCH 15/28] adjust lat/lon return argument ordering

---
 mllam_data_prep/ops/cropping.py | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/mllam_data_prep/ops/cropping.py b/mllam_data_prep/ops/cropping.py
index 1141e40..213215d 100644
--- a/mllam_data_prep/ops/cropping.py
+++ b/mllam_data_prep/ops/cropping.py
@@ -21,9 +21,9 @@ def _get_latlon_coords(da: xr.DataArray) -> tuple:
         The latitude and longitude coordinates.
     """
     if "latitude" in da.coords and "longitude" in da.coords:
-        return (da.latitude, da.longitude)
+        return (da.longitude, da.latitude)
     elif "lat" in da.coords and "lon" in da.coords:
-        return (da.lat, da.lon)
+        return (da.lon, da.lat)
     else:
         raise Exception("Could not find lat/lon coordinates in DataArray.")
 
@@ -50,8 +50,8 @@ def create_convex_hull_mask(ds: xr.Dataset, ds_reference: xr.Dataset) -> xr.Data
         A dataset containing lat lon coordinates for points in `ds` making up
         the convex hull.
     """
-    da_lat, da_lon = _get_latlon_coords(ds)
-    da_lat_ref, da_lon_ref = _get_latlon_coords(ds_reference)
+    da_lon, da_lat = _get_latlon_coords(ds)
+    da_lon_ref, da_lat_ref = _get_latlon_coords(ds_reference)
 
     assert da_lat.dims == da_lon.dims
     assert da_lat_ref.dims == da_lon_ref.dims

From 071d6ecde7ab62e3d147b9c90e0ea2f8d0bef307 Mon Sep 17 00:00:00 2001
From: Leif Denby <leif@denby.eu>
Date: Thu, 27 Mar 2025 12:22:40 +0100
Subject: [PATCH 16/28] remove derived variables from era5 crop example

---
 example.era5_cropped.yaml |   18 -
 pdm.lock                  | 3019 ++++++++++++++++++++++++++++++-------
 2 files changed, 2452 insertions(+), 585 deletions(-)

diff --git a/example.era5_cropped.yaml b/example.era5_cropped.yaml
index a73a416..dcd92be 100644
--- a/example.era5_cropped.yaml
+++ b/example.era5_cropped.yaml
@@ -58,24 +58,6 @@ inputs:
     dims: [time, longitude, latitude, level]
     variables:
       - mean_sea_level_pressure
-    derived_variables:
-      # derive variables to be used as forcings
-      toa_radiation:
-        kwargs:
-          time: ds_input.time
-          lat: ds_input.latitude
-          lon: ds_input.longitude
-        function: mllam_data_prep.ops.derive_variable.physical_field.calculate_toa_radiation
-      hour_of_day_sin:
-        kwargs:
-          time: ds_input.time
-          component: sin
-        function: mllam_data_prep.ops.derive_variable.time_components.calculate_hour_of_day
-      hour_of_day_cos:
-        kwargs:
-          time: ds_input.time
-          component: cos
-        function: mllam_data_prep.ops.derive_variable.time_components.calculate_hour_of_day
     dim_mapping:
       time:
         method: rename
diff --git a/pdm.lock b/pdm.lock
index 1d24148..a715199 100644
--- a/pdm.lock
+++ b/pdm.lock
@@ -2,89 +2,149 @@
 # It is not intended for manual editing.
 
 [metadata]
-groups = ["default", "dev"]
+groups = ["default", "dask-distributed", "dev", "latlon-domain-crop"]
 strategy = ["inherit_metadata"]
 lock_version = "4.5.0"
-content_hash = "sha256:b6b664c0870d66936516c282c038c66dbd6f579cedeef4276b6ffdfdea2b0a14"
+content_hash = "sha256:937d75cb4b8d8b7b143a6679903ba80edbea487a87fc5d165ed081c734cde985"
 
 [[metadata.targets]]
 requires_python = ">=3.9"
 
+[[package]]
+name = "aiohappyeyeballs"
+version = "2.6.1"
+requires_python = ">=3.9"
+summary = "Happy Eyeballs for asyncio"
+groups = ["default", "latlon-domain-crop"]
+files = [
+    {file = "aiohappyeyeballs-2.6.1-py3-none-any.whl", hash = "sha256:f349ba8f4b75cb25c99c5c2d84e997e485204d2902a9597802b0371f09331fb8"},
+    {file = "aiohappyeyeballs-2.6.1.tar.gz", hash = "sha256:c3f9d0113123803ccadfdf3f0faa505bc78e6a72d1cc4806cbd719826e943558"},
+]
+
 [[package]]
 name = "aiohttp"
-version = "3.9.5"
-requires_python = ">=3.8"
+version = "3.11.14"
+requires_python = ">=3.9"
 summary = "Async http client/server framework (asyncio)"
-groups = ["default"]
+groups = ["default", "latlon-domain-crop"]
 dependencies = [
+    "aiohappyeyeballs>=2.3.0",
     "aiosignal>=1.1.2",
-    "async-timeout<5.0,>=4.0; python_version < \"3.11\"",
+    "async-timeout<6.0,>=4.0; python_version < \"3.11\"",
     "attrs>=17.3.0",
     "frozenlist>=1.1.1",
     "multidict<7.0,>=4.5",
-    "yarl<2.0,>=1.0",
-]
-files = [
-    {file = "aiohttp-3.9.5-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:fcde4c397f673fdec23e6b05ebf8d4751314fa7c24f93334bf1f1364c1c69ac7"},
-    {file = "aiohttp-3.9.5-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:5d6b3f1fabe465e819aed2c421a6743d8debbde79b6a8600739300630a01bf2c"},
-    {file = "aiohttp-3.9.5-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:6ae79c1bc12c34082d92bf9422764f799aee4746fd7a392db46b7fd357d4a17a"},
-    {file = "aiohttp-3.9.5-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:4d3ebb9e1316ec74277d19c5f482f98cc65a73ccd5430540d6d11682cd857430"},
-    {file = "aiohttp-3.9.5-cp310-cp310-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:84dabd95154f43a2ea80deffec9cb44d2e301e38a0c9d331cc4aa0166fe28ae3"},
-    {file = "aiohttp-3.9.5-cp310-cp310-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:c8a02fbeca6f63cb1f0475c799679057fc9268b77075ab7cf3f1c600e81dd46b"},
-    {file = "aiohttp-3.9.5-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:c26959ca7b75ff768e2776d8055bf9582a6267e24556bb7f7bd29e677932be72"},
-    {file = "aiohttp-3.9.5-cp310-cp310-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:714d4e5231fed4ba2762ed489b4aec07b2b9953cf4ee31e9871caac895a839c0"},
-    {file = "aiohttp-3.9.5-cp310-cp310-musllinux_1_1_aarch64.whl", hash = "sha256:e7a6a8354f1b62e15d48e04350f13e726fa08b62c3d7b8401c0a1314f02e3558"},
-    {file = "aiohttp-3.9.5-cp310-cp310-musllinux_1_1_i686.whl", hash = "sha256:c413016880e03e69d166efb5a1a95d40f83d5a3a648d16486592c49ffb76d0db"},
-    {file = "aiohttp-3.9.5-cp310-cp310-musllinux_1_1_ppc64le.whl", hash = "sha256:ff84aeb864e0fac81f676be9f4685f0527b660f1efdc40dcede3c251ef1e867f"},
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-    {file = "aiohttp-3.9.5-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:702e2c7c187c1a498a4e2b03155d52658fdd6fda882d3d7fbb891a5cf108bb10"},
-    {file = "aiohttp-3.9.5-cp310-cp310-win32.whl", hash = "sha256:67c3119f5ddc7261d47163ed86d760ddf0e625cd6246b4ed852e82159617b5fb"},
-    {file = "aiohttp-3.9.5-cp310-cp310-win_amd64.whl", hash = "sha256:471f0ef53ccedec9995287f02caf0c068732f026455f07db3f01a46e49d76bbb"},
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 ]
 
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+summary = "Parallel PyData with Task Scheduling"
+groups = ["dask-distributed"]
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+requires_python = ">=3.8"
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 groups = ["default"]
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+groups = ["dask-distributed"]
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 [[package]]
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@@ -549,30 +1287,178 @@ files = [
 
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 [[package]]
@@ -580,7 +1466,7 @@ name = "locket"
 version = "1.0.0"
 requires_python = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*"
 summary = "File-based locks for Python on Linux and Windows"
-groups = ["default"]
+groups = ["default", "dask-distributed"]
 files = [
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@@ -588,8 +1474,8 @@ files = [
 
 [[package]]
 name = "loguru"
-version = "0.7.2"
-requires_python = ">=3.5"
+version = "0.7.3"
+requires_python = "<4.0,>=3.5"
 summary = "Python logging made (stupidly) simple"
 groups = ["default"]
 dependencies = [
@@ -598,8 +1484,8 @@ dependencies = [
     "win32-setctime>=1.0.0; sys_platform == \"win32\"",
 ]
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 requires_python = ">=3.6"
 summary = "A Python Parser"
 groups = ["dev"]
-marker = "python_version > \"3.6\""
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 version = "1.4.2"
 requires_python = ">=3.9"
 summary = "Appendable key-value storage"
-groups = ["default"]
+groups = ["default", "dask-distributed"]
 dependencies = [
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     "toolz",
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 version = "4.9.0"
 summary = "Pexpect allows easy control of interactive console applications."
 groups = ["dev"]
-marker = "sys_platform != \"win32\" and python_version > \"3.6\""
+marker = "sys_platform != \"win32\""
 dependencies = [
     "ptyprocess>=0.5",
 ]
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 [[package]]
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-requires_python = ">=3.8"
+version = "4.3.7"
+requires_python = ">=3.9"
 summary = "A small Python package for determining appropriate platform-specific dirs, e.g. a `user data dir`."
 groups = ["dev"]
 files = [
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 [[package]]
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 name = "pre-commit"
-version = "3.7.1"
+version = "4.2.0"
 requires_python = ">=3.9"
 summary = "A framework for managing and maintaining multi-language pre-commit hooks."
 groups = ["dev"]
@@ -916,40 +2172,178 @@ dependencies = [
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 ]
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 dependencies = [
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 [[package]]
@@ -1066,7 +2716,7 @@ name = "requests"
 version = "2.32.3"
 requires_python = ">=3.8"
 summary = "Python HTTP for Humans."
-groups = ["default"]
+groups = ["default", "latlon-domain-crop"]
 dependencies = [
     "certifi>=2017.4.17",
     "charset-normalizer<4,>=2",
@@ -1078,42 +2728,162 @@ files = [
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 ]
 
+[[package]]
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+requires_python = ">=3.4"
+summary = "OAuthlib authentication support for Requests."
+groups = ["latlon-domain-crop"]
+dependencies = [
+    "oauthlib>=3.0.0",
+    "requests>=2.0.0",
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-groups = ["default", "dev"]
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 summary = "Virtual Python Environment builder"
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 dependencies = [
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 dependencies = [
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 ]
@@ -1234,19 +3062,19 @@ files = [
 
 [[package]]
 name = "win32-setctime"
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+version = "1.2.0"
 requires_python = ">=3.5"
 summary = "A small Python utility to set file creation time on Windows"
 groups = ["default"]
 marker = "sys_platform == \"win32\""
 files = [
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 requires_python = ">=3.9"
 summary = "N-D labeled arrays and datasets in Python"
 groups = ["default"]
@@ -1256,69 +3084,115 @@ dependencies = [
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+    {file = "yarl-1.18.3-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:8d39d351e7faf01483cc7ff7c0213c412e38e5a340238826be7e0e4da450fdc8"},
+    {file = "yarl-1.18.3-cp313-cp313-win32.whl", hash = "sha256:61ee62ead9b68b9123ec24bc866cbef297dd266175d53296e2db5e7f797f902d"},
+    {file = "yarl-1.18.3-cp313-cp313-win_amd64.whl", hash = "sha256:578e281c393af575879990861823ef19d66e2b1d0098414855dd367e234f5b3c"},
+    {file = "yarl-1.18.3-cp39-cp39-macosx_10_9_universal2.whl", hash = "sha256:61e5e68cb65ac8f547f6b5ef933f510134a6bf31bb178be428994b0cb46c2a04"},
+    {file = "yarl-1.18.3-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:fe57328fbc1bfd0bd0514470ac692630f3901c0ee39052ae47acd1d90a436719"},
+    {file = "yarl-1.18.3-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:a440a2a624683108a1b454705ecd7afc1c3438a08e890a1513d468671d90a04e"},
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+    {file = "yarl-1.18.3-cp39-cp39-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:b4f6450109834af88cb4cc5ecddfc5380ebb9c228695afc11915a0bf82116789"},
+    {file = "yarl-1.18.3-cp39-cp39-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:a9ca04806f3be0ac6d558fffc2fdf8fcef767e0489d2684a21912cc4ed0cd1b8"},
+    {file = "yarl-1.18.3-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:77a6e85b90a7641d2e07184df5557132a337f136250caafc9ccaa4a2a998ca2c"},
+    {file = "yarl-1.18.3-cp39-cp39-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:6333c5a377c8e2f5fae35e7b8f145c617b02c939d04110c76f29ee3676b5f9a5"},
+    {file = "yarl-1.18.3-cp39-cp39-musllinux_1_2_aarch64.whl", hash = "sha256:0b3c92fa08759dbf12b3a59579a4096ba9af8dd344d9a813fc7f5070d86bbab1"},
+    {file = "yarl-1.18.3-cp39-cp39-musllinux_1_2_armv7l.whl", hash = "sha256:4ac515b860c36becb81bb84b667466885096b5fc85596948548b667da3bf9f24"},
+    {file = "yarl-1.18.3-cp39-cp39-musllinux_1_2_i686.whl", hash = "sha256:045b8482ce9483ada4f3f23b3774f4e1bf4f23a2d5c912ed5170f68efb053318"},
+    {file = "yarl-1.18.3-cp39-cp39-musllinux_1_2_ppc64le.whl", hash = "sha256:a4bb030cf46a434ec0225bddbebd4b89e6471814ca851abb8696170adb163985"},
+    {file = "yarl-1.18.3-cp39-cp39-musllinux_1_2_s390x.whl", hash = "sha256:54d6921f07555713b9300bee9c50fb46e57e2e639027089b1d795ecd9f7fa910"},
+    {file = "yarl-1.18.3-cp39-cp39-musllinux_1_2_x86_64.whl", hash = "sha256:1d407181cfa6e70077df3377938c08012d18893f9f20e92f7d2f314a437c30b1"},
+    {file = "yarl-1.18.3-cp39-cp39-win32.whl", hash = "sha256:ac36703a585e0929b032fbaab0707b75dc12703766d0b53486eabd5139ebadd5"},
+    {file = "yarl-1.18.3-cp39-cp39-win_amd64.whl", hash = "sha256:ba87babd629f8af77f557b61e49e7c7cac36f22f871156b91e10a6e9d4f829e9"},
+    {file = "yarl-1.18.3-py3-none-any.whl", hash = "sha256:b57f4f58099328dfb26c6a771d09fb20dbbae81d20cfb66141251ea063bd101b"},
+    {file = "yarl-1.18.3.tar.gz", hash = "sha256:ac1801c45cbf77b6c99242eeff4fffb5e4e73a800b5c4ad4fc0be5def634d2e1"},
 ]
 
 [[package]]
@@ -1339,13 +3213,24 @@ files = [
 ]
 
 [[package]]
-name = "zipp"
-version = "3.19.2"
+name = "zict"
+version = "3.0.0"
 requires_python = ">=3.8"
+summary = "Mutable mapping tools"
+groups = ["dask-distributed"]
+files = [
+    {file = "zict-3.0.0-py2.py3-none-any.whl", hash = "sha256:5796e36bd0e0cc8cf0fbc1ace6a68912611c1dbd74750a3f3026b9b9d6a327ae"},
+    {file = "zict-3.0.0.tar.gz", hash = "sha256:e321e263b6a97aafc0790c3cfb3c04656b7066e6738c37fffcca95d803c9fba5"},
+]
+
+[[package]]
+name = "zipp"
+version = "3.21.0"
+requires_python = ">=3.9"
 summary = "Backport of pathlib-compatible object wrapper for zip files"
-groups = ["default"]
+groups = ["default", "dask-distributed", "dev", "latlon-domain-crop"]
 marker = "python_version < \"3.12\""
 files = [
-    {file = "zipp-3.19.2-py3-none-any.whl", hash = "sha256:f091755f667055f2d02b32c53771a7a6c8b47e1fdbc4b72a8b9072b3eef8015c"},
-    {file = "zipp-3.19.2.tar.gz", hash = "sha256:bf1dcf6450f873a13e952a29504887c89e6de7506209e5b1bcc3460135d4de19"},
+    {file = "zipp-3.21.0-py3-none-any.whl", hash = "sha256:ac1bbe05fd2991f160ebce24ffbac5f6d11d83dc90891255885223d42b3cd931"},
+    {file = "zipp-3.21.0.tar.gz", hash = "sha256:2c9958f6430a2040341a52eb608ed6dd93ef4392e02ffe219417c1b28b5dd1f4"},
 ]

From 8400c24a7bf38a7b9d390d8c9a4038cee9ff77e7 Mon Sep 17 00:00:00 2001
From: Leif Denby <leif@denby.eu>
Date: Thu, 27 Mar 2025 14:38:13 +0100
Subject: [PATCH 17/28] tweak cropping config docstring

---
 mllam_data_prep/config.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/mllam_data_prep/config.py b/mllam_data_prep/config.py
index 16934cd..b7a0ab4 100644
--- a/mllam_data_prep/config.py
+++ b/mllam_data_prep/config.py
@@ -309,9 +309,9 @@ class ConvexHullCropping:
     coordinates of an *interior* dataset (this will typically be the
     "limited-area" domain when doing Limited Area Modelling) and then including
     all points that are within a margin of the convex hull boundary. In addition
-    to including the points inside the convex hull, you can also include the
-    points inside the convex hull of the interior dataset by setting the
-    `include_interior` attribute to `True`.
+    to including the points inside the defined margin around the convex hull,
+    you can also include the points inside the convex hull of the interior
+    dataset by setting the `include_interior` attribute to `True`.
 
     Attributes
     ----------

From 4110e31b17d0ed7d720a529c12477e59850b0302 Mon Sep 17 00:00:00 2001
From: Hauke Schulz <43613877+observingClouds@users.noreply.github.com>
Date: Wed, 14 May 2025 08:55:42 +0200
Subject: [PATCH 18/28] improve performance

---
 mllam_data_prep/config.py       | 498 --------------------------------
 mllam_data_prep/ops/cropping.py |  24 +-
 pyproject.toml                  |   5 +
 3 files changed, 26 insertions(+), 501 deletions(-)
 delete mode 100644 mllam_data_prep/config.py

diff --git a/mllam_data_prep/config.py b/mllam_data_prep/config.py
deleted file mode 100644
index 16934cd..0000000
--- a/mllam_data_prep/config.py
+++ /dev/null
@@ -1,498 +0,0 @@
-from dataclasses import dataclass, field
-from typing import Any, Dict, List, Optional, Union
-
-import dataclass_wizard
-import xarray as xr
-from dataclass_wizard import JSONWizard
-from deepdiff import DeepDiff
-from packaging.version import Version
-
-
-class InvalidConfigException(Exception):
-    pass
-
-
-def validate_config(config_inputs):
-    """
-    Validate that, in the config:
-    - either `variables` or `derived_variables` are present in the config
-    - if both `variables` and `derived_variables` are present, that they don't
-      add the same variables to the dataset
-
-    Parameters
-    ----------
-    config_inputs: Dict[str, InputDataset]
-
-    Returns
-    -------
-    """
-
-    for input_dataset_name, input_dataset in config_inputs.items():
-        if not input_dataset.variables and not input_dataset.derived_variables:
-            raise InvalidConfigException(
-                f"Input dataset '{input_dataset_name}' is missing the keys `variables` and/or"
-                " `derived_variables`. Make sure that you update the config so that the input"
-                f" dataset '{input_dataset_name}' contains at least either a `variables` or"
-                " `derived_variables` section."
-            )
-        elif input_dataset.variables and input_dataset.derived_variables:
-            # Check so that there are no overlapping variables
-            if isinstance(input_dataset.variables, list):
-                variable_vars = input_dataset.variables
-            elif isinstance(input_dataset.variables, dict):
-                variable_vars = input_dataset.variables.keys()
-            else:
-                raise TypeError(
-                    f"Expected an instance of list or dict, but got {type(input_dataset.variables)}."
-                )
-            derived_variable_vars = input_dataset.derived_variables.keys()
-            common_vars = list(set(variable_vars) & set(derived_variable_vars))
-            if len(common_vars) > 0:
-                raise InvalidConfigException(
-                    "Both `variables` and `derived_variables` include the following variables name(s):"
-                    f" '{', '.join(common_vars)}'. This is not allowed. Make sure that there"
-                    " are no overlapping variable names between `variables` and `derived_variables`,"
-                    f" either by renaming or removing '{', '.join(common_vars)}' from one of them."
-                )
-
-
-@dataclass
-class Range:
-    """
-    Defines a range for a variable to be used for selection, i.e.
-    `xarray.Dataset.sel({var_name}: slice({start}, {end}, {step}))`, the variable
-    name is the key in the dictionary and the slice object is created from the
-    `start`, `end`, and `step` attributes.
-
-    Attributes
-    ----------
-    start: str
-        The start of the range, e.g. "1990-09-03T00:00", 0, or 0.0.
-    end: str
-        The end of the range, e.g. "1990-09-04T00:00", 1, or 1.0.
-    step: str
-        The step size for the range, e.g. "PT3H", 1, or 1.0. If not given
-        then the entire range will be selected.
-    """
-
-    start: Union[str, int, float]
-    end: Union[str, int, float]
-    step: Optional[Union[str, int, float]] = None
-
-
-@dataclass
-class ValueSelection:
-    """
-    Defines a selection on the coordinate values of a variable, the
-    `values` attribute can either be a list of values to select or a
-    `Range` object to select a range of values. This is used to create
-    a slice object for the selection. Optionally, the `units` attribute can be
-    used to specify the units of the values which will used to ensure that
-    the `units` attribute of the variable has the same value.
-
-    Attributes:
-        values: The values to select.
-        units: The units of the values.
-    """
-
-    values: Union[List[Union[float, int]], Range]
-    units: Optional[str] = None
-
-
-@dataclass
-class DerivedVariable:
-    """
-    Defines a derived variables, where the function (for calculating the variable) and
-    the kwargs (arguments to function) are specified. kwargs can contain both arguments
-    which should extract/select data from the input dataset, in which case they should
-    have the "ds_input." prefix to distinguish them from other argument that should not
-    be extracted from the dataset (e.g. a string to indicate if the sine or cosine
-    component should be extracted).
-
-    Optionally, attributes to the derived variable can be specified in `attrs`, e.g.
-    {"attrs": "units": "W*m**-2, "long_name": "top-of-the-atmosphere radiation"}.
-    In case a function does not return an `xr.DataArray` with the required attributes
-    (`units` and `long_name`) set, these have to be specified in `attrs`.
-
-    Attributes:
-        kwargs: Variables required for calculating the derived variable.
-        function: Function used to calculate the derived variable.
-        attrs: Attributes (e.g. `units` and `long_name`) to set for the derived variable.
-    """
-
-    kwargs: Dict[str, str]
-    function: str
-    attrs: Optional[Dict[str, str]] = field(default_factory=dict)
-
-
-@dataclass
-class DimMapping:
-    """
-    Defines the process for mapping dimensions and variables from an input
-    dataset to a single new dimension (as in dimension in the
-    output dataset of the dataset generation).
-
-    There are three methods implemented for mapping:
-    - "rename":
-        Renames a dimension in the dataset to a new name.
-
-        E.g. adding a dim-mapping as `{"time": {"method": "rename", "dim": "analysis_time"}}`
-        will rename the "analysis_time" dimension in the input dataset to "time" dimension in the output.
-
-    - "stack_variables_by_var_name":
-        Stacks all variables along a new dimension that is mapped to the output dimensions name given.
-
-        E.g. adding a dim-mapping as
-        `{"state_feature": {"method": "stack_variables_by_var_name", "name_format": "{var_name}{altitude}m", dims: [altitude]}}`
-        will stack all variables in the input dataset along the "state_feature" dimension in the output
-        and the coordinate values will be given as f"{var_name}{altitude}m" where `var_name` is the name
-        of the variable and `altitude` is the value of the "altitude" coordinate.
-        If any dimensions are specified in the `dims` attribute, then the these dimensions will
-        also be stacked into this new dimension, and the `name_format` attribute can be used to
-        use the coordinate values from the stacked dimensions in the new coordinate values.
-
-    - "stack":
-        Stacks the provided coordinates and maps the result to the output dimension.
-
-        E.g. `{"grid_index": {"method": "stack", "dims": ["x", "y"]}}` will stack the "x" and "y"
-        dimensions in the input dataset into a new "grid_index" dimension in the output.
-
-    Attributes:
-        method: The method used for mapping.
-        dims: The dimensions to be mapped.
-        name_format: The format for naming the mapped dimensions.
-
-    Attributes
-    ----------
-    method: str
-        The method used for mapping. The options are:
-        - "rename": Renames a dimension in the dataset to a new name.
-        - "stack_variables_by_var_name": Stacks all variables along a new dimension that is mapped to the output dimensions name given.
-        - "stack": Stacks the provided coordinates and maps the result to the output dimension.
-    dims: List[str]
-        The dimensions to be mapped when using the "stack" or "stack_variables_by_var_name" methods.
-    dim: str
-        The dimension to be renamed when using the "rename" method.
-    name_format: str
-        The format for naming the mapped dimensions when using the "stack_variables_by_var_name" method.
-    """
-
-    method: str
-    dims: Optional[List[str]] = None
-    dim: Optional[str] = None
-    name_format: Optional[str] = field(default=None)
-    coord_ranges: Optional[Dict[str, Range]] = field(default_factory=dict)
-
-
-@dataclass
-class InputDataset:
-    """
-    Definition of a single input dataset which will be mapped to one the
-    variables that have been defined as output variables in the produced dataset
-    (i.e. the input variables for model architecture being targeted by the dataset).
-    The definition for a input dataset includes setting
-        1) the path to the dataset,
-        2) the expected dimensions of the dataset,
-        3) the variables to select from the dataset (and optionally subsection
-           along the coordinates for each variable) or the variables to derive
-           from the dataset, and finally
-        4) the method by which the dimensions and variables of the dataset are
-           mapped to one of the output variables (this includes stacking of all
-           the selected variables into a new single variable along a new coordinate,
-           and may include renaming and stacking dimensions existing dimensions).
-
-    Attributes
-    ----------
-    path: str
-        Path to the dataset, e.g. the path to a zarr dataset or netCDF file.
-        This can be anything that can be passed to `xarray.open_dataset`
-    dims: List[str]
-        List of the expected dimensions of the dataset. E.g. `["time", "x", "y"]`.
-        These will be checked to ensure consistency of the dataset being read.
-    dim_mapping: Dict[str, DimMapping]
-        Mapping of the variables and dimensions in the input dataset to the dimensions of the
-        output variable (`target_output_variable`). The key is the name of the output dimension to map to
-        and the ´DimMapping´ describes how to map the dimensions and variables of the input dataset
-        to this input dimension for the output variable.
-    target_output_variable: str
-        The name of the output variable (i.e. the name of a variable that that is expected by
-        the architecture to exist in the training dataset). If multiple datasets map to the same variable,
-        then the data from all datasets will be concatenated along the dimension that isn't shared
-        (e.g. two datasets that coincide in space and time will only differ in the feature dimension,
-        so the two will be combined by concatenating along the feature dimension).
-        If a single shared coordinate cannot be found then an exception will be raised.
-    variables: Union[List[str], Dict[str, Dict[str, ValueSelection]]]
-        List of the variables to select from the dataset. E.g. `["temperature", "precipitation"]`
-        or a dictionary where the keys are the variable names and the values are dictionaries
-        defining the selection for each variable. E.g. `{"temperature": levels: {"values": [1000, 950, 900]}}`
-        would select the "temperature" variable and only the levels 1000, 950, and 900.
-    derived_variables: Dict[str, DerivedVariable]
-        Dictionary of variables to derive from the dataset, where the keys are the names variables will be given and
-        the values are `DerivedVariable` definitions that specify how to derive a variable.
-    """
-
-    path: str
-    dims: List[str]
-    dim_mapping: Dict[str, DimMapping]
-    target_output_variable: str
-    variables: Optional[Union[List[str], Dict[str, Dict[str, ValueSelection]]]] = None
-    derived_variables: Optional[Dict[str, DerivedVariable]] = None
-    attributes: Optional[Dict[str, Any]] = field(default_factory=dict)
-    coord_ranges: Optional[Dict[str, Range]] = None
-
-
-@dataclass
-class Statistics:
-    """
-    Define the statistics to compute for the output dataset, this includes defining
-    the the statistics to compute and the dimensions to compute the statistics over.
-    The statistics will be computed for each variable in the output dataset seperately.
-
-    Attributes
-    ----------
-    ops: List[str]
-        The statistics to compute, e.g. ["mean", "std", "min", "max"].
-    dims: List[str]
-        The dimensions to compute the statistics over, e.g. ["time", "grid_index"].
-    """
-
-    ops: List[str]
-    dims: List[str]
-
-
-@dataclass
-class Split:
-    """
-    Define the `start` and `end` coordinate value (e.g. time) for a split of the dataset and optionally
-    the statistics to compute for the split.
-
-    Attributes
-    ----------
-    start: str
-        The start of the split, e.g. "1990-09-03T00:00".
-    end: str
-        The end of the split, e.g. "1990-09-04T00:00".
-    compute_statistics: StatisticsInput
-        The statistics to compute for the split.
-    """
-
-    start: str
-    end: str
-    compute_statistics: Optional[Statistics] = None
-
-
-@dataclass
-class Splitting:
-    """
-    dim: str
-        The dimension to split the dataset along, e.g. "time", this must be provided if splits are defined.
-
-    splits: Dict[str, Split]
-        Defines the splits of the dataset, the keys are the names of the splits and the values
-        are the `Split` objects defining the start and end of the split. Optionally, the
-        `compute_statistics` attribute can be used to define the statistics to compute for the split.
-    """
-
-    dim: str
-    splits: Dict[str, Split]
-
-
-@dataclass
-class ConvexHullCropping:
-    """
-    Define the method applied for cropping the spatial domain before writing
-    the transformed output dataset. This is typically used when you want to
-    create a dataset to provide data in a boundary around a limited-area
-    domain.
-
-    The cropping is done by creating a convex hull around the spatial
-    coordinates of an *interior* dataset (this will typically be the
-    "limited-area" domain when doing Limited Area Modelling) and then including
-    all points that are within a margin of the convex hull boundary. In addition
-    to including the points inside the convex hull, you can also include the
-    points inside the convex hull of the interior dataset by setting the
-    `include_interior` attribute to `True`.
-
-    Attributes
-    ----------
-    margin_width_degrees: float
-        The width (in degrees) of the margin applied to the convex hull
-        boundary of the interior dataset used to define the cropping domain.
-    interior_dataset_config_path: str
-        The path to the configuration file for the dataset defining the interior domain
-    include_interior_points: bool
-        Whether to include the points inside the convex hull of the interior dataset
-    """
-
-    margin_width_degrees: float
-    interior_dataset_config_path: str
-    include_interior_points: bool = False
-
-
-@dataclass
-class Output:
-    """
-    Definition of the output dataset that will be created by the dataset generation, you should
-    adapt this to the architecture of the model that you are going to using the dataset with. This
-    includes defining what input variables the architecture expects (and the dimensions of each),
-    the expected value range for each coordinate, and the chunking information for each dimension.
-
-    Attributes
-    ----------
-    variables: Dict[str, List[str]]
-        Defines the variables of the produced output, i.e. the input variables for the model
-        architecture. The keys are the variable names to create and the values are lists of
-        the dimensions. E.g. `{"static": ["grid_index", "feature"], "state": ["time",
-        "grid_index", "state_feature"]}` would define that the architecture expects a variable
-        named "static" with dimensions "grid_index" and "feature" and a variable named "state" with
-        dimensions "time", "grid_index", and "state_feature".
-
-    coord_ranges: Dict[str, Range]
-        Defines the expected value range for each coordinate. The keys are the
-        name of the coordinate and the values are the range, e.g.
-        `{"time": {"start": "1990-09-03T00:00", "end": "1990-09-04T00:00", "step": "PT3H"}}`
-        would define that the "time" coordinate should have values between
-        "1990-09-03T00:00" and "1990-09-04T00:00" with a step size of 3 hours.
-        These range definitions are both used to ensure that the input dataset
-        has the expected range and to select the correct values from the input
-        dataset. If not given then the entire range will be selected.
-
-    chunking: Dict[str, int]
-        Defines the chunking information for each dimension. The keys are the
-        names of the dimensions and the values are the chunk size for that dimension.
-        If chunking is not specified for a dimension, then the entire dimension
-        will be a single chunk.
-
-    splitting: Splitting
-        Defines the splits of the dataset (e.g. train, test, validation), the dimension to split
-        the dataset along, and optionally the statistics to compute for each split.
-
-    domain_cropping: ConvexHullCropping
-        Defines the method applied for cropping the spatial domain before writing
-        the transformed output dataset. This is typically used when you want to
-        create a dataset to provide data in a boundary around a limited-area
-        domain.
-    """
-
-    variables: Dict[str, List[str]]
-    coord_ranges: Dict[str, Range] = field(default_factory=dict)
-    chunking: Dict[str, int] = field(default_factory=dict)
-    splitting: Optional[Splitting] = None
-    domain_cropping: Optional[ConvexHullCropping] = None
-
-
-@dataclass
-class Config(dataclass_wizard.JSONWizard, dataclass_wizard.YAMLWizard):
-    """Configuration for the model.
-
-    Attributes:
-        schema_version: Version of the config file schema.
-        dataset_version: Version of the dataset itself.
-        architecture: Information about the model architecture this dataset is intended for.
-        inputs: Input datasets for the model.
-
-    Attributes
-    ----------
-    output: Output
-        Information about the structure of the output from mllam-data-prep, you should set this
-        to matchthe model architecture this dataset is intended for. This
-        covers defining what input variables the architecture expects (and the dimensions of each),
-        the expected value range for each coordinate, and the chunking information for each dimension.
-    inputs: Dict[str, InputDataset]
-        Input datasets for the model. The keys are the names of the datasets and the values are
-        the input dataset configurations.
-    extra: Dict[str, Any]
-        Extra information to include in the config file. This will be ignored by the
-        `mllam_data_prep` library, but can be used to include additional information
-        that is useful for the user.
-    schema_version: str
-        Version string for the config file schema.
-    dataset_version: str
-        Version string for the dataset itself.
-    """
-
-    output: Output
-    inputs: Dict[str, InputDataset]
-    schema_version: str
-    dataset_version: str
-    extra: Dict[str, Any] = field(default_factory=dict)
-
-    def __post_init__(self):
-        validate_config(self.inputs)
-
-    class _(JSONWizard.Meta):
-        raise_on_unknown_json_key = True
-
-
-class UnsupportedMllamDataPrepVersion(Exception):
-    pass
-
-
-def find_config_differences(
-    config: Config, ds_existing: xr.Dataset
-) -> Union[None, dict]:
-    """
-    Compare the provided config against the one the provided dataset is created
-    from (which is stored in the `creation_config` attribute), and return the
-    differences.
-
-    Parameters
-    ----------
-    config : Config
-        The configuration object to compare against
-    ds_existing : xr.Dataset
-        The existing dataset to compare against
-
-    Returns
-    -------
-    Union[None, dict]
-        If the configurations are the same, returns None. If they are different, returns
-        a dictionary of the differences.
-
-    Raises
-    ------
-    UnsupportedMllamDataPrepVersion
-        If the existing dataset was created with an older version of mllam-data-prep
-        that does not have the `creation_config` attribute
-
-    """
-    required_mdp_version = Version("v0.6.0")
-
-    config_mdp_version = Version(ds_existing.attrs["mdp_version"])
-    if config_mdp_version < required_mdp_version:
-        raise UnsupportedMllamDataPrepVersion(
-            "The existing dataset was created with an older version of mllam-data-prep "
-            f"({config_mdp_version}), and does not have the creation_config attribute "
-            f"(added in v{required_mdp_version}). Please delete the existing dataset "
-            "or set overwrite='always' to overwrite it."
-        )
-    else:
-        existing_config_yaml = ds_existing.attrs.get("creation_config", None)
-        if existing_config_yaml is None:
-            raise ValueError(
-                "The provided dataset does not have a creation_config attribute"
-            )
-        existing_config = Config.from_yaml(existing_config_yaml)
-        if existing_config != config:
-            differences = DeepDiff(
-                existing_config.to_dict(), config.to_dict(), ignore_order=True
-            ).to_dict()
-            return differences
-
-        return None
-
-
-if __name__ == "__main__":
-    import argparse
-
-    argparser = argparse.ArgumentParser()
-    argparser.add_argument(
-        "-f", help="Path to the yaml file to load.", default="example.danra.yaml"
-    )
-    args = argparser.parse_args()
-
-    assert args.f.endswith(".yaml"), "Config file must have a .yaml extension."
-    config = Config.from_yaml_file(args.f)
-    import rich
-
-    rich.print(config)
diff --git a/mllam_data_prep/ops/cropping.py b/mllam_data_prep/ops/cropping.py
index 213215d..eaecb47 100644
--- a/mllam_data_prep/ops/cropping.py
+++ b/mllam_data_prep/ops/cropping.py
@@ -1,8 +1,12 @@
 from typing import Tuple, Union
 
 import numpy as np
+import shapely
 import spherical_geometry as sg
 import xarray as xr
+from loguru import logger
+from shapely import Polygon, contains
+from shapely.geometry import MultiPoint
 from spherical_geometry.polygon import SphericalPolygon
 
 
@@ -62,9 +66,23 @@ def create_convex_hull_mask(ds: xr.Dataset, ds_reference: xr.Dataset) -> xr.Data
     chull_lam = SphericalPolygon.convex_hull(da_ref_xyz.values)
 
     # call .load() to avoid using dask arrays in the following apply_ufunc
-    da_interior_mask = xr.apply_ufunc(
-        chull_lam.contains_lonlat, da_lon.load(), da_lat.load(), vectorize=True
-    ).astype(bool)
+    logger.info("Creating convex hull mask...")
+    # Convert convex hull points to a Shapely Polygon in 3D
+    x = Polygon(chull_lam._polygons[0]._points)
+    shapely.prepare(x)
+    points_xyz = _latlon_to_unit_sphere_xyz(da_lat=da_lat.load(), da_lon=da_lon.load())
+    # Reshape to (N, 3) for vectorized operation
+    pts = points_xyz.values.reshape(-1, 3)
+    # Create a shapely MultiPoint geometry for all points at once
+    pts_geom = MultiPoint(pts)
+    # Vectorized contains: returns a boolean array
+    mask_flat = contains(x, pts_geom.geoms)
+    da_interior_mask = xr.DataArray(
+        mask_flat.reshape(points_xyz.shape[:-1]).astype(bool),
+        coords=da_lat.coords,
+        dims=da_lat.dims,
+    )
+    logger.info("Convex hull mask created.")
     da_interior_mask.attrs[
         "long_name"
     ] = "contained in convex hull of source dataset (da_ref)"
diff --git a/pyproject.toml b/pyproject.toml
index 8cc0d68..4951a6e 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -27,6 +27,7 @@ dependencies = [
     "psutil>=5.7.2",
     "packaging>=23.1",
     "deepdiff>=8.2.0",
+    "shapely"
 ]
 requires-python = ">=3.9"
 readme = "README.md"
@@ -43,6 +44,10 @@ latlon-domain-crop = [
     "gcsfs>=2024.10.0",
     "jinja2>=3.1.4",
 ]
+
+[tool.uv.sources]
+#spherical_geometry = { path = "../../GitProjects/spherical_geometry" , editable = true}
+
 [build-system]
 requires = ["pdm-backend"]
 build-backend = "pdm.backend"

From c416254c0b099c359dcee0b90f3220d5ba8270ea Mon Sep 17 00:00:00 2001
From: Hauke Schulz <43613877+observingClouds@users.noreply.github.com>
Date: Wed, 14 May 2025 09:22:52 +0200
Subject: [PATCH 19/28] remove accidentally commited changes

---
 pyproject.toml | 3 ---
 1 file changed, 3 deletions(-)

diff --git a/pyproject.toml b/pyproject.toml
index 4951a6e..d5380cb 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -45,9 +45,6 @@ latlon-domain-crop = [
     "jinja2>=3.1.4",
 ]
 
-[tool.uv.sources]
-#spherical_geometry = { path = "../../GitProjects/spherical_geometry" , editable = true}
-
 [build-system]
 requires = ["pdm-backend"]
 build-backend = "pdm.backend"

From 7357453b140ff63ded85dcbab482a8a6e3e1bc40 Mon Sep 17 00:00:00 2001
From: Hauke Schulz <43613877+observingClouds@users.noreply.github.com>
Date: Wed, 14 May 2025 09:27:14 +0200
Subject: [PATCH 20/28] remove accidentally commited changes

---
 mllam_data_prep/config.py | 498 ++++++++++++++++++++++++++++++++++++++
 1 file changed, 498 insertions(+)
 create mode 100644 mllam_data_prep/config.py

diff --git a/mllam_data_prep/config.py b/mllam_data_prep/config.py
new file mode 100644
index 0000000..16934cd
--- /dev/null
+++ b/mllam_data_prep/config.py
@@ -0,0 +1,498 @@
+from dataclasses import dataclass, field
+from typing import Any, Dict, List, Optional, Union
+
+import dataclass_wizard
+import xarray as xr
+from dataclass_wizard import JSONWizard
+from deepdiff import DeepDiff
+from packaging.version import Version
+
+
+class InvalidConfigException(Exception):
+    pass
+
+
+def validate_config(config_inputs):
+    """
+    Validate that, in the config:
+    - either `variables` or `derived_variables` are present in the config
+    - if both `variables` and `derived_variables` are present, that they don't
+      add the same variables to the dataset
+
+    Parameters
+    ----------
+    config_inputs: Dict[str, InputDataset]
+
+    Returns
+    -------
+    """
+
+    for input_dataset_name, input_dataset in config_inputs.items():
+        if not input_dataset.variables and not input_dataset.derived_variables:
+            raise InvalidConfigException(
+                f"Input dataset '{input_dataset_name}' is missing the keys `variables` and/or"
+                " `derived_variables`. Make sure that you update the config so that the input"
+                f" dataset '{input_dataset_name}' contains at least either a `variables` or"
+                " `derived_variables` section."
+            )
+        elif input_dataset.variables and input_dataset.derived_variables:
+            # Check so that there are no overlapping variables
+            if isinstance(input_dataset.variables, list):
+                variable_vars = input_dataset.variables
+            elif isinstance(input_dataset.variables, dict):
+                variable_vars = input_dataset.variables.keys()
+            else:
+                raise TypeError(
+                    f"Expected an instance of list or dict, but got {type(input_dataset.variables)}."
+                )
+            derived_variable_vars = input_dataset.derived_variables.keys()
+            common_vars = list(set(variable_vars) & set(derived_variable_vars))
+            if len(common_vars) > 0:
+                raise InvalidConfigException(
+                    "Both `variables` and `derived_variables` include the following variables name(s):"
+                    f" '{', '.join(common_vars)}'. This is not allowed. Make sure that there"
+                    " are no overlapping variable names between `variables` and `derived_variables`,"
+                    f" either by renaming or removing '{', '.join(common_vars)}' from one of them."
+                )
+
+
+@dataclass
+class Range:
+    """
+    Defines a range for a variable to be used for selection, i.e.
+    `xarray.Dataset.sel({var_name}: slice({start}, {end}, {step}))`, the variable
+    name is the key in the dictionary and the slice object is created from the
+    `start`, `end`, and `step` attributes.
+
+    Attributes
+    ----------
+    start: str
+        The start of the range, e.g. "1990-09-03T00:00", 0, or 0.0.
+    end: str
+        The end of the range, e.g. "1990-09-04T00:00", 1, or 1.0.
+    step: str
+        The step size for the range, e.g. "PT3H", 1, or 1.0. If not given
+        then the entire range will be selected.
+    """
+
+    start: Union[str, int, float]
+    end: Union[str, int, float]
+    step: Optional[Union[str, int, float]] = None
+
+
+@dataclass
+class ValueSelection:
+    """
+    Defines a selection on the coordinate values of a variable, the
+    `values` attribute can either be a list of values to select or a
+    `Range` object to select a range of values. This is used to create
+    a slice object for the selection. Optionally, the `units` attribute can be
+    used to specify the units of the values which will used to ensure that
+    the `units` attribute of the variable has the same value.
+
+    Attributes:
+        values: The values to select.
+        units: The units of the values.
+    """
+
+    values: Union[List[Union[float, int]], Range]
+    units: Optional[str] = None
+
+
+@dataclass
+class DerivedVariable:
+    """
+    Defines a derived variables, where the function (for calculating the variable) and
+    the kwargs (arguments to function) are specified. kwargs can contain both arguments
+    which should extract/select data from the input dataset, in which case they should
+    have the "ds_input." prefix to distinguish them from other argument that should not
+    be extracted from the dataset (e.g. a string to indicate if the sine or cosine
+    component should be extracted).
+
+    Optionally, attributes to the derived variable can be specified in `attrs`, e.g.
+    {"attrs": "units": "W*m**-2, "long_name": "top-of-the-atmosphere radiation"}.
+    In case a function does not return an `xr.DataArray` with the required attributes
+    (`units` and `long_name`) set, these have to be specified in `attrs`.
+
+    Attributes:
+        kwargs: Variables required for calculating the derived variable.
+        function: Function used to calculate the derived variable.
+        attrs: Attributes (e.g. `units` and `long_name`) to set for the derived variable.
+    """
+
+    kwargs: Dict[str, str]
+    function: str
+    attrs: Optional[Dict[str, str]] = field(default_factory=dict)
+
+
+@dataclass
+class DimMapping:
+    """
+    Defines the process for mapping dimensions and variables from an input
+    dataset to a single new dimension (as in dimension in the
+    output dataset of the dataset generation).
+
+    There are three methods implemented for mapping:
+    - "rename":
+        Renames a dimension in the dataset to a new name.
+
+        E.g. adding a dim-mapping as `{"time": {"method": "rename", "dim": "analysis_time"}}`
+        will rename the "analysis_time" dimension in the input dataset to "time" dimension in the output.
+
+    - "stack_variables_by_var_name":
+        Stacks all variables along a new dimension that is mapped to the output dimensions name given.
+
+        E.g. adding a dim-mapping as
+        `{"state_feature": {"method": "stack_variables_by_var_name", "name_format": "{var_name}{altitude}m", dims: [altitude]}}`
+        will stack all variables in the input dataset along the "state_feature" dimension in the output
+        and the coordinate values will be given as f"{var_name}{altitude}m" where `var_name` is the name
+        of the variable and `altitude` is the value of the "altitude" coordinate.
+        If any dimensions are specified in the `dims` attribute, then the these dimensions will
+        also be stacked into this new dimension, and the `name_format` attribute can be used to
+        use the coordinate values from the stacked dimensions in the new coordinate values.
+
+    - "stack":
+        Stacks the provided coordinates and maps the result to the output dimension.
+
+        E.g. `{"grid_index": {"method": "stack", "dims": ["x", "y"]}}` will stack the "x" and "y"
+        dimensions in the input dataset into a new "grid_index" dimension in the output.
+
+    Attributes:
+        method: The method used for mapping.
+        dims: The dimensions to be mapped.
+        name_format: The format for naming the mapped dimensions.
+
+    Attributes
+    ----------
+    method: str
+        The method used for mapping. The options are:
+        - "rename": Renames a dimension in the dataset to a new name.
+        - "stack_variables_by_var_name": Stacks all variables along a new dimension that is mapped to the output dimensions name given.
+        - "stack": Stacks the provided coordinates and maps the result to the output dimension.
+    dims: List[str]
+        The dimensions to be mapped when using the "stack" or "stack_variables_by_var_name" methods.
+    dim: str
+        The dimension to be renamed when using the "rename" method.
+    name_format: str
+        The format for naming the mapped dimensions when using the "stack_variables_by_var_name" method.
+    """
+
+    method: str
+    dims: Optional[List[str]] = None
+    dim: Optional[str] = None
+    name_format: Optional[str] = field(default=None)
+    coord_ranges: Optional[Dict[str, Range]] = field(default_factory=dict)
+
+
+@dataclass
+class InputDataset:
+    """
+    Definition of a single input dataset which will be mapped to one the
+    variables that have been defined as output variables in the produced dataset
+    (i.e. the input variables for model architecture being targeted by the dataset).
+    The definition for a input dataset includes setting
+        1) the path to the dataset,
+        2) the expected dimensions of the dataset,
+        3) the variables to select from the dataset (and optionally subsection
+           along the coordinates for each variable) or the variables to derive
+           from the dataset, and finally
+        4) the method by which the dimensions and variables of the dataset are
+           mapped to one of the output variables (this includes stacking of all
+           the selected variables into a new single variable along a new coordinate,
+           and may include renaming and stacking dimensions existing dimensions).
+
+    Attributes
+    ----------
+    path: str
+        Path to the dataset, e.g. the path to a zarr dataset or netCDF file.
+        This can be anything that can be passed to `xarray.open_dataset`
+    dims: List[str]
+        List of the expected dimensions of the dataset. E.g. `["time", "x", "y"]`.
+        These will be checked to ensure consistency of the dataset being read.
+    dim_mapping: Dict[str, DimMapping]
+        Mapping of the variables and dimensions in the input dataset to the dimensions of the
+        output variable (`target_output_variable`). The key is the name of the output dimension to map to
+        and the ´DimMapping´ describes how to map the dimensions and variables of the input dataset
+        to this input dimension for the output variable.
+    target_output_variable: str
+        The name of the output variable (i.e. the name of a variable that that is expected by
+        the architecture to exist in the training dataset). If multiple datasets map to the same variable,
+        then the data from all datasets will be concatenated along the dimension that isn't shared
+        (e.g. two datasets that coincide in space and time will only differ in the feature dimension,
+        so the two will be combined by concatenating along the feature dimension).
+        If a single shared coordinate cannot be found then an exception will be raised.
+    variables: Union[List[str], Dict[str, Dict[str, ValueSelection]]]
+        List of the variables to select from the dataset. E.g. `["temperature", "precipitation"]`
+        or a dictionary where the keys are the variable names and the values are dictionaries
+        defining the selection for each variable. E.g. `{"temperature": levels: {"values": [1000, 950, 900]}}`
+        would select the "temperature" variable and only the levels 1000, 950, and 900.
+    derived_variables: Dict[str, DerivedVariable]
+        Dictionary of variables to derive from the dataset, where the keys are the names variables will be given and
+        the values are `DerivedVariable` definitions that specify how to derive a variable.
+    """
+
+    path: str
+    dims: List[str]
+    dim_mapping: Dict[str, DimMapping]
+    target_output_variable: str
+    variables: Optional[Union[List[str], Dict[str, Dict[str, ValueSelection]]]] = None
+    derived_variables: Optional[Dict[str, DerivedVariable]] = None
+    attributes: Optional[Dict[str, Any]] = field(default_factory=dict)
+    coord_ranges: Optional[Dict[str, Range]] = None
+
+
+@dataclass
+class Statistics:
+    """
+    Define the statistics to compute for the output dataset, this includes defining
+    the the statistics to compute and the dimensions to compute the statistics over.
+    The statistics will be computed for each variable in the output dataset seperately.
+
+    Attributes
+    ----------
+    ops: List[str]
+        The statistics to compute, e.g. ["mean", "std", "min", "max"].
+    dims: List[str]
+        The dimensions to compute the statistics over, e.g. ["time", "grid_index"].
+    """
+
+    ops: List[str]
+    dims: List[str]
+
+
+@dataclass
+class Split:
+    """
+    Define the `start` and `end` coordinate value (e.g. time) for a split of the dataset and optionally
+    the statistics to compute for the split.
+
+    Attributes
+    ----------
+    start: str
+        The start of the split, e.g. "1990-09-03T00:00".
+    end: str
+        The end of the split, e.g. "1990-09-04T00:00".
+    compute_statistics: StatisticsInput
+        The statistics to compute for the split.
+    """
+
+    start: str
+    end: str
+    compute_statistics: Optional[Statistics] = None
+
+
+@dataclass
+class Splitting:
+    """
+    dim: str
+        The dimension to split the dataset along, e.g. "time", this must be provided if splits are defined.
+
+    splits: Dict[str, Split]
+        Defines the splits of the dataset, the keys are the names of the splits and the values
+        are the `Split` objects defining the start and end of the split. Optionally, the
+        `compute_statistics` attribute can be used to define the statistics to compute for the split.
+    """
+
+    dim: str
+    splits: Dict[str, Split]
+
+
+@dataclass
+class ConvexHullCropping:
+    """
+    Define the method applied for cropping the spatial domain before writing
+    the transformed output dataset. This is typically used when you want to
+    create a dataset to provide data in a boundary around a limited-area
+    domain.
+
+    The cropping is done by creating a convex hull around the spatial
+    coordinates of an *interior* dataset (this will typically be the
+    "limited-area" domain when doing Limited Area Modelling) and then including
+    all points that are within a margin of the convex hull boundary. In addition
+    to including the points inside the convex hull, you can also include the
+    points inside the convex hull of the interior dataset by setting the
+    `include_interior` attribute to `True`.
+
+    Attributes
+    ----------
+    margin_width_degrees: float
+        The width (in degrees) of the margin applied to the convex hull
+        boundary of the interior dataset used to define the cropping domain.
+    interior_dataset_config_path: str
+        The path to the configuration file for the dataset defining the interior domain
+    include_interior_points: bool
+        Whether to include the points inside the convex hull of the interior dataset
+    """
+
+    margin_width_degrees: float
+    interior_dataset_config_path: str
+    include_interior_points: bool = False
+
+
+@dataclass
+class Output:
+    """
+    Definition of the output dataset that will be created by the dataset generation, you should
+    adapt this to the architecture of the model that you are going to using the dataset with. This
+    includes defining what input variables the architecture expects (and the dimensions of each),
+    the expected value range for each coordinate, and the chunking information for each dimension.
+
+    Attributes
+    ----------
+    variables: Dict[str, List[str]]
+        Defines the variables of the produced output, i.e. the input variables for the model
+        architecture. The keys are the variable names to create and the values are lists of
+        the dimensions. E.g. `{"static": ["grid_index", "feature"], "state": ["time",
+        "grid_index", "state_feature"]}` would define that the architecture expects a variable
+        named "static" with dimensions "grid_index" and "feature" and a variable named "state" with
+        dimensions "time", "grid_index", and "state_feature".
+
+    coord_ranges: Dict[str, Range]
+        Defines the expected value range for each coordinate. The keys are the
+        name of the coordinate and the values are the range, e.g.
+        `{"time": {"start": "1990-09-03T00:00", "end": "1990-09-04T00:00", "step": "PT3H"}}`
+        would define that the "time" coordinate should have values between
+        "1990-09-03T00:00" and "1990-09-04T00:00" with a step size of 3 hours.
+        These range definitions are both used to ensure that the input dataset
+        has the expected range and to select the correct values from the input
+        dataset. If not given then the entire range will be selected.
+
+    chunking: Dict[str, int]
+        Defines the chunking information for each dimension. The keys are the
+        names of the dimensions and the values are the chunk size for that dimension.
+        If chunking is not specified for a dimension, then the entire dimension
+        will be a single chunk.
+
+    splitting: Splitting
+        Defines the splits of the dataset (e.g. train, test, validation), the dimension to split
+        the dataset along, and optionally the statistics to compute for each split.
+
+    domain_cropping: ConvexHullCropping
+        Defines the method applied for cropping the spatial domain before writing
+        the transformed output dataset. This is typically used when you want to
+        create a dataset to provide data in a boundary around a limited-area
+        domain.
+    """
+
+    variables: Dict[str, List[str]]
+    coord_ranges: Dict[str, Range] = field(default_factory=dict)
+    chunking: Dict[str, int] = field(default_factory=dict)
+    splitting: Optional[Splitting] = None
+    domain_cropping: Optional[ConvexHullCropping] = None
+
+
+@dataclass
+class Config(dataclass_wizard.JSONWizard, dataclass_wizard.YAMLWizard):
+    """Configuration for the model.
+
+    Attributes:
+        schema_version: Version of the config file schema.
+        dataset_version: Version of the dataset itself.
+        architecture: Information about the model architecture this dataset is intended for.
+        inputs: Input datasets for the model.
+
+    Attributes
+    ----------
+    output: Output
+        Information about the structure of the output from mllam-data-prep, you should set this
+        to matchthe model architecture this dataset is intended for. This
+        covers defining what input variables the architecture expects (and the dimensions of each),
+        the expected value range for each coordinate, and the chunking information for each dimension.
+    inputs: Dict[str, InputDataset]
+        Input datasets for the model. The keys are the names of the datasets and the values are
+        the input dataset configurations.
+    extra: Dict[str, Any]
+        Extra information to include in the config file. This will be ignored by the
+        `mllam_data_prep` library, but can be used to include additional information
+        that is useful for the user.
+    schema_version: str
+        Version string for the config file schema.
+    dataset_version: str
+        Version string for the dataset itself.
+    """
+
+    output: Output
+    inputs: Dict[str, InputDataset]
+    schema_version: str
+    dataset_version: str
+    extra: Dict[str, Any] = field(default_factory=dict)
+
+    def __post_init__(self):
+        validate_config(self.inputs)
+
+    class _(JSONWizard.Meta):
+        raise_on_unknown_json_key = True
+
+
+class UnsupportedMllamDataPrepVersion(Exception):
+    pass
+
+
+def find_config_differences(
+    config: Config, ds_existing: xr.Dataset
+) -> Union[None, dict]:
+    """
+    Compare the provided config against the one the provided dataset is created
+    from (which is stored in the `creation_config` attribute), and return the
+    differences.
+
+    Parameters
+    ----------
+    config : Config
+        The configuration object to compare against
+    ds_existing : xr.Dataset
+        The existing dataset to compare against
+
+    Returns
+    -------
+    Union[None, dict]
+        If the configurations are the same, returns None. If they are different, returns
+        a dictionary of the differences.
+
+    Raises
+    ------
+    UnsupportedMllamDataPrepVersion
+        If the existing dataset was created with an older version of mllam-data-prep
+        that does not have the `creation_config` attribute
+
+    """
+    required_mdp_version = Version("v0.6.0")
+
+    config_mdp_version = Version(ds_existing.attrs["mdp_version"])
+    if config_mdp_version < required_mdp_version:
+        raise UnsupportedMllamDataPrepVersion(
+            "The existing dataset was created with an older version of mllam-data-prep "
+            f"({config_mdp_version}), and does not have the creation_config attribute "
+            f"(added in v{required_mdp_version}). Please delete the existing dataset "
+            "or set overwrite='always' to overwrite it."
+        )
+    else:
+        existing_config_yaml = ds_existing.attrs.get("creation_config", None)
+        if existing_config_yaml is None:
+            raise ValueError(
+                "The provided dataset does not have a creation_config attribute"
+            )
+        existing_config = Config.from_yaml(existing_config_yaml)
+        if existing_config != config:
+            differences = DeepDiff(
+                existing_config.to_dict(), config.to_dict(), ignore_order=True
+            ).to_dict()
+            return differences
+
+        return None
+
+
+if __name__ == "__main__":
+    import argparse
+
+    argparser = argparse.ArgumentParser()
+    argparser.add_argument(
+        "-f", help="Path to the yaml file to load.", default="example.danra.yaml"
+    )
+    args = argparser.parse_args()
+
+    assert args.f.endswith(".yaml"), "Config file must have a .yaml extension."
+    config = Config.from_yaml_file(args.f)
+    import rich
+
+    rich.print(config)

From 3d5fffc1ab90b11681c247efc5da2ca18db429f0 Mon Sep 17 00:00:00 2001
From: Hauke Schulz <43613877+observingClouds@users.noreply.github.com>
Date: Fri, 26 Sep 2025 10:10:02 +0200
Subject: [PATCH 21/28] revert erronous improvements

shapely does not account for z coordinate
---
 mllam_data_prep/ops/cropping.py | 24 +++---------------------
 pyproject.toml                  |  1 -
 2 files changed, 3 insertions(+), 22 deletions(-)

diff --git a/mllam_data_prep/ops/cropping.py b/mllam_data_prep/ops/cropping.py
index eaecb47..213215d 100644
--- a/mllam_data_prep/ops/cropping.py
+++ b/mllam_data_prep/ops/cropping.py
@@ -1,12 +1,8 @@
 from typing import Tuple, Union
 
 import numpy as np
-import shapely
 import spherical_geometry as sg
 import xarray as xr
-from loguru import logger
-from shapely import Polygon, contains
-from shapely.geometry import MultiPoint
 from spherical_geometry.polygon import SphericalPolygon
 
 
@@ -66,23 +62,9 @@ def create_convex_hull_mask(ds: xr.Dataset, ds_reference: xr.Dataset) -> xr.Data
     chull_lam = SphericalPolygon.convex_hull(da_ref_xyz.values)
 
     # call .load() to avoid using dask arrays in the following apply_ufunc
-    logger.info("Creating convex hull mask...")
-    # Convert convex hull points to a Shapely Polygon in 3D
-    x = Polygon(chull_lam._polygons[0]._points)
-    shapely.prepare(x)
-    points_xyz = _latlon_to_unit_sphere_xyz(da_lat=da_lat.load(), da_lon=da_lon.load())
-    # Reshape to (N, 3) for vectorized operation
-    pts = points_xyz.values.reshape(-1, 3)
-    # Create a shapely MultiPoint geometry for all points at once
-    pts_geom = MultiPoint(pts)
-    # Vectorized contains: returns a boolean array
-    mask_flat = contains(x, pts_geom.geoms)
-    da_interior_mask = xr.DataArray(
-        mask_flat.reshape(points_xyz.shape[:-1]).astype(bool),
-        coords=da_lat.coords,
-        dims=da_lat.dims,
-    )
-    logger.info("Convex hull mask created.")
+    da_interior_mask = xr.apply_ufunc(
+        chull_lam.contains_lonlat, da_lon.load(), da_lat.load(), vectorize=True
+    ).astype(bool)
     da_interior_mask.attrs[
         "long_name"
     ] = "contained in convex hull of source dataset (da_ref)"
diff --git a/pyproject.toml b/pyproject.toml
index d9332bf..86838f0 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -28,7 +28,6 @@ dependencies = [
     "psutil>=5.7.2",
     "packaging>=23.1",
     "deepdiff>=8.2.0",
-    "shapely"
 ]
 requires-python = ">=3.9"
 readme = "README.md"

From a9921e130b140ee54ff3b35d8a40bb3a4b17ee07 Mon Sep 17 00:00:00 2001
From: Leif Denby <leif@denby.eu>
Date: Tue, 25 Nov 2025 20:45:03 +0100
Subject: [PATCH 22/28] add era5 crop test checking for same variables+coords

---
 tests/data.py                      | 25 ++++++++-
 tests/test_convex_hull_cropping.py | 88 ++++++++++++++++++++++++++++++
 2 files changed, 111 insertions(+), 2 deletions(-)

diff --git a/tests/data.py b/tests/data.py
index 3a332c5..612ac02 100644
--- a/tests/data.py
+++ b/tests/data.py
@@ -302,8 +302,19 @@ def create_static_dataset(
 
 def _add_latlon(ds: xr.Dataset) -> xr.Dataset:
     """
-    Add latitude and longitude coordinates to the dataset using a local equal
-    area projection centered on Denmark.
+    Add latitude and longitude coordinates (named `lat` and `lon`) to the
+    dataset using a local equal area projection centered on Denmark with the
+    coordinates `x` and `y` being the projected coordinates in meters.
+
+    Parameters
+    ----------
+    ds : xarray.Dataset
+        The input dataset with `x` and `y` coordinates, modified in-place
+
+    Returns
+    -------
+    xarray.Dataset
+        The dataset with added `lat` and `lon` coordinates
     """
     if pyproj is None:
         raise ImportError("pyproj is required for this function")
@@ -361,6 +372,11 @@ def create_data_collection(
         Number of analysis times, by default NT_ANALYSIS
     nt_forecast : int, optional
         Number of forecast times, by default NT_FORECAST
+    add_latlon : bool, optional
+        Whether to add latitude and longitude coordinates to the datasets. In
+        this case, the x- and y-coordinates will be interpreted as local equal
+        area projection coordinates (in meters) centered on Copenhagen, by
+        default False
 
     Returns
     -------
@@ -452,6 +468,11 @@ def create_input_datasets_and_config(
         Number of grid points in x-direction, by default NX
     ny : int, optional
         Number of grid points in y-direction, by default NY
+    add_latlon : bool, optional
+        Whether to add latitude and longitude coordinates to the datasets. In
+        this case, the x- and y-coordinates will be interpreted as local equal
+        area projection coordinates (in meters) centered on Copenhagen, by
+        default False
 
     Returns
     -------
diff --git a/tests/test_convex_hull_cropping.py b/tests/test_convex_hull_cropping.py
index bbcd584..7d27780 100644
--- a/tests/test_convex_hull_cropping.py
+++ b/tests/test_convex_hull_cropping.py
@@ -1,3 +1,4 @@
+import copy
 import tempfile
 from pathlib import Path
 
@@ -5,6 +6,7 @@
 import pytest
 
 import mllam_data_prep as mdp
+import mllam_data_prep.config as mdp_config
 import tests.data as testdata
 from mllam_data_prep.ops import cropping
 
@@ -123,3 +125,89 @@ def test_create_cropped_dataset(include_interior_points):
         interior_dataset_config_path=fp_lam_config,
     )
     mdp.create_dataset(config=config_global)
+
+
+def test_crop_era5_with_generated_lam_domain():
+    """
+    Test cropping an ERA5 dataset with a generated LAM domain to ensure that
+    coordinates of ERA5 domain have been carried over into resulting dataset,
+    i.e. the same variables should be present whether the domain is cropped or
+    not.
+    """
+
+    era5_config = mdp_config.Config(
+        schema_version="v0.5.0",
+        dataset_version="v1.0.0",
+        output=mdp_config.Output(
+            variables=dict(
+                state=["time", "grid_index", "state_feature"],
+            ),
+            coord_ranges=dict(
+                time=mdp_config.Range(
+                    start="1990-09-03T00:00", end="1990-09-09T00:00", step="PT6H"
+                )
+            ),
+            chunking=dict(time=1),
+        ),
+        inputs={
+            "era_height_levels": mdp_config.InputDataset(
+                path="simplecache::gs://weatherbench2/datasets/era5/1959-2023_01_10-6h-64x32_equiangular_conservative.zarr",
+                dims=["time", "longitude", "latitude", "level"],
+                variables={
+                    "u_component_of_wind": dict(
+                        level=mdp_config.ValueSelection(values=[1000], units="hPa")
+                    )
+                },
+                dim_mapping={
+                    "time": mdp_config.DimMapping(method="rename", dim="time"),
+                    "state_feature": mdp_config.DimMapping(
+                        method="stack_variables_by_var_name",
+                        dims=["level"],
+                        name_format="{var_name}{level}hPa",
+                    ),
+                    "grid_index": mdp_config.DimMapping(
+                        method="stack", dims=["longitude", "latitude"]
+                    ),
+                },
+                target_output_variable="state",
+            )
+        },
+    )
+
+    # create uncropped dataset
+    ds_uncropped = mdp.create_dataset(config=era5_config)
+
+    tmpdir = tempfile.TemporaryDirectory()
+
+    lam_config = testdata.create_input_datasets_and_config(
+        identifier="danra_lam",
+        data_categories=["state"],
+        tmpdir=tmpdir,
+        xlim=[-2.0, 2.0],
+        ylim=[-2.0, 2.0],
+        nx=50,
+        ny=50,
+        add_latlon=True,
+    )
+    # save the LAM config to a temporary file
+    lam_config_path = Path(tmpdir.name) / "danra_lam_config.yaml"
+    lam_config.to_yaml_file(lam_config_path)
+
+    # create cropped dataset
+    era5_config_cropped = copy.deepcopy(era5_config)
+    era5_config_cropped.output.domain_cropping = mdp_config.ConvexHullCropping(
+        margin_width_degrees=10,
+        interior_dataset_config_path=lam_config_path.as_posix(),
+    )
+
+    ds_cropped = mdp.create_dataset(config=era5_config_cropped)
+
+    # check that the cropped dataset has the same variables and coordinates as the uncropped one, allowing for fewer grid points
+    for var in ds_uncropped.data_vars:
+        assert var in ds_cropped.data_vars
+        for coord in ds_uncropped[var].coords:
+            assert coord in ds_cropped[var].coords
+
+    import ipdb
+
+    ipdb.set_trace()

From b8cd2661698b7bcdb27ec70bf666849a2e27c0a2 Mon Sep 17 00:00:00 2001
From: Leif Denby <leif@denby.eu>
Date: Tue, 25 Nov 2025 20:46:12 +0100
Subject: [PATCH 23/28] remove debug statement

---
 tests/test_convex_hull_cropping.py | 7 ++-----
 1 file changed, 2 insertions(+), 5 deletions(-)

diff --git a/tests/test_convex_hull_cropping.py b/tests/test_convex_hull_cropping.py
index 7d27780..d9d3042 100644
--- a/tests/test_convex_hull_cropping.py
+++ b/tests/test_convex_hull_cropping.py
@@ -202,12 +202,9 @@ def test_crop_era5_with_generated_lam_domain():
 
     ds_cropped = mdp.create_dataset(config=era5_config_cropped)
 
-    # check that the cropped dataset has the same variables and coordinates as the uncropped one, allowing for fewer grid points
+    # check that the cropped dataset has the same variables and coordinates as
+    # the uncropped one, allowing for fewer grid points
     for var in ds_uncropped.data_vars:
         assert var in ds_cropped.data_vars
         for coord in ds_uncropped[var].coords:
             assert coord in ds_cropped[var].coords
-
-    import ipdb
-
-    ipdb.set_trace()

From cf127e65d849b3c914c46f63acbfa4b7c307b8ad Mon Sep 17 00:00:00 2001
From: Leif Denby <leif@denby.eu>
Date: Tue, 25 Nov 2025 22:48:13 +0100
Subject: [PATCH 24/28] document domain cropping in README

---
 README.md | 21 +++++++++++++++++++++
 1 file changed, 21 insertions(+)

diff --git a/README.md b/README.md
index 7350562..9203101 100644
--- a/README.md
+++ b/README.md
@@ -285,6 +285,27 @@ The `output` section defines three things:
 3. `chunking`: the chunk sizes to use when writing the training dataset to zarr. This is optional, but can be used to optimise the performance of the zarr dataset. By default the chunk sizes are set to the size of the dimension, but this can be overridden by setting the chunk size in the configuration file. A common choice is to set the dimension along which you are batching to align with the of each training item (e.g. if you are training a model with time-step roll-out of 10 timesteps, you might choose a chunksize of 10 along the time dimension).
 4. Splitting and calculation of statistics of the output variables, using the `splitting` section. The `output.splitting.splits` attribute defines the individual splits to create (for example `train`, `val` and `test`) and `output.splitting.dim` defines the dimension to split along. The `compute_statistics` can be optionally set for a given split to calculate the statistical properties requested (for example `mean`, `std`) any method available on `xarray.Dataset.{op}` can be used. In addition methods prefixed by `diff_` (so the operational would be listed as `diff_{op}`) to compute a statistic based on difference of consecutive time-steps, e.g. `diff_mean` to compute the `mean` of the difference between consecutive timesteps (these are used for normalisating increments). The `dims` attribute defines the dimensions to calculate the statistics over (for example `grid_index` and `time`).
 
+In addition the `output` section can also contain a configuration for cropping the output dataset using the convex hull of coordinates from a different dataset. This is used for example when creating training datasets from limited area modelling (LAM) setups, where a separate dataset is used for the boundary data. The example above doesn't include this section, but [see below](#cropping-the-output-dataset-using-convex-hull-of-another-dataset) for an example of how to use this feature.
+
+#### Cropping the output dataset using convex hull of another dataset
+
+When creating training datasets for limited area models (LAMs) it is often useful to crop the training dataset to the convex hull of the coordinates of another dataset (for example a dataset containing boundary data). This can be done by adding a `domain_cropping` section to the `output` section of the configuration file. A full example where ERA5 is cropped is given in [example.danra.yaml](example.danra.yaml). The relevant section is reproduced here for completeness:
+
+```yaml
+output:
+  ...
+  domain_cropping:
+    margin_width_degrees: 10
+    interior_dataset_config_path: example.danra.yaml
+```
+
+The `domain_cropping` section has two required attributes, and one optional attribute:
+- `margin_width_degrees`: the width (in degrees) of the margin to add around the convex hull of the coordinates of the interior dataset. This allows you to control how much extra area to include around the convex hull of the interior dataset (e.g. how thick to make the boundary region for LAM boundary forcing datasets).
+- `interior_dataset_config_path`: the path to the configuration file of the interior dataset. This is used to load the interior dataset and extract the coordinates to calculate the convex hull from.
+- `include_interior_points`: optional, defaults to `false`. If set to `true` the points inside the convex hull of the interior dataset will also be included in the cropped output dataset. If set to `false` only the margin region around the convex hull will be included. This can be useful if you not only want to create a boundary forcing dataset from a global simulation for LAM, but also want to include the interior points of the global simulation that overlap with the LAM domain.
+
+Details on how the convex hull cropping is actually done can be found in the jupyter notebook in [docs/domain-cropping.ipynb](docs/domain-cropping.ipynb).
+
 ### The `inputs` section
 
 ```yaml

From 63e31493a830c7887c22fd344f82c0fd9b72ccee Mon Sep 17 00:00:00 2001
From: Leif Denby <leif@denby.eu>
Date: Wed, 26 Nov 2025 09:08:35 +0100
Subject: [PATCH 25/28] changelog

---
 CHANGELOG.md | 8 ++++++++
 1 file changed, 8 insertions(+)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index 105c9d4..19765aa 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -5,6 +5,14 @@ All notable changes to this project will be documented in this file.
 The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.1.0/),
 and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).
 
+## [Unreleased](https://github.com/mllam/mllam-data-prep)
+
+[All changes](https://github.com/mllam/mllam-data-prep/compare/HEAD...v0.6.1)
+
+### Added
+
+- add support for cropping a dataset using the convex hull of the lat/lon coordinates of another dataset (can be used for creating boundary data in Limited Area Modelling setups) [\#45](https://github.com/mllam/mllam-data-prep/pull/45), @leifdenby
+
 ## [v0.6.1](https://github.com/mllam/mllam-data-prep/release/tag/v0.6.1)
 
 [All changes](https://github.com/mllam/mllam-data-prep/compare/v0.6.1...v0.6.0)

From 1bc3576802edd81d86a6bf6be1404157d9f58c02 Mon Sep 17 00:00:00 2001
From: Leif Denby <leif@denby.eu>
Date: Wed, 26 Nov 2025 09:17:04 +0100
Subject: [PATCH 26/28] clarify notebook details cropping implementation

---
 docs/domain-cropping.ipynb | 1913 +-----------------------------------
 1 file changed, 24 insertions(+), 1889 deletions(-)

diff --git a/docs/domain-cropping.ipynb b/docs/domain-cropping.ipynb
index f1a76e0..d8ead71 100644
--- a/docs/domain-cropping.ipynb
+++ b/docs/domain-cropping.ipynb
@@ -6,14 +6,14 @@
    "source": [
     "# Cropping a domain using the lat/lon convex hull of another domain\n",
     "\n",
-    "This notebook demonstrates how convex hull based cropping works in `mllam-data-prep`.\n",
+    "This notebook demonstrates how convex hull based cropping is implemented in `mllam-data-prep`. To actually use this feature see [the details in the README](../README.md#cropping-a-domain-using-the-latlon-convex-hull-of-another-domain).\n",
     "\n",
     "This method crops a domain using the lat/lon convex hull of another domain. This is useful when a) you have two datasets on two different overlapping domains where you only want to keep the overlapping part, or b) in the case where you want to run limited-area simulations and need to create a dataset that provides the boundary conditions for the limited-area domain."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -39,7 +39,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -72,24 +72,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\u001b[32m2025-01-08 17:48:34.313\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmllam_data_prep.create_dataset\u001b[0m:\u001b[36mcreate_dataset\u001b[0m:\u001b[36m144\u001b[0m - \u001b[1mLoading dataset lam_surface_analysis from /var/folders/n8/kcprfkd918967drydbc_bphw0000gr/T/tmp0tflm74d/lam/surface_analysis_c6f5cd46-48fd-4f02-8a6a-fde52bf31d69.zarr\u001b[0m\n",
-      "\u001b[32m2025-01-08 17:48:34.359\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmllam_data_prep.create_dataset\u001b[0m:\u001b[36mcreate_dataset\u001b[0m:\u001b[36m169\u001b[0m - \u001b[1mMapping dimensions and variables for dataset lam_surface_analysis to state\u001b[0m\n",
-      "\u001b[32m2025-01-08 17:48:34.362\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmllam_data_prep.create_dataset\u001b[0m:\u001b[36m_merge_dataarrays_by_target\u001b[0m:\u001b[36m49\u001b[0m - \u001b[1mMerging dataarrays for target variable `state`\u001b[0m\n",
-      "\u001b[32m2025-01-08 17:48:34.363\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmllam_data_prep.create_dataset\u001b[0m:\u001b[36mcreate_dataset\u001b[0m:\u001b[36m204\u001b[0m - \u001b[1mChunking dataset with {}\u001b[0m\n",
-      "\u001b[32m2025-01-08 17:48:34.364\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmllam_data_prep.create_dataset\u001b[0m:\u001b[36mcreate_dataset\u001b[0m:\u001b[36m144\u001b[0m - \u001b[1mLoading dataset global_surface_analysis from /var/folders/n8/kcprfkd918967drydbc_bphw0000gr/T/tmp0tflm74d/global/surface_analysis_8c2f2bb4-5767-4180-9b5a-374afa4ce05d.zarr\u001b[0m\n",
-      "\u001b[32m2025-01-08 17:48:34.367\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmllam_data_prep.create_dataset\u001b[0m:\u001b[36mcreate_dataset\u001b[0m:\u001b[36m169\u001b[0m - \u001b[1mMapping dimensions and variables for dataset global_surface_analysis to state\u001b[0m\n",
-      "\u001b[32m2025-01-08 17:48:34.369\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmllam_data_prep.create_dataset\u001b[0m:\u001b[36m_merge_dataarrays_by_target\u001b[0m:\u001b[36m49\u001b[0m - \u001b[1mMerging dataarrays for target variable `state`\u001b[0m\n",
-      "\u001b[32m2025-01-08 17:48:34.370\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmllam_data_prep.create_dataset\u001b[0m:\u001b[36mcreate_dataset\u001b[0m:\u001b[36m204\u001b[0m - \u001b[1mChunking dataset with {}\u001b[0m\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "ds_lam = mdp.create_dataset(config=config_lam)\n",
     "ds_global = mdp.create_dataset(config=config_global)"
@@ -97,1794 +82,18 @@
   },
   {
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-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 200kB\n",
-       "Dimensions:                       (time: 5, grid_index: 2500, state_feature: 1)\n",
-       "Coordinates:\n",
-       "  * time                          (time) datetime64[ns] 40B 2000-01-01 ... 20...\n",
-       "    lat                           (grid_index) float64 20kB dask.array&lt;chunksize=(2500,), meta=np.ndarray&gt;\n",
-       "    lon                           (grid_index) float64 20kB dask.array&lt;chunksize=(2500,), meta=np.ndarray&gt;\n",
-       "    x                             (grid_index) float64 20kB dask.array&lt;chunksize=(2500,), meta=np.ndarray&gt;\n",
-       "    y                             (grid_index) float64 20kB dask.array&lt;chunksize=(2500,), meta=np.ndarray&gt;\n",
-       "  * state_feature                 (state_feature) &lt;U15 60B &#x27;pres_seasurface&#x27;\n",
-       "    state_feature_units           (state_feature) &lt;U1 4B dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;\n",
-       "    state_feature_long_name       (state_feature) &lt;U1 4B dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;\n",
-       "    state_feature_source_dataset  (state_feature) &lt;U20 80B dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;\n",
-       "  * grid_index                    (grid_index) int64 20kB 0 1 2 ... 2498 2499\n",
-       "Data variables:\n",
-       "    state                         (state_feature, time, grid_index) float64 100kB dask.array&lt;chunksize=(1, 5, 2500), meta=np.ndarray&gt;\n",
-       "Attributes:\n",
-       "    schema_version:   v0.5.0\n",
-       "    dataset_version:  v0.1.0\n",
-       "    created_on:       2025-01-08T17:48:34\n",
-       "    created_with:     mllam-data-prep (https://github.com/mllam/mllam-data-prep)\n",
-       "    mdp_version:      v0.5.0</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-0a9b7847-79ff-42d7-89f4-9e95d648a0c6' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-0a9b7847-79ff-42d7-89f4-9e95d648a0c6' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 5</li><li><span class='xr-has-index'>grid_index</span>: 2500</li><li><span class='xr-has-index'>state_feature</span>: 1</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-51023d5e-36f4-4db6-842d-97622a318f9b' class='xr-section-summary-in' type='checkbox'  checked><label for='section-51023d5e-36f4-4db6-842d-97622a318f9b' class='xr-section-summary' >Coordinates: <span>(10)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2000-01-01 ... 2000-01-02</div><input id='attrs-df9088d6-dfff-4838-93b1-30c96722b589' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-df9088d6-dfff-4838-93b1-30c96722b589' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-40be614c-12f2-479c-b038-8104fb9da3ba' class='xr-var-data-in' type='checkbox'><label for='data-40be614c-12f2-479c-b038-8104fb9da3ba' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;2000-01-01T00:00:00.000000000&#x27;, &#x27;2000-01-01T06:00:00.000000000&#x27;,\n",
-       "       &#x27;2000-01-01T12:00:00.000000000&#x27;, &#x27;2000-01-01T18:00:00.000000000&#x27;,\n",
-       "       &#x27;2000-01-02T00:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lat</span></div><div class='xr-var-dims'>(grid_index)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(2500,), meta=np.ndarray&gt;</div><input id='attrs-f4684728-b371-464d-be26-93648e335fd7' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f4684728-b371-464d-be26-93648e335fd7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-946960ac-4912-4c7d-a356-05e9c8ababa2' class='xr-var-data-in' type='checkbox'><label for='data-946960ac-4912-4c7d-a356-05e9c8ababa2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table style=\"border-collapse: collapse;\">\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 19.53 kiB </td>\n",
-       "                        <td> 19.53 kiB </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (2500,) </td>\n",
-       "                        <td> (2500,) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
-       "        <td>\n",
-       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
-       "\n",
-       "  <!-- Horizontal lines -->\n",
-       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
-       "\n",
-       "  <!-- Vertical lines -->\n",
-       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
-       "\n",
-       "  <!-- Colored Rectangle -->\n",
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-       "\n",
-       "  <!-- Text -->\n",
-       "  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2500</text>\n",
-       "  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n",
-       "</svg>\n",
-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon</span></div><div class='xr-var-dims'>(grid_index)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(2500,), meta=np.ndarray&gt;</div><input id='attrs-dadb082e-4ec7-4844-b5a2-7df5a28ceee0' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-dadb082e-4ec7-4844-b5a2-7df5a28ceee0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-411747c4-f650-4da7-a4ef-4ba98adb63e5' class='xr-var-data-in' type='checkbox'><label for='data-411747c4-f650-4da7-a4ef-4ba98adb63e5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table style=\"border-collapse: collapse;\">\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 19.53 kiB </td>\n",
-       "                        <td> 19.53 kiB </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (2500,) </td>\n",
-       "                        <td> (2500,) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
-       "        <td>\n",
-       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
-       "\n",
-       "  <!-- Horizontal lines -->\n",
-       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
-       "\n",
-       "  <!-- Vertical lines -->\n",
-       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
-       "\n",
-       "  <!-- Colored Rectangle -->\n",
-       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
-       "\n",
-       "  <!-- Text -->\n",
-       "  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2500</text>\n",
-       "  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n",
-       "</svg>\n",
-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>x</span></div><div class='xr-var-dims'>(grid_index)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(2500,), meta=np.ndarray&gt;</div><input id='attrs-6abb7f7d-bdc9-4d4f-8d5c-b3583967350d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6abb7f7d-bdc9-4d4f-8d5c-b3583967350d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-876824a2-af04-4499-b105-dbec48fdd4a4' class='xr-var-data-in' type='checkbox'><label for='data-876824a2-af04-4499-b105-dbec48fdd4a4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table style=\"border-collapse: collapse;\">\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 19.53 kiB </td>\n",
-       "                        <td> 19.53 kiB </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (2500,) </td>\n",
-       "                        <td> (2500,) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 1 chunks in 1 graph layer </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
-       "        <td>\n",
-       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
-       "\n",
-       "  <!-- Horizontal lines -->\n",
-       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
-       "\n",
-       "  <!-- Vertical lines -->\n",
-       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
-       "\n",
-       "  <!-- Colored Rectangle -->\n",
-       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
-       "\n",
-       "  <!-- Text -->\n",
-       "  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2500</text>\n",
-       "  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n",
-       "</svg>\n",
-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>y</span></div><div class='xr-var-dims'>(grid_index)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(2500,), meta=np.ndarray&gt;</div><input id='attrs-85719a5c-38f2-4cb5-bfce-316d135b8c92' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-85719a5c-38f2-4cb5-bfce-316d135b8c92' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2e6cd5fa-e8f5-4a42-b070-73df5fca764c' class='xr-var-data-in' type='checkbox'><label for='data-2e6cd5fa-e8f5-4a42-b070-73df5fca764c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table style=\"border-collapse: collapse;\">\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 19.53 kiB </td>\n",
-       "                        <td> 19.53 kiB </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (2500,) </td>\n",
-       "                        <td> (2500,) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 1 chunks in 1 graph layer </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
-       "        <td>\n",
-       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
-       "\n",
-       "  <!-- Horizontal lines -->\n",
-       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
-       "\n",
-       "  <!-- Vertical lines -->\n",
-       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
-       "\n",
-       "  <!-- Colored Rectangle -->\n",
-       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
-       "\n",
-       "  <!-- Text -->\n",
-       "  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2500</text>\n",
-       "  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n",
-       "</svg>\n",
-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>state_feature</span></div><div class='xr-var-dims'>(state_feature)</div><div class='xr-var-dtype'>&lt;U15</div><div class='xr-var-preview xr-preview'>&#x27;pres_seasurface&#x27;</div><input id='attrs-47b3b0ab-77a3-4cc8-8279-2b0bcff7a96a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-47b3b0ab-77a3-4cc8-8279-2b0bcff7a96a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8057a714-5090-4d4f-b6d2-3339df8472e8' class='xr-var-data-in' type='checkbox'><label for='data-8057a714-5090-4d4f-b6d2-3339df8472e8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;pres_seasurface&#x27;], dtype=&#x27;&lt;U15&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>state_feature_units</span></div><div class='xr-var-dims'>(state_feature)</div><div class='xr-var-dtype'>&lt;U1</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;</div><input id='attrs-e3b7f5f5-ba1e-4b84-86a5-fe43477e0d05' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e3b7f5f5-ba1e-4b84-86a5-fe43477e0d05' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f1356a48-66d6-41b1-a813-3382d1077dda' class='xr-var-data-in' type='checkbox'><label for='data-f1356a48-66d6-41b1-a813-3382d1077dda' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table style=\"border-collapse: collapse;\">\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 4 B </td>\n",
-       "                        <td> 4 B </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (1,) </td>\n",
-       "                        <td> (1,) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 1 chunks in 1 graph layer </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> <U1 numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
-       "        <td>\n",
-       "        <svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
-       "\n",
-       "  <!-- Horizontal lines -->\n",
-       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
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-       "\n",
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-       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
-       "\n",
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-       "\n",
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-       "  <text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1</text>\n",
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-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>state_feature_long_name</span></div><div class='xr-var-dims'>(state_feature)</div><div class='xr-var-dtype'>&lt;U1</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;</div><input id='attrs-93404203-a0dc-4090-a2f2-3afb07df9ee5' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-93404203-a0dc-4090-a2f2-3afb07df9ee5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b0b40428-a023-4bc8-80dc-b058e1b20b0e' class='xr-var-data-in' type='checkbox'><label for='data-b0b40428-a023-4bc8-80dc-b058e1b20b0e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table style=\"border-collapse: collapse;\">\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 4 B </td>\n",
-       "                        <td> 4 B </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (1,) </td>\n",
-       "                        <td> (1,) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 1 chunks in 1 graph layer </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> <U1 numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
-       "        <td>\n",
-       "        <svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
-       "\n",
-       "  <!-- Horizontal lines -->\n",
-       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"0\" y1=\"120\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
-       "\n",
-       "  <!-- Vertical lines -->\n",
-       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
-       "\n",
-       "  <!-- Colored Rectangle -->\n",
-       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
-       "\n",
-       "  <!-- Text -->\n",
-       "  <text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1</text>\n",
-       "  <text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,60.000000)\">1</text>\n",
-       "</svg>\n",
-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>state_feature_source_dataset</span></div><div class='xr-var-dims'>(state_feature)</div><div class='xr-var-dtype'>&lt;U20</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;</div><input id='attrs-808fcccf-507d-4459-8c5f-b66f0c6f51bb' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-808fcccf-507d-4459-8c5f-b66f0c6f51bb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ce74496f-ec75-47d9-b544-701f9836a628' class='xr-var-data-in' type='checkbox'><label for='data-ce74496f-ec75-47d9-b544-701f9836a628' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table style=\"border-collapse: collapse;\">\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 80 B </td>\n",
-       "                        <td> 80 B </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (1,) </td>\n",
-       "                        <td> (1,) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 1 chunks in 1 graph layer </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> <U20 numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
-       "        <td>\n",
-       "        <svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
-       "\n",
-       "  <!-- Horizontal lines -->\n",
-       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"0\" y1=\"120\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
-       "\n",
-       "  <!-- Vertical lines -->\n",
-       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
-       "\n",
-       "  <!-- Colored Rectangle -->\n",
-       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
-       "\n",
-       "  <!-- Text -->\n",
-       "  <text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1</text>\n",
-       "  <text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,60.000000)\">1</text>\n",
-       "</svg>\n",
-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>grid_index</span></div><div class='xr-var-dims'>(grid_index)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 ... 2496 2497 2498 2499</div><input id='attrs-14c3b76c-3be4-489f-9720-6f85a9dad28f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-14c3b76c-3be4-489f-9720-6f85a9dad28f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-db640cb7-c12a-4d57-aebb-219c0b2ca4a3' class='xr-var-data-in' type='checkbox'><label for='data-db640cb7-c12a-4d57-aebb-219c0b2ca4a3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([   0,    1,    2, ..., 2497, 2498, 2499])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-c73a93cb-30be-4b3e-a622-5d8f0b2ddb78' class='xr-section-summary-in' type='checkbox'  checked><label for='section-c73a93cb-30be-4b3e-a622-5d8f0b2ddb78' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>state</span></div><div class='xr-var-dims'>(state_feature, time, grid_index)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 5, 2500), meta=np.ndarray&gt;</div><input id='attrs-221a8b74-8bb8-410a-af3d-c79607fca35a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-221a8b74-8bb8-410a-af3d-c79607fca35a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-330eda23-55d7-4689-8267-19e0e5ee9f1d' class='xr-var-data-in' type='checkbox'><label for='data-330eda23-55d7-4689-8267-19e0e5ee9f1d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>variables_mapping_dim :</span></dt><dd>state_feature</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table style=\"border-collapse: collapse;\">\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 97.66 kiB </td>\n",
-       "                        <td> 97.66 kiB </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (1, 5, 2500) </td>\n",
-       "                        <td> (1, 5, 2500) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 1 chunks in 4 graph layers </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
-       "        <td>\n",
-       "        <svg width=\"194\" height=\"90\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
-       "\n",
-       "  <!-- Horizontal lines -->\n",
-       "  <line x1=\"10\" y1=\"0\" x2=\"24\" y2=\"14\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"10\" y1=\"25\" x2=\"24\" y2=\"40\" style=\"stroke-width:2\" />\n",
-       "\n",
-       "  <!-- Vertical lines -->\n",
-       "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"25\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"40\" style=\"stroke-width:2\" />\n",
-       "\n",
-       "  <!-- Colored Rectangle -->\n",
-       "  <polygon points=\"10.0,0.0 24.9485979497544,14.948597949754403 24.9485979497544,40.36121446433689 10.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
-       "\n",
-       "  <!-- Horizontal lines -->\n",
-       "  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"24\" y1=\"14\" x2=\"144\" y2=\"14\" style=\"stroke-width:2\" />\n",
-       "\n",
-       "  <!-- Vertical lines -->\n",
-       "  <line x1=\"10\" y1=\"0\" x2=\"24\" y2=\"14\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"130\" y1=\"0\" x2=\"144\" y2=\"14\" style=\"stroke-width:2\" />\n",
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-       "\n",
-       "  <!-- Horizontal lines -->\n",
-       "  <line x1=\"24\" y1=\"14\" x2=\"144\" y2=\"14\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"24\" y1=\"40\" x2=\"144\" y2=\"40\" style=\"stroke-width:2\" />\n",
-       "\n",
-       "  <!-- Vertical lines -->\n",
-       "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"40\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"144\" y1=\"14\" x2=\"144\" y2=\"40\" style=\"stroke-width:2\" />\n",
-       "\n",
-       "  <!-- Colored Rectangle -->\n",
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-       "\n",
-       "  <!-- Text -->\n",
-       "  <text x=\"84.948598\" y=\"60.361214\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2500</text>\n",
-       "  <text x=\"164.948598\" y=\"27.654906\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,164.948598,27.654906)\">5</text>\n",
-       "  <text x=\"7.474299\" y=\"52.886915\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,7.474299,52.886915)\">1</text>\n",
-       "</svg>\n",
-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-eb7c8fce-ba14-41f9-aff3-b3a59b5d4bb4' class='xr-section-summary-in' type='checkbox'  ><label for='section-eb7c8fce-ba14-41f9-aff3-b3a59b5d4bb4' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-e4d99767-0003-4d74-810e-02aec372313b' class='xr-index-data-in' type='checkbox'/><label for='index-e4d99767-0003-4d74-810e-02aec372313b' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2000-01-01 00:00:00&#x27;, &#x27;2000-01-01 06:00:00&#x27;,\n",
-       "               &#x27;2000-01-01 12:00:00&#x27;, &#x27;2000-01-01 18:00:00&#x27;,\n",
-       "               &#x27;2000-01-02 00:00:00&#x27;],\n",
-       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>state_feature</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-e4b8e693-58d9-4d95-9c69-55fa91cab792' class='xr-index-data-in' type='checkbox'/><label for='index-e4b8e693-58d9-4d95-9c69-55fa91cab792' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;pres_seasurface&#x27;], dtype=&#x27;object&#x27;, name=&#x27;state_feature&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>grid_index</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-2c7564cc-59e0-42a4-9a01-8da00664d50f' class='xr-index-data-in' type='checkbox'/><label for='index-2c7564cc-59e0-42a4-9a01-8da00664d50f' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([   0,    1,    2,    3,    4,    5,    6,    7,    8,    9,\n",
-       "       ...\n",
-       "       2490, 2491, 2492, 2493, 2494, 2495, 2496, 2497, 2498, 2499],\n",
-       "      dtype=&#x27;int64&#x27;, name=&#x27;grid_index&#x27;, length=2500))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-dacf8850-451f-41e7-b5ee-6e26ef6fb0e4' class='xr-section-summary-in' type='checkbox'  checked><label for='section-dacf8850-451f-41e7-b5ee-6e26ef6fb0e4' class='xr-section-summary' >Attributes: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>schema_version :</span></dt><dd>v0.5.0</dd><dt><span>dataset_version :</span></dt><dd>v0.1.0</dd><dt><span>created_on :</span></dt><dd>2025-01-08T17:48:34</dd><dt><span>created_with :</span></dt><dd>mllam-data-prep (https://github.com/mllam/mllam-data-prep)</dd><dt><span>mdp_version :</span></dt><dd>v0.5.0</dd></dl></div></li></ul></div></div>"
-      ],
-      "text/plain": [
-       "<xarray.Dataset> Size: 200kB\n",
-       "Dimensions:                       (time: 5, grid_index: 2500, state_feature: 1)\n",
-       "Coordinates:\n",
-       "  * time                          (time) datetime64[ns] 40B 2000-01-01 ... 20...\n",
-       "    lat                           (grid_index) float64 20kB dask.array<chunksize=(2500,), meta=np.ndarray>\n",
-       "    lon                           (grid_index) float64 20kB dask.array<chunksize=(2500,), meta=np.ndarray>\n",
-       "    x                             (grid_index) float64 20kB dask.array<chunksize=(2500,), meta=np.ndarray>\n",
-       "    y                             (grid_index) float64 20kB dask.array<chunksize=(2500,), meta=np.ndarray>\n",
-       "  * state_feature                 (state_feature) <U15 60B 'pres_seasurface'\n",
-       "    state_feature_units           (state_feature) <U1 4B dask.array<chunksize=(1,), meta=np.ndarray>\n",
-       "    state_feature_long_name       (state_feature) <U1 4B dask.array<chunksize=(1,), meta=np.ndarray>\n",
-       "    state_feature_source_dataset  (state_feature) <U20 80B dask.array<chunksize=(1,), meta=np.ndarray>\n",
-       "  * grid_index                    (grid_index) int64 20kB 0 1 2 ... 2498 2499\n",
-       "Data variables:\n",
-       "    state                         (state_feature, time, grid_index) float64 100kB dask.array<chunksize=(1, 5, 2500), meta=np.ndarray>\n",
-       "Attributes:\n",
-       "    schema_version:   v0.5.0\n",
-       "    dataset_version:  v0.1.0\n",
-       "    created_on:       2025-01-08T17:48:34\n",
-       "    created_with:     mllam-data-prep (https://github.com/mllam/mllam-data-prep)\n",
-       "    mdp_version:      v0.5.0"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "ds_lam"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
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-       ".xr-attrs {\n",
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-       "\n",
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-       ".xr-var-attrs,\n",
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-       ".xr-index-data {\n",
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-       "\n",
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-       "\n",
-       ".xr-attrs dt,\n",
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-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "  float: left;\n",
-       "  padding-right: 10px;\n",
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-       "\n",
-       ".xr-attrs dt {\n",
-       "  font-weight: normal;\n",
-       "  grid-column: 1;\n",
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-       "\n",
-       ".xr-attrs dt:hover span {\n",
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-       "\n",
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-       "  grid-column: 2;\n",
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-       "\n",
-       ".xr-icon-database,\n",
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-       ".xr-no-icon {\n",
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-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 50kB\n",
-       "Dimensions:                       (time: 5, grid_index: 625, state_feature: 1)\n",
-       "Coordinates:\n",
-       "  * time                          (time) datetime64[ns] 40B 2000-01-01 ... 20...\n",
-       "    lat                           (grid_index) float64 5kB dask.array&lt;chunksize=(625,), meta=np.ndarray&gt;\n",
-       "    lon                           (grid_index) float64 5kB dask.array&lt;chunksize=(625,), meta=np.ndarray&gt;\n",
-       "    x                             (grid_index) float64 5kB dask.array&lt;chunksize=(625,), meta=np.ndarray&gt;\n",
-       "    y                             (grid_index) float64 5kB dask.array&lt;chunksize=(625,), meta=np.ndarray&gt;\n",
-       "  * state_feature                 (state_feature) &lt;U15 60B &#x27;pres_seasurface&#x27;\n",
-       "    state_feature_units           (state_feature) &lt;U1 4B dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;\n",
-       "    state_feature_long_name       (state_feature) &lt;U1 4B dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;\n",
-       "    state_feature_source_dataset  (state_feature) &lt;U23 92B dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;\n",
-       "  * grid_index                    (grid_index) int64 5kB 0 1 2 3 ... 622 623 624\n",
-       "Data variables:\n",
-       "    state                         (state_feature, time, grid_index) float64 25kB dask.array&lt;chunksize=(1, 5, 625), meta=np.ndarray&gt;\n",
-       "Attributes:\n",
-       "    schema_version:   v0.5.0\n",
-       "    dataset_version:  v0.1.0\n",
-       "    created_on:       2025-01-08T17:48:34\n",
-       "    created_with:     mllam-data-prep (https://github.com/mllam/mllam-data-prep)\n",
-       "    mdp_version:      v0.5.0</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-9d7c4eb9-348a-470b-9834-3bd6abf6e15a' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-9d7c4eb9-348a-470b-9834-3bd6abf6e15a' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 5</li><li><span class='xr-has-index'>grid_index</span>: 625</li><li><span class='xr-has-index'>state_feature</span>: 1</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-c9f536c3-ad5e-4f81-a985-dca62b32e0f9' class='xr-section-summary-in' type='checkbox'  checked><label for='section-c9f536c3-ad5e-4f81-a985-dca62b32e0f9' class='xr-section-summary' >Coordinates: <span>(10)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2000-01-01 ... 2000-01-02</div><input id='attrs-168b2970-43b4-4c13-bd15-082a7145e249' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-168b2970-43b4-4c13-bd15-082a7145e249' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-47f23b8a-5d3f-4ff2-a159-13ff3811de1c' class='xr-var-data-in' type='checkbox'><label for='data-47f23b8a-5d3f-4ff2-a159-13ff3811de1c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;2000-01-01T00:00:00.000000000&#x27;, &#x27;2000-01-01T06:00:00.000000000&#x27;,\n",
-       "       &#x27;2000-01-01T12:00:00.000000000&#x27;, &#x27;2000-01-01T18:00:00.000000000&#x27;,\n",
-       "       &#x27;2000-01-02T00:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lat</span></div><div class='xr-var-dims'>(grid_index)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(625,), meta=np.ndarray&gt;</div><input id='attrs-e520eddb-5b88-45ef-9bc4-15f7df9af498' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e520eddb-5b88-45ef-9bc4-15f7df9af498' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fe34eed4-e850-4d20-b7e7-8d0bc72e2d5f' class='xr-var-data-in' type='checkbox'><label for='data-fe34eed4-e850-4d20-b7e7-8d0bc72e2d5f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
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-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 4.88 kiB </td>\n",
-       "                        <td> 4.88 kiB </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (625,) </td>\n",
-       "                        <td> (625,) </td>\n",
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-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n",
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-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
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-       "\n",
-       "  <!-- Text -->\n",
-       "  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >625</text>\n",
-       "  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n",
-       "</svg>\n",
-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon</span></div><div class='xr-var-dims'>(grid_index)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(625,), meta=np.ndarray&gt;</div><input id='attrs-71a12dc7-2c15-4752-b8c0-f9a2cb9d4acc' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-71a12dc7-2c15-4752-b8c0-f9a2cb9d4acc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3dfa0318-063c-4530-b3c4-1d9861db45e9' class='xr-var-data-in' type='checkbox'><label for='data-3dfa0318-063c-4530-b3c4-1d9861db45e9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table style=\"border-collapse: collapse;\">\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 4.88 kiB </td>\n",
-       "                        <td> 4.88 kiB </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (625,) </td>\n",
-       "                        <td> (625,) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
-       "        <td>\n",
-       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
-       "\n",
-       "  <!-- Horizontal lines -->\n",
-       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
-       "\n",
-       "  <!-- Vertical lines -->\n",
-       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
-       "\n",
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-       "\n",
-       "  <!-- Text -->\n",
-       "  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >625</text>\n",
-       "  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n",
-       "</svg>\n",
-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>x</span></div><div class='xr-var-dims'>(grid_index)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(625,), meta=np.ndarray&gt;</div><input id='attrs-dff0d4ea-6c18-4a1a-acb5-eb0783c7d37e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-dff0d4ea-6c18-4a1a-acb5-eb0783c7d37e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-18535fd0-9ec7-45ac-9cfa-18199fa8c4ab' class='xr-var-data-in' type='checkbox'><label for='data-18535fd0-9ec7-45ac-9cfa-18199fa8c4ab' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table style=\"border-collapse: collapse;\">\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 4.88 kiB </td>\n",
-       "                        <td> 4.88 kiB </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (625,) </td>\n",
-       "                        <td> (625,) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 1 chunks in 1 graph layer </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
-       "        <td>\n",
-       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
-       "\n",
-       "  <!-- Horizontal lines -->\n",
-       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
-       "\n",
-       "  <!-- Vertical lines -->\n",
-       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
-       "\n",
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-       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
-       "\n",
-       "  <!-- Text -->\n",
-       "  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >625</text>\n",
-       "  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n",
-       "</svg>\n",
-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>y</span></div><div class='xr-var-dims'>(grid_index)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(625,), meta=np.ndarray&gt;</div><input id='attrs-ac5c0f48-2b98-403f-a7d4-fc18633d08f5' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ac5c0f48-2b98-403f-a7d4-fc18633d08f5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d191ec81-eb23-49b1-b30e-a28a8dd62441' class='xr-var-data-in' type='checkbox'><label for='data-d191ec81-eb23-49b1-b30e-a28a8dd62441' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table style=\"border-collapse: collapse;\">\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 4.88 kiB </td>\n",
-       "                        <td> 4.88 kiB </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (625,) </td>\n",
-       "                        <td> (625,) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 1 chunks in 1 graph layer </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
-       "        <td>\n",
-       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
-       "\n",
-       "  <!-- Horizontal lines -->\n",
-       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
-       "\n",
-       "  <!-- Vertical lines -->\n",
-       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
-       "\n",
-       "  <!-- Colored Rectangle -->\n",
-       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
-       "\n",
-       "  <!-- Text -->\n",
-       "  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >625</text>\n",
-       "  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n",
-       "</svg>\n",
-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>state_feature</span></div><div class='xr-var-dims'>(state_feature)</div><div class='xr-var-dtype'>&lt;U15</div><div class='xr-var-preview xr-preview'>&#x27;pres_seasurface&#x27;</div><input id='attrs-60316e24-9a9d-4323-84ca-f3d7064f553e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-60316e24-9a9d-4323-84ca-f3d7064f553e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ce9fd0f4-d8ce-4b8c-9b60-12c7a8d3796c' class='xr-var-data-in' type='checkbox'><label for='data-ce9fd0f4-d8ce-4b8c-9b60-12c7a8d3796c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;pres_seasurface&#x27;], dtype=&#x27;&lt;U15&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>state_feature_units</span></div><div class='xr-var-dims'>(state_feature)</div><div class='xr-var-dtype'>&lt;U1</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;</div><input id='attrs-fbbdc307-16ee-48a5-bd13-bcf726b2b5ae' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-fbbdc307-16ee-48a5-bd13-bcf726b2b5ae' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b52e986d-c140-4426-9afc-15a47c6c7fe0' class='xr-var-data-in' type='checkbox'><label for='data-b52e986d-c140-4426-9afc-15a47c6c7fe0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table style=\"border-collapse: collapse;\">\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 4 B </td>\n",
-       "                        <td> 4 B </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (1,) </td>\n",
-       "                        <td> (1,) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 1 chunks in 1 graph layer </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> <U1 numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
-       "        <td>\n",
-       "        <svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
-       "\n",
-       "  <!-- Horizontal lines -->\n",
-       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"0\" y1=\"120\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
-       "\n",
-       "  <!-- Vertical lines -->\n",
-       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
-       "\n",
-       "  <!-- Colored Rectangle -->\n",
-       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
-       "\n",
-       "  <!-- Text -->\n",
-       "  <text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1</text>\n",
-       "  <text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,60.000000)\">1</text>\n",
-       "</svg>\n",
-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>state_feature_long_name</span></div><div class='xr-var-dims'>(state_feature)</div><div class='xr-var-dtype'>&lt;U1</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;</div><input id='attrs-a774260b-8f93-4609-afbe-547378de0848' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a774260b-8f93-4609-afbe-547378de0848' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c6368c46-236a-453f-9f5b-8a85faa29d6a' class='xr-var-data-in' type='checkbox'><label for='data-c6368c46-236a-453f-9f5b-8a85faa29d6a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table style=\"border-collapse: collapse;\">\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 4 B </td>\n",
-       "                        <td> 4 B </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (1,) </td>\n",
-       "                        <td> (1,) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 1 chunks in 1 graph layer </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> <U1 numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
-       "        <td>\n",
-       "        <svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
-       "\n",
-       "  <!-- Horizontal lines -->\n",
-       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"0\" y1=\"120\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
-       "\n",
-       "  <!-- Vertical lines -->\n",
-       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
-       "\n",
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-       "\n",
-       "  <!-- Text -->\n",
-       "  <text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1</text>\n",
-       "  <text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,60.000000)\">1</text>\n",
-       "</svg>\n",
-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>state_feature_source_dataset</span></div><div class='xr-var-dims'>(state_feature)</div><div class='xr-var-dtype'>&lt;U23</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;</div><input id='attrs-25cc5d88-a251-46a0-8dc6-180f5afdcbfd' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-25cc5d88-a251-46a0-8dc6-180f5afdcbfd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c97c57fc-0480-4b62-af4f-401816eecf4f' class='xr-var-data-in' type='checkbox'><label for='data-c97c57fc-0480-4b62-af4f-401816eecf4f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table style=\"border-collapse: collapse;\">\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 92 B </td>\n",
-       "                        <td> 92 B </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (1,) </td>\n",
-       "                        <td> (1,) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 1 chunks in 1 graph layer </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> <U23 numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
-       "        <td>\n",
-       "        <svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
-       "\n",
-       "  <!-- Horizontal lines -->\n",
-       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"0\" y1=\"120\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
-       "\n",
-       "  <!-- Vertical lines -->\n",
-       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"120\" style=\"stroke-width:2\" />\n",
-       "\n",
-       "  <!-- Colored Rectangle -->\n",
-       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
-       "\n",
-       "  <!-- Text -->\n",
-       "  <text x=\"60.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1</text>\n",
-       "  <text x=\"140.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,60.000000)\">1</text>\n",
-       "</svg>\n",
-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>grid_index</span></div><div class='xr-var-dims'>(grid_index)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 620 621 622 623 624</div><input id='attrs-b927b3c2-6627-4d85-9001-34cd31b05b17' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b927b3c2-6627-4d85-9001-34cd31b05b17' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-52be8376-5da1-4691-9c12-a0cc766e8a81' class='xr-var-data-in' type='checkbox'><label for='data-52be8376-5da1-4691-9c12-a0cc766e8a81' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  0,   1,   2, ..., 622, 623, 624])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-8f7dbca4-6eea-4afe-bfba-7fa7b1026a0f' class='xr-section-summary-in' type='checkbox'  checked><label for='section-8f7dbca4-6eea-4afe-bfba-7fa7b1026a0f' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>state</span></div><div class='xr-var-dims'>(state_feature, time, grid_index)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 5, 625), meta=np.ndarray&gt;</div><input id='attrs-6c9f3f0e-39ad-4dc7-b6be-4631196a48ed' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6c9f3f0e-39ad-4dc7-b6be-4631196a48ed' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5f427456-fa2e-4410-8ae5-0d5f6b85e3c8' class='xr-var-data-in' type='checkbox'><label for='data-5f427456-fa2e-4410-8ae5-0d5f6b85e3c8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>variables_mapping_dim :</span></dt><dd>state_feature</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table style=\"border-collapse: collapse;\">\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 24.41 kiB </td>\n",
-       "                        <td> 24.41 kiB </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (1, 5, 625) </td>\n",
-       "                        <td> (1, 5, 625) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 1 chunks in 4 graph layers </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
-       "        <td>\n",
-       "        <svg width=\"194\" height=\"90\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
-       "\n",
-       "  <!-- Horizontal lines -->\n",
-       "  <line x1=\"10\" y1=\"0\" x2=\"24\" y2=\"14\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"10\" y1=\"25\" x2=\"24\" y2=\"40\" style=\"stroke-width:2\" />\n",
-       "\n",
-       "  <!-- Vertical lines -->\n",
-       "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"25\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"40\" style=\"stroke-width:2\" />\n",
-       "\n",
-       "  <!-- Colored Rectangle -->\n",
-       "  <polygon points=\"10.0,0.0 24.9485979497544,14.948597949754403 24.9485979497544,40.36121446433689 10.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
-       "\n",
-       "  <!-- Horizontal lines -->\n",
-       "  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"24\" y1=\"14\" x2=\"144\" y2=\"14\" style=\"stroke-width:2\" />\n",
-       "\n",
-       "  <!-- Vertical lines -->\n",
-       "  <line x1=\"10\" y1=\"0\" x2=\"24\" y2=\"14\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"130\" y1=\"0\" x2=\"144\" y2=\"14\" style=\"stroke-width:2\" />\n",
-       "\n",
-       "  <!-- Colored Rectangle -->\n",
-       "  <polygon points=\"10.0,0.0 130.0,0.0 144.9485979497544,14.948597949754403 24.9485979497544,14.948597949754403\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
-       "\n",
-       "  <!-- Horizontal lines -->\n",
-       "  <line x1=\"24\" y1=\"14\" x2=\"144\" y2=\"14\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"24\" y1=\"40\" x2=\"144\" y2=\"40\" style=\"stroke-width:2\" />\n",
-       "\n",
-       "  <!-- Vertical lines -->\n",
-       "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"40\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"144\" y1=\"14\" x2=\"144\" y2=\"40\" style=\"stroke-width:2\" />\n",
-       "\n",
-       "  <!-- Colored Rectangle -->\n",
-       "  <polygon points=\"24.9485979497544,14.948597949754403 144.9485979497544,14.948597949754403 144.9485979497544,40.36121446433689 24.9485979497544,40.36121446433689\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
-       "\n",
-       "  <!-- Text -->\n",
-       "  <text x=\"84.948598\" y=\"60.361214\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >625</text>\n",
-       "  <text x=\"164.948598\" y=\"27.654906\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,164.948598,27.654906)\">5</text>\n",
-       "  <text x=\"7.474299\" y=\"52.886915\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,7.474299,52.886915)\">1</text>\n",
-       "</svg>\n",
-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-f25ac1a5-2266-415f-a048-fd801872dfed' class='xr-section-summary-in' type='checkbox'  ><label for='section-f25ac1a5-2266-415f-a048-fd801872dfed' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-1e8b54dc-ed6a-4fc0-ba2d-97b091f346da' class='xr-index-data-in' type='checkbox'/><label for='index-1e8b54dc-ed6a-4fc0-ba2d-97b091f346da' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2000-01-01 00:00:00&#x27;, &#x27;2000-01-01 06:00:00&#x27;,\n",
-       "               &#x27;2000-01-01 12:00:00&#x27;, &#x27;2000-01-01 18:00:00&#x27;,\n",
-       "               &#x27;2000-01-02 00:00:00&#x27;],\n",
-       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>state_feature</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-cf1143d0-595d-4554-aa39-eae722c57de2' class='xr-index-data-in' type='checkbox'/><label for='index-cf1143d0-595d-4554-aa39-eae722c57de2' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;pres_seasurface&#x27;], dtype=&#x27;object&#x27;, name=&#x27;state_feature&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>grid_index</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-7338622f-e91b-4bd8-b871-445ab906b5e4' class='xr-index-data-in' type='checkbox'/><label for='index-7338622f-e91b-4bd8-b871-445ab906b5e4' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,\n",
-       "       ...\n",
-       "       615, 616, 617, 618, 619, 620, 621, 622, 623, 624],\n",
-       "      dtype=&#x27;int64&#x27;, name=&#x27;grid_index&#x27;, length=625))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-7f05fac1-1643-4991-92ac-1c193752e0eb' class='xr-section-summary-in' type='checkbox'  checked><label for='section-7f05fac1-1643-4991-92ac-1c193752e0eb' class='xr-section-summary' >Attributes: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>schema_version :</span></dt><dd>v0.5.0</dd><dt><span>dataset_version :</span></dt><dd>v0.1.0</dd><dt><span>created_on :</span></dt><dd>2025-01-08T17:48:34</dd><dt><span>created_with :</span></dt><dd>mllam-data-prep (https://github.com/mllam/mllam-data-prep)</dd><dt><span>mdp_version :</span></dt><dd>v0.5.0</dd></dl></div></li></ul></div></div>"
-      ],
-      "text/plain": [
-       "<xarray.Dataset> Size: 50kB\n",
-       "Dimensions:                       (time: 5, grid_index: 625, state_feature: 1)\n",
-       "Coordinates:\n",
-       "  * time                          (time) datetime64[ns] 40B 2000-01-01 ... 20...\n",
-       "    lat                           (grid_index) float64 5kB dask.array<chunksize=(625,), meta=np.ndarray>\n",
-       "    lon                           (grid_index) float64 5kB dask.array<chunksize=(625,), meta=np.ndarray>\n",
-       "    x                             (grid_index) float64 5kB dask.array<chunksize=(625,), meta=np.ndarray>\n",
-       "    y                             (grid_index) float64 5kB dask.array<chunksize=(625,), meta=np.ndarray>\n",
-       "  * state_feature                 (state_feature) <U15 60B 'pres_seasurface'\n",
-       "    state_feature_units           (state_feature) <U1 4B dask.array<chunksize=(1,), meta=np.ndarray>\n",
-       "    state_feature_long_name       (state_feature) <U1 4B dask.array<chunksize=(1,), meta=np.ndarray>\n",
-       "    state_feature_source_dataset  (state_feature) <U23 92B dask.array<chunksize=(1,), meta=np.ndarray>\n",
-       "  * grid_index                    (grid_index) int64 5kB 0 1 2 3 ... 622 623 624\n",
-       "Data variables:\n",
-       "    state                         (state_feature, time, grid_index) float64 25kB dask.array<chunksize=(1, 5, 625), meta=np.ndarray>\n",
-       "Attributes:\n",
-       "    schema_version:   v0.5.0\n",
-       "    dataset_version:  v0.1.0\n",
-       "    created_on:       2025-01-08T17:48:34\n",
-       "    created_with:     mllam-data-prep (https://github.com/mllam/mllam-data-prep)\n",
-       "    mdp_version:      v0.5.0"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "ds_global"
    ]
@@ -1898,7 +107,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1907,7 +116,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1916,20 +125,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "ds_reference = ds_lam\n",
     "ds = ds_global\n",
@@ -1954,7 +152,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1963,30 +161,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<cartopy.mpl.gridliner.Gridliner at 0x13f02f3b0>"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1000x400 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "fig, ax = plt.subplots(subplot_kw=dict(projection=ccrs.PlateCarree()), figsize=(10, 4))\n",
     "xr.plot.scatter(ax=ax, ds=ds_reference, x=\"lon\", y=\"lat\", label=\"ds_ref\", marker=\".\", transform=ccrs.PlateCarree())\n",
@@ -2004,7 +181,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2016,30 +193,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<cartopy.mpl.gridliner.Gridliner at 0x13f19bcb0>"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1000x400 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "fig, ax = plt.subplots(subplot_kw=dict(projection=ccrs.PlateCarree()), figsize=(10, 4))\n",
     "xr.plot.scatter(ax=ax, ds=ds_reference, x=\"lon\", y=\"lat\", label=\"ds_ref\", marker=\".\", transform=ccrs.PlateCarree())\n",
@@ -2051,7 +207,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2063,30 +219,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<cartopy.mpl.gridliner.Gridliner at 0x13f1b7da0>"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1000x400 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "fig, ax = plt.subplots(subplot_kw=dict(projection=ccrs.PlateCarree()), figsize=(10, 4))\n",
     "xr.plot.scatter(ax=ax, ds=ds_reference, x=\"lon\", y=\"lat\", label=\"ds_ref\", marker=\".\", transform=ccrs.PlateCarree())\n",
@@ -2106,7 +241,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": ".venv",
+   "display_name": "mllam-data-prep",
    "language": "python",
    "name": "python3"
   },
@@ -2120,7 +255,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.4"
+   "version": "3.10.16"
   }
  },
  "nbformat": 4,

From 1674642eaac291c53ba81aa5077a8499af37b55e Mon Sep 17 00:00:00 2001
From: Leif Denby <leif@denby.eu>
Date: Tue, 13 Jan 2026 09:27:50 +0100
Subject: [PATCH 27/28] rm optional dep group for latlon crop

---
 pyproject.toml | 10 ++++------
 1 file changed, 4 insertions(+), 6 deletions(-)

diff --git a/pyproject.toml b/pyproject.toml
index 86838f0..a8a941e 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -28,6 +28,10 @@ dependencies = [
     "psutil>=5.7.2",
     "packaging>=23.1",
     "deepdiff>=8.2.0",
+    "spherical-geometry>=1.3.2",
+    "cartopy>=0.23.0",
+    "gcsfs>=2024.10.0",
+    "jinja2>=3.1.4",
 ]
 requires-python = ">=3.9"
 readme = "README.md"
@@ -38,12 +42,6 @@ dask-distributed = [
     "dask[distributed]>=2024.7.1",
     "bokeh!=3.0.*,>=2.4.2",
 ]
-latlon-domain-crop = [
-    "spherical-geometry>=1.3.2",
-    "cartopy>=0.23.0",
-    "gcsfs>=2024.10.0",
-    "jinja2>=3.1.4",
-]
 
 [build-system]
 requires = ["pdm-backend"]

From 758d0c3046ae755d64d6dffe30f1f523194ce20d Mon Sep 17 00:00:00 2001
From: Leif Denby <leif@denby.eu>
Date: Tue, 13 Jan 2026 09:55:45 +0100
Subject: [PATCH 28/28] unpin dataclass-wizard version

---
 pyproject.toml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/pyproject.toml b/pyproject.toml
index a8a941e..5e659fa 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -21,7 +21,7 @@ dependencies = [
     "isodate>=0.6.1",
     "requests>=2.31.0",
     "aiohttp>=3.9.3",
-    "dataclass-wizard==0.30.0",
+    "dataclass-wizard>=0.29.2",
     "semver>=3.0.2",
     "rich>=13.7.1",
     "dask>=2024.2.1",