From 74c89ae0d5b1b2a23203cbf6ff7a3cf56030fba3 Mon Sep 17 00:00:00 2001
From: Leif Denby <leif@denby.eu>
Date: Tue, 12 Mar 2024 09:15:55 +0000
Subject: [PATCH 1/4] add draft config for global+regional data

---
 example.danra.yaml | 27 +++++++++++++++++++--------
 1 file changed, 19 insertions(+), 8 deletions(-)

diff --git a/example.danra.yaml b/example.danra.yaml
index 19fb07b..49c54f1 100644
--- a/example.danra.yaml
+++ b/example.danra.yaml
@@ -24,10 +24,6 @@ inputs:
         altitude:
           sel: [100, ]
           units: m
-      v:
-        altitude:
-          sel: [100, ]
-          units: m
     dim_mapping:
       time: time
       state_feature:
@@ -37,13 +33,21 @@ inputs:
       grid_index:
         method: flatten
         dims: [x, y]
+    subsetting:
+      type: latlon-bbox
+      coordinates:
+        # give WESN bounding box around Denmark in degrees
+        wesn: [10, 50, 20, 60]
     target_architecture_variable: state
 
-  danra_surface:
-    path: ~/Desktop/mldev/single_levels.zarr
+  era_height_levels:
+    path: ~/Desktop/mldev/era5.zarr
     dims: [time, x, y]
     variables:
-      - pres_seasurface
+      u:
+        altitude:
+          sel: [100, ]
+          units: m
     dim_mapping:
       time: time
       grid_index:
@@ -52,4 +56,11 @@ inputs:
       forcing_feature:
         method: stack_variables_by_var_name
         name_format: f"{var_name}"
-    target_architecture_variable: forcing
+    subsetting:
+      type: latlon-bbox-frame
+      coordinates:
+        # give WESN bounding box around Denmark in degrees
+        wesn: [10, 50, 20, 60]
+        # width in degrees
+        latlonwidth: 0.25
+    target_architecture_variable: state

From bdb2cbe96a7cac3084d014340679d6bc835b4b05 Mon Sep 17 00:00:00 2001
From: Leif Denby <lcd@dmi.dk>
Date: Tue, 12 Mar 2024 14:21:07 +0000
Subject: [PATCH 2/4] add lateral boundary by latlon bbox notebook

---
 notebooks/lateral-boundaries.ipynb | 6656 ++++++++++++++++++++++++++++
 1 file changed, 6656 insertions(+)
 create mode 100644 notebooks/lateral-boundaries.ipynb

diff --git a/notebooks/lateral-boundaries.ipynb b/notebooks/lateral-boundaries.ipynb
new file mode 100644
index 0000000..6ea34fa
--- /dev/null
+++ b/notebooks/lateral-boundaries.ipynb
@@ -0,0 +1,6656 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import xarray as xr\n",
+    "import matplotlib.pyplot as plt\n",
+    "import cartopy.crs as ccrs\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fp_danra = \"/dmidata/projects/cloudphysics/danra/data/v0.3.0/height_levels.zarr/\"\n",
+    "url_era5 = \"gs://gcp-public-data-arco-era5/ar/1959-2022-full_37-1h-0p25deg-chunk-1.zarr-v2\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "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",
+       "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n",
+       "<path d=\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
+       "<path d=\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
+       "</symbol>\n",
+       "<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n",
+       "<path d=\"M28.681 7.159c-0.694-0.947-1.662-2.053-2.724-3.116s-2.169-2.030-3.116-2.724c-1.612-1.182-2.393-1.319-2.841-1.319h-15.5c-1.378 0-2.5 1.121-2.5 2.5v27c0 1.378 1.122 2.5 2.5 2.5h23c1.378 0 2.5-1.122 2.5-2.5v-19.5c0-0.448-0.137-1.23-1.319-2.841zM24.543 5.457c0.959 0.959 1.712 1.825 2.268 2.543h-4.811v-4.811c0.718 0.556 1.584 1.309 2.543 2.268zM28 29.5c0 0.271-0.229 0.5-0.5 0.5h-23c-0.271 0-0.5-0.229-0.5-0.5v-27c0-0.271 0.229-0.5 0.5-0.5 0 0 15.499-0 15.5 0v7c0 0.552 0.448 1 1 1h7v19.5z\"></path>\n",
+       "<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;\n",
+       "Dimensions:                                           (time: 552264, lat: 721,\n",
+       "                                                       lon: 1440, level: 37)\n",
+       "Coordinates:\n",
+       "  * lat                                               (lat) float32 90.0 ... ...\n",
+       "  * level                                             (level) int64 1 2 ... 1000\n",
+       "  * lon                                               (lon) float32 0.0 ... 3...\n",
+       "  * time                                              (time) datetime64[ns] 1...\n",
+       "Data variables: (12/31)\n",
+       "    10m_u_component_of_wind                           (time, lat, lon) float32 dask.array&lt;chunksize=(48, 721, 1440), meta=np.ndarray&gt;\n",
+       "    10m_v_component_of_wind                           (time, lat, lon) float32 dask.array&lt;chunksize=(48, 721, 1440), meta=np.ndarray&gt;\n",
+       "    2m_temperature                                    (time, lat, lon) float32 dask.array&lt;chunksize=(48, 721, 1440), meta=np.ndarray&gt;\n",
+       "    angle_of_sub_gridscale_orography                  (lat, lon) float32 dask.array&lt;chunksize=(721, 1440), meta=np.ndarray&gt;\n",
+       "    anisotropy_of_sub_gridscale_orography             (lat, lon) float32 dask.array&lt;chunksize=(721, 1440), meta=np.ndarray&gt;\n",
+       "    geopotential                                      (time, level, lat, lon) float32 dask.array&lt;chunksize=(48, 37, 721, 1440), meta=np.ndarray&gt;\n",
+       "    ...                                                ...\n",
+       "    total_precipitation                               (time, lat, lon) float32 dask.array&lt;chunksize=(48, 721, 1440), meta=np.ndarray&gt;\n",
+       "    type_of_high_vegetation                           (lat, lon) float32 dask.array&lt;chunksize=(721, 1440), meta=np.ndarray&gt;\n",
+       "    type_of_low_vegetation                            (lat, lon) float32 dask.array&lt;chunksize=(721, 1440), meta=np.ndarray&gt;\n",
+       "    u_component_of_wind                               (time, level, lat, lon) float32 dask.array&lt;chunksize=(48, 37, 721, 1440), meta=np.ndarray&gt;\n",
+       "    v_component_of_wind                               (time, level, lat, lon) float32 dask.array&lt;chunksize=(48, 37, 721, 1440), meta=np.ndarray&gt;\n",
+       "    vertical_velocity                                 (time, level, lat, lon) float32 dask.array&lt;chunksize=(48, 37, 721, 1440), 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-206a4751-ac04-45b0-9f6b-0c6bf3cecc33' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-206a4751-ac04-45b0-9f6b-0c6bf3cecc33' 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>: 552264</li><li><span class='xr-has-index'>lat</span>: 721</li><li><span class='xr-has-index'>lon</span>: 1440</li><li><span class='xr-has-index'>level</span>: 37</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-6a3714e4-1d93-4b46-8fb5-71c61bfccdb7' class='xr-section-summary-in' type='checkbox'  checked><label for='section-6a3714e4-1d93-4b46-8fb5-71c61bfccdb7' 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'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>90.0 89.75 89.5 ... -89.75 -90.0</div><input id='attrs-f0fd45fd-428f-4149-9b11-380de3acebd6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f0fd45fd-428f-4149-9b11-380de3acebd6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-868c3dd6-5a8f-438a-8cd2-4497813fcdf5' class='xr-var-data-in' type='checkbox'><label for='data-868c3dd6-5a8f-438a-8cd2-4497813fcdf5' title='Show/Hide data 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>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><pre>array([ 90.  ,  89.75,  89.5 , ..., -89.5 , -89.75, -90.  ], dtype=float32)</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'>1 2 3 5 7 ... 900 925 950 975 1000</div><input id='attrs-d11a6d82-c339-41bc-bf76-0dfe49667866' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d11a6d82-c339-41bc-bf76-0dfe49667866' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a665a74f-8a44-4999-b655-9296db0689f8' class='xr-var-data-in' type='checkbox'><label for='data-a665a74f-8a44-4999-b655-9296db0689f8' title='Show/Hide data 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([   1,    2,    3,    5,    7,   10,   20,   30,   50,   70,  100,  125,\n",
+       "        150,  175,  200,  225,  250,  300,  350,  400,  450,  500,  550,  600,\n",
+       "        650,  700,  750,  775,  800,  825,  850,  875,  900,  925,  950,  975,\n",
+       "       1000])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0 0.25 0.5 ... 359.2 359.5 359.8</div><input id='attrs-20421289-44ee-49c7-ba05-31d016472533' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-20421289-44ee-49c7-ba05-31d016472533' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fdd7ab6e-9979-4a10-99f7-8f8a1943d18b' class='xr-var-data-in' type='checkbox'><label for='data-fdd7ab6e-9979-4a10-99f7-8f8a1943d18b' title='Show/Hide data 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>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>array([0.0000e+00, 2.5000e-01, 5.0000e-01, ..., 3.5925e+02, 3.5950e+02,\n",
+       "       3.5975e+02], dtype=float32)</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 ... 2021-12-31T23:00:00</div><input id='attrs-5542f7f1-2133-413a-ab87-fe7778ca9c7c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5542f7f1-2133-413a-ab87-fe7778ca9c7c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-130f35b6-e038-42c9-b455-9d8fedd18507' class='xr-var-data-in' type='checkbox'><label for='data-130f35b6-e038-42c9-b455-9d8fedd18507' title='Show/Hide data 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;1959-01-01T01:00:00.000000000&#x27;,\n",
+       "       &#x27;1959-01-01T02:00:00.000000000&#x27;, ..., &#x27;2021-12-31T21:00:00.000000000&#x27;,\n",
+       "       &#x27;2021-12-31T22:00:00.000000000&#x27;, &#x27;2021-12-31T23:00:00.000000000&#x27;],\n",
+       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-41349960-85f9-4a26-b97b-2bd59fd21d5d' class='xr-section-summary-in' type='checkbox'  ><label for='section-41349960-85f9-4a26-b97b-2bd59fd21d5d' class='xr-section-summary' >Data variables: <span>(31)</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, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(48, 721, 1440), meta=np.ndarray&gt;</div><input id='attrs-31602b59-cdd8-4783-b927-a732b95700a8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-31602b59-cdd8-4783-b927-a732b95700a8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-179d0791-cb77-44c1-adb3-a1637e9e44cf' class='xr-var-data-in' type='checkbox'><label for='data-179d0791-cb77-44c1-adb3-a1637e9e44cf' title='Show/Hide data 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> 2.09 TiB </td>\n",
+       "                        <td> 190.11 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (552264, 721, 1440) </td>\n",
+       "                        <td> (48, 721, 1440) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 11506 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",
+       "        <svg width=\"156\" height=\"146\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"10\" y1=\"25\" x2=\"80\" y2=\"96\" 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=\"13\" y1=\"3\" x2=\"13\" y2=\"29\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"17\" y2=\"32\" />\n",
+       "  <line x1=\"21\" y1=\"11\" x2=\"21\" y2=\"36\" />\n",
+       "  <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=\"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=\"69\" />\n",
+       "  <line x1=\"58\" y1=\"48\" x2=\"58\" y2=\"73\" />\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",
+       "  <line x1=\"76\" y1=\"66\" x2=\"76\" y2=\"92\" />\n",
+       "  <line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"96\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"10.0,0.0 80.58823529411765,70.58823529411765 80.58823529411765,96.00085180870013 10.0,25.41261651458249\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"35\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"13\" y1=\"3\" x2=\"39\" y2=\"3\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"42\" y2=\"7\" />\n",
+       "  <line x1=\"21\" y1=\"11\" x2=\"46\" y2=\"11\" />\n",
+       "  <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=\"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=\"79\" y2=\"44\" />\n",
+       "  <line x1=\"58\" y1=\"48\" x2=\"83\" y2=\"48\" />\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",
+       "  <line x1=\"76\" y1=\"66\" x2=\"102\" y2=\"66\" />\n",
+       "  <line x1=\"80\" y1=\"70\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"35\" y1=\"0\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"10.0,0.0 35.41261651458249,0.0 106.00085180870013,70.58823529411765 80.58823529411765,70.58823529411765\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"80\" y1=\"70\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"80\" y1=\"96\" x2=\"106\" y2=\"96\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"96\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"106\" y1=\"70\" x2=\"106\" y2=\"96\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"80.58823529411765,70.58823529411765 106.00085180870013,70.58823529411765 106.00085180870013,96.00085180870013 80.58823529411765,96.00085180870013\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"93.294544\" y=\"116.000852\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1440</text>\n",
+       "  <text x=\"126.000852\" y=\"83.294544\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,126.000852,83.294544)\">721</text>\n",
+       "  <text x=\"35.294118\" y=\"80.706734\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,35.294118,80.706734)\">552264</text>\n",
+       "</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, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(48, 721, 1440), meta=np.ndarray&gt;</div><input id='attrs-994f102c-626e-48dd-940e-37800cebdb8e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-994f102c-626e-48dd-940e-37800cebdb8e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-acf32e54-070e-4ed8-b7ae-4421809f5aad' class='xr-var-data-in' type='checkbox'><label for='data-acf32e54-070e-4ed8-b7ae-4421809f5aad' title='Show/Hide data 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> 2.09 TiB </td>\n",
+       "                        <td> 190.11 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (552264, 721, 1440) </td>\n",
+       "                        <td> (48, 721, 1440) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 11506 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",
+       "        <svg width=\"156\" height=\"146\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"10\" y1=\"25\" x2=\"80\" y2=\"96\" 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=\"13\" y1=\"3\" x2=\"13\" y2=\"29\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"17\" y2=\"32\" />\n",
+       "  <line x1=\"21\" y1=\"11\" x2=\"21\" y2=\"36\" />\n",
+       "  <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=\"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=\"69\" />\n",
+       "  <line x1=\"58\" y1=\"48\" x2=\"58\" y2=\"73\" />\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",
+       "  <line x1=\"76\" y1=\"66\" x2=\"76\" y2=\"92\" />\n",
+       "  <line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"96\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"10.0,0.0 80.58823529411765,70.58823529411765 80.58823529411765,96.00085180870013 10.0,25.41261651458249\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"35\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"13\" y1=\"3\" x2=\"39\" y2=\"3\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"42\" y2=\"7\" />\n",
+       "  <line x1=\"21\" y1=\"11\" x2=\"46\" y2=\"11\" />\n",
+       "  <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=\"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=\"79\" y2=\"44\" />\n",
+       "  <line x1=\"58\" y1=\"48\" x2=\"83\" y2=\"48\" />\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",
+       "  <line x1=\"76\" y1=\"66\" x2=\"102\" y2=\"66\" />\n",
+       "  <line x1=\"80\" y1=\"70\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"35\" y1=\"0\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"10.0,0.0 35.41261651458249,0.0 106.00085180870013,70.58823529411765 80.58823529411765,70.58823529411765\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"80\" y1=\"70\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"80\" y1=\"96\" x2=\"106\" y2=\"96\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"96\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"106\" y1=\"70\" x2=\"106\" y2=\"96\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"80.58823529411765,70.58823529411765 106.00085180870013,70.58823529411765 106.00085180870013,96.00085180870013 80.58823529411765,96.00085180870013\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"93.294544\" y=\"116.000852\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1440</text>\n",
+       "  <text x=\"126.000852\" y=\"83.294544\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,126.000852,83.294544)\">721</text>\n",
+       "  <text x=\"35.294118\" y=\"80.706734\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,35.294118,80.706734)\">552264</text>\n",
+       "</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, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(48, 721, 1440), meta=np.ndarray&gt;</div><input id='attrs-67f81c8b-e7ca-4ce7-b9ba-2768ec787f2b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-67f81c8b-e7ca-4ce7-b9ba-2768ec787f2b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-460dd4af-da8e-4cd2-81fb-80003922e1c2' class='xr-var-data-in' type='checkbox'><label for='data-460dd4af-da8e-4cd2-81fb-80003922e1c2' title='Show/Hide data 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",
+       "                <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> 2.09 TiB </td>\n",
+       "                        <td> 190.11 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (552264, 721, 1440) </td>\n",
+       "                        <td> (48, 721, 1440) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 11506 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",
+       "        <svg width=\"156\" height=\"146\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"10\" y1=\"25\" x2=\"80\" y2=\"96\" 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=\"13\" y1=\"3\" x2=\"13\" y2=\"29\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"17\" y2=\"32\" />\n",
+       "  <line x1=\"21\" y1=\"11\" x2=\"21\" y2=\"36\" />\n",
+       "  <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=\"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=\"69\" />\n",
+       "  <line x1=\"58\" y1=\"48\" x2=\"58\" y2=\"73\" />\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",
+       "  <line x1=\"76\" y1=\"66\" x2=\"76\" y2=\"92\" />\n",
+       "  <line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"96\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"10.0,0.0 80.58823529411765,70.58823529411765 80.58823529411765,96.00085180870013 10.0,25.41261651458249\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"35\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"13\" y1=\"3\" x2=\"39\" y2=\"3\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"42\" y2=\"7\" />\n",
+       "  <line x1=\"21\" y1=\"11\" x2=\"46\" y2=\"11\" />\n",
+       "  <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=\"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=\"79\" y2=\"44\" />\n",
+       "  <line x1=\"58\" y1=\"48\" x2=\"83\" y2=\"48\" />\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",
+       "  <line x1=\"76\" y1=\"66\" x2=\"102\" y2=\"66\" />\n",
+       "  <line x1=\"80\" y1=\"70\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"35\" y1=\"0\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"10.0,0.0 35.41261651458249,0.0 106.00085180870013,70.58823529411765 80.58823529411765,70.58823529411765\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"80\" y1=\"70\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"80\" y1=\"96\" x2=\"106\" y2=\"96\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"96\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"106\" y1=\"70\" x2=\"106\" y2=\"96\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"80.58823529411765,70.58823529411765 106.00085180870013,70.58823529411765 106.00085180870013,96.00085180870013 80.58823529411765,96.00085180870013\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"93.294544\" y=\"116.000852\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1440</text>\n",
+       "  <text x=\"126.000852\" y=\"83.294544\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,126.000852,83.294544)\">721</text>\n",
+       "  <text x=\"35.294118\" y=\"80.706734\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,35.294118,80.706734)\">552264</text>\n",
+       "</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'>(lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(721, 1440), meta=np.ndarray&gt;</div><input id='attrs-36850b55-9706-4cba-b246-4da927387d94' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-36850b55-9706-4cba-b246-4da927387d94' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3a0fe18e-36e5-4773-819b-46131f06beea' class='xr-var-data-in' type='checkbox'><label for='data-3a0fe18e-36e5-4773-819b-46131f06beea' title='Show/Hide data 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> 3.96 MiB </td>\n",
+       "                        <td> 3.96 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (721, 1440) </td>\n",
+       "                        <td> (721, 1440) </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",
+       "        <svg width=\"170\" height=\"110\" 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=\"60\" x2=\"120\" y2=\"60\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"60\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"60\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,60.083333333333336 0.0,60.083333333333336\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"80.083333\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1440</text>\n",
+       "  <text x=\"140.000000\" y=\"30.041667\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,30.041667)\">721</text>\n",
+       "</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'>(lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(721, 1440), meta=np.ndarray&gt;</div><input id='attrs-1efc08d9-59ac-4750-89e5-eb6f5e724de9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1efc08d9-59ac-4750-89e5-eb6f5e724de9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-154c4280-7319-4eb5-9e73-d555534bdfa9' class='xr-var-data-in' type='checkbox'><label for='data-154c4280-7319-4eb5-9e73-d555534bdfa9' title='Show/Hide data 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> 3.96 MiB </td>\n",
+       "                        <td> 3.96 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (721, 1440) </td>\n",
+       "                        <td> (721, 1440) </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",
+       "        <svg width=\"170\" height=\"110\" 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=\"60\" x2=\"120\" y2=\"60\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"60\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"60\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,60.083333333333336 0.0,60.083333333333336\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"80.083333\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1440</text>\n",
+       "  <text x=\"140.000000\" y=\"30.041667\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,30.041667)\">721</text>\n",
+       "</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, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(48, 37, 721, 1440), meta=np.ndarray&gt;</div><input id='attrs-6984f5db-e409-461f-a12a-695562a01c88' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6984f5db-e409-461f-a12a-695562a01c88' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-659c9f36-2015-4da0-8d91-ea20b7e49648' class='xr-var-data-in' type='checkbox'><label for='data-659c9f36-2015-4da0-8d91-ea20b7e49648' title='Show/Hide data 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",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 77.18 TiB </td>\n",
+       "                        <td> 6.87 GiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (552264, 37, 721, 1440) </td>\n",
+       "                        <td> (48, 37, 721, 1440) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 11506 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",
+       "        <svg width=\"470\" height=\"90\" 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=\"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=\"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=\"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=\"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=\"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=\"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=\"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=\"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",
+       "  <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:#8B4903A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >552264</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",
+       "\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"190\" y1=\"0\" x2=\"204\" y2=\"14\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"190\" y1=\"25\" x2=\"204\" y2=\"40\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"190\" y1=\"0\" x2=\"190\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"204\" y1=\"14\" x2=\"204\" y2=\"40\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"190.0,0.0 204.9485979497544,14.948597949754403 204.9485979497544,40.36121446433689 190.0,25.41261651458249\" 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",
+       "  <line x1=\"215\" y1=\"0\" x2=\"230\" y2=\"14\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"190.0,0.0 215.41261651458248,0.0 230.36121446433688,14.948597949754403 204.9485979497544,14.948597949754403\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\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",
+       "  <polygon points=\"204.9485979497544,14.948597949754403 230.36121446433688,14.948597949754403 230.36121446433688,40.36121446433689 204.9485979497544,40.36121446433689\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"217.654906\" y=\"60.361214\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1440</text>\n",
+       "  <text x=\"250.361214\" y=\"27.654906\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,250.361214,27.654906)\">721</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)\">37</text>\n",
+       "</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'>(lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(721, 1440), meta=np.ndarray&gt;</div><input id='attrs-ba884afd-aa87-424a-81f4-c5a9c8243a2f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ba884afd-aa87-424a-81f4-c5a9c8243a2f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-25b63f77-0bde-4d69-b814-d7fe25cdb87f' class='xr-var-data-in' type='checkbox'><label for='data-25b63f77-0bde-4d69-b814-d7fe25cdb87f' title='Show/Hide data 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",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 3.96 MiB </td>\n",
+       "                        <td> 3.96 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (721, 1440) </td>\n",
+       "                        <td> (721, 1440) </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",
+       "        <svg width=\"170\" height=\"110\" 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=\"60\" x2=\"120\" y2=\"60\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"60\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"60\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,60.083333333333336 0.0,60.083333333333336\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"80.083333\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1440</text>\n",
+       "  <text x=\"140.000000\" y=\"30.041667\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,30.041667)\">721</text>\n",
+       "</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'>(lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(721, 1440), meta=np.ndarray&gt;</div><input id='attrs-16347a3e-0aca-46d1-96de-d8441108ce67' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-16347a3e-0aca-46d1-96de-d8441108ce67' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1306ab1a-e8e4-4687-8a1e-c2180a37cd6a' class='xr-var-data-in' type='checkbox'><label for='data-1306ab1a-e8e4-4687-8a1e-c2180a37cd6a' title='Show/Hide data 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",
+       "                <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> 3.96 MiB </td>\n",
+       "                        <td> 3.96 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (721, 1440) </td>\n",
+       "                        <td> (721, 1440) </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",
+       "        <svg width=\"170\" height=\"110\" 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=\"60\" x2=\"120\" y2=\"60\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"60\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"60\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,60.083333333333336 0.0,60.083333333333336\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"80.083333\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1440</text>\n",
+       "  <text x=\"140.000000\" y=\"30.041667\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,30.041667)\">721</text>\n",
+       "</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'>(lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(721, 1440), meta=np.ndarray&gt;</div><input id='attrs-0badc767-8f97-4b4c-98e5-23b6899bb88d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0badc767-8f97-4b4c-98e5-23b6899bb88d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e120a213-7bae-4075-848f-86f8a308b6c3' class='xr-var-data-in' type='checkbox'><label for='data-e120a213-7bae-4075-848f-86f8a308b6c3' title='Show/Hide data 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> 3.96 MiB </td>\n",
+       "                        <td> 3.96 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (721, 1440) </td>\n",
+       "                        <td> (721, 1440) </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",
+       "        <svg width=\"170\" height=\"110\" 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=\"60\" x2=\"120\" y2=\"60\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"60\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"60\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,60.083333333333336 0.0,60.083333333333336\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"80.083333\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1440</text>\n",
+       "  <text x=\"140.000000\" y=\"30.041667\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,30.041667)\">721</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lake_depth</span></div><div class='xr-var-dims'>(lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(721, 1440), meta=np.ndarray&gt;</div><input id='attrs-3a9dcd3b-71df-44db-ba6a-9d07d9aa1f64' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3a9dcd3b-71df-44db-ba6a-9d07d9aa1f64' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-999ec503-a0c5-472c-8c0f-d93450abe716' class='xr-var-data-in' type='checkbox'><label for='data-999ec503-a0c5-472c-8c0f-d93450abe716' title='Show/Hide data 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 total depth</dd><dt><span>short_name :</span></dt><dd>dl</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> 3.96 MiB </td>\n",
+       "                        <td> 3.96 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (721, 1440) </td>\n",
+       "                        <td> (721, 1440) </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",
+       "        <svg width=\"170\" height=\"110\" 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=\"60\" x2=\"120\" y2=\"60\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"60\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"60\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,60.083333333333336 0.0,60.083333333333336\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"80.083333\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1440</text>\n",
+       "  <text x=\"140.000000\" y=\"30.041667\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,30.041667)\">721</text>\n",
+       "</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'>(lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(721, 1440), meta=np.ndarray&gt;</div><input id='attrs-0e0507a1-0618-4627-bf2b-d963fc3ef483' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0e0507a1-0618-4627-bf2b-d963fc3ef483' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-49c23b87-ef79-4bf6-a8a5-47341eabe8e2' class='xr-var-data-in' type='checkbox'><label for='data-49c23b87-ef79-4bf6-a8a5-47341eabe8e2' title='Show/Hide data 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> 3.96 MiB </td>\n",
+       "                        <td> 3.96 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (721, 1440) </td>\n",
+       "                        <td> (721, 1440) </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",
+       "        <svg width=\"170\" height=\"110\" 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=\"60\" x2=\"120\" y2=\"60\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"60\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"60\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,60.083333333333336 0.0,60.083333333333336\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"80.083333\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1440</text>\n",
+       "  <text x=\"140.000000\" y=\"30.041667\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,30.041667)\">721</text>\n",
+       "</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'>(lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(721, 1440), meta=np.ndarray&gt;</div><input id='attrs-dc317855-af06-4109-b622-9e1a61148ee6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-dc317855-af06-4109-b622-9e1a61148ee6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6b487db2-1eb2-4b10-955b-edff37344fd6' class='xr-var-data-in' type='checkbox'><label for='data-6b487db2-1eb2-4b10-955b-edff37344fd6' title='Show/Hide data 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",
+       "                <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> 3.96 MiB </td>\n",
+       "                        <td> 3.96 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (721, 1440) </td>\n",
+       "                        <td> (721, 1440) </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",
+       "        <svg width=\"170\" height=\"110\" 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=\"60\" x2=\"120\" y2=\"60\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"60\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"60\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,60.083333333333336 0.0,60.083333333333336\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"80.083333\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1440</text>\n",
+       "  <text x=\"140.000000\" y=\"30.041667\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,30.041667)\">721</text>\n",
+       "</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, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(48, 721, 1440), meta=np.ndarray&gt;</div><input id='attrs-fc72efee-846d-4bcd-96ed-e527a1b67f08' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fc72efee-846d-4bcd-96ed-e527a1b67f08' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e6aa3d5a-a62a-45be-9471-383357dfbb5d' class='xr-var-data-in' type='checkbox'><label for='data-e6aa3d5a-a62a-45be-9471-383357dfbb5d' title='Show/Hide data 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",
+       "                        <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> 2.09 TiB </td>\n",
+       "                        <td> 190.11 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (552264, 721, 1440) </td>\n",
+       "                        <td> (48, 721, 1440) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 11506 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",
+       "        <svg width=\"156\" height=\"146\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"10\" y1=\"25\" x2=\"80\" y2=\"96\" 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=\"13\" y1=\"3\" x2=\"13\" y2=\"29\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"17\" y2=\"32\" />\n",
+       "  <line x1=\"21\" y1=\"11\" x2=\"21\" y2=\"36\" />\n",
+       "  <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=\"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=\"69\" />\n",
+       "  <line x1=\"58\" y1=\"48\" x2=\"58\" y2=\"73\" />\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",
+       "  <line x1=\"76\" y1=\"66\" x2=\"76\" y2=\"92\" />\n",
+       "  <line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"96\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"10.0,0.0 80.58823529411765,70.58823529411765 80.58823529411765,96.00085180870013 10.0,25.41261651458249\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"35\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"13\" y1=\"3\" x2=\"39\" y2=\"3\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"42\" y2=\"7\" />\n",
+       "  <line x1=\"21\" y1=\"11\" x2=\"46\" y2=\"11\" />\n",
+       "  <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=\"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=\"79\" y2=\"44\" />\n",
+       "  <line x1=\"58\" y1=\"48\" x2=\"83\" y2=\"48\" />\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",
+       "  <line x1=\"76\" y1=\"66\" x2=\"102\" y2=\"66\" />\n",
+       "  <line x1=\"80\" y1=\"70\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"35\" y1=\"0\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"10.0,0.0 35.41261651458249,0.0 106.00085180870013,70.58823529411765 80.58823529411765,70.58823529411765\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"80\" y1=\"70\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"80\" y1=\"96\" x2=\"106\" y2=\"96\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"96\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"106\" y1=\"70\" x2=\"106\" y2=\"96\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"80.58823529411765,70.58823529411765 106.00085180870013,70.58823529411765 106.00085180870013,96.00085180870013 80.58823529411765,96.00085180870013\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"93.294544\" y=\"116.000852\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1440</text>\n",
+       "  <text x=\"126.000852\" y=\"83.294544\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,126.000852,83.294544)\">721</text>\n",
+       "  <text x=\"35.294118\" y=\"80.706734\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,35.294118,80.706734)\">552264</text>\n",
+       "</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, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(48, 721, 1440), meta=np.ndarray&gt;</div><input id='attrs-471fdb4d-9be7-4818-a7f4-d3e502b3b967' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-471fdb4d-9be7-4818-a7f4-d3e502b3b967' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-aab37796-267f-4fd2-bae9-f331589c8c74' class='xr-var-data-in' type='checkbox'><label for='data-aab37796-267f-4fd2-bae9-f331589c8c74' title='Show/Hide data 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",
+       "                <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> 2.09 TiB </td>\n",
+       "                        <td> 190.11 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (552264, 721, 1440) </td>\n",
+       "                        <td> (48, 721, 1440) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 11506 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",
+       "        <svg width=\"156\" height=\"146\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"10\" y1=\"25\" x2=\"80\" y2=\"96\" 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=\"13\" y1=\"3\" x2=\"13\" y2=\"29\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"17\" y2=\"32\" />\n",
+       "  <line x1=\"21\" y1=\"11\" x2=\"21\" y2=\"36\" />\n",
+       "  <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=\"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=\"69\" />\n",
+       "  <line x1=\"58\" y1=\"48\" x2=\"58\" y2=\"73\" />\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",
+       "  <line x1=\"76\" y1=\"66\" x2=\"76\" y2=\"92\" />\n",
+       "  <line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"96\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"10.0,0.0 80.58823529411765,70.58823529411765 80.58823529411765,96.00085180870013 10.0,25.41261651458249\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"35\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"13\" y1=\"3\" x2=\"39\" y2=\"3\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"42\" y2=\"7\" />\n",
+       "  <line x1=\"21\" y1=\"11\" x2=\"46\" y2=\"11\" />\n",
+       "  <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=\"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=\"79\" y2=\"44\" />\n",
+       "  <line x1=\"58\" y1=\"48\" x2=\"83\" y2=\"48\" />\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",
+       "  <line x1=\"76\" y1=\"66\" x2=\"102\" y2=\"66\" />\n",
+       "  <line x1=\"80\" y1=\"70\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"35\" y1=\"0\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"10.0,0.0 35.41261651458249,0.0 106.00085180870013,70.58823529411765 80.58823529411765,70.58823529411765\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"80\" y1=\"70\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"80\" y1=\"96\" x2=\"106\" y2=\"96\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"96\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"106\" y1=\"70\" x2=\"106\" y2=\"96\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"80.58823529411765,70.58823529411765 106.00085180870013,70.58823529411765 106.00085180870013,96.00085180870013 80.58823529411765,96.00085180870013\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"93.294544\" y=\"116.000852\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1440</text>\n",
+       "  <text x=\"126.000852\" y=\"83.294544\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,126.000852,83.294544)\">721</text>\n",
+       "  <text x=\"35.294118\" y=\"80.706734\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,35.294118,80.706734)\">552264</text>\n",
+       "</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, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(48, 721, 1440), meta=np.ndarray&gt;</div><input id='attrs-0e2ee9d9-f829-4ff9-bb4a-0ad9e8fe08c7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0e2ee9d9-f829-4ff9-bb4a-0ad9e8fe08c7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dcf82163-cc05-4a40-928a-d0bb13187191' class='xr-var-data-in' type='checkbox'><label for='data-dcf82163-cc05-4a40-928a-d0bb13187191' title='Show/Hide data 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",
+       "                <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> 2.09 TiB </td>\n",
+       "                        <td> 190.11 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (552264, 721, 1440) </td>\n",
+       "                        <td> (48, 721, 1440) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 11506 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",
+       "        <svg width=\"156\" height=\"146\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"10\" y1=\"25\" x2=\"80\" y2=\"96\" 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=\"13\" y1=\"3\" x2=\"13\" y2=\"29\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"17\" y2=\"32\" />\n",
+       "  <line x1=\"21\" y1=\"11\" x2=\"21\" y2=\"36\" />\n",
+       "  <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=\"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=\"69\" />\n",
+       "  <line x1=\"58\" y1=\"48\" x2=\"58\" y2=\"73\" />\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",
+       "  <line x1=\"76\" y1=\"66\" x2=\"76\" y2=\"92\" />\n",
+       "  <line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"96\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"10.0,0.0 80.58823529411765,70.58823529411765 80.58823529411765,96.00085180870013 10.0,25.41261651458249\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"35\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"13\" y1=\"3\" x2=\"39\" y2=\"3\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"42\" y2=\"7\" />\n",
+       "  <line x1=\"21\" y1=\"11\" x2=\"46\" y2=\"11\" />\n",
+       "  <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=\"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=\"79\" y2=\"44\" />\n",
+       "  <line x1=\"58\" y1=\"48\" x2=\"83\" y2=\"48\" />\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",
+       "  <line x1=\"76\" y1=\"66\" x2=\"102\" y2=\"66\" />\n",
+       "  <line x1=\"80\" y1=\"70\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"35\" y1=\"0\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"10.0,0.0 35.41261651458249,0.0 106.00085180870013,70.58823529411765 80.58823529411765,70.58823529411765\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"80\" y1=\"70\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"80\" y1=\"96\" x2=\"106\" y2=\"96\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"96\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"106\" y1=\"70\" x2=\"106\" y2=\"96\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"80.58823529411765,70.58823529411765 106.00085180870013,70.58823529411765 106.00085180870013,96.00085180870013 80.58823529411765,96.00085180870013\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"93.294544\" y=\"116.000852\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1440</text>\n",
+       "  <text x=\"126.000852\" y=\"83.294544\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,126.000852,83.294544)\">721</text>\n",
+       "  <text x=\"35.294118\" y=\"80.706734\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,35.294118,80.706734)\">552264</text>\n",
+       "</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'>(lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(721, 1440), meta=np.ndarray&gt;</div><input id='attrs-74e14e42-abd8-4be9-af68-654c8017afae' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-74e14e42-abd8-4be9-af68-654c8017afae' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7519ac24-aa6d-4058-b39b-492ff5b209d1' class='xr-var-data-in' type='checkbox'><label for='data-7519ac24-aa6d-4058-b39b-492ff5b209d1' title='Show/Hide data 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",
+       "                <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> 3.96 MiB </td>\n",
+       "                        <td> 3.96 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (721, 1440) </td>\n",
+       "                        <td> (721, 1440) </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",
+       "        <svg width=\"170\" height=\"110\" 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=\"60\" x2=\"120\" y2=\"60\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"60\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"60\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,60.083333333333336 0.0,60.083333333333336\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"80.083333\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1440</text>\n",
+       "  <text x=\"140.000000\" y=\"30.041667\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,30.041667)\">721</text>\n",
+       "</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'>(lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(721, 1440), meta=np.ndarray&gt;</div><input id='attrs-2206ef6b-1428-4272-811a-5526a00cfeae' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2206ef6b-1428-4272-811a-5526a00cfeae' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-60d9904b-cdbe-442c-80ee-7be177449290' class='xr-var-data-in' type='checkbox'><label for='data-60d9904b-cdbe-442c-80ee-7be177449290' title='Show/Hide data 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",
+       "                <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> 3.96 MiB </td>\n",
+       "                        <td> 3.96 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (721, 1440) </td>\n",
+       "                        <td> (721, 1440) </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",
+       "        <svg width=\"170\" height=\"110\" 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=\"60\" x2=\"120\" y2=\"60\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"60\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"60\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,60.083333333333336 0.0,60.083333333333336\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"80.083333\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1440</text>\n",
+       "  <text x=\"140.000000\" y=\"30.041667\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,30.041667)\">721</text>\n",
+       "</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, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(48, 37, 721, 1440), meta=np.ndarray&gt;</div><input id='attrs-d1b233f0-b34d-4a36-97be-3f7aff50d919' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d1b233f0-b34d-4a36-97be-3f7aff50d919' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9df61de0-bc0a-4e8f-8caf-ba421d9d6a37' class='xr-var-data-in' type='checkbox'><label for='data-9df61de0-bc0a-4e8f-8caf-ba421d9d6a37' title='Show/Hide data 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",
+       "                <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> 77.18 TiB </td>\n",
+       "                        <td> 6.87 GiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (552264, 37, 721, 1440) </td>\n",
+       "                        <td> (48, 37, 721, 1440) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 11506 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",
+       "        <svg width=\"470\" height=\"90\" 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=\"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=\"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=\"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=\"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=\"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=\"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=\"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=\"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",
+       "  <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:#8B4903A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >552264</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",
+       "\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"190\" y1=\"0\" x2=\"204\" y2=\"14\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"190\" y1=\"25\" x2=\"204\" y2=\"40\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"190\" y1=\"0\" x2=\"190\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"204\" y1=\"14\" x2=\"204\" y2=\"40\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"190.0,0.0 204.9485979497544,14.948597949754403 204.9485979497544,40.36121446433689 190.0,25.41261651458249\" 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",
+       "  <line x1=\"215\" y1=\"0\" x2=\"230\" y2=\"14\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"190.0,0.0 215.41261651458248,0.0 230.36121446433688,14.948597949754403 204.9485979497544,14.948597949754403\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\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",
+       "  <polygon points=\"204.9485979497544,14.948597949754403 230.36121446433688,14.948597949754403 230.36121446433688,40.36121446433689 204.9485979497544,40.36121446433689\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"217.654906\" y=\"60.361214\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1440</text>\n",
+       "  <text x=\"250.361214\" y=\"27.654906\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,250.361214,27.654906)\">721</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)\">37</text>\n",
+       "</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'>(lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(721, 1440), meta=np.ndarray&gt;</div><input id='attrs-8199fee6-73ee-463a-ada8-2bae5a8a6ee0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8199fee6-73ee-463a-ada8-2bae5a8a6ee0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f2ceae66-3268-4a7d-82c1-401f396029ee' class='xr-var-data-in' type='checkbox'><label for='data-f2ceae66-3268-4a7d-82c1-401f396029ee' title='Show/Hide data 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",
+       "                <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> 3.96 MiB </td>\n",
+       "                        <td> 3.96 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (721, 1440) </td>\n",
+       "                        <td> (721, 1440) </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",
+       "        <svg width=\"170\" height=\"110\" 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=\"60\" x2=\"120\" y2=\"60\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"60\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"60\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,60.083333333333336 0.0,60.083333333333336\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"80.083333\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1440</text>\n",
+       "  <text x=\"140.000000\" y=\"30.041667\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,30.041667)\">721</text>\n",
+       "</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'>(lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(721, 1440), meta=np.ndarray&gt;</div><input id='attrs-48cce45f-0932-4801-aa90-2bd58ceafc3a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-48cce45f-0932-4801-aa90-2bd58ceafc3a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-689ea885-232a-4ed6-a8d3-a4ec52caf23d' class='xr-var-data-in' type='checkbox'><label for='data-689ea885-232a-4ed6-a8d3-a4ec52caf23d' title='Show/Hide data 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",
+       "                <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> 3.96 MiB </td>\n",
+       "                        <td> 3.96 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (721, 1440) </td>\n",
+       "                        <td> (721, 1440) </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",
+       "        <svg width=\"170\" height=\"110\" 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=\"60\" x2=\"120\" y2=\"60\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"60\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"60\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,60.083333333333336 0.0,60.083333333333336\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"80.083333\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1440</text>\n",
+       "  <text x=\"140.000000\" y=\"30.041667\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,30.041667)\">721</text>\n",
+       "</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, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(48, 721, 1440), meta=np.ndarray&gt;</div><input id='attrs-be5fc87c-9971-48ec-8d5d-c9cc7d60f386' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-be5fc87c-9971-48ec-8d5d-c9cc7d60f386' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0816fae2-5cdf-4123-96b3-0c56af6d4013' class='xr-var-data-in' type='checkbox'><label for='data-0816fae2-5cdf-4123-96b3-0c56af6d4013' title='Show/Hide data 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",
+       "                <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> 2.09 TiB </td>\n",
+       "                        <td> 190.11 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (552264, 721, 1440) </td>\n",
+       "                        <td> (48, 721, 1440) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 11506 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",
+       "        <svg width=\"156\" height=\"146\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"10\" y1=\"25\" x2=\"80\" y2=\"96\" 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=\"13\" y1=\"3\" x2=\"13\" y2=\"29\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"17\" y2=\"32\" />\n",
+       "  <line x1=\"21\" y1=\"11\" x2=\"21\" y2=\"36\" />\n",
+       "  <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=\"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=\"69\" />\n",
+       "  <line x1=\"58\" y1=\"48\" x2=\"58\" y2=\"73\" />\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",
+       "  <line x1=\"76\" y1=\"66\" x2=\"76\" y2=\"92\" />\n",
+       "  <line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"96\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"10.0,0.0 80.58823529411765,70.58823529411765 80.58823529411765,96.00085180870013 10.0,25.41261651458249\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"35\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"13\" y1=\"3\" x2=\"39\" y2=\"3\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"42\" y2=\"7\" />\n",
+       "  <line x1=\"21\" y1=\"11\" x2=\"46\" y2=\"11\" />\n",
+       "  <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=\"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=\"79\" y2=\"44\" />\n",
+       "  <line x1=\"58\" y1=\"48\" x2=\"83\" y2=\"48\" />\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",
+       "  <line x1=\"76\" y1=\"66\" x2=\"102\" y2=\"66\" />\n",
+       "  <line x1=\"80\" y1=\"70\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"35\" y1=\"0\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"10.0,0.0 35.41261651458249,0.0 106.00085180870013,70.58823529411765 80.58823529411765,70.58823529411765\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"80\" y1=\"70\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"80\" y1=\"96\" x2=\"106\" y2=\"96\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"96\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"106\" y1=\"70\" x2=\"106\" y2=\"96\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"80.58823529411765,70.58823529411765 106.00085180870013,70.58823529411765 106.00085180870013,96.00085180870013 80.58823529411765,96.00085180870013\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"93.294544\" y=\"116.000852\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1440</text>\n",
+       "  <text x=\"126.000852\" y=\"83.294544\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,126.000852,83.294544)\">721</text>\n",
+       "  <text x=\"35.294118\" y=\"80.706734\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,35.294118,80.706734)\">552264</text>\n",
+       "</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, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(48, 37, 721, 1440), meta=np.ndarray&gt;</div><input id='attrs-d18edfb3-cbdb-4919-b8a6-42d506f65107' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d18edfb3-cbdb-4919-b8a6-42d506f65107' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f7b65e0e-e895-4e4b-8126-58acf5a8336e' class='xr-var-data-in' type='checkbox'><label for='data-f7b65e0e-e895-4e4b-8126-58acf5a8336e' title='Show/Hide data 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",
+       "                <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> 77.18 TiB </td>\n",
+       "                        <td> 6.87 GiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (552264, 37, 721, 1440) </td>\n",
+       "                        <td> (48, 37, 721, 1440) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 11506 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",
+       "        <svg width=\"470\" height=\"90\" 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=\"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=\"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=\"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=\"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=\"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=\"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=\"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=\"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",
+       "  <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:#8B4903A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >552264</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",
+       "\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"190\" y1=\"0\" x2=\"204\" y2=\"14\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"190\" y1=\"25\" x2=\"204\" y2=\"40\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"190\" y1=\"0\" x2=\"190\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"204\" y1=\"14\" x2=\"204\" y2=\"40\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"190.0,0.0 204.9485979497544,14.948597949754403 204.9485979497544,40.36121446433689 190.0,25.41261651458249\" 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",
+       "  <line x1=\"215\" y1=\"0\" x2=\"230\" y2=\"14\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"190.0,0.0 215.41261651458248,0.0 230.36121446433688,14.948597949754403 204.9485979497544,14.948597949754403\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\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",
+       "  <polygon points=\"204.9485979497544,14.948597949754403 230.36121446433688,14.948597949754403 230.36121446433688,40.36121446433689 204.9485979497544,40.36121446433689\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"217.654906\" y=\"60.361214\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1440</text>\n",
+       "  <text x=\"250.361214\" y=\"27.654906\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,250.361214,27.654906)\">721</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)\">37</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>toa_incident_solar_radiation</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(48, 721, 1440), meta=np.ndarray&gt;</div><input id='attrs-7562f572-cff6-44ac-93f2-2d5d20253b95' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7562f572-cff6-44ac-93f2-2d5d20253b95' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bb17843d-9fa0-4426-9912-cc3e0d09d345' class='xr-var-data-in' type='checkbox'><label for='data-bb17843d-9fa0-4426-9912-cc3e0d09d345' title='Show/Hide data 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>TOA incident solar radiation</dd><dt><span>short_name :</span></dt><dd>tisr</dd><dt><span>units :</span></dt><dd>J 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> 2.09 TiB </td>\n",
+       "                        <td> 190.11 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (552264, 721, 1440) </td>\n",
+       "                        <td> (48, 721, 1440) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 11506 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",
+       "        <svg width=\"156\" height=\"146\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"10\" y1=\"25\" x2=\"80\" y2=\"96\" 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=\"13\" y1=\"3\" x2=\"13\" y2=\"29\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"17\" y2=\"32\" />\n",
+       "  <line x1=\"21\" y1=\"11\" x2=\"21\" y2=\"36\" />\n",
+       "  <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=\"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=\"69\" />\n",
+       "  <line x1=\"58\" y1=\"48\" x2=\"58\" y2=\"73\" />\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",
+       "  <line x1=\"76\" y1=\"66\" x2=\"76\" y2=\"92\" />\n",
+       "  <line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"96\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"10.0,0.0 80.58823529411765,70.58823529411765 80.58823529411765,96.00085180870013 10.0,25.41261651458249\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"35\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"13\" y1=\"3\" x2=\"39\" y2=\"3\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"42\" y2=\"7\" />\n",
+       "  <line x1=\"21\" y1=\"11\" x2=\"46\" y2=\"11\" />\n",
+       "  <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=\"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=\"79\" y2=\"44\" />\n",
+       "  <line x1=\"58\" y1=\"48\" x2=\"83\" y2=\"48\" />\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",
+       "  <line x1=\"76\" y1=\"66\" x2=\"102\" y2=\"66\" />\n",
+       "  <line x1=\"80\" y1=\"70\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"35\" y1=\"0\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"10.0,0.0 35.41261651458249,0.0 106.00085180870013,70.58823529411765 80.58823529411765,70.58823529411765\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"80\" y1=\"70\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"80\" y1=\"96\" x2=\"106\" y2=\"96\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"96\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"106\" y1=\"70\" x2=\"106\" y2=\"96\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"80.58823529411765,70.58823529411765 106.00085180870013,70.58823529411765 106.00085180870013,96.00085180870013 80.58823529411765,96.00085180870013\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"93.294544\" y=\"116.000852\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1440</text>\n",
+       "  <text x=\"126.000852\" y=\"83.294544\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,126.000852,83.294544)\">721</text>\n",
+       "  <text x=\"35.294118\" y=\"80.706734\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,35.294118,80.706734)\">552264</text>\n",
+       "</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, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(48, 721, 1440), meta=np.ndarray&gt;</div><input id='attrs-0de97b9d-9746-4154-ae13-3bef109e432d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0de97b9d-9746-4154-ae13-3bef109e432d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5594b978-b02a-4e0a-a847-563b4a7a4ada' class='xr-var-data-in' type='checkbox'><label for='data-5594b978-b02a-4e0a-a847-563b4a7a4ada' title='Show/Hide data 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> 2.09 TiB </td>\n",
+       "                        <td> 190.11 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (552264, 721, 1440) </td>\n",
+       "                        <td> (48, 721, 1440) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 11506 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",
+       "        <svg width=\"156\" height=\"146\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"10\" y1=\"25\" x2=\"80\" y2=\"96\" 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=\"13\" y1=\"3\" x2=\"13\" y2=\"29\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"17\" y2=\"32\" />\n",
+       "  <line x1=\"21\" y1=\"11\" x2=\"21\" y2=\"36\" />\n",
+       "  <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=\"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=\"69\" />\n",
+       "  <line x1=\"58\" y1=\"48\" x2=\"58\" y2=\"73\" />\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",
+       "  <line x1=\"76\" y1=\"66\" x2=\"76\" y2=\"92\" />\n",
+       "  <line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"96\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"10.0,0.0 80.58823529411765,70.58823529411765 80.58823529411765,96.00085180870013 10.0,25.41261651458249\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"35\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"13\" y1=\"3\" x2=\"39\" y2=\"3\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"42\" y2=\"7\" />\n",
+       "  <line x1=\"21\" y1=\"11\" x2=\"46\" y2=\"11\" />\n",
+       "  <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=\"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=\"79\" y2=\"44\" />\n",
+       "  <line x1=\"58\" y1=\"48\" x2=\"83\" y2=\"48\" />\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",
+       "  <line x1=\"76\" y1=\"66\" x2=\"102\" y2=\"66\" />\n",
+       "  <line x1=\"80\" y1=\"70\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"35\" y1=\"0\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"10.0,0.0 35.41261651458249,0.0 106.00085180870013,70.58823529411765 80.58823529411765,70.58823529411765\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"80\" y1=\"70\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"80\" y1=\"96\" x2=\"106\" y2=\"96\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"96\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"106\" y1=\"70\" x2=\"106\" y2=\"96\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"80.58823529411765,70.58823529411765 106.00085180870013,70.58823529411765 106.00085180870013,96.00085180870013 80.58823529411765,96.00085180870013\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"93.294544\" y=\"116.000852\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1440</text>\n",
+       "  <text x=\"126.000852\" y=\"83.294544\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,126.000852,83.294544)\">721</text>\n",
+       "  <text x=\"35.294118\" y=\"80.706734\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,35.294118,80.706734)\">552264</text>\n",
+       "</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, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(48, 721, 1440), meta=np.ndarray&gt;</div><input id='attrs-011a7c38-99f2-4a09-b600-3667543b3b63' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-011a7c38-99f2-4a09-b600-3667543b3b63' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5e7c958b-b185-4d2f-8635-3a3929513a55' class='xr-var-data-in' type='checkbox'><label for='data-5e7c958b-b185-4d2f-8635-3a3929513a55' title='Show/Hide data 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",
+       "                <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> 2.09 TiB </td>\n",
+       "                        <td> 190.11 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (552264, 721, 1440) </td>\n",
+       "                        <td> (48, 721, 1440) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 11506 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",
+       "        <svg width=\"156\" height=\"146\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"10\" y1=\"25\" x2=\"80\" y2=\"96\" 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=\"13\" y1=\"3\" x2=\"13\" y2=\"29\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"17\" y2=\"32\" />\n",
+       "  <line x1=\"21\" y1=\"11\" x2=\"21\" y2=\"36\" />\n",
+       "  <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=\"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=\"69\" />\n",
+       "  <line x1=\"58\" y1=\"48\" x2=\"58\" y2=\"73\" />\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",
+       "  <line x1=\"76\" y1=\"66\" x2=\"76\" y2=\"92\" />\n",
+       "  <line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"96\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"10.0,0.0 80.58823529411765,70.58823529411765 80.58823529411765,96.00085180870013 10.0,25.41261651458249\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"35\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"13\" y1=\"3\" x2=\"39\" y2=\"3\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"42\" y2=\"7\" />\n",
+       "  <line x1=\"21\" y1=\"11\" x2=\"46\" y2=\"11\" />\n",
+       "  <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=\"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=\"79\" y2=\"44\" />\n",
+       "  <line x1=\"58\" y1=\"48\" x2=\"83\" y2=\"48\" />\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",
+       "  <line x1=\"76\" y1=\"66\" x2=\"102\" y2=\"66\" />\n",
+       "  <line x1=\"80\" y1=\"70\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"35\" y1=\"0\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"10.0,0.0 35.41261651458249,0.0 106.00085180870013,70.58823529411765 80.58823529411765,70.58823529411765\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"80\" y1=\"70\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"80\" y1=\"96\" x2=\"106\" y2=\"96\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"96\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"106\" y1=\"70\" x2=\"106\" y2=\"96\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"80.58823529411765,70.58823529411765 106.00085180870013,70.58823529411765 106.00085180870013,96.00085180870013 80.58823529411765,96.00085180870013\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"93.294544\" y=\"116.000852\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1440</text>\n",
+       "  <text x=\"126.000852\" y=\"83.294544\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,126.000852,83.294544)\">721</text>\n",
+       "  <text x=\"35.294118\" y=\"80.706734\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,35.294118,80.706734)\">552264</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>total_precipitation</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(48, 721, 1440), meta=np.ndarray&gt;</div><input id='attrs-ede30d0d-6add-4986-b8c2-e7a59e9ddf35' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ede30d0d-6add-4986-b8c2-e7a59e9ddf35' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-548297b6-8eed-46c0-bcb8-b2f6330c4b54' class='xr-var-data-in' type='checkbox'><label for='data-548297b6-8eed-46c0-bcb8-b2f6330c4b54' title='Show/Hide data 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> 2.09 TiB </td>\n",
+       "                        <td> 190.11 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (552264, 721, 1440) </td>\n",
+       "                        <td> (48, 721, 1440) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 11506 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",
+       "        <svg width=\"156\" height=\"146\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"10\" y1=\"25\" x2=\"80\" y2=\"96\" 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=\"13\" y1=\"3\" x2=\"13\" y2=\"29\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"17\" y2=\"32\" />\n",
+       "  <line x1=\"21\" y1=\"11\" x2=\"21\" y2=\"36\" />\n",
+       "  <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=\"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=\"69\" />\n",
+       "  <line x1=\"58\" y1=\"48\" x2=\"58\" y2=\"73\" />\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",
+       "  <line x1=\"76\" y1=\"66\" x2=\"76\" y2=\"92\" />\n",
+       "  <line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"96\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"10.0,0.0 80.58823529411765,70.58823529411765 80.58823529411765,96.00085180870013 10.0,25.41261651458249\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"35\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"13\" y1=\"3\" x2=\"39\" y2=\"3\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"42\" y2=\"7\" />\n",
+       "  <line x1=\"21\" y1=\"11\" x2=\"46\" y2=\"11\" />\n",
+       "  <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=\"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=\"79\" y2=\"44\" />\n",
+       "  <line x1=\"58\" y1=\"48\" x2=\"83\" y2=\"48\" />\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",
+       "  <line x1=\"76\" y1=\"66\" x2=\"102\" y2=\"66\" />\n",
+       "  <line x1=\"80\" y1=\"70\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"35\" y1=\"0\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"10.0,0.0 35.41261651458249,0.0 106.00085180870013,70.58823529411765 80.58823529411765,70.58823529411765\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"80\" y1=\"70\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"80\" y1=\"96\" x2=\"106\" y2=\"96\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"96\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"106\" y1=\"70\" x2=\"106\" y2=\"96\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"80.58823529411765,70.58823529411765 106.00085180870013,70.58823529411765 106.00085180870013,96.00085180870013 80.58823529411765,96.00085180870013\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"93.294544\" y=\"116.000852\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1440</text>\n",
+       "  <text x=\"126.000852\" y=\"83.294544\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,126.000852,83.294544)\">721</text>\n",
+       "  <text x=\"35.294118\" y=\"80.706734\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,35.294118,80.706734)\">552264</text>\n",
+       "</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'>(lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(721, 1440), meta=np.ndarray&gt;</div><input id='attrs-deb29833-8bfb-450a-8d7a-f6ad60b7ac28' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-deb29833-8bfb-450a-8d7a-f6ad60b7ac28' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ad63722b-669f-4910-8b5a-81eb8797d9fe' class='xr-var-data-in' type='checkbox'><label for='data-ad63722b-669f-4910-8b5a-81eb8797d9fe' title='Show/Hide data 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> 3.96 MiB </td>\n",
+       "                        <td> 3.96 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (721, 1440) </td>\n",
+       "                        <td> (721, 1440) </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",
+       "        <svg width=\"170\" height=\"110\" 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=\"60\" x2=\"120\" y2=\"60\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"60\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"60\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,60.083333333333336 0.0,60.083333333333336\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"80.083333\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1440</text>\n",
+       "  <text x=\"140.000000\" y=\"30.041667\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,30.041667)\">721</text>\n",
+       "</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'>(lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(721, 1440), meta=np.ndarray&gt;</div><input id='attrs-cdb2b364-7e62-4a38-a9e4-dd3d81953881' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cdb2b364-7e62-4a38-a9e4-dd3d81953881' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4d3edc6a-ff7a-4343-bdb1-70bab6e901ec' class='xr-var-data-in' type='checkbox'><label for='data-4d3edc6a-ff7a-4343-bdb1-70bab6e901ec' title='Show/Hide data 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> 3.96 MiB </td>\n",
+       "                        <td> 3.96 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (721, 1440) </td>\n",
+       "                        <td> (721, 1440) </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",
+       "        <svg width=\"170\" height=\"110\" 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=\"60\" x2=\"120\" y2=\"60\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"60\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"60\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,60.083333333333336 0.0,60.083333333333336\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"80.083333\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1440</text>\n",
+       "  <text x=\"140.000000\" y=\"30.041667\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,30.041667)\">721</text>\n",
+       "</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, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(48, 37, 721, 1440), meta=np.ndarray&gt;</div><input id='attrs-beee5f6c-940b-4ff5-9c01-04717026a30d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-beee5f6c-940b-4ff5-9c01-04717026a30d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-95c0e34b-c847-49c6-92a2-83ee74a124c4' class='xr-var-data-in' type='checkbox'><label for='data-95c0e34b-c847-49c6-92a2-83ee74a124c4' title='Show/Hide data 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> 77.18 TiB </td>\n",
+       "                        <td> 6.87 GiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (552264, 37, 721, 1440) </td>\n",
+       "                        <td> (48, 37, 721, 1440) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 11506 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",
+       "        <svg width=\"470\" height=\"90\" 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=\"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=\"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=\"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=\"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=\"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=\"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=\"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=\"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",
+       "  <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:#8B4903A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >552264</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",
+       "\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"190\" y1=\"0\" x2=\"204\" y2=\"14\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"190\" y1=\"25\" x2=\"204\" y2=\"40\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"190\" y1=\"0\" x2=\"190\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"204\" y1=\"14\" x2=\"204\" y2=\"40\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"190.0,0.0 204.9485979497544,14.948597949754403 204.9485979497544,40.36121446433689 190.0,25.41261651458249\" 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",
+       "  <line x1=\"215\" y1=\"0\" x2=\"230\" y2=\"14\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"190.0,0.0 215.41261651458248,0.0 230.36121446433688,14.948597949754403 204.9485979497544,14.948597949754403\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\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",
+       "  <polygon points=\"204.9485979497544,14.948597949754403 230.36121446433688,14.948597949754403 230.36121446433688,40.36121446433689 204.9485979497544,40.36121446433689\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"217.654906\" y=\"60.361214\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1440</text>\n",
+       "  <text x=\"250.361214\" y=\"27.654906\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,250.361214,27.654906)\">721</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)\">37</text>\n",
+       "</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, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(48, 37, 721, 1440), meta=np.ndarray&gt;</div><input id='attrs-5206f8df-5785-43ad-b69e-e5cc21e8f94b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5206f8df-5785-43ad-b69e-e5cc21e8f94b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a3f80f68-c51a-4af6-a922-b9ef9b77eda1' class='xr-var-data-in' type='checkbox'><label for='data-a3f80f68-c51a-4af6-a922-b9ef9b77eda1' title='Show/Hide data 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> 77.18 TiB </td>\n",
+       "                        <td> 6.87 GiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (552264, 37, 721, 1440) </td>\n",
+       "                        <td> (48, 37, 721, 1440) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 11506 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",
+       "        <svg width=\"470\" height=\"90\" 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=\"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=\"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=\"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=\"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=\"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=\"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=\"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=\"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",
+       "  <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:#8B4903A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >552264</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",
+       "\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"190\" y1=\"0\" x2=\"204\" y2=\"14\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"190\" y1=\"25\" x2=\"204\" y2=\"40\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"190\" y1=\"0\" x2=\"190\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"204\" y1=\"14\" x2=\"204\" y2=\"40\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"190.0,0.0 204.9485979497544,14.948597949754403 204.9485979497544,40.36121446433689 190.0,25.41261651458249\" 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",
+       "  <line x1=\"215\" y1=\"0\" x2=\"230\" y2=\"14\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"190.0,0.0 215.41261651458248,0.0 230.36121446433688,14.948597949754403 204.9485979497544,14.948597949754403\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\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",
+       "  <polygon points=\"204.9485979497544,14.948597949754403 230.36121446433688,14.948597949754403 230.36121446433688,40.36121446433689 204.9485979497544,40.36121446433689\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"217.654906\" y=\"60.361214\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1440</text>\n",
+       "  <text x=\"250.361214\" y=\"27.654906\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,250.361214,27.654906)\">721</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)\">37</text>\n",
+       "</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, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(48, 37, 721, 1440), meta=np.ndarray&gt;</div><input id='attrs-ba4f1c39-6d4e-43dd-99a2-af6dc8deb50b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ba4f1c39-6d4e-43dd-99a2-af6dc8deb50b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2733907a-b3c9-4156-a77b-bf7f0e25a058' class='xr-var-data-in' type='checkbox'><label for='data-2733907a-b3c9-4156-a77b-bf7f0e25a058' title='Show/Hide data 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> 77.18 TiB </td>\n",
+       "                        <td> 6.87 GiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (552264, 37, 721, 1440) </td>\n",
+       "                        <td> (48, 37, 721, 1440) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 11506 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",
+       "        <svg width=\"470\" height=\"90\" 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=\"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=\"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=\"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=\"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=\"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=\"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=\"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=\"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",
+       "  <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:#8B4903A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >552264</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",
+       "\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"190\" y1=\"0\" x2=\"204\" y2=\"14\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"190\" y1=\"25\" x2=\"204\" y2=\"40\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"190\" y1=\"0\" x2=\"190\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"204\" y1=\"14\" x2=\"204\" y2=\"40\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"190.0,0.0 204.9485979497544,14.948597949754403 204.9485979497544,40.36121446433689 190.0,25.41261651458249\" 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",
+       "  <line x1=\"215\" y1=\"0\" x2=\"230\" y2=\"14\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"190.0,0.0 215.41261651458248,0.0 230.36121446433688,14.948597949754403 204.9485979497544,14.948597949754403\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\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",
+       "  <polygon points=\"204.9485979497544,14.948597949754403 230.36121446433688,14.948597949754403 230.36121446433688,40.36121446433689 204.9485979497544,40.36121446433689\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"217.654906\" y=\"60.361214\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1440</text>\n",
+       "  <text x=\"250.361214\" y=\"27.654906\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,250.361214,27.654906)\">721</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)\">37</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-06e9e1f8-4bd4-43e5-b55e-16634b5fe2da' class='xr-section-summary-in' type='checkbox'  ><label for='section-06e9e1f8-4bd4-43e5-b55e-16634b5fe2da' 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>lat</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-1f57c8de-37c9-4744-a5cb-f00aef966008' class='xr-index-data-in' type='checkbox'/><label for='index-1f57c8de-37c9-4744-a5cb-f00aef966008' 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([  90.0,  89.75,   89.5,  89.25,   89.0,  88.75,   88.5,  88.25,   88.0,\n",
+       "        87.75,\n",
+       "       ...\n",
+       "       -87.75,  -88.0, -88.25,  -88.5, -88.75,  -89.0, -89.25,  -89.5, -89.75,\n",
+       "        -90.0],\n",
+       "      dtype=&#x27;float32&#x27;, name=&#x27;lat&#x27;, length=721))</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-afe045bc-a9e4-4af5-9de4-f679c41e7179' class='xr-index-data-in' type='checkbox'/><label for='index-afe045bc-a9e4-4af5-9de4-f679c41e7179' 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([   1,    2,    3,    5,    7,   10,   20,   30,   50,   70,  100,  125,\n",
+       "        150,  175,  200,  225,  250,  300,  350,  400,  450,  500,  550,  600,\n",
+       "        650,  700,  750,  775,  800,  825,  850,  875,  900,  925,  950,  975,\n",
+       "       1000],\n",
+       "      dtype=&#x27;int64&#x27;, name=&#x27;level&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lon</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-5b839066-5474-42c7-bf0f-2a11d49ca844' class='xr-index-data-in' type='checkbox'/><label for='index-5b839066-5474-42c7-bf0f-2a11d49ca844' 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,   0.25,    0.5,   0.75,    1.0,   1.25,    1.5,   1.75,    2.0,\n",
+       "         2.25,\n",
+       "       ...\n",
+       "        357.5, 357.75,  358.0, 358.25,  358.5, 358.75,  359.0, 359.25,  359.5,\n",
+       "       359.75],\n",
+       "      dtype=&#x27;float32&#x27;, name=&#x27;lon&#x27;, length=1440))</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-49e0d522-320b-4be0-9f8a-467caab9f043' class='xr-index-data-in' type='checkbox'/><label for='index-49e0d522-320b-4be0-9f8a-467caab9f043' 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 01:00:00&#x27;,\n",
+       "               &#x27;1959-01-01 02:00:00&#x27;, &#x27;1959-01-01 03:00:00&#x27;,\n",
+       "               &#x27;1959-01-01 04:00:00&#x27;, &#x27;1959-01-01 05:00:00&#x27;,\n",
+       "               &#x27;1959-01-01 06:00:00&#x27;, &#x27;1959-01-01 07:00:00&#x27;,\n",
+       "               &#x27;1959-01-01 08:00:00&#x27;, &#x27;1959-01-01 09:00:00&#x27;,\n",
+       "               ...\n",
+       "               &#x27;2021-12-31 14:00:00&#x27;, &#x27;2021-12-31 15:00:00&#x27;,\n",
+       "               &#x27;2021-12-31 16:00:00&#x27;, &#x27;2021-12-31 17:00:00&#x27;,\n",
+       "               &#x27;2021-12-31 18:00:00&#x27;, &#x27;2021-12-31 19:00:00&#x27;,\n",
+       "               &#x27;2021-12-31 20:00:00&#x27;, &#x27;2021-12-31 21:00:00&#x27;,\n",
+       "               &#x27;2021-12-31 22:00:00&#x27;, &#x27;2021-12-31 23:00:00&#x27;],\n",
+       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=552264, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-a7e4043c-be5a-4acb-a134-8bbd620117d1' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-a7e4043c-be5a-4acb-a134-8bbd620117d1' 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>\n",
+       "Dimensions:                                           (time: 552264, lat: 721,\n",
+       "                                                       lon: 1440, level: 37)\n",
+       "Coordinates:\n",
+       "  * lat                                               (lat) float32 90.0 ... ...\n",
+       "  * level                                             (level) int64 1 2 ... 1000\n",
+       "  * lon                                               (lon) float32 0.0 ... 3...\n",
+       "  * time                                              (time) datetime64[ns] 1...\n",
+       "Data variables: (12/31)\n",
+       "    10m_u_component_of_wind                           (time, lat, lon) float32 dask.array<chunksize=(48, 721, 1440), meta=np.ndarray>\n",
+       "    10m_v_component_of_wind                           (time, lat, lon) float32 dask.array<chunksize=(48, 721, 1440), meta=np.ndarray>\n",
+       "    2m_temperature                                    (time, lat, lon) float32 dask.array<chunksize=(48, 721, 1440), meta=np.ndarray>\n",
+       "    angle_of_sub_gridscale_orography                  (lat, lon) float32 dask.array<chunksize=(721, 1440), meta=np.ndarray>\n",
+       "    anisotropy_of_sub_gridscale_orography             (lat, lon) float32 dask.array<chunksize=(721, 1440), meta=np.ndarray>\n",
+       "    geopotential                                      (time, level, lat, lon) float32 dask.array<chunksize=(48, 37, 721, 1440), meta=np.ndarray>\n",
+       "    ...                                                ...\n",
+       "    total_precipitation                               (time, lat, lon) float32 dask.array<chunksize=(48, 721, 1440), meta=np.ndarray>\n",
+       "    type_of_high_vegetation                           (lat, lon) float32 dask.array<chunksize=(721, 1440), meta=np.ndarray>\n",
+       "    type_of_low_vegetation                            (lat, lon) float32 dask.array<chunksize=(721, 1440), meta=np.ndarray>\n",
+       "    u_component_of_wind                               (time, level, lat, lon) float32 dask.array<chunksize=(48, 37, 721, 1440), meta=np.ndarray>\n",
+       "    v_component_of_wind                               (time, level, lat, lon) float32 dask.array<chunksize=(48, 37, 721, 1440), meta=np.ndarray>\n",
+       "    vertical_velocity                                 (time, level, lat, lon) float32 dask.array<chunksize=(48, 37, 721, 1440), meta=np.ndarray>"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ds_era5 = xr.open_zarr(url_era5, chunks={\"time\": 48}, consolidated=True,)\n",
+    "ds_era5 = ds_era5.rename(dict(longitude=\"lon\", latitude=\"lat\"))\n",
+    "ds_era5"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "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",
+       "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n",
+       "<path d=\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
+       "<path d=\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
+       "</symbol>\n",
+       "<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n",
+       "<path d=\"M28.681 7.159c-0.694-0.947-1.662-2.053-2.724-3.116s-2.169-2.030-3.116-2.724c-1.612-1.182-2.393-1.319-2.841-1.319h-15.5c-1.378 0-2.5 1.121-2.5 2.5v27c0 1.378 1.122 2.5 2.5 2.5h23c1.378 0 2.5-1.122 2.5-2.5v-19.5c0-0.448-0.137-1.23-1.319-2.841zM24.543 5.457c0.959 0.959 1.712 1.825 2.268 2.543h-4.811v-4.811c0.718 0.556 1.584 1.309 2.543 2.268zM28 29.5c0 0.271-0.229 0.5-0.5 0.5h-23c-0.271 0-0.5-0.229-0.5-0.5v-27c0-0.271 0.229-0.5 0.5-0.5 0 0 15.499-0 15.5 0v7c0 0.552 0.448 1 1 1h7v19.5z\"></path>\n",
+       "<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;\n",
+       "Dimensions:   (altitude: 1, y: 589, x: 789, time: 29120)\n",
+       "Coordinates:\n",
+       "  * altitude  (altitude) int64 100\n",
+       "    lat       (y, x) float64 dask.array&lt;chunksize=(256, 256), meta=np.ndarray&gt;\n",
+       "    lon       (y, x) float64 dask.array&lt;chunksize=(256, 256), meta=np.ndarray&gt;\n",
+       "  * time      (time) datetime64[ns] 1990-09-01 ... 2000-08-18T21:00:00\n",
+       "  * x         (x) float64 -1.999e+06 -1.997e+06 ... -3.175e+04 -2.925e+04\n",
+       "  * y         (y) float64 -6.095e+05 -6.07e+05 -6.045e+05 ... 8.58e+05 8.605e+05\n",
+       "Data variables:\n",
+       "    r         (altitude, time, y, x) float64 dask.array&lt;chunksize=(1, 256, 256, 256), meta=np.ndarray&gt;\n",
+       "    t         (altitude, time, y, x) float64 dask.array&lt;chunksize=(1, 256, 256, 256), meta=np.ndarray&gt;\n",
+       "    u         (altitude, time, y, x) float64 dask.array&lt;chunksize=(1, 256, 256, 256), meta=np.ndarray&gt;\n",
+       "    v         (altitude, time, y, x) float64 dask.array&lt;chunksize=(1, 256, 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-5846b125-12ae-489b-a62b-04bc317145f9' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-5846b125-12ae-489b-a62b-04bc317145f9' 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>: 29120</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-6550e345-ea9b-4543-b4fe-b6d0a93ad17f' class='xr-section-summary-in' type='checkbox'  checked><label for='section-6550e345-ea9b-4543-b4fe-b6d0a93ad17f' 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-b37b5d13-8518-4bef-8d8b-56809524458d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b37b5d13-8518-4bef-8d8b-56809524458d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8d53701c-019b-49a5-9f08-dfe2a35094a2' class='xr-var-data-in' type='checkbox'><label for='data-8d53701c-019b-49a5-9f08-dfe2a35094a2' title='Show/Hide data 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-cbb66280-34c1-4a5e-abce-95c8f79c20a6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cbb66280-34c1-4a5e-abce-95c8f79c20a6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ffad41bf-73e8-47c0-9707-da748bf94d54' class='xr-var-data-in' type='checkbox'><label for='data-ffad41bf-73e8-47c0-9707-da748bf94d54' title='Show/Hide data 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-a948e48a-e5cc-41bf-9482-ab720498fd67' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a948e48a-e5cc-41bf-9482-ab720498fd67' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e8ec945a-4472-47e3-a21e-953bac2bc3e3' class='xr-var-data-in' type='checkbox'><label for='data-e8ec945a-4472-47e3-a21e-953bac2bc3e3' title='Show/Hide data 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 ... 2000-08-18T21:00:00</div><input id='attrs-31973714-a226-4274-97a2-f30a25829639' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-31973714-a226-4274-97a2-f30a25829639' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-07294a84-7dfb-4759-8ab8-d1462ee0d6d1' class='xr-var-data-in' type='checkbox'><label for='data-07294a84-7dfb-4759-8ab8-d1462ee0d6d1' title='Show/Hide data 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;2000-08-18T15:00:00.000000000&#x27;,\n",
+       "       &#x27;2000-08-18T18:00:00.000000000&#x27;, &#x27;2000-08-18T21:00:00.000000000&#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-8fb89613-3129-45db-9000-96506a62e31a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8fb89613-3129-45db-9000-96506a62e31a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5c8cb316-53c7-4ba9-a8df-10c38266969e' class='xr-var-data-in' type='checkbox'><label for='data-5c8cb316-53c7-4ba9-a8df-10c38266969e' title='Show/Hide data 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-14e6ef26-fd32-4494-aa4e-406461b10acf' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-14e6ef26-fd32-4494-aa4e-406461b10acf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-71b2cafb-eb92-49e0-b944-359cef7808cc' class='xr-var-data-in' type='checkbox'><label for='data-71b2cafb-eb92-49e0-b944-359cef7808cc' title='Show/Hide data 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-f92f6e56-7548-4b6d-9841-64c790d8d52d' class='xr-section-summary-in' type='checkbox'  checked><label for='section-f92f6e56-7548-4b6d-9841-64c790d8d52d' 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, 256, 256, 256), meta=np.ndarray&gt;</div><input id='attrs-e0c5ddb7-5e47-4727-9e4e-c35bf61fd97f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e0c5ddb7-5e47-4727-9e4e-c35bf61fd97f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-62ab3efd-6144-4066-917d-1bd03e097140' class='xr-var-data-in' type='checkbox'><label for='data-62ab3efd-6144-4066-917d-1bd03e097140' title='Show/Hide data 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> 100.83 GiB </td>\n",
+       "                        <td> 128.00 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (1, 29120, 589, 789) </td>\n",
+       "                        <td> (1, 256, 256, 256) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1368 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=\"341\" height=\"149\" 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",
+       "  <line x1=\"25\" y1=\"0\" x2=\"25\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 25.412616514582485,0.0 25.412616514582485,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\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=\"165\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"95\" y1=\"12\" x2=\"165\" y2=\"83\" />\n",
+       "  <line x1=\"95\" y1=\"25\" x2=\"165\" y2=\"95\" />\n",
+       "  <line x1=\"95\" y1=\"29\" x2=\"165\" y2=\"99\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"95\" y1=\"0\" x2=\"95\" y2=\"29\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"96\" y1=\"1\" x2=\"96\" y2=\"30\" />\n",
+       "  <line x1=\"99\" y1=\"4\" x2=\"99\" y2=\"33\" />\n",
+       "  <line x1=\"101\" y1=\"6\" x2=\"101\" y2=\"35\" />\n",
+       "  <line x1=\"103\" y1=\"8\" x2=\"103\" y2=\"37\" />\n",
+       "  <line x1=\"105\" y1=\"10\" x2=\"105\" y2=\"39\" />\n",
+       "  <line x1=\"108\" y1=\"13\" x2=\"108\" y2=\"42\" />\n",
+       "  <line x1=\"109\" y1=\"14\" x2=\"109\" y2=\"43\" />\n",
+       "  <line x1=\"112\" y1=\"17\" x2=\"112\" y2=\"46\" />\n",
+       "  <line x1=\"114\" y1=\"19\" x2=\"114\" y2=\"48\" />\n",
+       "  <line x1=\"116\" y1=\"21\" x2=\"116\" y2=\"50\" />\n",
+       "  <line x1=\"119\" y1=\"24\" x2=\"119\" y2=\"53\" />\n",
+       "  <line x1=\"121\" y1=\"26\" x2=\"121\" y2=\"55\" />\n",
+       "  <line x1=\"123\" y1=\"28\" x2=\"123\" y2=\"57\" />\n",
+       "  <line x1=\"125\" y1=\"30\" x2=\"125\" y2=\"59\" />\n",
+       "  <line x1=\"127\" y1=\"32\" x2=\"127\" y2=\"61\" />\n",
+       "  <line x1=\"130\" y1=\"35\" x2=\"130\" y2=\"64\" />\n",
+       "  <line x1=\"132\" y1=\"37\" x2=\"132\" y2=\"66\" />\n",
+       "  <line x1=\"134\" y1=\"39\" x2=\"134\" y2=\"68\" />\n",
+       "  <line x1=\"136\" y1=\"41\" x2=\"136\" y2=\"70\" />\n",
+       "  <line x1=\"139\" y1=\"44\" x2=\"139\" y2=\"73\" />\n",
+       "  <line x1=\"140\" y1=\"45\" x2=\"140\" y2=\"75\" />\n",
+       "  <line x1=\"143\" y1=\"48\" x2=\"143\" y2=\"77\" />\n",
+       "  <line x1=\"145\" y1=\"50\" x2=\"145\" y2=\"79\" />\n",
+       "  <line x1=\"147\" y1=\"52\" x2=\"147\" y2=\"81\" />\n",
+       "  <line x1=\"150\" y1=\"55\" x2=\"150\" y2=\"84\" />\n",
+       "  <line x1=\"152\" y1=\"57\" x2=\"152\" y2=\"86\" />\n",
+       "  <line x1=\"154\" y1=\"59\" x2=\"154\" y2=\"88\" />\n",
+       "  <line x1=\"156\" y1=\"61\" x2=\"156\" y2=\"90\" />\n",
+       "  <line x1=\"158\" y1=\"63\" x2=\"158\" y2=\"93\" />\n",
+       "  <line x1=\"160\" y1=\"65\" x2=\"160\" y2=\"94\" />\n",
+       "  <line x1=\"163\" y1=\"68\" x2=\"163\" y2=\"97\" />\n",
+       "  <line x1=\"165\" y1=\"70\" x2=\"165\" y2=\"99\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"95.0,0.0 165.58823529411765,70.58823529411765 165.58823529411765,99.6823464191901 95.0,29.09411112507245\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"95\" y1=\"0\" x2=\"125\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"96\" y1=\"1\" x2=\"127\" y2=\"1\" />\n",
+       "  <line x1=\"99\" y1=\"4\" x2=\"130\" y2=\"4\" />\n",
+       "  <line x1=\"101\" y1=\"6\" x2=\"131\" y2=\"6\" />\n",
+       "  <line x1=\"103\" y1=\"8\" x2=\"134\" y2=\"8\" />\n",
+       "  <line x1=\"105\" y1=\"10\" x2=\"136\" y2=\"10\" />\n",
+       "  <line x1=\"108\" y1=\"13\" x2=\"138\" y2=\"13\" />\n",
+       "  <line x1=\"109\" y1=\"14\" x2=\"140\" y2=\"14\" />\n",
+       "  <line x1=\"112\" y1=\"17\" x2=\"143\" y2=\"17\" />\n",
+       "  <line x1=\"114\" y1=\"19\" x2=\"145\" y2=\"19\" />\n",
+       "  <line x1=\"116\" y1=\"21\" x2=\"147\" y2=\"21\" />\n",
+       "  <line x1=\"119\" y1=\"24\" x2=\"149\" y2=\"24\" />\n",
+       "  <line x1=\"121\" y1=\"26\" x2=\"151\" y2=\"26\" />\n",
+       "  <line x1=\"123\" y1=\"28\" x2=\"154\" y2=\"28\" />\n",
+       "  <line x1=\"125\" y1=\"30\" x2=\"156\" y2=\"30\" />\n",
+       "  <line x1=\"127\" y1=\"32\" x2=\"158\" y2=\"32\" />\n",
+       "  <line x1=\"130\" y1=\"35\" x2=\"161\" y2=\"35\" />\n",
+       "  <line x1=\"132\" y1=\"37\" x2=\"163\" y2=\"37\" />\n",
+       "  <line x1=\"134\" y1=\"39\" x2=\"165\" y2=\"39\" />\n",
+       "  <line x1=\"136\" y1=\"41\" x2=\"167\" y2=\"41\" />\n",
+       "  <line x1=\"139\" y1=\"44\" x2=\"169\" y2=\"44\" />\n",
+       "  <line x1=\"140\" y1=\"45\" x2=\"171\" y2=\"45\" />\n",
+       "  <line x1=\"143\" y1=\"48\" x2=\"174\" y2=\"48\" />\n",
+       "  <line x1=\"145\" y1=\"50\" x2=\"176\" y2=\"50\" />\n",
+       "  <line x1=\"147\" y1=\"52\" x2=\"178\" y2=\"52\" />\n",
+       "  <line x1=\"150\" y1=\"55\" x2=\"181\" y2=\"55\" />\n",
+       "  <line x1=\"152\" y1=\"57\" x2=\"182\" y2=\"57\" />\n",
+       "  <line x1=\"154\" y1=\"59\" x2=\"185\" y2=\"59\" />\n",
+       "  <line x1=\"156\" y1=\"61\" x2=\"187\" y2=\"61\" />\n",
+       "  <line x1=\"158\" y1=\"63\" x2=\"189\" y2=\"63\" />\n",
+       "  <line x1=\"160\" y1=\"65\" x2=\"191\" y2=\"65\" />\n",
+       "  <line x1=\"163\" y1=\"68\" x2=\"194\" y2=\"68\" />\n",
+       "  <line x1=\"165\" y1=\"70\" x2=\"196\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"95\" y1=\"0\" x2=\"165\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"104\" y1=\"0\" x2=\"175\" y2=\"70\" />\n",
+       "  <line x1=\"114\" y1=\"0\" x2=\"185\" y2=\"70\" />\n",
+       "  <line x1=\"124\" y1=\"0\" x2=\"195\" y2=\"70\" />\n",
+       "  <line x1=\"125\" y1=\"0\" x2=\"196\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"95.0,0.0 125.78441972720654,0.0 196.3726550213242,70.58823529411765 165.58823529411765,70.58823529411765\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"165\" y1=\"70\" x2=\"196\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"165\" y1=\"83\" x2=\"196\" y2=\"83\" />\n",
+       "  <line x1=\"165\" y1=\"95\" x2=\"196\" y2=\"95\" />\n",
+       "  <line x1=\"165\" y1=\"99\" x2=\"196\" y2=\"99\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"165\" y1=\"70\" x2=\"165\" y2=\"99\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"175\" y1=\"70\" x2=\"175\" y2=\"99\" />\n",
+       "  <line x1=\"185\" y1=\"70\" x2=\"185\" y2=\"99\" />\n",
+       "  <line x1=\"195\" y1=\"70\" x2=\"195\" y2=\"99\" />\n",
+       "  <line x1=\"196\" y1=\"70\" x2=\"196\" y2=\"99\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"165.58823529411765,70.58823529411765 196.3726550213242,70.58823529411765 196.3726550213242,99.6823464191901 165.58823529411765,99.6823464191901\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"180.980445\" y=\"119.682346\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >789</text>\n",
+       "  <text x=\"216.372655\" y=\"85.135291\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,216.372655,85.135291)\">589</text>\n",
+       "  <text x=\"120.294118\" y=\"84.388229\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,120.294118,84.388229)\">29120</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, 256, 256, 256), meta=np.ndarray&gt;</div><input id='attrs-98802385-e8e8-42c2-9336-a706434ed9dd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-98802385-e8e8-42c2-9336-a706434ed9dd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-27e4c0e3-83af-4850-a101-aed81d14034a' class='xr-var-data-in' type='checkbox'><label for='data-27e4c0e3-83af-4850-a101-aed81d14034a' title='Show/Hide data 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> 100.83 GiB </td>\n",
+       "                        <td> 128.00 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (1, 29120, 589, 789) </td>\n",
+       "                        <td> (1, 256, 256, 256) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1368 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=\"341\" height=\"149\" 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",
+       "  <line x1=\"25\" y1=\"0\" x2=\"25\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 25.412616514582485,0.0 25.412616514582485,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\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=\"165\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"95\" y1=\"12\" x2=\"165\" y2=\"83\" />\n",
+       "  <line x1=\"95\" y1=\"25\" x2=\"165\" y2=\"95\" />\n",
+       "  <line x1=\"95\" y1=\"29\" x2=\"165\" y2=\"99\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"95\" y1=\"0\" x2=\"95\" y2=\"29\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"96\" y1=\"1\" x2=\"96\" y2=\"30\" />\n",
+       "  <line x1=\"99\" y1=\"4\" x2=\"99\" y2=\"33\" />\n",
+       "  <line x1=\"101\" y1=\"6\" x2=\"101\" y2=\"35\" />\n",
+       "  <line x1=\"103\" y1=\"8\" x2=\"103\" y2=\"37\" />\n",
+       "  <line x1=\"105\" y1=\"10\" x2=\"105\" y2=\"39\" />\n",
+       "  <line x1=\"108\" y1=\"13\" x2=\"108\" y2=\"42\" />\n",
+       "  <line x1=\"109\" y1=\"14\" x2=\"109\" y2=\"43\" />\n",
+       "  <line x1=\"112\" y1=\"17\" x2=\"112\" y2=\"46\" />\n",
+       "  <line x1=\"114\" y1=\"19\" x2=\"114\" y2=\"48\" />\n",
+       "  <line x1=\"116\" y1=\"21\" x2=\"116\" y2=\"50\" />\n",
+       "  <line x1=\"119\" y1=\"24\" x2=\"119\" y2=\"53\" />\n",
+       "  <line x1=\"121\" y1=\"26\" x2=\"121\" y2=\"55\" />\n",
+       "  <line x1=\"123\" y1=\"28\" x2=\"123\" y2=\"57\" />\n",
+       "  <line x1=\"125\" y1=\"30\" x2=\"125\" y2=\"59\" />\n",
+       "  <line x1=\"127\" y1=\"32\" x2=\"127\" y2=\"61\" />\n",
+       "  <line x1=\"130\" y1=\"35\" x2=\"130\" y2=\"64\" />\n",
+       "  <line x1=\"132\" y1=\"37\" x2=\"132\" y2=\"66\" />\n",
+       "  <line x1=\"134\" y1=\"39\" x2=\"134\" y2=\"68\" />\n",
+       "  <line x1=\"136\" y1=\"41\" x2=\"136\" y2=\"70\" />\n",
+       "  <line x1=\"139\" y1=\"44\" x2=\"139\" y2=\"73\" />\n",
+       "  <line x1=\"140\" y1=\"45\" x2=\"140\" y2=\"75\" />\n",
+       "  <line x1=\"143\" y1=\"48\" x2=\"143\" y2=\"77\" />\n",
+       "  <line x1=\"145\" y1=\"50\" x2=\"145\" y2=\"79\" />\n",
+       "  <line x1=\"147\" y1=\"52\" x2=\"147\" y2=\"81\" />\n",
+       "  <line x1=\"150\" y1=\"55\" x2=\"150\" y2=\"84\" />\n",
+       "  <line x1=\"152\" y1=\"57\" x2=\"152\" y2=\"86\" />\n",
+       "  <line x1=\"154\" y1=\"59\" x2=\"154\" y2=\"88\" />\n",
+       "  <line x1=\"156\" y1=\"61\" x2=\"156\" y2=\"90\" />\n",
+       "  <line x1=\"158\" y1=\"63\" x2=\"158\" y2=\"93\" />\n",
+       "  <line x1=\"160\" y1=\"65\" x2=\"160\" y2=\"94\" />\n",
+       "  <line x1=\"163\" y1=\"68\" x2=\"163\" y2=\"97\" />\n",
+       "  <line x1=\"165\" y1=\"70\" x2=\"165\" y2=\"99\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"95.0,0.0 165.58823529411765,70.58823529411765 165.58823529411765,99.6823464191901 95.0,29.09411112507245\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"95\" y1=\"0\" x2=\"125\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"96\" y1=\"1\" x2=\"127\" y2=\"1\" />\n",
+       "  <line x1=\"99\" y1=\"4\" x2=\"130\" y2=\"4\" />\n",
+       "  <line x1=\"101\" y1=\"6\" x2=\"131\" y2=\"6\" />\n",
+       "  <line x1=\"103\" y1=\"8\" x2=\"134\" y2=\"8\" />\n",
+       "  <line x1=\"105\" y1=\"10\" x2=\"136\" y2=\"10\" />\n",
+       "  <line x1=\"108\" y1=\"13\" x2=\"138\" y2=\"13\" />\n",
+       "  <line x1=\"109\" y1=\"14\" x2=\"140\" y2=\"14\" />\n",
+       "  <line x1=\"112\" y1=\"17\" x2=\"143\" y2=\"17\" />\n",
+       "  <line x1=\"114\" y1=\"19\" x2=\"145\" y2=\"19\" />\n",
+       "  <line x1=\"116\" y1=\"21\" x2=\"147\" y2=\"21\" />\n",
+       "  <line x1=\"119\" y1=\"24\" x2=\"149\" y2=\"24\" />\n",
+       "  <line x1=\"121\" y1=\"26\" x2=\"151\" y2=\"26\" />\n",
+       "  <line x1=\"123\" y1=\"28\" x2=\"154\" y2=\"28\" />\n",
+       "  <line x1=\"125\" y1=\"30\" x2=\"156\" y2=\"30\" />\n",
+       "  <line x1=\"127\" y1=\"32\" x2=\"158\" y2=\"32\" />\n",
+       "  <line x1=\"130\" y1=\"35\" x2=\"161\" y2=\"35\" />\n",
+       "  <line x1=\"132\" y1=\"37\" x2=\"163\" y2=\"37\" />\n",
+       "  <line x1=\"134\" y1=\"39\" x2=\"165\" y2=\"39\" />\n",
+       "  <line x1=\"136\" y1=\"41\" x2=\"167\" y2=\"41\" />\n",
+       "  <line x1=\"139\" y1=\"44\" x2=\"169\" y2=\"44\" />\n",
+       "  <line x1=\"140\" y1=\"45\" x2=\"171\" y2=\"45\" />\n",
+       "  <line x1=\"143\" y1=\"48\" x2=\"174\" y2=\"48\" />\n",
+       "  <line x1=\"145\" y1=\"50\" x2=\"176\" y2=\"50\" />\n",
+       "  <line x1=\"147\" y1=\"52\" x2=\"178\" y2=\"52\" />\n",
+       "  <line x1=\"150\" y1=\"55\" x2=\"181\" y2=\"55\" />\n",
+       "  <line x1=\"152\" y1=\"57\" x2=\"182\" y2=\"57\" />\n",
+       "  <line x1=\"154\" y1=\"59\" x2=\"185\" y2=\"59\" />\n",
+       "  <line x1=\"156\" y1=\"61\" x2=\"187\" y2=\"61\" />\n",
+       "  <line x1=\"158\" y1=\"63\" x2=\"189\" y2=\"63\" />\n",
+       "  <line x1=\"160\" y1=\"65\" x2=\"191\" y2=\"65\" />\n",
+       "  <line x1=\"163\" y1=\"68\" x2=\"194\" y2=\"68\" />\n",
+       "  <line x1=\"165\" y1=\"70\" x2=\"196\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"95\" y1=\"0\" x2=\"165\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"104\" y1=\"0\" x2=\"175\" y2=\"70\" />\n",
+       "  <line x1=\"114\" y1=\"0\" x2=\"185\" y2=\"70\" />\n",
+       "  <line x1=\"124\" y1=\"0\" x2=\"195\" y2=\"70\" />\n",
+       "  <line x1=\"125\" y1=\"0\" x2=\"196\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"95.0,0.0 125.78441972720654,0.0 196.3726550213242,70.58823529411765 165.58823529411765,70.58823529411765\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"165\" y1=\"70\" x2=\"196\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"165\" y1=\"83\" x2=\"196\" y2=\"83\" />\n",
+       "  <line x1=\"165\" y1=\"95\" x2=\"196\" y2=\"95\" />\n",
+       "  <line x1=\"165\" y1=\"99\" x2=\"196\" y2=\"99\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"165\" y1=\"70\" x2=\"165\" y2=\"99\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"175\" y1=\"70\" x2=\"175\" y2=\"99\" />\n",
+       "  <line x1=\"185\" y1=\"70\" x2=\"185\" y2=\"99\" />\n",
+       "  <line x1=\"195\" y1=\"70\" x2=\"195\" y2=\"99\" />\n",
+       "  <line x1=\"196\" y1=\"70\" x2=\"196\" y2=\"99\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"165.58823529411765,70.58823529411765 196.3726550213242,70.58823529411765 196.3726550213242,99.6823464191901 165.58823529411765,99.6823464191901\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"180.980445\" y=\"119.682346\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >789</text>\n",
+       "  <text x=\"216.372655\" y=\"85.135291\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,216.372655,85.135291)\">589</text>\n",
+       "  <text x=\"120.294118\" y=\"84.388229\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,120.294118,84.388229)\">29120</text>\n",
+       "</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, 256, 256, 256), meta=np.ndarray&gt;</div><input id='attrs-3ffe8b36-87dd-45f6-b6c2-1f85984b7fed' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3ffe8b36-87dd-45f6-b6c2-1f85984b7fed' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5bd5872a-429b-4b6f-bf6c-fd560fa73a5f' class='xr-var-data-in' type='checkbox'><label for='data-5bd5872a-429b-4b6f-bf6c-fd560fa73a5f' title='Show/Hide data 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> 100.83 GiB </td>\n",
+       "                        <td> 128.00 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (1, 29120, 589, 789) </td>\n",
+       "                        <td> (1, 256, 256, 256) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1368 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=\"341\" height=\"149\" 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",
+       "  <line x1=\"25\" y1=\"0\" x2=\"25\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 25.412616514582485,0.0 25.412616514582485,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\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=\"165\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"95\" y1=\"12\" x2=\"165\" y2=\"83\" />\n",
+       "  <line x1=\"95\" y1=\"25\" x2=\"165\" y2=\"95\" />\n",
+       "  <line x1=\"95\" y1=\"29\" x2=\"165\" y2=\"99\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"95\" y1=\"0\" x2=\"95\" y2=\"29\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"96\" y1=\"1\" x2=\"96\" y2=\"30\" />\n",
+       "  <line x1=\"99\" y1=\"4\" x2=\"99\" y2=\"33\" />\n",
+       "  <line x1=\"101\" y1=\"6\" x2=\"101\" y2=\"35\" />\n",
+       "  <line x1=\"103\" y1=\"8\" x2=\"103\" y2=\"37\" />\n",
+       "  <line x1=\"105\" y1=\"10\" x2=\"105\" y2=\"39\" />\n",
+       "  <line x1=\"108\" y1=\"13\" x2=\"108\" y2=\"42\" />\n",
+       "  <line x1=\"109\" y1=\"14\" x2=\"109\" y2=\"43\" />\n",
+       "  <line x1=\"112\" y1=\"17\" x2=\"112\" y2=\"46\" />\n",
+       "  <line x1=\"114\" y1=\"19\" x2=\"114\" y2=\"48\" />\n",
+       "  <line x1=\"116\" y1=\"21\" x2=\"116\" y2=\"50\" />\n",
+       "  <line x1=\"119\" y1=\"24\" x2=\"119\" y2=\"53\" />\n",
+       "  <line x1=\"121\" y1=\"26\" x2=\"121\" y2=\"55\" />\n",
+       "  <line x1=\"123\" y1=\"28\" x2=\"123\" y2=\"57\" />\n",
+       "  <line x1=\"125\" y1=\"30\" x2=\"125\" y2=\"59\" />\n",
+       "  <line x1=\"127\" y1=\"32\" x2=\"127\" y2=\"61\" />\n",
+       "  <line x1=\"130\" y1=\"35\" x2=\"130\" y2=\"64\" />\n",
+       "  <line x1=\"132\" y1=\"37\" x2=\"132\" y2=\"66\" />\n",
+       "  <line x1=\"134\" y1=\"39\" x2=\"134\" y2=\"68\" />\n",
+       "  <line x1=\"136\" y1=\"41\" x2=\"136\" y2=\"70\" />\n",
+       "  <line x1=\"139\" y1=\"44\" x2=\"139\" y2=\"73\" />\n",
+       "  <line x1=\"140\" y1=\"45\" x2=\"140\" y2=\"75\" />\n",
+       "  <line x1=\"143\" y1=\"48\" x2=\"143\" y2=\"77\" />\n",
+       "  <line x1=\"145\" y1=\"50\" x2=\"145\" y2=\"79\" />\n",
+       "  <line x1=\"147\" y1=\"52\" x2=\"147\" y2=\"81\" />\n",
+       "  <line x1=\"150\" y1=\"55\" x2=\"150\" y2=\"84\" />\n",
+       "  <line x1=\"152\" y1=\"57\" x2=\"152\" y2=\"86\" />\n",
+       "  <line x1=\"154\" y1=\"59\" x2=\"154\" y2=\"88\" />\n",
+       "  <line x1=\"156\" y1=\"61\" x2=\"156\" y2=\"90\" />\n",
+       "  <line x1=\"158\" y1=\"63\" x2=\"158\" y2=\"93\" />\n",
+       "  <line x1=\"160\" y1=\"65\" x2=\"160\" y2=\"94\" />\n",
+       "  <line x1=\"163\" y1=\"68\" x2=\"163\" y2=\"97\" />\n",
+       "  <line x1=\"165\" y1=\"70\" x2=\"165\" y2=\"99\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"95.0,0.0 165.58823529411765,70.58823529411765 165.58823529411765,99.6823464191901 95.0,29.09411112507245\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"95\" y1=\"0\" x2=\"125\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"96\" y1=\"1\" x2=\"127\" y2=\"1\" />\n",
+       "  <line x1=\"99\" y1=\"4\" x2=\"130\" y2=\"4\" />\n",
+       "  <line x1=\"101\" y1=\"6\" x2=\"131\" y2=\"6\" />\n",
+       "  <line x1=\"103\" y1=\"8\" x2=\"134\" y2=\"8\" />\n",
+       "  <line x1=\"105\" y1=\"10\" x2=\"136\" y2=\"10\" />\n",
+       "  <line x1=\"108\" y1=\"13\" x2=\"138\" y2=\"13\" />\n",
+       "  <line x1=\"109\" y1=\"14\" x2=\"140\" y2=\"14\" />\n",
+       "  <line x1=\"112\" y1=\"17\" x2=\"143\" y2=\"17\" />\n",
+       "  <line x1=\"114\" y1=\"19\" x2=\"145\" y2=\"19\" />\n",
+       "  <line x1=\"116\" y1=\"21\" x2=\"147\" y2=\"21\" />\n",
+       "  <line x1=\"119\" y1=\"24\" x2=\"149\" y2=\"24\" />\n",
+       "  <line x1=\"121\" y1=\"26\" x2=\"151\" y2=\"26\" />\n",
+       "  <line x1=\"123\" y1=\"28\" x2=\"154\" y2=\"28\" />\n",
+       "  <line x1=\"125\" y1=\"30\" x2=\"156\" y2=\"30\" />\n",
+       "  <line x1=\"127\" y1=\"32\" x2=\"158\" y2=\"32\" />\n",
+       "  <line x1=\"130\" y1=\"35\" x2=\"161\" y2=\"35\" />\n",
+       "  <line x1=\"132\" y1=\"37\" x2=\"163\" y2=\"37\" />\n",
+       "  <line x1=\"134\" y1=\"39\" x2=\"165\" y2=\"39\" />\n",
+       "  <line x1=\"136\" y1=\"41\" x2=\"167\" y2=\"41\" />\n",
+       "  <line x1=\"139\" y1=\"44\" x2=\"169\" y2=\"44\" />\n",
+       "  <line x1=\"140\" y1=\"45\" x2=\"171\" y2=\"45\" />\n",
+       "  <line x1=\"143\" y1=\"48\" x2=\"174\" y2=\"48\" />\n",
+       "  <line x1=\"145\" y1=\"50\" x2=\"176\" y2=\"50\" />\n",
+       "  <line x1=\"147\" y1=\"52\" x2=\"178\" y2=\"52\" />\n",
+       "  <line x1=\"150\" y1=\"55\" x2=\"181\" y2=\"55\" />\n",
+       "  <line x1=\"152\" y1=\"57\" x2=\"182\" y2=\"57\" />\n",
+       "  <line x1=\"154\" y1=\"59\" x2=\"185\" y2=\"59\" />\n",
+       "  <line x1=\"156\" y1=\"61\" x2=\"187\" y2=\"61\" />\n",
+       "  <line x1=\"158\" y1=\"63\" x2=\"189\" y2=\"63\" />\n",
+       "  <line x1=\"160\" y1=\"65\" x2=\"191\" y2=\"65\" />\n",
+       "  <line x1=\"163\" y1=\"68\" x2=\"194\" y2=\"68\" />\n",
+       "  <line x1=\"165\" y1=\"70\" x2=\"196\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"95\" y1=\"0\" x2=\"165\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"104\" y1=\"0\" x2=\"175\" y2=\"70\" />\n",
+       "  <line x1=\"114\" y1=\"0\" x2=\"185\" y2=\"70\" />\n",
+       "  <line x1=\"124\" y1=\"0\" x2=\"195\" y2=\"70\" />\n",
+       "  <line x1=\"125\" y1=\"0\" x2=\"196\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"95.0,0.0 125.78441972720654,0.0 196.3726550213242,70.58823529411765 165.58823529411765,70.58823529411765\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"165\" y1=\"70\" x2=\"196\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"165\" y1=\"83\" x2=\"196\" y2=\"83\" />\n",
+       "  <line x1=\"165\" y1=\"95\" x2=\"196\" y2=\"95\" />\n",
+       "  <line x1=\"165\" y1=\"99\" x2=\"196\" y2=\"99\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"165\" y1=\"70\" x2=\"165\" y2=\"99\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"175\" y1=\"70\" x2=\"175\" y2=\"99\" />\n",
+       "  <line x1=\"185\" y1=\"70\" x2=\"185\" y2=\"99\" />\n",
+       "  <line x1=\"195\" y1=\"70\" x2=\"195\" y2=\"99\" />\n",
+       "  <line x1=\"196\" y1=\"70\" x2=\"196\" y2=\"99\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"165.58823529411765,70.58823529411765 196.3726550213242,70.58823529411765 196.3726550213242,99.6823464191901 165.58823529411765,99.6823464191901\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"180.980445\" y=\"119.682346\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >789</text>\n",
+       "  <text x=\"216.372655\" y=\"85.135291\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,216.372655,85.135291)\">589</text>\n",
+       "  <text x=\"120.294118\" y=\"84.388229\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,120.294118,84.388229)\">29120</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, 256, 256, 256), meta=np.ndarray&gt;</div><input id='attrs-efd13aa6-cd01-45fd-b82b-11f779322544' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-efd13aa6-cd01-45fd-b82b-11f779322544' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-679c2532-24f6-4d59-9d2e-c7a67a9e331f' class='xr-var-data-in' type='checkbox'><label for='data-679c2532-24f6-4d59-9d2e-c7a67a9e331f' title='Show/Hide data 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> 100.83 GiB </td>\n",
+       "                        <td> 128.00 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (1, 29120, 589, 789) </td>\n",
+       "                        <td> (1, 256, 256, 256) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1368 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=\"341\" height=\"149\" 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",
+       "  <line x1=\"25\" y1=\"0\" x2=\"25\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 25.412616514582485,0.0 25.412616514582485,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\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=\"165\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"95\" y1=\"12\" x2=\"165\" y2=\"83\" />\n",
+       "  <line x1=\"95\" y1=\"25\" x2=\"165\" y2=\"95\" />\n",
+       "  <line x1=\"95\" y1=\"29\" x2=\"165\" y2=\"99\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"95\" y1=\"0\" x2=\"95\" y2=\"29\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"96\" y1=\"1\" x2=\"96\" y2=\"30\" />\n",
+       "  <line x1=\"99\" y1=\"4\" x2=\"99\" y2=\"33\" />\n",
+       "  <line x1=\"101\" y1=\"6\" x2=\"101\" y2=\"35\" />\n",
+       "  <line x1=\"103\" y1=\"8\" x2=\"103\" y2=\"37\" />\n",
+       "  <line x1=\"105\" y1=\"10\" x2=\"105\" y2=\"39\" />\n",
+       "  <line x1=\"108\" y1=\"13\" x2=\"108\" y2=\"42\" />\n",
+       "  <line x1=\"109\" y1=\"14\" x2=\"109\" y2=\"43\" />\n",
+       "  <line x1=\"112\" y1=\"17\" x2=\"112\" y2=\"46\" />\n",
+       "  <line x1=\"114\" y1=\"19\" x2=\"114\" y2=\"48\" />\n",
+       "  <line x1=\"116\" y1=\"21\" x2=\"116\" y2=\"50\" />\n",
+       "  <line x1=\"119\" y1=\"24\" x2=\"119\" y2=\"53\" />\n",
+       "  <line x1=\"121\" y1=\"26\" x2=\"121\" y2=\"55\" />\n",
+       "  <line x1=\"123\" y1=\"28\" x2=\"123\" y2=\"57\" />\n",
+       "  <line x1=\"125\" y1=\"30\" x2=\"125\" y2=\"59\" />\n",
+       "  <line x1=\"127\" y1=\"32\" x2=\"127\" y2=\"61\" />\n",
+       "  <line x1=\"130\" y1=\"35\" x2=\"130\" y2=\"64\" />\n",
+       "  <line x1=\"132\" y1=\"37\" x2=\"132\" y2=\"66\" />\n",
+       "  <line x1=\"134\" y1=\"39\" x2=\"134\" y2=\"68\" />\n",
+       "  <line x1=\"136\" y1=\"41\" x2=\"136\" y2=\"70\" />\n",
+       "  <line x1=\"139\" y1=\"44\" x2=\"139\" y2=\"73\" />\n",
+       "  <line x1=\"140\" y1=\"45\" x2=\"140\" y2=\"75\" />\n",
+       "  <line x1=\"143\" y1=\"48\" x2=\"143\" y2=\"77\" />\n",
+       "  <line x1=\"145\" y1=\"50\" x2=\"145\" y2=\"79\" />\n",
+       "  <line x1=\"147\" y1=\"52\" x2=\"147\" y2=\"81\" />\n",
+       "  <line x1=\"150\" y1=\"55\" x2=\"150\" y2=\"84\" />\n",
+       "  <line x1=\"152\" y1=\"57\" x2=\"152\" y2=\"86\" />\n",
+       "  <line x1=\"154\" y1=\"59\" x2=\"154\" y2=\"88\" />\n",
+       "  <line x1=\"156\" y1=\"61\" x2=\"156\" y2=\"90\" />\n",
+       "  <line x1=\"158\" y1=\"63\" x2=\"158\" y2=\"93\" />\n",
+       "  <line x1=\"160\" y1=\"65\" x2=\"160\" y2=\"94\" />\n",
+       "  <line x1=\"163\" y1=\"68\" x2=\"163\" y2=\"97\" />\n",
+       "  <line x1=\"165\" y1=\"70\" x2=\"165\" y2=\"99\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"95.0,0.0 165.58823529411765,70.58823529411765 165.58823529411765,99.6823464191901 95.0,29.09411112507245\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"95\" y1=\"0\" x2=\"125\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"96\" y1=\"1\" x2=\"127\" y2=\"1\" />\n",
+       "  <line x1=\"99\" y1=\"4\" x2=\"130\" y2=\"4\" />\n",
+       "  <line x1=\"101\" y1=\"6\" x2=\"131\" y2=\"6\" />\n",
+       "  <line x1=\"103\" y1=\"8\" x2=\"134\" y2=\"8\" />\n",
+       "  <line x1=\"105\" y1=\"10\" x2=\"136\" y2=\"10\" />\n",
+       "  <line x1=\"108\" y1=\"13\" x2=\"138\" y2=\"13\" />\n",
+       "  <line x1=\"109\" y1=\"14\" x2=\"140\" y2=\"14\" />\n",
+       "  <line x1=\"112\" y1=\"17\" x2=\"143\" y2=\"17\" />\n",
+       "  <line x1=\"114\" y1=\"19\" x2=\"145\" y2=\"19\" />\n",
+       "  <line x1=\"116\" y1=\"21\" x2=\"147\" y2=\"21\" />\n",
+       "  <line x1=\"119\" y1=\"24\" x2=\"149\" y2=\"24\" />\n",
+       "  <line x1=\"121\" y1=\"26\" x2=\"151\" y2=\"26\" />\n",
+       "  <line x1=\"123\" y1=\"28\" x2=\"154\" y2=\"28\" />\n",
+       "  <line x1=\"125\" y1=\"30\" x2=\"156\" y2=\"30\" />\n",
+       "  <line x1=\"127\" y1=\"32\" x2=\"158\" y2=\"32\" />\n",
+       "  <line x1=\"130\" y1=\"35\" x2=\"161\" y2=\"35\" />\n",
+       "  <line x1=\"132\" y1=\"37\" x2=\"163\" y2=\"37\" />\n",
+       "  <line x1=\"134\" y1=\"39\" x2=\"165\" y2=\"39\" />\n",
+       "  <line x1=\"136\" y1=\"41\" x2=\"167\" y2=\"41\" />\n",
+       "  <line x1=\"139\" y1=\"44\" x2=\"169\" y2=\"44\" />\n",
+       "  <line x1=\"140\" y1=\"45\" x2=\"171\" y2=\"45\" />\n",
+       "  <line x1=\"143\" y1=\"48\" x2=\"174\" y2=\"48\" />\n",
+       "  <line x1=\"145\" y1=\"50\" x2=\"176\" y2=\"50\" />\n",
+       "  <line x1=\"147\" y1=\"52\" x2=\"178\" y2=\"52\" />\n",
+       "  <line x1=\"150\" y1=\"55\" x2=\"181\" y2=\"55\" />\n",
+       "  <line x1=\"152\" y1=\"57\" x2=\"182\" y2=\"57\" />\n",
+       "  <line x1=\"154\" y1=\"59\" x2=\"185\" y2=\"59\" />\n",
+       "  <line x1=\"156\" y1=\"61\" x2=\"187\" y2=\"61\" />\n",
+       "  <line x1=\"158\" y1=\"63\" x2=\"189\" y2=\"63\" />\n",
+       "  <line x1=\"160\" y1=\"65\" x2=\"191\" y2=\"65\" />\n",
+       "  <line x1=\"163\" y1=\"68\" x2=\"194\" y2=\"68\" />\n",
+       "  <line x1=\"165\" y1=\"70\" x2=\"196\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"95\" y1=\"0\" x2=\"165\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"104\" y1=\"0\" x2=\"175\" y2=\"70\" />\n",
+       "  <line x1=\"114\" y1=\"0\" x2=\"185\" y2=\"70\" />\n",
+       "  <line x1=\"124\" y1=\"0\" x2=\"195\" y2=\"70\" />\n",
+       "  <line x1=\"125\" y1=\"0\" x2=\"196\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"95.0,0.0 125.78441972720654,0.0 196.3726550213242,70.58823529411765 165.58823529411765,70.58823529411765\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"165\" y1=\"70\" x2=\"196\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"165\" y1=\"83\" x2=\"196\" y2=\"83\" />\n",
+       "  <line x1=\"165\" y1=\"95\" x2=\"196\" y2=\"95\" />\n",
+       "  <line x1=\"165\" y1=\"99\" x2=\"196\" y2=\"99\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"165\" y1=\"70\" x2=\"165\" y2=\"99\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"175\" y1=\"70\" x2=\"175\" y2=\"99\" />\n",
+       "  <line x1=\"185\" y1=\"70\" x2=\"185\" y2=\"99\" />\n",
+       "  <line x1=\"195\" y1=\"70\" x2=\"195\" y2=\"99\" />\n",
+       "  <line x1=\"196\" y1=\"70\" x2=\"196\" y2=\"99\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"165.58823529411765,70.58823529411765 196.3726550213242,70.58823529411765 196.3726550213242,99.6823464191901 165.58823529411765,99.6823464191901\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"180.980445\" y=\"119.682346\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >789</text>\n",
+       "  <text x=\"216.372655\" y=\"85.135291\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,216.372655,85.135291)\">589</text>\n",
+       "  <text x=\"120.294118\" y=\"84.388229\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,120.294118,84.388229)\">29120</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-0c400b99-fc0f-42e0-ab41-f5c32f00533f' class='xr-section-summary-in' type='checkbox'  ><label for='section-0c400b99-fc0f-42e0-ab41-f5c32f00533f' 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-3b5db321-f42d-4301-a3a0-17034a88748c' class='xr-index-data-in' type='checkbox'/><label for='index-3b5db321-f42d-4301-a3a0-17034a88748c' 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-3166046d-e372-41d4-8fee-47fe306ebdb8' class='xr-index-data-in' type='checkbox'/><label for='index-3166046d-e372-41d4-8fee-47fe306ebdb8' 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",
+       "               ...\n",
+       "               &#x27;2000-08-17 18:00:00&#x27;, &#x27;2000-08-17 21:00:00&#x27;,\n",
+       "               &#x27;2000-08-18 00:00:00&#x27;, &#x27;2000-08-18 03:00:00&#x27;,\n",
+       "               &#x27;2000-08-18 06:00:00&#x27;, &#x27;2000-08-18 09:00:00&#x27;,\n",
+       "               &#x27;2000-08-18 12:00:00&#x27;, &#x27;2000-08-18 15:00:00&#x27;,\n",
+       "               &#x27;2000-08-18 18:00:00&#x27;, &#x27;2000-08-18 21:00:00&#x27;],\n",
+       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=29120, 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-94607c26-5fd5-499d-a2b9-f14eafc3d21d' class='xr-index-data-in' type='checkbox'/><label for='index-94607c26-5fd5-499d-a2b9-f14eafc3d21d' 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-bd2e971b-ecc3-4a23-b0e7-03e27aa143ab' class='xr-index-data-in' type='checkbox'/><label for='index-bd2e971b-ecc3-4a23-b0e7-03e27aa143ab' 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-1e050aec-2b84-4e51-b81d-828a5fd823bb' class='xr-section-summary-in' type='checkbox'  checked><label for='section-1e050aec-2b84-4e51-b81d-828a5fd823bb' 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>\n",
+       "Dimensions:   (altitude: 1, y: 589, x: 789, time: 29120)\n",
+       "Coordinates:\n",
+       "  * altitude  (altitude) int64 100\n",
+       "    lat       (y, x) float64 dask.array<chunksize=(256, 256), meta=np.ndarray>\n",
+       "    lon       (y, x) float64 dask.array<chunksize=(256, 256), meta=np.ndarray>\n",
+       "  * time      (time) datetime64[ns] 1990-09-01 ... 2000-08-18T21:00:00\n",
+       "  * x         (x) float64 -1.999e+06 -1.997e+06 ... -3.175e+04 -2.925e+04\n",
+       "  * y         (y) float64 -6.095e+05 -6.07e+05 -6.045e+05 ... 8.58e+05 8.605e+05\n",
+       "Data variables:\n",
+       "    r         (altitude, time, y, x) float64 dask.array<chunksize=(1, 256, 256, 256), meta=np.ndarray>\n",
+       "    t         (altitude, time, y, x) float64 dask.array<chunksize=(1, 256, 256, 256), meta=np.ndarray>\n",
+       "    u         (altitude, time, y, x) float64 dask.array<chunksize=(1, 256, 256, 256), meta=np.ndarray>\n",
+       "    v         (altitude, time, y, x) float64 dask.array<chunksize=(1, 256, 256, 256), meta=np.ndarray>\n",
+       "Attributes:\n",
+       "    description:  All prognostic variables for 10-year period on reduced levels"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ds_danra_hl = xr.open_zarr(fp_danra)\n",
+    "ds_danra_hl"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<cartopy.mpl.feature_artist.FeatureArtist at 0x7f31c97dad70>"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAj8AAAGFCAYAAAASFZK5AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy81sbWrAAAACXBIWXMAAA9hAAAPYQGoP6dpAADDxElEQVR4nOyddZxUVfvAv3d6trtgi+5upEtQVMAAVEIFsVB5fW1fQP1hK6JiImCADQYlKAtS0tK99HbN7k7P3N8fw44M7C5bswHn+/nMZ3fOPfGcuXfmPvecJyRZlmUEAoFAIBAIrhEUNS2AQCAQCAQCQXUilB+BQCAQCATXFEL5EQgEAoFAcE0hlB+BQCAQCATXFEL5EQgEAoFAcE0hlB+BQCAQCATXFEL5EQgEAoFAcE2hqmkBBAKBQCAQeGI2m7FarZXuR6PRoNPpqkCiqwuh/AgEAoFAUIswm80kxvuRmu6odF9RUVEkJycLBegShPIjEAgEAkEtwmq1kpru4NSOBAL8K26dYsh3Et/xJFarVSg/lyCUH4FAIBAIaiF+/hJ+/lKF2zupeNurHaH8CAQCgUBQC3HIThyVyL7pkJ1VJ8xVhvD2EggEAoFAcE0hVn4EAoFAIKiFOJFxUvGln8q0vdoRyo9AIBAIBLUQJ04qs3FVudZXN2LbSyAQCAQCwTWFWPkRCAQCgaAW4pBlHHLFt64q0/ZqRyg/AoFAIBDUQoTNj/cQ214CgUAgEAiuKcTKj0AgEAgEtRAnMg6x8uMVhPIjEAgEAkEtRGx7eQ+h/AgEAoFAUAsRBs/eQ9j8CAQCgUAguKYQKz8CgUAgENRCnBdelWkvKB6h/AgEAoFAUAtxVNLguTJtr3bEtpdAIBAIBIJrCrHyIxAIBAJBLcQhu16VaS8oHqH8CAQCgUBQCxE2P95DbHsJBAKBQCC4phArPwKBQCAQ1EKcSDiQKtVeUDxC+REIBAKBoBbilF2vyrQXFI/Y9hIIBAKBQHBNIVZ+BAKBQCCohTgque1VmbZXO0L5EQgEAoGgFiKUH+8hlB+BQCAQCGohTlnCKVfC4LkSba92hM2PQCAQCASCawqx8iMQCAQCQS1EbHt5D6H8CAQCgUBQC3GgwFGJDRpHFcpytSG2vQQCgUAgEFxTiJUfgUAgEAhqIXIlDZ5lYfBcIkL5EQgEAoGgFiJsfryH2PYSCAQCgUAAwPr16xk+fDgxMTFIksTSpUuv2GbdunV07NgRnU5HgwYN+Oijj0qs+8033yBJErfcckvVCV0BhPIjEAgEAkEtxCErKv0qL4WFhbRt25b333+/TPWTk5MZNmwYvXr1YteuXTz77LNMnTqVH3/88bK6p06d4oknnqBXr17llquqEdteAoFAIBDUQpxIOCuxRuGk/JlNhw4dytChQ8tc/6OPPiIuLo7Zs2cD0Lx5c7Zv386bb77JqFGj3PUcDgd33nknM2fO5K+//iI3N7fcslUlYuVHIBAIBIJaSJHNT2VeAAaDweNlsViqTMbNmzczePBgj7IhQ4awfft2bDabu+zFF18kPDyce++9t8rGrgxC+REIBAKB4ComNjaWwMBA9+uVV16psr5TU1OJjIz0KIuMjMRut5OZmQnAxo0bmTdvHp9++mmVjVtZxLaXQCAQCAS1kIra7fzb3rXtdebMGQICAtzlWq220rJdjCR5epXJF8aVJIn8/HzuuusuPv30U8LCwqp03MoglB+BQCAQCGohLpufSiQ2vdA2ICDAQ/mpSqKiokhNTfUoS09PR6VSERoayv79+zl58iTDhw//Vy6nEwCVSsXhw4dp2LChV2QrDaH8CAQCgUAgqBDdu3fn119/9Sj7/fff6dSpE2q1mmbNmrF3716P488//zz5+fm8++67xMbGVqe4boTyIxAIBAJBLcRZydxeFfH2Kigo4NixY+73ycnJ7N69m5CQEOLi4njmmWc4d+4cX3zxBQBTpkzh/fffZ9q0aUyaNInNmzczb948Fi9eDIBOp6NVq1YeYwQFBQFcVl6dCOVHIBAIBIJaSFXZ/JSH7du3069fP/f7adOmATB+/HgWLFhASkoKp0+fdh9PTExk+fLlPP7443zwwQfExMQwZ84cDzf32ogkyxX4dAQCgUAgEHgFg8FAYGAg3+xugY+/ssL9GPMdjG53gLy8PK/Z/NRVxMqPQCAQCAS1ECeKag9yeK0glB+BQCAQCGohDlnCUYnM7JVpe7UjghwKBAKBQCC4phArPwKBQCAQ1EIclfT2cohtrxIRyo9AIBAIBLUQp6zAWQlvL6fwZyoRofwIBAKBQFALESs/3kPY/AgEAoFAILimECs/AoFAIBDUQpxUzmPLWXWiXHUI5UcgEAgEglpI5eP8iM2dkhCfjEAgEAgEgmsKofzUcU6ePIkkSSxYsMBdtmnTJmbMmEFubu5l9fv27Uvfvn3d741GIzNmzCApKckr8kmSxIwZM7zSd2XZsGED9913Hx07dkSr1SJJEidPniyx/nvvvUezZs3QarUkJiYyc+ZMbDbbZfXS09OZMGECYWFh+Pj40L17d/74448yyZSQkIAkSUiSxMMPP1zRqRVLu3bt3H3feOONFepj0aJFzJ49u9hjtflcV5b8/HyefPJJBg8eTHh4eKlzlWWZOXPmuK+V6OhoHnjgAXJyci6rm5KSwoQJE4iIiECn09GmTRvmzZtXbL+Vua6K2LlzJwMHDsTPz4+goCBGjhzJiRMnKiVXSRQUFPDYY48RExODTqejXbt2fPPNN5fVK+/38FqiKLdXZV6C4hGfzFXIpk2bmDlzZrHKz9y5c5k7d677vdFoZObMmV5Tfmozf/zxB2vWrCEuLo4ePXqUWvf//u//ePTRRxk5ciSrVq3iwQcfZNasWTz00EMe9SwWCwMGDOCPP/7g3Xff5eeffyYyMpLrr7+edevWlUmuYcOGsXnzZp544okKz604vvzySzZv3kxUVFSF+yhN+dm8eTP33XdfhfuuzWRlZfHJJ59gsVi45ZZbSq37xBNP8Pjjj3PzzTfz22+/8fTTT7No0SIGDRrkoSzn5eVx3XXX8ccff/D666/z888/06FDB+677z7efvttjz6r4ro6dOgQffv2xWq18t133/H5559z5MgRevXqRUZGRoXkKo2RI0eycOFCpk+fzooVK+jcuTNjxoxh0aJFHvXK8z281nAiVfolKAFZUKdJTk6WAXn+/PnusjfeeEMG5OTk5Cu2z8jIkAF5+vTpXpHPm31XFofD4f6/tM8sMzNT1ul08uTJkz3K/+///k+WJEnev3+/u+yDDz6QAXnTpk3uMpvNJrdo0ULu0qXLFWWKj4+Xx48fX/7JlIP4+Hj5hhtuqFDbG264QY6Pj69ageoATqdTdjqdsiyX/p05e/asrFQq5UceecSjfNGiRTIgf/LJJ+6yV155RQbk7du3e9QdPHiw7OvrK+fk5LjLKntdybIs33bbbXJYWJicl5fnLjt58qSsVqvlJ598skJylcSyZctkQF60aJFH+aBBg+SYmBjZbre7y8r6PbyWyMvLkwF5zo5u8qeHr6vwa86ObjLgcc4FLsTKTy3l2LFjTJw4kcaNG+Pj40O9evUYPnw4e/fuLbXdjBkz+O9//wtAYmKie5ujaGXn4m2vkydPEh4eDsDMmTPddSdMmADAhAkTSEhIKHYMSfJ8ojAYDEyaNInQ0FD8/Py4/vrrOXLkSLEyHj16lLFjxxIREYFWq6V58+Z88MEHZfxkqg6FomyX/8qVKzGbzUycONGjfOLEiciyzNKlS91lS5YsoWnTpnTv3t1dplKpuOuuu9i6dSvnzp2rkKxJSUlIksSiRYt46qmniI6Oxs/Pj+HDh5OWlkZ+fj6TJ08mLCyMsLAwJk6cSEFBQYXGKom+ffuybNkyTp065b5WLr4OLt0KWrBgAZIk8eeff7qvjYCAAMaNG0dhYSGpqancfvvtBAUFER0dzRNPPHHZNqLVauXll192byGFh4czceJEj5WK6uDSuZbEli1bcDgcDBs2zKO8aJvxxx9/dJdt3LiRyMhIOnbseFndwsJCVq5c6S6r7HVlt9v57bffGDVqlEd27/j4ePr168eSJUsqJFdJLFmyBD8/P2677TaP8okTJ3L+/Hn+/vtvd1lZv4fXImLby3sIb69ayvnz5wkNDeXVV18lPDyc7OxsFi5cSNeuXdm1axdNmzYttt19991HdnY27733Hj/99BPR0dEAtGjR4rK60dHRrFy5kuuvv557773XvWVRpBCVFVmWueWWW9i0aRP/+9//6Ny5Mxs3bmTo0KGX1T1w4AA9evQgLi6Ot956i6ioKFatWsXUqVPJzMxk+vTppY7lcDiQyxC1VKFQVNmP6r59+wBo3bq1R3l0dDRhYWHu40V1e/XqdVkfbdq0AWD//v3Uq1evwrI8++yz9OvXjwULFnDy5EmeeOIJxowZg0qlom3btixevJhdu3bx7LPP4u/vz5w5cyo81qXMnTuXyZMnc/z4cY+b5ZW47777GDlyJN98841bNrvdzuHDhxk5ciSTJ09mzZo1vPbaa8TExDBt2jQAnE4nN998M3/99RdPPvkkPXr04NSpU0yfPp2+ffuyfft29Hp9iePKsozD4SiTjCpV1fwUWq1WALRarUe5Wq1GkiT27NnjUffSehe33bNnD6NHjwYqf10dP34ck8nkrn9pH6tXr8ZsNqPT6colV0ns27eP5s2bX/a5Fo2/b98+scVVBiof5FAoPyUhlJ9aSu/evendu7f7vcPh4IYbbqBly5Z8/PHHJe69169fn7i4OADat29f7MpNEVqt1v10V79+fbp161YhWVetWsXatWt59913mTp1KgCDBg1Co9Hw3HPPedSdNm0a/v7+bNiwwf0EOmjQICwWC6+++ipTp04lODi4xLEGDBhQJhuH8ePHexiBV4asrCy0Wi2+vr6XHQsJCSErK8ujbkhISLH1io5XhjZt2jB//nz3+0OHDjF79mymTp3KG2+8Abg+z82bN/P1119XqfLTokULgoKC0Gq15bpWbrzxRt58800P2RYvXszbb7/N448/DsDAgQNZtWoVX3/9tVv5+e6771i5ciU//vgjI0eOdPfXtm1bOnfuzIIFC3jggQdKHHfhwoWXrdaVRFkU6rJQ9JCxceNG+vXr5y7ftGkTsix7nP8WLVqwZs0aTp8+7f7OgssAGKjS66roeEl9yLJMTk4O0dHR5ZKrtPEaNGhQYXkFAm8jlJ9ait1u5/XXX+err77i2LFjHtsBBw8erEHJLmft2rUA3HnnnR7lY8eO9VB+zGYzf/zxBw888AA+Pj7Y7Xb3sWHDhvH++++zZcuWYleMivj444/Jz8+/okxhYWHlnUaplLblcemx8tQtL5d6aTVv3hyAG2644bLypUuXUlBQgJ+fX6XGrCzFybx06dJiZf7999/d73/77TeCgoIYPny4x7XSrl07oqKiSEpKKlX5GT58ONu2bauiWZSNtm3b0rt3b9544w2aNm3KoEGDOHDgAFOmTEGpVHqsRk6ePJkPP/yQO++8k48++oioqCi++eYbvv32W+Dy7aCyXFdOpxOn0+lRrlQqy9VHWeUqbmXt4pUeb34PrhWcsoSzMkEOK9H2akcoP7WUadOm8cEHH/DUU0/Rp08fgoODUSgU3HfffZhMppoWz4OsrCxUKhWhoaEe5Zd6FWVlZWG323nvvfd47733iu0rMzOz1LEaNWpU5m2vqiI0NBSz2YzRaMTHx8fjWHZ2todtRGhoaLFPtdnZ2UDxT97l4dL2Go2m1HKz2Vzjyk95ZDabze73aWlp5ObmuutfypWulZCQEAIDAysicqX4/vvvmTBhArfffjvgmtfjjz/OmjVrPDwwmzdvzpIlS7j//vtp1aoVALGxsbz11ls88sgjHttYZb2u7rnnHhYuXOg+3qdPH5KSktzfzZL6kCSJoKCgcsm1bt06j9UtgOTkZBISErz+PbhWcFZy20sEOSwZofzUUr766ivGjRvHrFmzPMozMzPdP1LeRqfTYbFYLiu/9KYTGhqK3W4nKyvLQwFKTU31qBccHIxSqeTuu+++zEW8iMTExFJlqoltryJbn71799K1a1d3eWpqKpmZme4bRFHd4ozSi8ourisonbCwMEJDQ0s0sPX39y+1fU1sewFERESwfPly0tPTSU1NJT4+Hr1ez9y5c7n11ls96g4dOpRTp05x7Ngx7HY7TZo04bvvvgPw2PYu63U1Y8YMj/hQRZ9Rw4YN0ev1JfbRqFEjdDpdueTq2LHjZStrMTExbnkXL16M3W73WA0S34PyUfms7kL5KQmh/NRSJEm6zOhw2bJlnDt3jkaNGpXatqhdWVaISqubkJBAeno6aWlpREZGAi4jzVWrVnnU69evH6+//jpff/212+YHuCyeh4+PD/369WPXrl20adOmxCf60qiJba/rr78enU7HggULPJSfIm+mi+O+jBgxggcffJC///7bXddut/PVV1/RtWtX982hrqLVaqtt5fHGG2/km2++weFweHzuZaUmtr0uJiIigoiICADmzJlDYWFhsYErJUmicePGgOv79e6779KuXTsP5aes11VCQkKxdn4qlYrhw4fz008/8frrr7uVotOnT7N27Vq37VV55PL396dTp07Fzn3EiBF8+umn/Pjjj9xxxx3u8oULFxITE1Oh8ykQVCVC+aml3HjjjSxYsIBmzZrRpk0bduzYwRtvvEH9+vWv2LZopeLdd99l/PjxqNVqmjZtWuyTsr+/P/Hx8fz8888MGDCAkJAQwsLCSEhI4I477uB///sfo0eP5r///S9ms5k5c+Zcts8/ePBgevfuzZNPPklhYSGdOnVi48aNfPnll5eN9+6773LdddfRq1cvHnjgARISEsjPz+fYsWP8+uuv/Pnnn6XOrSQvt4qQkZHhXkUqeiJdsWIF4eHhhIeH06dPH8C1RP/888/zwgsvEBISwuDBg9m2bRszZszgvvvu8/Cku+eee/jggw+47bbbePXVV4mIiGDu3LkcPnyYNWvWVJnsVU3RDfNKkXVbt27NTz/9xIcffkjHjh1RKBQl3gAry+jRo/n6668ZNmwYjz76KF26dEGtVnP27FnWrl3LzTffzIgRI0psHxoaetlWbGVYsWIFhYWFbuX7wIED/PDDD4DLZq1oS/TTTz8FXKstubm5rFixgnnz5jFr1iw6dOjg0ecjjzxC3759CQ0N5cSJE8yZM4ezZ89etrpZFdfVzJkz6dy5MzfeeCNPP/00ZrOZ//3vf4SFhfGf//ynQnKVxNChQxk0aBAPPPAABoOBRo0asXjxYlauXMlXX33lYYdU1u/htYgDCUclAhVWpu1VT00FGBKUTk5OjnzvvffKERERso+Pj3zdddfJf/31l9ynTx+5T58+7nrFBTmUZVl+5pln5JiYGFmhUMiAvHbtWlmW5cvay7Isr1mzRm7fvr2s1WplwCPI3vLly+V27drJer1ebtCggfz+++/L06dPly+9dHJzc+V77rlHDgoKkn18fORBgwbJhw4dKjYYXHJysnzPPffI9erVk9VqtRweHi736NFDfvnllyv5qZWPtWvXykCxr0s/I1mW5XfffVdu0qSJrNFo5Li4OHn69Omy1Wq9rF5qaqo8btw4OSQkRNbpdHK3bt3k1atXl0mmkoIcFsn6/fffe5TPnz9fBuRt27Z5lBedo4yMjGLHuDTIYVhYmNytW7crypednS3feuutclBQkCxJksd1cOm5Lq9s48ePl319fT3KbDab/Oabb8pt27aVdTqd7OfnJzdr1ky+//775aNHj15R3qokPj6+xOvl4qB8H3/8sdy8eXPZx8dH9vPzk3v16iUvXbq02D5vvvlmOTo6Wlar1XJUVJQ8YcIE+eTJk8XWrcx1VcT27dvlAQMGyD4+PnJAQIB8yy23yMeOHauUXCWRn58vT506VY6KipI1Go3cpk0befHixZfVK+/38FqgKMjhzL8Hyq/tv77Cr5l/DxRBDktAkuUq3OwWCASVIiEhgT59+jBv3rwqjVUE/8ZIatSoEa1ateK3334DXCsYLVu25LfffrvMA0sgEFQ/BoOBwMBAZv49EJ1fxTdozAV2pnddQ15enkdwS4HI7SUQ1Dq++OIL1Gq1h/1UVdCxY0fUajWnTp3yKF+7di3du3cXio9AUMtw8O/WV8VegpIQNj8CQS3i119/dXvYFRnLVhWLFi3CaDQCeHgMPvTQQyV63wkEgppDeHt5D6H8CAS1iEtTaFQlxaU4EQgEgmsRofwIBAKBQFALqWxyUpHYtGSE8iMQCAQCQS1ERsJZCXd1Wbi6l4hQCwUCgUAgEFxTiJUfgUAgEAhqIWLby3sI5UcgEAgEglqIyOruPYRaWArr169n+PDhxMTEIEkSS5cu9TguyzIzZswgJiYGvV5P37592b9/v0edw4cP07NnT+rXr8+LL77oLh89ejRDhw71qLtixQokSeKFF17wKH/ppZe8mhNqxowZSJLk8bo4I3tqaipDhw4lJiaGBx98EKfTCcDTTz9N8+bNPfo6ePAgkiRx9913e5R/+eWXqNVqCgoKvDaPijB37lwSExPR6XR07NiRv/76y32spHnXFip63gD69u17WVtJkpgyZUq1z8Ob3zNwBY4sbq6vvvpqjc5rwoQJl8nUrVu3Wj+vV155hc6dO+Pv709ERAS33HILhw8f9qhTV89ZbcNxIat7ZV6C4hGfTCkUFhbStm1b3n///WKPv/7667z99tu8//77bNu2jaioKAYNGuSRePOhhx7i7rvv5ueff+bXX39l48aNgCsZ6IYNG7Db7e66SUlJxMbGsnbtWo9xkpKS6Nevnxdm+C8tW7YkJSXF/bo4+/Pzzz9P586dWbFiBSdPnmTx4sXuORw6dMgje3tpc+jSpQt+fn5enUd5+Pbbb3nsscd47rnn2LVrF7169WLo0KGcPn0aKHnetYmKnLciJk2a5NE2JSWF119/vbqn4NXvWREvvvjiZXN95JFHanRe4Eqae7FMy5cv9zheG+e1bt06HnroIbZs2cLq1aux2+0MHjyYwsJCd526es4E1w5i26sUhg4detnqTBGyLDN79myee+45Ro4cCbgyFkdGRrJo0SLuv/9+AHJzc2nfvj1t2rQhJiaGvLw8wKU4FBQUsH37dvfTXlJSEk8//TSPP/44RqMRHx8frFYrmzdvZs6cOV6dq0ql8lg1uJjc3FwGDRpE69atSUxMdM/huuuuQ61Wk5SUxOjRo91zeOihh5g1axbHjh1zZ6BPSkpizJgxXp1DeXn77be59957ue+++wCYPXs2q1at4sMPP+SVV14pcd61iYqctyJ8fHxKbFudePN7VoS/v3+1z7W0eRWh1WpLlas2zmvlypUe7+fPn09ERAQ7duygd+/edfqc1TbEtpf3ECs/FSQ5OZnU1FQGDx7sLtNqtfTp04dNmza5y1588UUGDRqEj48PCoWCIUOGANCkSRNiYmLcKyT5+fns3LmT2267jYYNG7qfgrZs2YLJZPL6ys/Ro0eJiYkhMTGR0aNHc+LECfexp59+mqlTp6LVatm1axfjxo0DwNfXl86dO3us8qxbt44BAwbQs2dPd/mZM2c4ceKE1+dQHqxWKzt27PA4f+DKUF90/kqad22iIuetLlHZ71ltJykpiYiICJo0acKkSZNIT0/3OF4X5lWktISEhABX/zmrTpwoKv0SFI/4ZCpI0VZPZGSkR3lkZKTHNtCwYcPIyMjg/PnzLFmyBKVS6T7Wt29fkpKSAPjrr79o0qQJ4eHh9OnTx11etI3UsGFDr82la9eufPHFF6xatYpPP/2U1NRUevToQVZWFgCdOnXi3LlznDlzhk2bNnlsXV08hwMHDmAymWjfvr3HHNauXYtWq6VHjx5em0N5yczMxOFwlHr+Spt3baAy5w1c9k5+fn4er4ULF9bEVEqkKr5nAE899dRlcy26PmuKoUOH8vXXX/Pnn3/y1ltvsW3bNvr37+9ObwK1f16yLDNt2jSuu+46WrVqBVzd50xw9SC2vSqJJHkuK8qyfFmZVqslPDz8srb9+vXjsccew2azkZSURN++fQHo06cP7733HuBSfvr37+8d4S9w8dJ869at6d69Ow0bNmThwoVMmzYNKHl7pV+/fsyaNYvz58+TlJTEddddh1KppE+fPu6tuqSkJLp164Zer/fqPCrClc5fadtKNU1lzhvAnXfeyXPPPedRVtX5xKqKynzPAP773/8yYcIEj7J69epVqYzl5Y477nD/36pVKzp16kR8fDzLli1zbxdB7Z7Xww8/zJ49e9iwYcNlx67Gc1bdOGQJRyW2rirT9mpHKD8VpOiGkpqaSnR0tLs8PT39sieekujXrx+FhYVs27aNtWvX8t///hdwKT/jxo0jOzubzZs3M378+KqfQCn4+vrSunVrjh49esW6PXv2RKPRkJSUxNq1a+nTpw/gWnXIy8vjyJEjrF279rIfsZomLCwMpVLp8SQK5Tt/tY3ynDeAwMBAt01WbaUqvmfgOt+1fa7R0dHEx8eX+fxBzc7rkUce4ZdffmH9+vXUr1/fXX4tnTNvI2x+vIfY9qogiYmJREVFsXr1aneZ1Wpl3bp1Zd7eadiwIbGxsfzyyy/s3r3brThER0eTkJDAW2+9hdlsrnZbGYvFwsGDBz1+uEpCr9fTtWtXkpKSWL9+vXv1SqVS0aNHD7744gtOnjxZq+x9ADQaDR07dvQ4fwCrV6+uVdtz5aE8562uUBXfs7pCVlYWZ86cqfXnT5ZlHn74YX766Sf+/PNPEhMTPY5fS+dMUHcRKz+lUFBQwLFjx9zvk5OT2b17NyEhIcTFxfHYY48xa9YsGjduTOPGjZk1axY+Pj6MHTu2zGP069ePuXPn0qhRI4+noqKtrwYNGhAXF1el87qUJ554guHDhxMXF0d6ejovv/wyBoOhzCtO/fr145133gGgQ4cO7vI+ffrw2muvuRWk2sa0adO4++676dSpE927d+eTTz7h9OnTNRLrpiJU9rwZjcbLVr60Wi3BwcHeELdEquN7lp+ff9lcfXx8CAgIqLJ5XEpp8woJCWHGjBmMGjWK6OhoTp48ybPPPktYWBgjRowo8xg1Ma+HHnqIRYsW8fPPP+Pv7+8ePzAwEL1ejyRJdfac1TZkWYGzElGaZRHhuWRkQYmsXbtWBi57jR8/XpZlWXY6nfL06dPlqKgoWavVyr1795b37t1brjHmz58vA/KUKVM8yr/88ksZkO+9996qmk6J3HHHHXJ0dLSsVqvlmJgYeeTIkfL+/fvL3L7oc7r++us9yv/66y8ZkAcMGFDVIlcZH3zwgRwfHy9rNBq5Q4cO8rp162papDJTmfPWp0+fYq/tIUOGeFnqy/H29yw+Pr7Y/u+//34vzchFafMyGo3y4MGD5fDwcFmtVstxcXHy+PHj5dOnT5e5/5qaV3FjAvL8+fPdderqOast5OXluX7/190uP7Djzgq/7l13uwzIeXl5NT2lWocky7LsBZ1KIBAIBAJBBTAYDAQGBnLvutvR+Kkr3I+1wMa8Pt+Rl5d3Ta2YlQWx7SUQCAQCQS3EKVfOaNkpljZKRCg/AoFAIBDUQpyVtPmpTNurHaH8CAQCgUBQC3Ei4aQSKz+VaHu1I9RCgUAgEAgE1xRi5UcgEAgEglqIiPDsPYTyIxAIBAJBLUTY/HgP8ckIBAKBQCC4phArPwKBQCAQ1EKcVDK3lzB4LhGh/ABmsxmr1VrTYggEAoGgDqDRaNDpdF4fR66kt5cslJ8SueaVH7PZTGJi4mU5ZAQCgUAgKI6oqCiSk5OrRQESeIdrXvmxWq2kpqZy5swZr4T/ttls/P777wwePBi1uuJhymsbYl51i6t9Xn5+fjz++OOkpKTw5ptvMnbsWCSp7j71Vuf5avPRe17tvwgNEjNjE5l+JhkrpYcelu12ctesxbBhE0gSAd264NelE+rwMK/JJzsc5P+9jdxVa5AtFgIH9CVo0ACPOnumPILBYCA2Nhar1ep15ccpV3LbS3h7lcg1r/wUERAQ4DXlpygT8dV20xHzqjtcrfP66quv8Pf354477qBFixYsW7aMpk2bXrGd0+lEoai9/h5XOl8N5rxVZWMpqmn1QilJ+Pj4oNTrUJSSUlKWZbJ++pmC7TsJ7NeHwP59UPr6elU2WZZJ//gzTIeO4NetCyE3XI/S3/+yetWdH0t4e3kPofwIBII6yQ8//MDkyZNZvHgxS5cuZdCgQaWu9hw8eJDFixezbNkydu7cCcDLL7/Mc889V10ie1CaAqOVJF6Na0ibj97Dco3lnjYk/UXB39sIv2sMfp07enUsR34+OSt+x7hvP448AxH3TcC3dSuvjimoHQjlRyAQ1ClOnz7NokWLmDlzJiNGjACgX79+JSo+WVlZNG/enIyMjMuObd68udLyVOUqzLWO8eBhsn/+lcABfb2q+NiysyncsYvcNWuRJPDv3g1tfFytU3zEtpf3EMqPQCCocXbv3k12djb9+/cvsY7dbufZZ5/ljTfeQKfTMW7cON566y3++OOPUvu22WxuxWf06NF88803AHTt2pXffvvtsvpCmale7Ll5mE8kYz5ylILtO9A3b0bwjcOqfBzZ4aBg63YMGzdjPXMWFAoCenYn6PrBKP28u61WUURuL+8hlB+BQFCt2Gw2/vzzT/7880927tzJ+vXr3aEmfvnlF4YPH15su/fee4833niDoUOH8sEHH5CYmIjNZitxnIKCAn777Te+++47d9kv+/e5/99x7KhQdGoIWZaxnj1H3tp1FO7YBYAqPIzAAf0J7N8HqQrtsWSnE+OefeQsW4EtPQOf1i0JGtAPXdMmKH30VTaONxArP95DKD8CgaBaeemll3jppZeKPfbxxx9z4sQJ2rRpQ4cOHQgMDHQf++KLLwBYsWIF1113Hcop9+EbFOhhG+O0WDDuP0jhrn8w7tl7Wf/mY8fw69oFv47t0DVs4J0JCi5DdjoxHTsBcQ3J/Po7co8fx56ZiTLAn9DbR+HbuhXKgMsNjCtDzorfKfxnL/aMDGS7HX2zpoSPvwtt/XpVOo6gbiKUH4FAUK089thjZGRksGXLFg4ePIjFYsHPz4+GDRty/vx5Hn/iCWS7HUmrxb97Vwp378FZWIBss7v7OH/+PPzvRWInjoO4huQsX0Xe8eNYTp5GttnQxNZ31w3s3xd9syZoYuuj0OvrtAt8XcFpteE0FqIKCsJy+gyZ33yPMjsHBl2PI9+AvnkTfFrcjL5pEySlskrHduTnYz2XQu7K31EGBRI8/Aa0CXHoEuKrdJzqQKz8eA+h/AgEAq+RmZnJsmXL2LRpE8ePH2dXoB+BfXqR8scazEePoYmtT9h1PfDr2hnDBaUkzmLh1JPPIVssGJLW439dD9QR4agCAtA1bIDl7FnSPp7n6v+b7+HmkRjW/YXZZAJAE1sfXUK8y64D8G3XBm18XM18ANcYxoOHyP39DywnkgFQR0Viz85BHRFO1IOTAYiccp/XPNiyl60g73eXDZiucUMiJtyN0s/PK2NVB0L58R5C+REIBFVCcfYzKe99iPnYcY8yn1Yt8OvYHsvJk1jPnCVz8XfIdjsB1/UAQNJoCOjVE3uegaAhAy/bpvBp0Zy4V14i44uv4eQpADQx0Uh6PZJGjaOgENOx42hi6yMplUharZdmLLgYy5mzpH+2AHV0FAAKPz80MTFo6tcjdMRNaIuJm1PV5G90ee/59+hG6G0jq9R2SHB1IZQfgUBQJipiHBw0aACZubnYc3LB4UDbqAHZvyzHevacR72Ltz4kSSL01hGl9qv00RMx8W7su/YAEP3oQ9dcPJyaRJZlDGvXIcsy6pAQjAcPU7B1G+qoKKKnPoSkVIBCUaktRvPxZDK/+wFkGd+OHdBER6FrkIDC1xeHIR97djbKAH8UOj229HQsp8/gLDQCENCn11Wh+IiVH+8hlB+BQAB4x8Vb36wJsS88g+x0IttspLz/EZbkZHw7tEcV4I/C3x9NRHiFtqUUWi3+3btUucyCknEUFJK7ajWmI0expaa5yxW+voSMuImAnt2RVFVzW5HtNvcYuctXAuDTrg3G3XuKb6BUom/RDL8O7VFHhFeJDDWNTOXc1SvyOLB+/XreeOMNduzYQUpKCkuWLOGWW24ptc26deuYNm0a+/fvJyYmhieffJIpU6a4j3/66ad88cUX7Nvn8rbs2LEjs2bNokuXmvv+CuVHILiG8HbEYLvBQP7mvzHu3Y/TaARJQrZYUfj6oPT3w3r6DOqoSHyaN0PXpJEwPq4jOK027JmZ5KxYhXHPPvQtm6ONi8W3Y3u0sbFIGjWKKk6bom/ahPC7x5Lx5SIA/Dp3RHEhzUVA7+vw69YFe3Y2st2OOiICdXgYCo2mSmW4FiksLKRt27ZMnDiRUaNGXbF+cnIyw4YNY9KkSXz11Vds3LiRBx98kPDwcHf7pKQkxowZQ48ePdDpdLz++usMHjyY/fv3U69ezXjfCeVHILiKKGn1pihdQlXiNJsxHzuONSXV9Tqfii0tDUmpxKdNa1RBgSDLSFoNTqPJtfUF2FLTSJ37MdrEBMJuH4UmJrpK5RJULdZz5zn3+tvu9xET7sa3fdtqGduvUwd8O7YHWcZ8/ASp738EQP6WrYSOugVtvZhqkaOmqIltr6FDhzJ06NAy1//oo4+Ii4tj9uzZADRv3pzt27fz5ptvupWfr7/+2qPNp59+yg8//MAff/zBuHHjyi1jVVBu5efcuXM89dRTrFixApPJRJMmTZg3bx4dO3bEZrPx/PPPs3z5ck6cOEFgYCADBw7k1VdfJSam5It0wYIFTJw48bJyk8nkzppbUFDAfffdx7p16+jTpw/z5s3D98JTwIQJE1i4cCGvvPIKTz/9tLv90qVLGTFiBLKwBRBcRdSGwHyW02dI/fATnEYTCr0OdXQ0ugYJBPTuiW+7tiUGjyvcs4+cX5dhS8/AknySc6+9hb5ZEyIn31vlLs+CilH4z17U4WFupdRRUPjvQUmqNsXn3yElTMeOk/XdjygDAggbfRuqsNBqlaGIBnPeYveESdU2XlUpPwaDwaNcq9WirSJHgM2bNzN48GCPsiFDhjBv3jxsNluxiXmNRiM2m42QkJAqkaEilEv5ycnJoWfPnvTr148VK1YQERHB8ePHCQoKAlwT2rlzJy+88AJt27YlJyeHxx57jJtuuont27eX2ndAQACHDx/2KNNdlG149uzZ+Pn58fvvv/POO+8we/Zsj4SEOp2O1157jfvvv5/g4ODyTEsgqJXUBiWnOAq27SDz2+9RR0QQ/tgjqENDkGW52G0PWZaRzWbshnxwOPBt0wrfNq2wnk/BsGkz+X9twnToCMYDh/Bt3bIGZiO4GKfVRvrnC93vVSHB2PNcN06Fnx8hw6s+7URpOIwmsn5cQuH2nWgTE4iYOE6sFFaA2NhYj/fTp09nxowZVdJ3amoqkZGRHmWRkZHY7XYyMzOJjr78fD399NPUq1ePgQMHVokMFaFcys9rr71GbGws8+fPd5clJCS4/w8MDGT16tUebd577z26dOnC6dOniYsr2ahRkiSioqJKPJ6bm0uTJk1o3bo1zZo1IzMz0+P4wIEDOXbsGK+88gqvv/56eaYlENQYtVXBKQlraioZXy0GQBkQQPrnC91GqcrgIHQJ8TgKjZiPHC22ffDwYQQN7I8mJpqwW0cSestNmE8ko01MqK4pCEpBoVHj06oFxn0HALBn5+DXpRNB1w9CFRJSrTZasiyT9sk8bKlphI25Hb8una4KD67yUFUrP2fOnCEgIMBdXlWrPkVcel0U7bYUd728/vrrLF68mKSkJI8FjuqmXMrPL7/8wpAhQ7jttttYt24d9erV48EHH2TSpJKXAfPy8pAkyb06VBIFBQXEx8fjcDho164dL730Eu3bt3cff/jhhxkwYADPPfccjRo1Ys2aNR7tlUols2bNYuzYsUydOpX69etfOoRAUGPUNSUHLuRE2ncA69lzyE4nTqMR84mT7uOmg4cAUOh1KAMCcJpMWM+dx5Z+efZ0VUgwCl9ftJc8gUoqFfomjb06D0H5cF6IpB04oC8hN91YY3JYz57DknySyPvvxadF8xqToyapKuUnICDAQ/mpSqKiokhNTfUoS09PR6VSERrquT355ptvMmvWLNasWUObNm28Ik9ZKZfyc+LECT788EOmTZvGs88+y9atW5k6dSparbZYoyWz2czTTz/N2LFjS/3gmzVrxoIFC2jdujUGg4F3332Xnj178s8//9C4seuHMSEhgaNHj5Kenk5kZGSxGuWIESNo164d06dPZ968eeWZGjabrdQkiRWlqE9v9F2TiHkVT5uP3iu2XFvDXk1F45dVDtnhJHfVavLXbUDp74ekUqHQ6/GPCEfZqAGyzY7TYsFpMuE0GnEYja6bpq0Alf5fe5+oh+5HGxdbykiVo7zzqitUx7xkp4xp/wHUEeGoIyOwpaRizchAOnsOvV4Pp89W6fjlnZOkVqPX69GpVLXq/Fbnb54sS8iVUH4q07asdO/enV9//dWj7Pfff6dTp04e9j5vvPEGL7/8MqtWraJTp05el+tKSHI5rIE1Gg2dOnVi06ZN7rKpU6eybds2Nm/e7FHXZrNx2223cfr0aZKSksqldTqdTjp06EDv3r2ZM2fOFetPmDCB3Nxcli5dyvr16+nfvz979uzhyJEjVzR4NhgMBAYGsmjRInx8fMoso0AgEAiuPYxGI2PHjiUvL89rqylF96WePz+MyrfiW1T2Qgsbb36/XLIWFBRw7NgxANq3b8/bb79Nv379CAkJIS4ujmeeeYZz5865Ew0nJyfTqlUr7r//fiZNmsTmzZuZMmUKixcvdnt7vf7667zwwgssWrSInj17usfy8/PDr4bSj5Rr5Sc6OpoWLVp4lDVv3pwff/zRo8xms3H77beTnJzMn3/+We4LRKFQ0LlzZ44eLd5uoDR69+7NkCFDePbZZ5kwYUKZ2w0ePNgrF7LNZmP16tUMGjSoWKt3b1JYWMjx48dJTU2lc+fObkPw2bNno1KpeOihhyq8h1+T8/Iml86rpJWcuoZWkpgZ24DpZ0644/zIDieFu3dTuGOXazur6CFBkv79vxjqPf0fVLXEqaC4eV0NVMe8DBs2k/PrMgL69sFpLEQTHYVv+3ZIWi2SoupXDCoyp5wVqzEkrUNTvx5hd9yGOiKsyuUqLxvGjq+2sZxIlQpyWJG227dvp1+/fu7306ZNA2D8+PEsWLCAlJQUTp8+7T6emJjI8uXLefzxx/nggw+IiYlhzpw5HjGC5s6di9Vq5dZbb/UYqyoNr8tLuZSfnj17XuaRdeTIEeLj/82WW6T4HD16lLVr116251cWZFlm9+7dtG7dutxtAV599VXatWtHkyZNytxGrVZ79Sbu7f4vZeXKlQwbNsy96qVSqbjuuuvo2bMnr732Gna7nR9++IFVq1ZVSvOu7nl5iyKbnKJ4OB3nfXRV3UyLsMgyFllGttvJ+nEp+Zv/RteoAb7DhqCOikQbW5+C7Tsx7j8ASKgjXcHj1BHhqMPDUQUF4gActeyzKZrX1YY356Xu0hHH5i2krViJOjqKiJ7dsekurDJ48bMsz5z0QwZAQiwZC7/GseYPwsfc7jW5ykp1/t7VRJyfvn37lrpbsmDBgsvK+vTpw86dO0tsc/LkyXLL4W3Kpfw8/vjj9OjRg1mzZnH77bezdetWPvnkEz755BMA7HY7t956Kzt37uS3337D4XC4DaFCQkLQXIi+OW7cOOrVq8crr7wCwMyZM+nWrRuNGzfGYDAwZ84cdu/ezQcffFChSbVu3Zo777yT9967Op7ay4rD4eD777/n/PnznDp1iqCgIIKDg/nll1/46KOPeP/990lKSiIkJITs7Gz++eefazLCbl00Pq4qUj/4GGNBAbLDgS0tndBRtxDQq6dHnYDreriTjAquXhRqNdEPT+HUU89jS0nFuG8/msiImhbLA0mS8GnWFHV4KI48w5UbCARlpFzKT+fOnVmyZAnPPPMML774IomJicyePZs777wTgLNnz/LLL78A0K5dO4+2a9eupW/fvgCcPn0axUUui7m5uUyePJnU1FQCAwNp374969evr1Tej5deeonvvvuuwu3rIkajkTFjxniU5eXlMXr0aA4dOuQuGzVqFH379mXUqFFV7vJYm7iWlZyScJpMKHx8kG02Yh5/pEI5tQRXD4W794AkEf3oQ+hqabgBy9lzWE6dIWLi3TUtSrVTFwye6yrljvB84403cuONxbs/JiQklCmaclJSksf7d955h3feeae8orgpbhkuPj4es9lc4T7rIv7+/mzbto3JkyezZ88eHA4H/fr1Izk5Gbvd7q73wgsvXBb0qq4jFJ3SKfpaqsPD8OnWBZwy9jwD8vETqIKDUQYFXnMxVK51ZLud3DV/4tOyea1VfAAKd+wChQKNF70Gaysiq7v3ELm9rjI6derEzp07cTqdOBwO1Go1//zzDz179sRsNtOmTRsCAwNrWsxKIRSd8mHYsAn7rn/gzbcwHjiEaceuYuupwsPQREf9myRSr3cbPzsMBszHkynctZuQW24isF/vap6FoKox7tuPPSOTiIk1k1uprOiaNCJ/63bOvfoW0Q9P8WroBMG1g1B+rlIUCoV7azEgIIDCQld+nl27djFq1KjLInHXVoSiUzlku53sJb8QcOHJXt+sCY6MTBQ6HZJWg/nocWSrFQB7Rib2jMxSenNR+M8eofxcBdjSM5E0GjRRkVeuXIP4NG9G7PNPkfrhJ6R9toB6T05D6edb02JVC2Lby3sI5ecaIDExkdOnTzNv3jw++OADunbtWtMiFYtQdLyAQgGShK6hK6N7xMRxBF6yNe202bCeO4/1zFmsKanY0tKRHQ5QSCjUGiSNBnVYKNqEeHQNElD6+9fETARVhPHAQdI+W+DKyWa1kvfnOoIG9a9psUpFodcTcc8Ezr32Flk//UzEuLE1LVK1IFdy20soPyUjlJ9rgBtuuIHDhw/z0EMPcejQoQqFH6hqhKJTPUgKBQF9emFYtx6mPID5yHGkxg086ijUanQJ8egS4kvoRXC1IMsyhvUbwOFAuuDs4DDUDS8qVVAgfl06YUhaj6V/H7T169W0SII6jFB+rgEyMzM5fvw406ZNY8+ePR6JaasDoejULME3DkW+kD8rbd58nFGRBN84FH2jhjUsmaC6cRgMmA4eJqBfH0JvGV7T4pQL2enEkLQeAFtq2jWh/MhULuTS1Rf5quoQ7h3XAD/99JM7bMCCBQswePlJr8GctzxeghrG4cDw10b3W0vySbK++7GUBhXDlpVF6ifzXCsLglqJ0t8flEoUmroXmPRib0S/Th1qUJLqoyjCc2VeguIRKz/XAPXq1WPDhg288MIL/Pnnn5jN5ipL5dHmo/euysi6VxO2jEzMx44DEDb6NixKJfqm5c+k7sjPB6USZTE58Ox5Bs6+6Apaqg6r+RQEguKR7XZwODDuO0DwsOtrWpxyowoLRd+07JH76zrC4Nl7COXnGkGtVvPqq69Wup9L00AIajdOqxVHoRHpQkh+nzatUVUgb5Pp8BFS536CpFETNWUy6shwlBelRSnYut39v8LXh9zf/yCgd08UOl3lJyGoMhQaDarwMGzp6TUtSrmRnU6cJpMwuBdUCUL5EZSK2Laqu8h2O2dnvY4jJ5eAC8bM5155HUWL5uibNQEkLKdPYz19FntODtaUVHA63e0Vvj5o4+NQ6H0o3OHK2yNbbaTM+TftjKRRI1ttHuPmLl8FgLZBgrArqoXoEuKxZdQ9V3HT4SM4C43oGl8715RTlpBEkEOvIJQfgRuh6FxdWFNSceTkAq5tKQBHfgEFSevdhqOl4Sw0YjpwyKNMExONPTcPp9EI4KH4SGo1vu3aoI6OQt+kMdrY+lU0E0FVogwOwnTkaE2LUS6sqamkz/8SXeNG6Bok1rQ41YYsV9LgWVgklIhQfq5hhLJzdaOJiSbszjtwFhRi/nMdAL5tWuPMzMCZX4CkUqGJrY86LBRVSAiq0BBUISEodFrsuXnYs3Nw5OWBQkIdEYE2LtZtdOq0WDj/5mxs6Rn4tGtDxPi7RHqMOoI6PAxHngGn2VxntiWN+w4AEDlporjOBFWCUH6uIa5lZcdpsWA9e851kw8OqmlxqgVJqcS/S2cArJv+BiDszjvwL8PjoCYyotQM3wqtloh7xpO99Bf8OrQXN6Q6hCokBADT0WP4tm5Vw9KUDUmhAFlG0mhqWpRqRRg8ew+h/FzFXMvKThGyw4E9P5/zb7yDs6AQVWgosf97pqbFqn688BuoiY4i6oHJVd+xwKvoGjZAHRVJ/obNdUb50dSLQbZasZ1PQVMvpqbFqTaE8uM9hPJzFSGUHU+cZjNpn853u3kD6BpeO/YCF6Nr4Irq7DSb4UJkX2taOmmfzMNpMlPvqWmo6njCW0HZkCTXDdF67rzL9kcGVXAQ6ojwGpaseJxmM8owV1R6y9lz15TyI/AeQvmpwwhl53KM+w+QteQXcDrRt2zhVnx0TZugb9yQoEEDaljCmiGwfx8Azr85G7l+fQp3emZ2l1Tip+BaInBAP7KX/kLqBx+7CiSJkJE3owoOxnT4MDhlQm66ocZtgmRZ5vzbc7ClpYNKiWH9BhR6vStS9eEj+LRojn/32pmrsCoQ3l7eQ/zi1SGEslM6aZ/Ox7hvv/u902Ry/x9264ha+2TrbWSHw20r4cgvwHSR4hM2+jb8unRCUipLbi/LOPLysJ5PwXo+BeOBg1iOJwMQ0Lc3oSNu8u4EBFWOf5dO+LZt7QpciUT2r8vI/nEpAKrwMBx5LoP38LvG1GwGdVnGnp3j+t/uwJaRSfq8BSBJqEJDMO4/SOHe/fi0aIZPq5aogq6u1Uvh7eU9qkX5OXfuHE899RQrVqzAZDLRpEkT5s2bR8eOHQHXj+vMmTP55JNPyMnJoWvXrnzwwQe0bNnS3cfhw4e55557OHXqFJMnT+Z///uf+1hCQgKnTp1i8+bNdOvWzV3+2GOPsXv3bpKSkqpjmlWOUHbKjizLSCrXDVwZHAROJ4XbdgAg6XSowq/dqMMZi77Fuf8gLF4MQPCNw/Dr1KFUw2/Z4cB06Aj5m//GuHdfifXUYTWfJFdQMRRaLYoLW6AR4+7EfF0PVIGBqCPCMe4/SMZXizn36puuPHAtmqH093dvmZUHp9VG7uo1aJ0yPDwVR24eBJYtwrykUBA5+R6yl/yCo9BIQJ/r0DdtgkKvI+3T+eBwYNp/ANPBg2R9/xOqiHC09euhCg1F6eeL0s/vmkmFISgfXld+cnJy6NmzJ/369WPFihVERERw/PhxgoKC3HVef/113n77bRYsWECTJk14+eWXGTRoEIcPH8b/QjTPhx56iLvvvpvOnTszZcoUBgwYQM+ePd196HQ6nnrqKdatW+ftKXkNoexUHNPBQ6jCw/Fp0xrjnr2E3zUGVVgoKbPfR6FRV+hH+2rBkV/gYe/s07pFqYqPw2gkfd4CzMdOFHs8aMhAAnr3qtkVAUGVIimV6Bs3cr/3admcek8/QfZPS8lc/B0AqpBggq4fjF/njmX27nNaLKR9+jnmo8fR6/Xw8FTOvvIGJCYQfMP1ZUpOqm/SmHpP/eeycp9WLclLSQVAEx+PNfkk9vQM7OkZHvV8O7avs99/18pPZQyeq1CYqwyvKz+vvfYasbGxHpnEExIS3P/Lsszs2bN57rnnGDlyJAALFy4kMjKSRYsWcf/99wOQm5tL+/btadOmDTExMeTl5XmMc//99/Phhx+yfPlyhg0b5u1pVQlC2akaZFkm7bMF4HC4CiQJWXaiDAwASaqTOYyqElVQII7TZwBQBgWS+uFnhI2+FX2TxshOJ5JS6b6ZFe7dT9a3P1zYDgFdk8aE3HQDBdt2YFj3Fz5tWl3zn+e1giowgIiJ47BlZWE9e56CnbvIXPQt9qxsgocNKbWtIz+fjEXfYTpw0F1WZKisqV+PvAMHsaWmEjv9uXLJJDud2NLTkdRa1BFhBPTqicLXB2tqGtaS2pjNOMwWctf8iW/7tnUq6rjw9vIeXld+fvnlF4YMGcJtt93GunXrqFevHg8++CCTJk0CIDk5mdTUVAYPHuxuo9Vq6dOnD5s2bXIrPy+++CKDBg3CZDJx4403MmSI55cvISGBKVOm8Mwzz3D99dejqKVxR4TCU/VIkkTkvRMo2LqNwt17QJbJ/PpblIGBKPQ6fDu0q2kRa5Sggf3JuWCjEzx0CAWfLyTto8886oSNvg1VaAjp8xa4V4WChw8jcEA/JEki89sfAAi9bWS1yi6oedShoahDQ/Fp04pT/30Gw4ZNKIMC0TVsUGIsKHPyKQ/FB0DfohkA1rPnkHQ6gm+6AbiQMFehROl7ecLci3HabJx98RUcBgMKHx93lPF6zz5JUEQ4Bdu2k7n4e3A6kTQaZKsVfbMmSFotOT//RsHmvyncsZPox6eWGsPqSvz1118Vblte5AuvyrQXFI/XlZ8TJ07w4YcfMm3aNJ599lm2bt3K1KlT0Wq1jBs3jtRU17JlZGSkR7vIyEhOnTrlfj9s2DAyMjIwGAyEhxdvuPr8888zf/58vv76a+6+++5yyWmz2bDZbFeuWE46f/w+M2Mb0Pnj97HIMto6uvx6KUXzqC3z0bZqQXCrFjjy8pCUSs689CpYrfh16YS+HB4rtW1eVYE2MoLQ6c8CYFy2wrX9cAmW9RuwSAr0Oh1KjRbCwwnv2d39EBF37wScZjOaWuYOfzWeL6il85Ik6o2+nfxNWzD+uhyjLBN84zACevW4rKqmZQsUNw9HodORv2ET9uxsrBs2wz2TiB5xC5punQDI+vYHCnbuBlweaEGDS/bGdEoS/jHRWGw2V6DG4GD8r+uOf5Tr3qHr2oWgBg2x5eSQ/vlCUCrxDQkh9+N5OE6dJmJgf0yHj5Dz/odoY+sT0K8v+iaNcBYWIjucKC6YWMgFBdgys7BlZWHPycWRn48jz4BstaL08+WOC9GmBXUbSZa9uyuo0Wjo1KkTmzZtcpdNnTqVbdu2sXnzZjZt2kTPnj05f/480dHR7jqTJk3izJkzrFy58opjJCQk8Nhjj/HYY4/x4osvMn/+fA4fPsyTTz55RYNng8FAYGAgixYtwsen9CcPgUAgEFzbGI1Gxo4dS15eHgEBZTPcLi9F96UGXzyL0qfi4QYcRjMnxs3yqqx1Fa+v/ERHR9OiRQuPsubNm/Pjjz8CEBUVBUBqaqqH8pOenn7ZalBZmDZtGnPnzmXu3Lnlajd48OAKXxxtPnqvxGNaSWJmbAOmnzmB5SqyPqvN88r+ZRn5Gzfj161Lud2wa/O8KkPRvO655x787xiFrmlT8jdtJufX5fi2aUXh/oPgcBDQpxfa2HrY8wzYs3MI6t8HhZ9fTYtfIlf7+arN85KdMtnf/4jx4CHqPfUECn3pN+manpPslEmZ/Z4rZtAFVMFB+F/XE1VIMI48A8hOlAEBqMNCUYaGolBffovcMHZ8NQqN2PfyEl5Xfnr27Mnhw4c9yo4cOUJ8fDwAiYmJREVFsXr1atq3bw+A1Wpl3bp1vPbaa+Uez8/PjxdeeIEZM2YwfPjwMrdTq9Wo1eoy1y+v7Y5Flmvtj1hlqI3z8hk2BEWTxmgT4iosW22cV1WgbNEMo8nE+VmvYUvPQNekMb6jbiHn8BEcBQWYVv7uUV9q1ACf5s1qSNqyc/H5kmWZ3BWrUAYFEdCj2xVa1m5q83XoKMjHotFQmJ1DQUYG2tj6ZWpXU3NyFBZgOOkypdDEROPXuSMBva9zB/i8ONKV88KrOHep8twnBLUXrys/jz/+OD169GDWrFncfvvtbN26lU8++YRPPvkEcBmrPvbYY8yaNYvGjRvTuHFjZs2ahY+PD2PHjq3QmJMnT+add95h8eLFdO1aNdE/haFy3UFSKtE3bVzTYtRKCnbswrTh3y1o85GjnHv1TRR6H1f8FUAdHYVfx/b4tm9XJ+P4mI8dJ3fVGiSNps4rP7URR0Eh2b/8RsH2nUgKCb+uXepEygmlnx8J77wOUHcS8VbS2wvh7VUiXld+OnfuzJIlS3jmmWd48cUXSUxMZPbs2dx5553uOk8++SQmk4kHH3zQHeTw999/d8f4KS9qtZqXXnqpwspTEULhEVytqEJD8evYHnV0FNazZ7FnZePfvYvrSfiCka3TZCL/722YDhwk+IahdSJCtsNownr2HACy1YrTbK7xFA1XC06TCcOGzeT9sRZZdhJy41D8una5opdWbaLOKD0XEBGevUe1RHi+8cYbufHGG0s8LkkSM2bMYMaMGRXq/+TJk5eVjRkzhjFjxpS7L6HwCK5mNPXr4bTZiJ764L8pLS4JBWDPzSNn5e8UbP7bXaZv2bxOKD9pn8zDknzS/V62O2pOmKsAe3YO9rw88tauw3ToCLLF4j7m36PbNaNY2jIzMWzYjGy38UpKZk2LI6gCRG6vC7T56L1r5otcW3EUGrGlp6ONiy0115Sg4ljPniNo3FiPz1eWZexZWVhOn6Fw1x6Me/a6j+kaNSRs9G2o60h6kKKo02GjbyPzm++x5+SISNTlRHY6MSStJ3/LVg/jYDeSRPSjD131v5e2rGwM6/7Cnp2Dcd9+FD4+qAIDmH/keLXJIIIceg+h/Fxgz5RHaLfg05oW45pFlmXOvfE2jpxcAKIemIw6OoqsH5dgOnwUVWAAUVMmoQoJrllB6zpKJboLEW6dZjNZS3/FdOiw+3MvQtewAcE33YAuIb4GhKw4Pm3bYNy7H12TxqijIkn96DNiHn2oTqxa1RachYVk//ybZ6FCAU6n639ZRhlQMZOE2ow9z4D5+AmsKSnIJjOGTVvA4UAVEkzoqFvw69oFhUbN7gmTCKyueFeyVDm7HaH8lIhQfi7ixNT/iG2vGsBpMiFpNARfP9idRyj1w09Q6PXuzOw2s9n1AyyoFKoAf0yHXJ5dhvUbcOTm4telM/qmjdHGxnL2lTfQNW5I5D3V6M5bRdiys8n55Td0jRuiDg0h+pEHSZnzAWmffk5A7+tQhYWhqReN0s+vztl+VCdKf39i/vs41nPnkR0OdA0SyN+4hfyt2/Fp0Ryfls1dQQavEixnzpL1/U9YTp0GXClglD4+aGKiUYWGEHbHrShFDLirDqH8XIJQgKoPWZZJn7fQlTVcoUDfpBHRTzxG7rIVmA4eRte40YUf2mB0DRuIrbAqwJ5nIH3eApAkdA0bEHH3WLenjjUlFafRiEJ3eQTo2kDW0l8xrF1HyKhbCOjZ3eN6cJrMpH08D0mtJmL8XYBrCyxy0j2kf76QrCW//Jv77QJ+nTsSdufoOpv00pto69dDFRCA8dAhcn//k8KdO1GFhaFr3LDOZEl3GI0U7tiFpNGUmozVeOAQllOnCb3jVnxbtaxVq1rC4Nl7COWnGIQCVD1Yz53HuHcfklqNf8/uGJLWYzp0hJDbRxF8/WA08XHixlTFxE5/FrPNjqRWodBqKdi1m7y/NqKtX8/95CvbrFjT0rGcOk32z78S0LsXwUMG1rDkIF9IP5P941LyN2wifNydmE8kw90NOTvrdRxWK9GPPYzyIi9RdXgY9Z76D7LTiT0zi/T5X2A9nwJAwbYdBPbviyYmutjxrjVkWcaRm4c9O5uC7TvJ/3sbOBxo6sUgabTYMzKxpabVtJglIjudOE1mnIWFWNPSyfr+RxyGfJBl8v/eir5xI3w7tL8sr5flxAkA/Lt1qX0rgiLIodcQyk8JCAXI+1jT010Z2G02Cnfucpdnf+eK/h025nb8u3WpKfGuShQ6HUqt6xfRlplFxoKvUPj6UPD3NrdNR+HO3RReyLcEYNq/v0aVH2taOsY9+7CeOw+AMsAfh8HA+TfeQe/nB3dPwKdZM4IH9CkxYaWkUKCOCEcVGgqSRNTDUzAdPIw6qvxR5K8mZFnGlpaG6dARCrbtcIcJUAYGEHz9YPx7dEPp50vOqjXIVusVs7nXFKeem4GzoMCjTB0ZQczjUynYuQvjnn0Y1m/AsH4jMY8/4rYBc1qtmA4dAUlyXf+1TPkRBs/eQyg/pSAUIO9i3PnPv+uySiW+7dtSuGefOytzkWGuwDsUuYTXf+5pJLUKy6nTFO76h/xNW0CWUfj5EjVlEpr69WpMxryk9WQv+QUAnzatCb11BH7duuDML8CWlYV/tCs9Ttidt5cparBfl06kz1uALSUNv47tvSp7bceWnkHGom9d14FSib5xI8LvHosqNARtfJzHKkhtWPkrjYCe3cldtdqjLHTUCFTBQfi2aY1staJNiHd5sf29jZDhw1yVZNmVlkOhwHL2HKqAAOFUcY0glJ8rcGLqfwAR/8cbBA8fhi0jA1tqGo6cXFSdOrrsMjQa4l97WWx5eRlbVhYKPz93kDp940boGzcidOTNWM6eQxMTg0JTc6H8jfsPYvhrI+CKKRN2x63uY4qQYFQhwSjLeY3omzUFXMbRuoaJVSdsHcGakkrB9p2YDh7Ceu48qtAQIu6biL5pYxQaTU2LV2GChw1xr0o5Cgo59+Y7pC/4grCxo13bX3kGl11hs6boGzdCdjpBklBotYSMuJnMRd+S8s57qKOjqP/0EzU8m0sQW1deQSg/ZUSsAnlS9JBdHoM6h9GEPSMDu8GA02zBWVCAJi4WSa1G6eeLw2DAv1dP9I0bCcWnGlCHhuIsKMB06Aj6Zk3c5ZJKVaMu7tbzKRg2bCJ/42Y0sfUJHj6MwH59qqRvhUaNpNO5U3lcLciyTOqHn2I+fOSyY8rAAJQBAch2B7aUFBS+vvi0aEbggH74tGqBQqutAYm9h0Knxb9rF3JX/k76Z/NRBgURdMNQUEjoGySSv2UrqR9/hkKvQ9egAf69erja6fWEj7m9hqX3RGx7eQ+h/JSDq1EBku12nBZruULUm08kk/Htj/DRR5z530ykxo3QxERj3HcApa8P+iaNsZ5PwZqejtNowmky4TSZPaLDAkhqFerISFRhoViST+E4eJiwu8bg27Z1VU9TUAy+HduTv20HaZ9+Tsgtw/Hr0qlGb4T23Fyyvv/JdR0FBBB0/WACB/Sr8tUnbf16WE6frtI+awOOnJziy/MMOPIM6Fs0J3BAX/zat3Un86zr2HPzsKakog4Pw2kyYTp0hLw//sRpMrvseDQaHLm55C5bAZJE7kVPa85CI8a9+9C3aoGk1aKOjKh1Nj8C73F1fAOqkatFAbKmpZO/YRP5W7cjW63E/OdRtGW07cj6cSn6C1FzA/v1IXX5Soy796Br1BDzseMYDxxCExWJOioSZb16KPQ6FHo9yoAAl9FpYCCSToekVrlXeDK//wnjvv34tmvjtTkLPJEUCiInTSTrhyVk/bCE7J9/w6dlc/x7dEPftMmVO6giZFmmYMtWspb+ikKrIfzusfi2b+u10AbauPoUbN915Yp1CEmSqP/cUy6PrTwDCq0GR0Ehuav/pHDXbmSrFXt2Nv6dO9a0qFWGbLdzZvpLl5Vr4+MIuflG1NHRKHRabOkZKAP8UWi1WM+nkLPyd0z7DgAg6fXomzUhcvI9ZH3/Eylz5hL38vTasxomvL28hlB+KkBdVYDs2TkU7v6Hwr37sZxIRuHnS0CPruT9uQ7zkaNlVn7s2Tno+vUGcLkKd+/q3j+X7XaQpHLduGxZ2ZiPHkMVHIxCXXM2JtciCrWa8DG3EzRogOva2Lmb1Lmf4NOmNb4d2uLTvJlX0xjYsrPJ/OYHzIeP4Ne1CyG3DEfp4904Q478AhwGA+nzvyBo6BA0V5HHlyRJqIJc0Yet51Mp+HvrBQPmeAJ696xh6aoWSaUicNAA8lb/gTYxgaCB/dDGxaHw8/Uw1i46v9bUNAzrN2A+dgKUSnA40DduiCowEHVQEJH3TuDsK2+Q8cUifNu3RZsQjyo0BEmSKNixi5xlK1D6+fJGRm51zvLCqzLtBcUhlJ8KUlcUIEd+Pk6bDdli5dyrbwKuJJXhd43Bt31b8tauA0lCm5hQaj/23FyMe/cj22w4jUYMf66DSVMAPG6O5VlONx07jmH9Box796MMDHAHpxNUP+qwUIIG9idwQD8KtmzFsHEzGQu+ctn/NGmEOiwM2eFAodUSNGRgpRQi2enEdOQoBdt2YPxnLwpfHyKn3IdP82ZVOKOS8W3fFpRKTAcPkf3zb0Tdf2+1jFvdaBPj0cREo/DREz5u7FVnR+coLCRv9R8ABPTqiU+rloDr+rKmpmHPyUGSFDiMhRT8vR3T4SMogwIJ6NGNwIH9L9vqV0eEE37naHJXrSbjy0WusuhoFHodlhPJ6Js1xXToMC+//HL1TlTgFYTyUwlquwJkOnqMtE/nI1ssKC6EZ/ft2J6IcXe68jr99DP5GzcTOGgAuisoP+kLvsKSfNLlFoorBHxFMSefJGfZCsxHj6OOjCB0xE0ue5OrPFFiXUCSJPy7d8W/e1dsWdkY9+7DeOAQpiNH3QHunFYr2vr10CYmlHnVxGE0Yj56HKfViiFpPdaz51BHhBM0eAABvXqi0FdfVGmfli3wadmCzG9/wHLmbLWNW91ICgU+rVuRu2o1llOn61yetiuh0OvR1K+H9ew5Mr7+BtORo2iiozFs2Ig9wzPzujoinLDRt+HXqUOpD2h+nTrg16kDjsJCzMeTKdy5G1l2EnrHrfh368LZWa/TIjqGPXv2eHt6LsS2l9cQyk8lqc0KUMGOXcgWCwG9euK0WJC0GvRNm5CzYhW5K10xMQL69CJ46OBS+7GmpWM5eYrAgf3d8TG0FXiKtJw6Tc6K3zEdPIQmJpqI+ybg06rlVfdEerWgDg0hsG9vAvv2xnL6DOffehfAZSsGKHz01H/uKRyGfJwmM0p/P5SBAaBQYM/KwpaRiT0jE8vZ8xj37HVHaNYmxBP1yAOulCU1eO6V/v7YM7NwGPJrVUqDqqTwnz3omjZBG1u/pkWpciSFgnr/fRyHIZ+0eQso2LIVcNn8hD04GVV4mKui3eHavirHVrzS1xffNq3wbdPKXeYoLMSelc3EZ5/j8ccfr9K5lIhQfryGUH6qgNqqAOkbNaRg898Y/tpI3P/NRHnBSNly+oy7jn+Prlf8UbCeOw+yjPV8CnaDAVVAQLnkKPxnL3lr12FJPok6MoLwCXfh27ZN7QslLygRTb0Ygq4fhOnwUZwmE7pGDcnfuJnTz80otZ2k0VxY4Rnoyq+k1aDQ62uFwuvfvSv5m7eQ8t5c9C1b4N+l01WX6kK229HWr+f1vHjW8ykYDxzEknyKgN49q9VgPv/vrVhOnnK/Dx11C9r4uCofJ+uHpSh0Wm644YbqU34EXqNcys+MGTOYOXOmR1lkZCSpqakAJf6gvf766/z3v/8t9tiCBQuYOHHiZeUmkwndhW2QgoIC7rvvPtatW0efPn2YN28evr6uG/mECRNYuHAhr7zyCk8//bS7/dKlSxkxYgRyNWV2q40KkE+rFugaNcB87ASnn5tO6O2jyN+0BevZcyiDAgkedj3qyJK3LawpqRgPHHQZUYaHYTpwkDMvvEjUg5PRXggWdyWM+/aT/vlCtAnxRNw7AZ9WLYTSUweRlEqChw4heOi/6Q0C+/XBcvo0qqAgFHo9jvx8HIZ8ZLsdVVgo6vBwlAH+tULRKQ5VcBCRUyaRu/J3Crb8jSMnh4iJ42parCpBlmUyFn6NPScXSVW1io+jsBBbegay3U7Or8uR9DrMh/6NL2Q5fYa4l/5XpWOWhnHvfvf/wTfd4BXFx7j/IIU7dxF+91giS/nNrHJkyfWqTHtBsZR75adly5asWbPG/V550RNFSkqKR90VK1Zw7733MmrUqFL7DAgI4PDhwx5luovsP2bPno2fnx+///4777zzDrNnz+a5557zqPvaa69x//33Exxcc6HJa1s0aGtKqsuz4QJZ3/2INjGB8PF34tO6VameVbb0DM7Pfh+cDlAowelAFRaKPTMLy9lzUEblx3zcNb7l5CkcBoNQfK4i1GGhqMNC/y24kGqiLqGtF0PkvRPIWPydO3fY1YDlxEkKd+3Gr2tn/DpV3r1ddjop3LmbnJW/X2ZPU4SkURN5/31o46pe+SiNiInjMB87jjY+zp2zq6op3P0P6uhofKs5JYrI6u49yq38qFQqoqKK/5G7tPznn3+mX79+NGjQoNQ+JUkqsU+A3NxcmjRpQuvWrWnWrBmZmZ5fvoEDB3Ls2DFeeeUVXn/99TLOxHvU5CpQUWZj2WLBabV5HAsddQv+Pbu7l8CdVhv2zEzUkRGXLYtnL1uB0kdPvaf+UylDZE1srPt/hV6PLMvYc3Kwnj6L5cxZHIWFKLRaNNFRqMPDsefl4SwsdDVQKpEuvFAoXH9lJ/acXGSbHW2DhBq3GxHULqznU8j9fQ2Fu/65LCVGaahCQlxebpu24Ne+bbUaYHsDSe36afdt16bSCoHTYiHjy8UY9+7Dp3Ur14pxWCgZXy3Gnpvnsisc1J/Avr1R+flVhfjlQhUchF/njq4krekZWM+fdwVZPZ+KJrY+QYMHVPo3wlFQiEKrqf7fGmHz4zXKrfwcPXqUmJgYtFotXbt2ZdasWcUqN2lpaSxbtoyFCxdesc+CggLi4+NxOBy0a9eOl156ifbt/9WwH374YQYMGMBzzz1Ho0aNPFaewLX6NGvWLMaOHcvUqVOpX7/mjfuqWwFy2mykvPOe59OrJLnimMREu3L3XAjc5SgowJqSStYPS7ClpqEKCyXy3gluewfZbsf4z14C+/etlOJjTj5JxsKvQKEgoPd1mA4fIee35dizXZFolYGBqIICcRQWYlj317+PKUWrQxeyjF+KpFEjKZU4l5nxad2S8HF31um8RIKK4bRYMJ8+B3ENyVm1mrzDRzEfO+4+rm1Q9txdvq1bYjpwkKxvf8CenUPIjUO9IXK1oQoJAUC22SvVj+nwUTK/+xFHfj4R903Et7XLndxhNGFLS0fbMJGQ4Tdc0Vu0qpGdTmxp6dizs7GlZ2A+kYz5RDLOAteDk9LfH1V4GLnLV6LQ6wjsfV2lxvPv1oX0zxdiPZ9y5cqCOkG5lJ+uXbvyxRdf0KRJE9LS0nj55Zfp0aMH+/fvJzQ01KPuwoUL8ff3Z+TIkaX22axZMxYsWEDr1q0xGAy8++679OzZk3/++YfGjRsDkJCQwNGjR0lPTycyMrJY7XvEiBG0a9eO6dOnM2/evPJMCwCbzYbNZrtyxXJw+IGpdP74faBi3lHlwWm1ojZbUF7yxKrXagm8kC7CabWSs3wVBZv/BkCtUhFy/WBMh49Q8MMSoh99CABZoUSv02HdtIXULVuJ+7+ZSApP+YvmU9q8CvcdQH9BHtvW7agjIwjp2AFdg0S0sfU9PGycViuOnFyU/n5ut3xZBpxOVxJCp8OV9BQJSa8HWcZ04CCZ336P8effCL299K3VslKWedVFrrZ5WZJPkv3tD6jNFhg4GOf+g+j9/fHv25uCv7cRNvZ2fNuWPVq4tl4M/tOmkv75FzjPnK3xz6my50v29cU3PAz5yFG0FYyabj1zlrwFX+KfmEDIlEmoI8Lcx2yFBej1eqJuHo62jC70VXUNOs1mMhZ+jflEMuBa5dLWr49/n17oEhLQ1otGcWEFKuvHpRQsXwXHTxA0ZBCaejEVGlPdohn5ej2K9Iwqv0+UirD58RqSXAmL4MLCQho2bMiTTz7JtGnTPI41a9aMQYMG8d5775WrT6fTSYcOHejduzdz5sy5Yv0JEyaQm5vL0qVLWb9+Pf3792fPnj0cOXKkTAbPBoOBwMBAFi1ahI9P2fNbCQQCgeDaw2g0MnbsWPLy8ggop+drWSm6L8W++6I7tlpFcJrMnHn0f16Vta5SKVd3X19fWrduzdGjRz3K//rrLw4fPsy3335b7j4VCgWdO3e+rM+y0Lt3b4YMGcKzzz7LhAkTytV28ODBXrk4bDYbq1evZvqZE1i8bH0mO5ycfflVnEYj+mZNiZh4t8fx1LmfYDl1GnV4GNqGDfBp0Rx908bk/r6GvD+SiJ35PAqdjvT5X2I6dBi/Th0JvuF6FMWkG9BKEjNjG1TLvIrDabOT+dUiTIeOEDigL0GDB1ZJvzU9L29xtczLsGETOb8ux69bF0JuHo5OqbhsXjnLV1KwYxf1n3+GokUGW3om5uMnsCSfxLdjB/RNGxXbv+xwkvnVIsynz6KJiiRy0uWeqNVBZc+X+fgJ0ud/gW+b1u5VUVtqGrmr/8CnVUtXlOsrYDp4iPQFX1Hv6f+gusSRxLj/IBlffA1AxD0TSvw8q3JOjrw8Uj/5HNliIfLeCajLYWAvO5wU7tpN3po/secZCBo8kMALKXrKSsp7H6KJiebgj0vKK7qgFlIp5cdisXDw4EF69erlUT5v3jw6duxI27ZX/oJdiizL7N69m9atK5bZ+9VXX6Vdu3Y0aVK+OBNqtRq1F/NKbbv/YZp+eOWVrMrgtFkpzMoCwLx/P4GX/MAE3jOOvKS/sJ47h2n/ATKS1hN+91hUjRth+m0FR558DlVIMJJGi0OSMG3YSN7Zs0Q/eH+JmbUtslwjN9P0L7/GuP8gERPHoW/ZvMplqKl5eZu6Oi+n1eZS0lf/QeCAfvgOH4ZVAunCXC6el9lqxeJwYEUGGXL/WEvOL8vcfeWdPEn9Z4oPvYFCImDcnSh27iJj4dfoz5wtc847b1CR82XPzePsR5+hjY/Dd8RNWGQZ2eHg7EefYs/OIWvHLhLatLqi52Xu0eOYTCYskgLHBRkcRhOmw4fJ/OYHdA0TCR52PVK9mHLJWNFrMP3n3zDnGYh+9CGcYaHl60Mhoe7YntC2rclZvpKUJT/jCA7Ct23Z7jNOqxVjfj4OY6BX7xOXIQyevUa5lJ8nnniC4cOHExcXR3p6Oi+//DIGg4Hx48e76xgMBr7//nveeqt4Y99x48ZRr149XnnlFQBmzpxJt27daNy4MQaDgTlz5rB7924++OCDCk2odevW3HnnneXebqsOvG0E7bjgJVUU8t1ptXoYAiu0WoKHuFZIZKeTzEXfkvHF1x7Lqpr69bGeP4/TaASlAmvySaznz9eq0PjW8ykU7t5D+F1j8GnZvKbFEVQSu8GAbLYgOx1Ykk9hOXMW2W5HUqmQ1GoceQZMR4/iLDQSfOMwggb1L7Ev44GD5K3bgE+rFu4ydUSE6xpXqgga1J+AXldO8Onbtg05YavIXPQt0Y8+VHuyfJcBe24ussXi8nK6kMohZ/kqt6NB6MibyxRyQte4IXlr/iR/4yaChg7BsHYdOctXIdtsaGLrEz72DpTV5N0lyzKm/QcJ6NPLM7xCOZFUKoKH34AtLYOsH35C17hRqYl0ZacT4z97XS7+WdmE3la6DWuVI2x+vEa5lJ+zZ88yZswYMjMzCQ8Pp1u3bmzZsoX4+H9vjN988w2yLDNmzJhi+zh9+jSKi754ubm5TJ48mdTUVAIDA2nfvj3r16+nS5cuFZwSvPTSS3z33XcVbu9NvKkAKfR6dE0aYz5yFCQJ2e6AEpygJIUC344dQKlEFRSIpNGQ88syLGfO4MjJRdeoIQofH4z79qOpRfFbZFkmZ/kqlAH+ZVq6F9RenDYbuStWkffnun89/SQJpb8fDkM+4Lqm1VGR+HfphH+P7qjDw0rsz1FoJHPxd+ibNCJi3J3uct/WLfF9tXzJKCWlksh7x3N+9vtkfLGIiHvH15kYVdrY+iiDgyjcvQd940bYc/PIW/On61hiAv49upWpH59mTQkc0I/cVWswrN+A02QmoG9vAvv0QhVSvfHULKdO4zSZ0MbHXrnyFZAkidDbRnB21hukvjcXv84d8WnbBnVoiLuO02ajYNsO8v5Iwp6Zia5xIyLG33XVRQC/limX8vPNN99csc7kyZOZPHlyiceTkpI83r/zzju888475RHDgwULFlxWFh8fj9lsrnCf3sYbCpD55ClS3nGtdoWMvBmf5s1Kf6Kx20n76FMAfNq2JmLiOIz7DmBLzyDo+kHYsrIp3LaD0DturVVPvfmb/8a4dx8R94wvVwZ5Qe3ClpFJ2qefY8vMInjYELdbumy2kL7gS3c9p8VCxLg7y3SzTftkHk6zhbDRt1XJtaGJiSZi/F2kffo5Ob8uJ+TmGyvdZ3XgNJtxGk2uuFpOpztDOZJExIS7ypXqwpaW7urTZCagXx9CbxnuDZFLxZ6bR+bi71BHRqAvY3DVK6EKCiJy8j0Ykv4iZ9lKsn/+DU1sfRR6PfasLOw5uSDL+LRpRcS4sV6JGl0mruFtL4PBUOa6FbHXFXePGqLKFSCHKyaOMsAfXcMGVwxsJtvtaBskYktNRd+sKZIkEfPoQ1jPp3DuNZdcIbfcREAZnxKrA+v5FLKX/Ix/965l3qsX1D6s51NInfsJCr2Oek8+juZCgFNbRibnP3sXbXws4ePvonD7TrJ/XY7C98pemLLsWh0IGjoYVVBQlcnq07I5IbfcRPaSn3GaTAQNG1Lu3HbVjeGvjeB0oqlfj5R3P3DnvQq9fVS5PhtbVjbGfa7UEZq4WIL69/WCtCUjOxwY/tpE7spVSFotUQ9MqtLVN32jhugbNcRpNmM8cBDjnv3IshPf2DaoQkLQN2nstYjRZeYaVn6CgoLKHFTS4XCUu3+h/NQgVakA6Romkvjum2Wur9DpiLkQ1wdchozGf/ZgTU1zlykDPTNdO602zCdOoG/UEKrT6I8LOcK+XIwqNIyQETdV69iCqiXzux+RtBqipz6I0t91jclOJ5nffIfTbEYbH0f2kl+wnDmLJjqqbCuPDgfIMqrAwCqXN6DPdaCQyF2xisLde4h65AG0FYwX422cViv5G7fg06Y1Wd98714xk9Qqd4DCsqIKCSZ66oModDrUMdHVHt04a8nP5G/YjH/3rgTfMNSdmLmqUeh0+HVoj1+H6k1dISidtWvXuv8/efIkTz/9NBMmTKB79+4AbN682Z3XsyII5aeGqYlUGNa0dCzJJ/Hr0glJocCalsG5Wa+5j+ubNyNwYD+XkoPrCaxg63Yyv/kegOjHH6nWiK6Fu/4hfcGXrmjOd42pVdtwgvIjqdXYM7PIWfE7Cp0W08HD2LJzkC9sVRs2bEYVGIA9IxN9i2Zl6/RCChTZCwHoJEkisPd1+HXsQOrcj0n76DMiJtyFNiHe69nSy4PsdJK99FccRiO2zEwkrRa/Th3IPneeqAcmuxXNsiJJErqGpacm8iamg0cIuK4HobeOqDEZapxreOWnT58+7v9ffPFF3n77bQ9b4ptuuonWrVvzySefeDhdlRWh/NQCqlMBSv9yEYXbdwKgjgjHsO4vCvcdcB1UKMDpJKB3T3SJCS5Phz37yFm+0r33r22YWK3734V79pGx6Bt8O7Qj/O6xdcboVFA8WT/9jP1Cbr78jZsBV/6porQBQdcPJrB/HyS1GsO6DWgTynatyVYLOJ3Ys7ORnU6vXCdKXx+i7r+P83M+IGXOXBR+fkRNuQ9tbM2n0yny3izYvhOf1i0x7t1P1AOTyfzuB3zatK5RJaYiOM1m7JmZaOIH1bQoNYvw9gJcqzwfffTRZeWdOnXivvvuq1CfQvmpJVSHAiTLMtbTZwBcKyg6HYW79wCgb9EMR54B6/kU0j6eB5IEKiXY7KijIgFQR0YQ/fADXlv+lp1OHAYD9qwcbBkZFO76B9Ohw/i0bU3YmNuF4lPHcdpsGNb9hToywuWVePwEOByYj7vSFATfcL1HsMryBKFTaLUE9L6OvD/Xkb91O+rQUEBGll2ejYH9+1aJnZgywJ/6Tz+B6egxMhd/h+GvjYSPvaPS/VaWvLXrKNi2g7C7xpD13Y8E9L4OR34+9swsIiaOq2nxyo2j0AiAqpyrVdVBm4+qL4yKJLtelWl/NRAbG8tHH310WQidjz/+mNjYinkACuWnFuFtBUiSJOo/95RHWeTke8lZtgLzkWPEvTwdw6YtFGzbgS0llcC+ffBt04r8v7fhMOQTed/EKldAnFYbpv0HyFm1GltKqscxl+Hrnfi2bycyt18FOHJyAQi9dQT6Jo1xWm3YUlPJ/f0PkJ34X3fl+DulETrqFvy6dKJw9x4cBQWuQknCcvIU6Qu+JHbGc5W2CTIdPkrq3I/d72uD67M9N4/cFb8T0Lc3+oYNkK1W9E0bk/3zb+hbNK/RII0VRbZaAZC0ImGxwOUVPmrUKFatWkW3bi4nnC1btnD8+HF+/PHHCvUpHqVrGSem/qfaxrLn5ZG99Bes584j2+2u6M/nU1zZ1CWJ4KGDke128jdsImhQf694PmR88RXpC768TPEBkO0OFFqdUHyuEuwXlJ8iI1yFRo02LpbI+yYQOemeUkMzlBVNTDTBQwcTPuZ2wsfcTtgdt6Jv0hhJqayUrZjTYnF7PhUROWUSAX16ldCi+shZvhJJqyF46GAktet5tnDXP9jS0gkaPKCGpasYRcrPxUFar0nkKniVk/Xr1zN8+HBiYmKQJImlS5desc26devo2LEjOp2OBg0aFLtF9eOPP9KiRQu0Wi0tWrRgyZKypwkZNmwYR44c4aabbiI7O5usrCxuvvlmjhw5wrBhw8ozPTdi5acWUl02QM5CI7b0DPf7/I2bUYWFoo2LJbB/XySlktw1f6KJiSag779bEIW7/yH7r03wZuVlDBl5M9q4WHSNG6GJicZ67jyOPAP5W7djOnAQ48GDIorzVYKj0LUaU17D27JiPn6C9C8X4TSZCb5+MMhOCnf/g+XUGYKuH4xCV/EEkYV79pH51WKPMkdBgfe2gIu5aTlNJhz5BUgqFcpglxuw+UQyBVu3EzrqFhQ6HbJajUKvp2DbDnRNm1SrY0JV4ixa+almr1KBK2F527ZtmThxIqNGjbpi/eTkZIYNG8akSZP46quv2LhxIw8++CDh4eHu9ps3b+aOO+7gpZdeYsSIESxZsoTbb7+dDRs20LVr1zLJFRsby6xZsyo1t4sRyk8tpToUIE1MNPGv/x9OswWFVuO+OVhT0zAdOowtJxfLiZME9Ovj8gq7KAaQXu96Sj/70qv43HxjmdxoHYVGJLXaI0+YOiSEoMEDMR9PJmXOXKxnz7mP6Ro3JKh/v6qcsqAGcZotgHduaLLDScZXi1H6+6ONrU/20l+QVCp0TRoRef+96JuX0WvsImzZ2Zj2H8RpsaAMCEBTLwbrufPomjRGExONT4uqU8odRhOZi77FfOw4st2OTqWCxYs589KrWJUKnBaLe9sQQBkUiDYuDuP+A2jj49xRmyWlkvC7x2I6dJigIXXXWLhoe9Kem1vzsXauMYYOHcrQoUPLXP+jjz4iLi6O2bNnA9C8eXO2b9/Om2++6VZ+Zs+ezaBBg3jmmWcAeOaZZ1i3bh2zZ89m8eLFxfa7Z88eWrVqhUKhYM+ePaXK0KZNmzLLW4RQfmox1aEAKbRa93aAw2gk57cVbi8cy+kzOM1m9E0bk7NiFbkrV7saSRKRk+5xtSkowHr2XJmUn9PP/s/9vzoiHG18HMoAf8wnT2E5nowmtj6RD0zCkZOLPTcXhVZL4T970cbHoo2Pq1VuxYLyU3QTM+4/WO6YM1fCdOAA9uwcYu4Zjza2Pg6jEYVGU+5Iz7IsYzp4iLw/12E+egyUShQaDU6TyVVBocCek0PE+LtQliH44pUwnzxFwd/bkG02jHv3uVJ5dO+G3sfVt3+PbhhzcpDUKjQxMSiDApHNZoyHDmNJPkXwsCEE9u3t8d3wadm8zq+WqkJDQKnEmpqGvknjmhanxpCopMHzhb+XRkvWarVoqyhkyObNmxk8eLBH2ZAhQ5g3bx42mw21Ws3mzZt5/PHHL6tTpDAVR7t27UhNTSUiIoJ27Vx2n3IxS6KSJIkgh1cj1aEA2TIykZFJ+2ie2w1ZFRqK7JTRJiZgOXnqX8UHUPr7kfbp59Df9WSZu/J3clf+TthdY/Dv3LHEcSLum0j6Z/NdY6ZneGy5AagCA8j8+hschnwkrdblceawI9vsSDod+iaN0Ddrir55U9QhIcUNIajF6Bs1RNsgEUPS+ipXfvK3bHOFYYitT+E/e0n/fCGhd9xaaoRyp9VK3pq12DIzUQUHEzRkEOmfL8R08BDa+DjC7hyNb9vWKLRaHEYTaZ99juV4Ms7CQlI//IToh6eg0FfMTsmem0fWj0sx7tmLKiQYh9GlXAVfPxjf9m3RXthOCxrQF30xP/g+rar286ttSEol6tAQ7FnZNS1KzVJFru6XekRNnz6dGTNmVEKwf0lNTSUyMtKjLDIyErvdTmZmJtHR0SXWSU293NaziOTkZMLDw93/VzVC+akDeEMBsmVmkv3rcmwpqe4YPhcTef+95Pzy2792EgoFuoYNMB89hqRWE3H32MvamI8dL1X58W3dksR338RptWLPyXWv8NhzcrDn5OHIy8O3fTv8unRye6jITieW02cwHT6C6eBhsn5YArJM4MD+LgNPsRpUp5AkqUzpKq6E7HC4MowfOQpz3sN87Dh+N7qW6q0prphBWd/+gCYyEl3DxGL7yFv9J7m/r0GbEE/hrn+QNBq3bJFTJnkYYCt99DiNJnxatSB42PWkvP8hKR98TMSEu1CHlZxstTgsZ86S+sFHSCo14XePxbdDO5AkYdh/CZJW6zZ8FlSOM2fOeOS/qqpVnyIuvXaLVmguLi+uTmnXfFHCdJvNxowZM3jhhRdo0KDq4lUJ5aeOUNUKUMaXi905f/x7dnctvdvtSBo10Q8/gCYyAqfNjvnwEUyHj4DTib51S/x7dsfvoidT3zZtiBp7W7nGVmg0aCIjIDLiinUlhQJdQjy6hHiChwzCaTJh+GsjOSt+x3z0GOHj7vTIxiyovciyjDUlhcB+fa5c+Qo4zRby1vzptj3zadHcpURwkYeQQoEy2GU7IjscmI4eQzabkbQ6LMknyf19DX7duxI++jZOPvE0kiRhOXUapb8/imJcrPWNGlK4bz+aejFEPXg/6fO/5Pxb71L/2SfLZcSdt+ZPlAEBRD/2MEqfyiuCVysKvR5bRmZNi1GzVFGE54CAgAol/ywLUVFRl63gpKeno1KpCA0NLbXOpatBxaFWq1myZAkvvPBC1QmNcHWvU1SVG7zDkO9WfACQZdRx9ZF0OmKeeBxtfByyw4E9w7UtpQwOAkki56efyfr+J4wHDrqb2vPzcBjyq0SusqDQ6wkaPJDoqQ/iMBg4/8bb5G/+G7kCe76C6sWZX4DTaEJdhh+80pAdDvLWrvMoCx9/p1uRCOzfF/+e3Qm741b39mjeH0mkffgp6fO/JO2jT8ld/QeBgwYQdsetACiDgshZtgJ7RqYrgeYlK4pOiwWnw4EjJxfjgYNoY+sT/dhDOI0mTIeOlEt+p8XqWulRCU+m0gjo2Q3z0WOYDh+taVFqjhpwdS8v3bt3Z/Xq1R5lv//+O506dUJ9wbmhpDo9evQo0xgjRowok8t9eRArP9cgCj9fQm8biSo0hLSPPiN/0xYAAvr2dq3IAAVbt2PPySXqwcmkL/wKZJmA3tdhTU0j85sfCJrxPOCy3Tk76zUiJo5H37T6DBN1iQnE/HcaWT8sIfOb7zH8tZGIieNQh5dvC0JQfVjTXdurlrNn0TVuVKG4PuYTyeT8tgJz8kkCBw0gqEkTADK++JqcQ0dQhQTj17E9jrw8cn7bhzoiHF2DRHSNG7r7iHpwMtqEeI+4PzGPPoRx/0EUej2ai5KWWlPTyP7lN0wHD7viXwGZi74j7uXpyBe81xTlTLgZfONQUma/R+6KlYTcPLzcn8G1gnRhBS/9i6+Je3m62BasJgoKCjh27Jj7fXJyMrt37yYkJIS4uDieeeYZzp07xxdffAHAlClTeP/995k2bRqTJk1i8+bNzJs3z8OL69FHH6V379689tpr3Hzzzfz888+sWbOGDRs2lEmmRo0a8dJLL7Fp0yY6duyIr6/nd27q1KnlnqdY+aljVMXqj6RQEHBdD/TNmhJ6x62Ejbmd2Bf/R+hF2dINm7agb94Me24ezkIjCr0ee24evu3a4MjLw3IhJUG9Jx5DFRJCzrLllZarvCh99ESMG0vMtKk4zRYyvv6mWG8AQe1AExONb4d25K1Zy5npL5L+xSKM+/Yj2+1lam88cJCU9z7EaTbj17EDeX+sJf3zBQA4TWb8e3RFodeTvfRXbJlZSBo1Wd//hCzL6BITqP8/l5ttwbYdlwU8VPr749+ti0cKDKfVSsrs97GlphMy4iZ8O7ns2fy6dgbAdOQoSFK5Y+looqPQJiSQl/QXTmvVJ2K9Wsj6/icAnAUFWE6drmFpaoai9BaVeZWX7du30759e9q3d2W5nzZtGu3bt+d//3N566akpHD69L/nIzExkeXLl5OUlES7du146aWXmDNnjkeMoB49evDNN98wf/582rRpw4IFC/j222/LHOPns88+IygoiB07dvDJJ5/wzjvvuF+leYyVhtdXfmbMmMHMmTM9yi628p4wYQILFy70ON61a1e2bNnifn/48GHuueceTp06xeTJk90nASAhIYFTp06xefNmd9hrgMcee4zdu3eTlJTkhVnVLKXZ/zgtFszHTqAKC3Wv4pSEJEnFesNYU1Oxnj5DxMS7yVm5Bn0Ll+tskXcKgKRzPZU5C03Yc/PwadWikrOqONr4OEJH3kTap/OxnjtfJ8P5XwsofXyIGH8X9lsMFPy9lYIdu0jbsROFj56oh6Zc8bw58gzgdBJx7wRyfl2OOiKcyNEue7PI++/Fconiazp6jNT3P6Jw1278OrRHHRpK8A3Xk7NsJaG33+oRb6rY8fLzcVosSGo1kkpF+F2jCb/zDneKF8P6DSDL5KxcjToiHHV4GEp/f2wZmdhSU7GmpGFLTcWWkYHCxxd1WAiq4GCsaelYz54j5OYbryjDtYo1JRV7do77vfnYcXQJ8TUoUQ1RA1nd+/btW+pD5IIFCy4r69OnDzt37iy131tvvZVbb721/AJRh729WrZsyZo1a9zvlZfsp19//fXMnz/f/V5zSUjzhx56iLvvvpvOnTszZcoUBgwYQM+e/+YB0ul0PPXUU6xb52kHcDVTpADJDgeO/AKUfr7YMjNdT8YFhQDomzcjoFdP9E0blyveifOC2236wq8BCLtjFLrEBHzbt3PF39Fo0F6I2ZL62eco/XwJubFiIcarCn3zZij8/CjYul0oP7UcVWAAQYMHEjR4INaUVNI+/dyVIHTM7aW282nTGr75HvORo0hqFbLDgTa+5BuivnEjtA0TyfpxKb5tWiOpVK7vgVLpWm26guKhDg0l6sHJ5K5aTda3P+DTvBmq4CD3cf/u3Sj8Zw/GvftcLtkX3TAUeh3qqCi08XH4deqAw2jCnpWFLTMLhV5P1EP3o2/U0GM8e04ullOn0DdrChV0ob8asKamce7VNwHw7diBwh070UTXfA61GqEGlJ/ayLRp04otlyQJnU5Ho0aNuPnmmwkpRwiUalF+VCoVUVFRJR7XarWlHs/NzaV9+/a0adOGmJgY8vLyPI7ff//9fPjhhyxfvrzCeT7qArIsk52dzYYNGzhy5Aj3Wq08//zzxdb169wRc/JJ0j6ZB0DwTTeg9PPDnpmF5fQZLGfO4DRbUAUGoAoPR6nXETTsejSREegaJBI5+R6s51PQNWzgXtZX+vq4A7sVhZ/H6STqwckoA2o2+7KkVOLXqQMF23YQPHwYChEWv06giY5C17ABttS0K9aVlAoUfr6YTyQT0Ps6CrZuJ3fVaphSsq2ZJjIS65mzLrdaXPFxclf/Sep7c1FHRwESqtBgdPFx6Fu2cNuV2LKyKdi6Hcvp01jPnkdSqXBazB59B/br7c48L9vt2LKycRgMqMPDUQYGlMtG5eLo6ZJWS1iPbvDo41dodfVhTj5Jyuz3AdC3bE7Y6Nsw7tuP9dy5Oh+4UVBxdu3axc6dO3E4HDRt2hRZljl69ChKpZJmzZoxd+5c/vOf/7BhwwZatCjbLkS12PwcPXqUmJgYEhMTGT16NCdOnPA4npSUREREBE2aNGHSpEmkp3vGnXnxxRcZNGgQPj4+KBQKhgwZ4nE8ISGBKVOm8Mwzz+C8YJR4tfHtt9/i7+9PWFgYt9xyCy+++CJz5swBLnhjXSBi4t0kvvsm2sQEdyJJgJxflpG56FvyN/8NCgUBva4j9Jbh+LZrg0KrxXL2HOffehdbZhYAPi1bEDRoALoGxcdIyVn664XxxqEKCiq2TnXj36MbzsJCCnf9U9OiCMqI8eAhCrZux3FhtbIkLKdOc/aVN5CtNvy7dUETHUXQkIEY1v0FQPYvyzDuO/BvNndcwTsLduxCoffh1BPPkP/3NtQR4URNuQ9Zdq202HNzKPh7O2mfzsd85F8jz5xlK8n7cy2yU8a/d0/qP/cUmlIe0CSVCk1kBPrGjVAFBZbbONd06DAoFMT893EC+/bGtP8AcGFr7RpAdjrJ+zPJrfj4tGtD5H0TXYlaHQ6kSuRlq8vUhM1PbeTmm29m4MCBnD9/nh07drBz507OnTvHoEGDGDNmDOfOnaN3796XRZEuDa+v/HTt2pUvvviCJk2akJaWxssvv0yPHj3Yv38/oaGhDB06lNtuu434+HiSk5N54YUX6N+/Pzt27HAHYho2bBgZGRkYDAZ3xMdLef7555k/fz5ff/01d999d7nltNls2GxVb3xY1Gdl+l67di2PPPII3bt3Z/LkybRr146EhAT38Z9++omJEye63pw5h7Z9O2xbtqK/sH3o17E9ISNvQZZlFOriT7nTYuHca29hWfcXvqNGUNJvtz0ji7yk9TgOHALAv17MZbYWNYU2KpKQdm2w/b0NTZfOJc6h1D4uNNJeZZ4ltW1ehvUbyFm20pV3S6/Hr2njUmVLm7cQjdWKJrY+9n0HyP10PrrGDYkc6gqrL59IdqdlUfj6ArIrKKG/P87CQtDr8Q0ORitJaBPiCXjmCXffsgzn33wHx979aJu5vMdUOTmEde0CQMH6jRSmpaNNTMC/Z3evZBrX+/ii1+nI+fBT9I0aEHLBuFqZk1drzlllKekatKVnkr7gS+xZWej1eoJvvhH/7t1cAd4NBnRqNX5hYbXmc3BQjXJUUYTnus4bb7zB6tWrPWIVBQQEMGPGDAYPHsyjjz7K//73v8vSbJSGJFeze0xhYSENGzbkySefLHYfLyUlhfj4eL755htGjhx5xf4SEhJ47LHHeOyxx3jxxReZP38+hw8f5sknnyyTwbPBYCAwMJBFixbhIwKOCQQCgaAUjEYjY8eOJS8vz2uBA4vuS4kzZ/0bZb8COM1mkqc/61VZqwM/Pz9+++03+vbt61GelJTE8OHDyc/P58SJE7Rr1+6yPGYlUe1xfnx9fWndujVHjxYfuCo6Opr4+PgSj5fGtGnTmDt3LnPnzi1328GDB3vl4rDZbKxevZpBgwa5Az6Vh1WrVnH77bfTvHlzDw+4izl79ixnz56la9eutP34/UrJa9iwiZzfVqCpF0P46NtRhYd6HE/76DPsBgPxD07m5RZteeT9OTiCAvFp1RJVSDCyw4lxz15MBw6hjo7Cv2c3UCgx7tlD3pq1Lg8OpRK/dm1wmsweARMlrZboh6dUKouzLEP6x5/htFiIeuRBJEX5nny0ksTM2AZMP3Oi1qxoVQW1aV7Zv60g/6+NHmWSVkvci1eO4Go9c5aU9z9y5X2TZfR6PZ9//jmPvPsOmVu2uuv59+xB/uYt7tg8UQ/dT8ZX37gilbdqgeXYcWwZmWjqxaBtkIhst1GweStBQwbi264tKR98jPPCFppPy+Zo6tfHUViIf6cOF2yFvEt1ny/Z4cSRk4stM8OVeqagEKfR6DIMd8qgUeHTpAm6xo2QVBVLKVO4bCXvPfAQUz/+CKtSQnbIIDvQxtZHU78exv2HsGVmYj56DPuF7XdVSDC6BomE3DqyQiu53sBhMl+5UlUhDJ4B17bXPffcw1tvvUXnzp2RJImtW7fyxBNPcMsttwCwdetWmlyI+1UWql35sVgsHDx4kF69ehV7PCsrizNnzhBdAet+Pz8/XnjhBWbMmMHw4eULHqZWqyuknHijf5vNxv/93//xxRdfkJycTNeuXfn0009LbJ+YmEhioss25+Aj0yqVBkPbszv+Pj6uZegVKy/L4aW/4XpSP/iY0598DrPfJXPLVswWM/IPS1CFBOM0mXCazGjq18P691ZSlv7ibuvbrg1+/fuia9wI08FDZC3+Dl3DBoTdeQfqC2HQnVDpH3vdwH6kzJmLavOWUhNbloZFlmtcSfAGtWFehhPJmE0mFP5+IMsofX2xpaWTvWs3vu3altpWjonGHhiALTUNSaPBr10bAHxH3IS9XRtyV6zCfOIk+gA/TIUuOyK/rp0htj6FOTmoI8LR14shaGB/zEePUbBjF4W7dmO/kEbBtPRXuGDPFj7xbvSNGqG8KIjhla5Ph9EIMlWS8R28c74cRiOmg4exnj2LLT0TW3q6y9avyF5SqUTp64vC18flii8pcBQUkvFHEgC6Rg0IHjYUbYOEctk25Se7osr7DB2E8pI5OQBtz25oAT/AabOBjDsUgBW51tzIndUoSGXtdq4Wm5+PP/6Yxx9/nNGjR2O/EBdMpVIxfvx43nnnHQCaNWvGZ599VuY+va78PPHEEwwfPpy4uDjS09N5+eWXMRgMjB8/noKCAmbMmMGoUaOIjo7m5MmTPPvss4SFhTFixIgKjTd58mTeeecdFi9eXOYASrWN119/3R0b6cYbb2TmzJmkpaWxe/dujh49yvjx42nYsGGJ7SuTByz/721kLvoWgMLtO0mz2wm9eTiqkGAAtPXrEf3ogxR85wpAFnrLcJStW2I6dARz8kkUPnp8W7dCExON5fQZbOkZyLKMJioSbWx9wJVeI3vpL/h26kD4naPdcVOqCl3DBvh370rWdz9iOXUa3zat0TVu6BVbDUH5CR4+DNPBQ+SudIW7l2UZ3w7tSF/wFRHjwbd9yQqQpFAQM20q1rPnMGzaQsHmv+FCcFddQjxRD0z+t7IsY04+SchNNwIQeusI8jduJuPLRUhqFfoWzdHG1QfJ5dZuOnSYwIH90CUmoE2IR+nnV+Y5yU4n2Ut/dRkoKxREjL8Lnzatak1UYtlup2DnLgq2bsd8PBmcTlShoS5lsHkzAiLCUYeHo4644Kl2yXdSlmVOPvZfAMzHTpAy5wMAEt5+rUzJhQt27sJy5kyZ5RXemoKL8fPz49NPP+Wdd97hxIkTyLJMw4YN8bvoO9quXbty9el15efs2bOMGTOGzMxMwsPD6datG1u2bCE+Ph6TycTevXv54osvyM3NJTo6mn79+rk9myqCWq3mpZdeYuzYy7OO1xVGjhzJkSNH2LVrF8uWLeO3337zOJ6YmFiq8lMZFDodCr0OXeNGGPfsw7h7D/bMLGL+86j7B1ETFUXUIw8CrjgcVoWEb7s2+F54Ci9CGxeLNi72sjGyl68ESUHoiJurXPEpIvS2kagjwjH8tZGCLVuR1Cp0jRq6EmB26iASStYguoR4tPFx6Bo3wp6VjS4xAVVYKEgK0r9cRJjFgn+3LiW2V2i16Bo2QB0eTubxkoOfBfbtTWDf3u73Ps2boq1fD6fDjvnIMQp37sZ04CDa+DhQKAns35egQQMqZGORv2kLhnV/Edi/L6ajx0j/fCEKvZ6QkTfh36VzufurLNaUVPI3bcGWmYk9Kxt7Vjay3Y6uaRNCR92CT6sW5fLSlCSJiPsmkP7ZAnSNGmI+dhwAR0EhqsCSzQVkp5P8LVvJ+v4nwrpW/+dQ5xHbXh74+fnRpk2bK1csA15Xfr755psSj+n1elatWlWp/k+ePHlZ2ZgxYxgzZkyl+q1Jmjdv7o56vXfvXl544QVOnDjB3r17GTlyJBMmTLhiHxVd/fFt2xrftq2xpqVj3LMPAOvZc1jPnHXdJC5QZEsjKRUewd3Kgj0rG01sfRRVtDVQHJLSdTML6NcHW1o6poOHMB44RNbSX8n+bQWho27Br0unWvNkfq0hSZIryN+FQH+y00nQkIEU7thJ1k8/l6r8wL/Rm4uyumct+QVtz+7Yc3PRxsddtsrntFg4/fy/keYDevWk3n8fd8X/KeM1YP7/9u47PMoqe+D4952WmUlvpAAhtFCD9AABXWQBRVBRFJFFsaPoguiqCCgqghVxXWAtiwoquOv+7FHBVUREQJr0JoRASA/JJJlk6vv7Y2AkEgIpkwzkfJ5nHs07b7k3wOTk3nPPPZxO6eYtaAMD0YWFoY+LRR8VicZsxvLjTxiaxxM+agThqkrx9z9w4vM0SjdsatDgR3U6KVr5LUXffo82OIiAFs0xdeyALiICU8f21S7XP5fA5K4E9uhO+d69xD34QLUVl1W3m4oDv1H4RRr2jKME908h8sbaVfdt0uq6XP0iC37qk2xs6seef/55HnvsMQIDA0lNTeW6665jypQp5/1hXZfpL90fihZqw0LPeY1l3XqK//c9GrOZuAfuO2vp/qDePcn/4EOKvl5F+JXnvzSxNhRFwRAbgyE2htDBl3mm3D731Dwq37OPqHE3nLHPk2hYhZ99ieXHtah2B2g0lfbXqpaiYEpqB0Dp+o3kndzpXTHoMXfuhLl7N4xt22A/egzr7r1oAs24y6wAWH78ifCrz297CVeZlcLPvqB0/UZPTS2XG1dJiTfo10WE4zxRROR1npFM1enEbfU8x1lcjLO4GF3ouf/91JWz2ELee8up+O0QYX8eTOjQIfU+fRQ19nqy//kWWa+8RkDrREydOqALC8NdXo6rpBRXaalnL66Mo7gsJRhatiD2gXsxtWsrv2gIvyLBjw8dPXqUpUuX0rVrV5555hlWr17N8ePHKS8vx2g0YjabSU5OZubMmRw+fJhly5YRHh5OXFwc0dHRPPbYY4Bn+X9tpwFrEwC5KypwnEwABYh/ZFq1H96qy0Xxd6s58cVXGDskUbFvP+V79xHYrWuV5wen9MFlKeHEF2moLhfhV13RYB+M2pBgosffhKlzR/KX/4fj8/9Os1vGV9rJWzSs0k2bPYEPYGzbmojTNtg9G1P7drRe8KK39kvs5HuwlpSgCw3FumcvZdu2k/fOe97ztaEhuMusBKX0wdylM9qQ4HMGPqrbs3Kx8NMvcJeXE3nDdQQP6Iei0eB2OHBk5+AsPEHhZ1+AonAi7RvcFTasu3ZjS/ck9zrz8inbso3QwZfV9ttTdducTuzZOThyc3FZSnHk5VG2ZZvne3HvXZjat6vX552iMZmIvX8S1u07Kf1lM5Yf1uIuK0PR69EGB6ENDkITFERgr54EJncloHUrn01tNwky7eUzEvz4UHJyMna7neXLl7Ns2TJ69OhB//79MZlM2Gw2SktL+fzzz+nSpQsA3bp1Q6PRkJ2dTU7O7+X+/7jXWU3VJAByFBZy/MUFnt9cFYWgPr0IOEdgkPPW25Tv3kvony/HdXLrEfUcRR3Dhl6OotVQ+OkX2I8dI+rGMbjKyrAdTsftcBCc0qdGCac1FdSjO4b4eHLfXkrmSws8WxVcPhiCffdMUbXwUVdRsW8/mqAgLN//QNmmLYRcOrBG9whIaOkdiTGcXM3lKCjAlp7hTbQ//spraIzGs44sucvLcRQU4i6z4rKWYVn9I7b0IxiT2hM97kZv0j94EnIDWrbwLNNuHk/O62/hyM3jxJdfgUZD5JjrcDtsWHfswtjh/JffVsVVVob92HHsx49jzzyO7dhxHDk53tVZik6HLioS8yXJRIwagTYw8Bx3rBuNXk9Qrx4E9fLs+q06nTXaO1DUgAQ/PiN/Y31Ip9NhP7kH1pQpU5g6deoZ57z00ku899576PV6/vKXv6A5+VtSSUkJW7duJSIiwlvpui7OFgCpbjdFq/6HPioKfbNoCv77CWgU4v56H/rY2HMu2a04eIjy3XsxdeqILT2dioOHiBw7xvvBWJ3Qy/+EPiaG/A8/4uhTz3oOarXgcuG2lhMxyrf7tBlimtH84akUrfqOoq9XUvy/1bR59CFI8E0yuahacJ9eBPfxVDQu+Wkdqrt+PrH1kZFog4Io/PQLSn7egC4slNAhg884z1lUROGnX1D26w5wubzHtcHBxP31Poxt21T/nKhImk//G+W793Dim2+xZxyl4KP/875//PmXMbZrQ+jlf8LUqWO1IyFuuwN7Zia241lwc1syn59PSWYmAIpejyE+DmPrVoQM7I+heTz6mBg0JmOjTilJ4OM7stTdd+RvrQ/89NNPXHXVVZhMJoYOHQrA448/zgMPPHDGjvYBAQHccccdZ9wjODiYSy+99IzjdfHHAMhRWEjh/32GdcdO7zFNUBAxd91ebTLj6cpP7odUvmcvuqgoYu+7G1MNftM1d+lEi+kPU7p5K7qwUIzt2pLxxDNoTA2zl4+i0xHUpydFX69EExiI7jxym4QPuVXqc/eAikPp3m0vAtq0pvDTzzHExxHQKgFDfDylmzZz4suv0RgMRFx9FcbWiWgCA9GYzWiMAec9ZaNoNJi7dsHUpTP2o8dwl1egBBiwZRyl8L+fUHHwEBUHD6FvFk3o0CEE9eqBcnJ3+YrD6ZTv2Uf5/gPYM4+D2405OBhunoCpc0eMl1+GoWUL9NFRMoUkRD2R4McHnnrqKYqLiykuLmbVqlXcfvvtACxdutS7VK9Dhw6N0rbTA6CSH9dh3bGTgMRWBCS0JLD7JZ5VWOeRBHpK2BVDMQ0djMtqRWs21+q3QI3JRMjAAVh37eH4y6+iOhzV1nqpb4rBAFotwf1TTu4LJRqSI78ARatFGxqCLjLCWwlcFxaKPjoaU8ekWo8umDomEXLZIMr37sORm4ei1WLduRvVZvOcoCgED+hH+MgRaM2mOvdFUZRK5R2Mia2wfP8DzsITaENDQKMh//0V5L+/An1MM5xFxag2G5qgIEwdkwju15eAVgkEN28OQMSoEY1elFKIi5EEPz7w5ZdfkpWVxd69e3n44d83UDwVBEVFRZGXl9dYzfMytmtD8XerMXXqQPgVtVt1pWgUFEWHrh62Bin4+FMUnY74qfd7Kz43BF1ICGHDh1KU9jXBzeNl2qsBWX76mYJ//xc4OX2iKKgOB5bVayqdF//wVG/uTk0oikLkdddUOqa63Tiyc7BlHvfk7MTG1L4D5yGobx+Kvl6Jq9iCq/j3fYccObmAZ7Q19r67K+XWycooAUjOjw9J8OMDer2ehIQEEhIS2Lx5M2lpaZXer68iTbV1avTH3KUzIYMvo/i7HwgdfFmjL/nWmEzYM45iz8mtVFOoPqlOJ9mL3qDit0PoY2NQ7XbcdgfhVwwlqE8vCv7zfzBa6pHUlOp248wvwG2zeb6nNptnCkur8UzVaLUYYpqhPW3VoruigoL/fkJg754E9eyOI78AZ0Ehik6LxmRGYzJSunETtiMZHH9pAZE3Xk9Iav86t1XRaDDEx2GIr/kWOrURctkg7JmZWHfs8h7TmM3ooiLRBgdRvmsP1p27z7mwQAhRfyT48THHaaueLrvsMj799FNCG6Dmx7mcCoBCBqV6Vtj8ur1RKtGeztQhCXvGUU8NFR9RnU4qfjsEgCP79xV1BR997GnDyaJ59uNZ0AAbWF5IVFXFumMX1h07CUrp4ylSCJTv3U/e+8txWar/c9OYzcROuhNDyxbYMo6i2u3gchF66cCzBrshAwdg3bmbnDeXUPDv/2LdsYtmt01o9EC9JrRmEzF33oY9K5uKg7/hyM3DnnmcivQj3gTrU6NAQpxOEp59R4IfH8nPz+f666/n8OHDvPbaawD88MMPrF+/nuHDhzdy6zy8I0DdulL4f58R1LNHo63ccFdUUPzdaoL7pxB6+Z989hyN0UjrV19CdbtxWUpQXS6cefnkvrMU92m7NduOZGBowsGPqqrYM49Tvm+/t+xAxYGD5P7rHQB04eGY2rXFdiSDnDeXeFYz/WWcJ1E4IADFYPAU/HO7UF1uVIed/OX/IWvh6wR260rpL5u9z7JnZVc70mfu2pkWs6Zz7Jl5lO/Zy5FHZmBs3464+yf5+ttQrwxxsZX+TrntDmzp6ZRt/RVTx8bJARQXAAlgfEKCHx8pKytjzZo13pGEDz/8kB49etCiRc3zFnzp0F8fInbXHqzbd+IstqCPjKj1vVzWcmxHjoDLjTGpXY02EnWeKAKXC0Wvb5B8B0Wj8a7s0kdGkDDvGaw7d1Hx7feAZ+RH73Kd16aNFxLV6cKek4PtSAZlv+7AkZePLjyMigMHvefETZlM2a87vHk3tvQjmDt3wllswdwtGUWvQxMc5CluufpHdJERxNx1+zkD5+D+KeQv/zelv2zG0LIFqtOJIysb5TwS7PVRkSS+/Jxn24gvv8bU6cIPFjQGPaak9piS2jd2U4RociT48ZFWrSovFR87dizdunXjrrvu4sorryQ+3n/m993l5Z7/qUPMUXE4nazXFnuH8TWBgUSOGY2pQxIaswlFUTz5IC4Xik6Ps6CAigMHcZWWog0N9eTaAJY1a3GVlmLq1JHA7pfUaOVZXSiKQmByV8K7eqpSl27aQllBIc1um3BR1DFRXZ6CeBkznqT81J/3SZX2WNNoyHp1YaX3rdt3Yt2+0/PnqNej6HSUbdlG2aYtniKAbdvgttnQnuP7FNCyBYaElqg2G9Hjb6Js668UZWWf9w9/RacjbOgQwoYOOa/zhbjgScKzz1z4n+p+LDs7m82bN3vzfjZs2MCGDRvo0qULTzzxBOXl5ZSWllJaWkpgYCDJycn079+/zhWda+ofI69h7LIPvBVja8NdUQEuF7GT70EXEU7h52nkvevZXiCgTWtUhwP7sczKm6BqtWhMJtylpZXuVbZlG2VbtuHIziHi6qtq3abaOLVha7OJf+HIojcoWbe+xtWG/c2JL7/G9uNPsHw5Qf36oGZmYe7cCUWno3SzJ4BBUTC2aY0xqR2GuDh0YaHeQEcJMKANCqo0ClZxOJ3ct5d6/v+3Qxx98hkSX3qu2nYYmsfT/KEp3q91kZGYk7v4tJK3EBcyyfnxHQl+fCgmJoahQ4eSlpbGnj170Ov1HDlyhNGjRzN27Ngqr+nevTtbt25t0HZeddVVREZGYln9I5FjRtfqHgEtW6Axm8l7fwXmzp2wHz3mfc+elY25U0dCBg5AExCA2+FAFxZKQGIrFL0e+9FjnPjqG/TR0YQNvZyy7TtBUQjs3nir4kwdkgjs1YOi/31PcGr/C3L6S1VVSjdtoei77zHpPQF1+MirCNJpKVm/kfzl/8bYtg1R42707HcVdP41joytE2kx8zEq9h+kfP8B9NFRNW6fxqCv1fJ1IYSoKwl+Gkh8fDx6vZ74+HiOHz+OxWLh3//+N9OmTaOsrMx73vTp0xu8bYGBgUyaNInXXnsN9dpRtZrm0QYFETv5Hgr+7xNK1q0HIKB1IuGjRmBsnVhtZdqAhJbE3nOn9+v6WM5cV25rOarN7vmvy33BBT+qqlL4yefevJ2Wz87wvOFyUvTDGoq+WklQv75E3XRDrXOsNAYD5q6dMXftXF/NFkKcTqa9fEaCn0ag0WhYuHAhM2fO5LbbbuO2226jXbt2xMU1TN2Rqvz5z3/m2Wef5ZeRo7ni689rdQ+31YpyMnGo2Z23EZjcpT6b2KAyX15AuaWE6PE3oei0lG7agqOgANXuQGM0oosMR9Hq0AQEoI9phjYs1K8K0zly87yBjy42BmeRZ8PZY/NewmqxENSnF5HXX+tXbRZCVCbTXr5To41iZs+ejaIolV6xsZ6lmw6Hg0cffZTk5GQCAwOJj4/nlltu4fjx49Xe85133jnjnoqiUFHx+7Lj0tJSbrrpJuLi4rjpppsqjZRMnDgRRVF47rnK+QaffPKJ336wnzhxgpkzZwLwr3/9i0GDBjVq4AN4N1StTeVpd0UFOf96l+yFr1Px2yE0ZjNFK7/lyONPkPXaItQLqDy/dfdeAFS7nRYzH8XUuRM5b71N3rIPsKz5ibItWyn+3/fkvfs+uUveJXvxGxydPYeM6U+Q8/ZSKg4dbuQeeOgiI9CfrFzsOlFE1oJ/ABCc0peWTz5O9Lgba7QaTwjRCNR6eIkq1Xjkp0uXLnz77bfer09t1Gm1WtmyZQuzZs3ikksu4cSJE0ydOpWrr76aTZs2VXvPkJAQ9u3bV+mY0fj7xpYLFiwgKCiIlStX8sorr7BgwQJmzJhR6dznn3+ee+65h/Dw8Jp2qcGtXbsWgKuvvrpSgFZUVERoaMOPIGRlZTF+/Hi6du1Kr169OJSaWuUO8GdTtOo7rNt3eL/WRUViiIvDnnGUioOHUB3O81rO7A9O5b2odgdZf1+E6nDgLrMSM+lOzJ06es9zWa3gVnFXlOPIycV29BhlW38l69WFmDomEXbl8PPeHNYXStdvxJGTS+jQIeB2E3Zys9nwEcNkryghRJNX4+BHp9N5R3tOFxoayqpVqyode+211+jbty8ZGRkkJJy9iNnpI0hVKSoqIikpieTkZDp27Eh+fn6l9//85z9z8OBB5s2bxwsvvFDDHjWcw4cPM23aND755BNGjhzJ8uXLAXC73cyfP59HH32UXr160bt3b+bNm9dglaCfeuopKioq+Oabb7xB5x93gK+OsV0bnPn56GNiCO6fgi48DICQS1M5/tICiv/3PeFX1m7vsIZ2alPKmLtvJ3/9L2gMeoIH9sfwh7+fWrNnebg2KBB9VBTmLp0JG/ZnrNt3cOKrlWS98hrRE/9CUI/uDd0FAEo3bsLctTMRI68EIMBPR0GFENWQnB+fqXHwc+DAAeLj4wkICCAlJYW5c+fSpk2bKs8tLi5GURTCwsKqvWdpaSmtWrXC5XLRvXt3nnnmGXr06OF9//7772fIkCHMmDGDdu3aVRp5As/o09y5c7n55pv561//6neFBAEuvfRS1q9f7/36559/JikpiczMzErn/fLLL/zyyy/07NmTO++884+3qXdut5v333+fhx566IzaQ+cbAJk7daw0KnLKqWq27tOmMC8UxrZtiG7TukbXKBoNgd0vwdwtmdx3llH48WcEdr+kwUfynEXF2I5kEPWXcQ36XCFE/ZKcH9+pUfCTkpLC0qVLSUpKIicnhzlz5jBgwAB27dpF5B924a6oqOCxxx7j5ptvJqSaHb87duzIO++8Q3JyMhaLhVdffZXU1FR+/fVX2rf3FD9LTEzkwIED5ObmEhMTU+UPk9GjR9O9e3eefPJJ/vWvf9WkW4AnZ+n0fbjqy6ncpf3793urPYNnmtBqtXqPabVabrvtNnr37k3Pnj1JSkrySXv+aOvWrbhcLvr27Vvl8/bd+1e6/fO1M46fGkmobkRBVTSYjEYcG35BMzAVfbOaL4duaOfTr3PSaolOHUDu/oPoiorRRTTsVKx97z5MgYFEJHdB84f+XGwjQNKvC8fF0idXXarBCr+hqHXIRi0rK6Nt27Y88sgjTJs2zXvc4XBwww03kJGRwerVq6sNfv7I7XbTs2dPLr30Uv7+97+f8/yJEydSVFTEJ598wpo1a7j88svZvn07+/fvZ/To0edMtrVYLISGhvLBBx9gNpurPVcIIUTTZrVaufnmmykuLq7Rz7aaOPVzqcPUuWgDjOe+4Cxctgr2LXjcp229UNVpqfupqsQHDhzwHnM4HNx4440cPnyY7777rsbfcI1GQ58+fSrd83xdeumlDB8+nMcff5yJEyfW6Nphw4bV618OVVVxuVyoqsqqVau4/fbbWbJkCVdeeaVfrEJbuHAhjz/+OAkJCXz77bfExMRUe/4fR38CFIWnWrbhyaOHqkygVVUo+uprLGt+Iu6v92GIb9zVbOfrXP06X7lvL8VZUEjctCneqtENoXzffnKXLCX6LzdhTu7qPV5f/fI30q8Lx8XSJ1d5A07jS86Pz9Qp+LHZbOzZs4dBgwYBvwc+Bw4c4Pvvvz9jKux8qKrKtm3bSE5OrlWbnnvuObp3705SUlKNrtPr9ej1dV+RlJ6ezssvv8wHH3xAYWEhSUlJvPDCC5SXl/PTTz9xzTXX1PkZNZGfn8+LL77I3/72N3bu3InL5aJTp04cOXKE8vJywsPDzytHas8D06rM/7GpaqUPMtXtpnzvPopW/g/b4XQirhmJGhd7wX3Y/bFfNVFx6DCFW3+l2cQJ2BUqb+nhQ/bsHLLeeQ9DyxZounSusv116Zc/k35dOC70Prklorgo1Cj4efjhhxk1ahQJCQnk5uYyZ84cLBYLt956K06nkzFjxrBlyxa++OILXC4X2dnZAERERHj3q7rlllto3rw58+bNAzwrjfr160f79u2xWCz8/e9/Z9u2bSxcuPCs7ahOcnIy48eP57XXzsxT8bWSkhJat/YkyTZv3pzevXuza9cu7/vR0dE+fX5GRgYbNmwgMzOTnJwcFEVh3bp1/PDDD2ddBRcXF4fb7fbW+anO2RKg3TYb1p27sW7fQfmBg7jLrAS0SiD2vrsxdahZEHoxKPt1B9qQYMwNuD1HxeF0ct56B21IMM0m/sUvRheFEHUjCc++U6Pg59ixY4wbN478/Hyio6Pp168f69evp1WrVqSnp/PZZ58Bnv2pTvf999/zpz/9CfD8gD79B21RURF333032dnZhIaG0qNHD9asWUPfvn1r3alnnnmGf//737W+vrasVivNmjUjNzeXzMxMMjMzvQnNX375JYMHD/bZsxctWsTkyZMBMJlM3tIBwcHBTJgwgaioKCZMmEBQUBC7d+9Gr9fTtWvXaksQVOXQXx+i9asv4Sw8AQltyX//Qwq2bEV1OAho1ZKQgQMwdexAQOvEJvcDWHW5sPzwI5YffyL0skEN0n/V7aZk7ToKPvkcY2Irmt1+C9rA89+jSwjhx2Tay2dqFPysWLHirO8lJiaeVyXf1atXV/r6lVde4ZVXXqlJMyp55513zjjWqlWrShWiG0pMTAzZ2dm8/fbbPPjgg1gsFm8ZgNTU1Hp/3urVq/nmm2/Ytm0bK1eu5M4772Tu3LlERUVV+4P31Cq6mlJVlcWLF3N09rMYbDZYvhxHfj5hVw4jsPsl6CMjatuVC5pl3XqsO3bizC/AkZdPyKUDCb/qCp8/11lcTPY//okjN4/gQQOIvPbqWu3LJoQQTU2NtrcQ1XO73dx6663ccccdjBw5koMHD3p3aM/KymLOnDmYzWa+/PLLGt33mWeeQVEUhg8fzubNm0lPT+ehhx5i8ODBLF26FK1Wy/z581m0aBHR0dE+G3F49NFHmTx5MmOGD6fZbbcAEDdlMmFDBjfZwAfA+usOynfvRR8bQ/xDU4i87hqfByGqqpL33grcNjvx0/5K1JjrJPAR4iJzatqrLi9RNfm0rEeqqvLVV18Bnro+r732GsePH2f8+PF06tSJ8vJyAHbu3MlVV1113vc9NXW2cuVKVq5c6T323HPP8cgjjzTY9NKpnKWUlBSW3X8/aWlp3vfcFRXYM7MA0IWH1WttG9Xtxl1WhjY4uN7uWVsuq5WyzVsp/WUT7gob2tBQKvYfQDHoiZ5ws8/3y3KVWXHk5FB+8BAV+w8QNuIKAlrVbOpSCHGBkGkvn5Hgpx5ptVo++OAD0tLS2LFjB4sXL0ar1TJ+/HjAs4fZ9ddfz6effsqYMWNo27btWe9VUFBAeno6b731Fp9++ing2RMsNzcXjUbD4MGDG7xuw9/+9jc++ugj5s2bh0ajITExEdvhIxRs3kLJhl9QbTbPiRoNQb17evb4immG+ZJutQrQVJcLy48/UfixJ5cs8ZUXUM4jMduXjr/8Ks78AgCCBw7AZbEQOmwIIf37+T7wKS0lY+ZTlVaPFX31DZqAAEL/NMinzxZCNAIJfnxGgp96NnToUIYOHUp2djZxcXGVqjpbLBbefvttAPbt21cp+HE6nRw4cIDt27fzySefePOroqKiGD9+PMOHD/dJ3lBNvf3228yYMYMnnniCpUuXkv3PN7HrdIQMSiWoVw/QarHu2MmJz38fFQpO7Q+qirlrF0ydOlQbwLjtdhxZ2dgyj1P48WeodjsAYSOuaPTAB6DZLePJffd9nAUFBPXuibF1os+f6bKWU/LzepyFJ9CGhuIqLv49AFJVCj/+FEPzOEzt2/m8LUIIcTGQ4MdH3G43gwYN8u6XFRgY6J32atasGZMmTaJNmza0atWKnTt3smvXLmwnR04SEhJYuHAhvXv3pkuXLgT60eqdzp078/HHH5Obm8vPP//MqlWr+Msv6yoFJoaYy3Fk51D6y2YArDt2ojEaKVm3Hn1sDFE33XBG0GA/nkXRyv9R9ut2cLsrvRf/twcJaNHc532riup0krvsA6zbtqMxm1B0OlyWEnSRkRiaN0yb8pZ9QPnuPSg6HbroaDQBBhx5+ZW+T/nL/0PzRx5EY6x9NVghhH9RTr7qcr2omgQ/PhIfH8+aNWtwOBykpaVRVlYGeFbFDRw4kISEBHbs2MG+ffvo0aMHt956K926dSM5OblWxSEbWni4J6enb9++HE5NPaP+T9TNY4m47loUrQZNQACqqmJLP0Lhx5+SteAfmJO7oouMQBsUhCM3l9JfNqOLiCBs2BAsP/6Eu8xKcGp/IkaNQHPa6FlDK9v6K9Zt2wFwW8vRx8YQPKAfwSl9UW0VOIqLcVutuB0ONHo9hhbNUbTaenm2227HfizTm8isOp04srLQhoWiDQzEVVLiPddZUEDFb4cxd+lUL88WQvgBmfbyGQl+GsiJEyew2+0E+0HSri/8sQCiotGgNf8etCiKgrF1InFTH6Bk7TrKtm2nPCcHV2kpiiGAyOuvJbB7N7LfWAJu1W8KJAb26oGxbRvQashe/CaOrGyKvl5F0derqjw/9M+XEzFqRK2f5yopoWzHLsq2bafiwEHP6I5Wi2LQo9o9G8+6ioqrvFYCHyGEOD8S/DQQjUZz0QY+VVFVlfLde3Hk5uKylOAqKcFdUQGKgqLRYGjRHF1UJLrQUFAUXMXFZP19Ea7SMmLvn9Ro01x/pGg03pVrzR99CHdJKY6CAlwlpaCAxmhEYzKhMRjIfGkBqtNZ42eoLhfWXXsoWb+B8j37QFUxtmtL5OirCWjTGkNcLIpWi+p248jNxXmiGLWigoA2rXGXl6Pa7Riax9d314UQjUwqPPuOBD+i3pw++mP5YS2FH3+KYjCgDQlBGxKMxhgAqorb5cZ+PAtHQSG4XJ6LFQVThySa3X4rhrjYRuzF2SmKgjYkGG3I70Gs6nJRvm8/lq2/otpsGGKr3yD2dPacXErXb6T0l824SkowtGzhHQHTBgWd+XyNBkNsLIbY074/obJTsxDVOfTXh+r1fhaLhdBHZ9brPc9Kpr18RoIfUW9UVeX7a8bQZ+Z0TnyRRsigVCLHjD77+W437vJycLvRmM31livTEFRVpejrlVjWrPXkAjWLJmzEcIL69DrjPGdBIY68fJx5eTjyC3Dk5Z38Oh+N2UxQ754E9etLgIzeiIvY9kkPkJaWxvZJD9TLJtJC1IUEP6LeXHnllXzzzTcABCS2IvwcuS+KRuOTfahUl8sngZSqqlh37sKRk4vtcDrWnbsJ+dOlBPftjT4+rlItI7fNhmXtOkp+3oAzL99zUKtFHxWJLioSc5fOBCQmYO7aBY38IBB+rL5GThwOR73cp8mR0RufkOBH1IuMjAy++eYb5s2bx+23306/FcsapR0Vh9PJWvAPAIIH9EMbEkxAy5YY27WptAxcdbtxFRXjttvA5QZFQRsWimquHIy5Skqw7txN+b4DVKSn4zpRhMZkxG13ENSvL5Gjrz6jDfacXLIXvo6rtJTAHpcQOfpq9LGx6MLD/KJWkWia6nv6R/ie5Pz4jgQ/os5cLhevvvoqiqJw66230qxZszNWf50P1en05gHpIsKrrVnjPFGEq6wMjcGA02LBXVqK2+HEun2H95yyHTtRHU7UigrQajG2SUTRanEUFHp2pT+Vb3Qac3g4vP02ee++T1lxERWH0kFVCWiVQOAl3TB37XzOYoL5H/4HTYCBuKmPoo9ounueCd+RQEaIupHgR9TJkSNHmDhxIuvWrWPOnDnExcV53ztXAOQsKqZ8335sGUexZRzFnnm8UkBiaNmCoN69CO7fF1dZGc6CQrQhIejCQsl8cT7uMuvZG6bV4i4pBSCwT08CmregIj0dXG7MXTp7pp8iI9AYjZ6VVKqK60QR2pO1c1SXC01QEJGjryawZw+0Qec3Ped2OLD9dpjIsWMk8BE1cnpAc6o+mOTHNHGS8OwzEvyIWlFPbq8wYMAAAgMDWbNmDQMHDjzv6+2Zx8l6bTHuigr0zaIJSGhJcJ9entwZjQZHXj7W3Xso/PRzCj/5rNJ+VqeLuft2dFFRaIODKPz4M0o3bgJAHxmBy1qOu7SUwEu6EZjclVAurb5Ria0IOJm30+z2W7Cd5ZnVcVs9Vbx1DbzvmvBvMlIjakOmvXxHgh9RY/n5+YwZM4YHH3yQlJQU3n///bNusnq20Z/8jz5GGxZGi/vvqXJZt7FNa4JT+uAoKKR8335UpxNThyRcJSW4iopxWSwYWjTHlNTee030+JuIGncj5Xv3Yd2xC0WnI+TSgeijo+qv8+fgPrmFSWNWpRYNR4Ia4VMy8uMzPg9+Zs+ezVNPPVXpWExMDNnZ2YBnBOGpp57ijTfe4MSJE6SkpLBw4UK6dOniPX/fvn3cfvvtHDlyhLvvvpsnnnjC+15iYiJHjhzh559/pl+/ft7jU6dOZdu2baxevdq3HWxitm3bxg033ODdh2zFihXevcdsNhu//fYbx48fJzMzk59//pnbb7/9jADIkZuH7dBhosbfVGXgczp9ZAT6Ab//uRLTrNrzFY0Gc+dOmDs3TrVjb/BjluDnYiDBjRAXpwYZ+enSpQvffvut92vtacuQX3jhBebPn88777xDUlISc+bMYejQoezbt89bEXny5MlMmDCBPn36MGnSJIYMGVJph3Oj0cijjz7KDz/80BDdaZLKy8t5+umnefHFF+natStpaWns3r2bQ4cOsXz5ctasWcOmTZu8QRFAUFAQr7/+Oh06dODGa6/lA7MejdFI/of/QRsWRmCPSxqxR75xKvhR9DKoeiGQ4Eb4M5n28p0G+YTW6XTExp5ZtVdVVRYsWMCMGTO47rrrAHj33XeJiYnhgw8+4J577gGgqKiIHj160K1bN+Lj4ykurry30T333MPixYtJS0tjxIja76skKissLOSFF15g7dq1bNmyBZfLxezZs3nkkUfYscOzqiolJQWz2cyQIUO48cYb6d69Oy1btiQ8PJygoCC++uorPv30U958800Ki4tRNApodcTcOfGirG9Tvu+A57/7D6KParjpNlE1CW7EBU2mvXymQYKfAwcOEB8fT0BAACkpKcydO5c2bdpw+PBhsrOzGTZsmPfcgIAALrvsMtatW+cNfp5++mmGDh1KeXk5I0eOZPjw4ZXun5iYyKRJk5g+fTpXXHEFGqmlUmc2m42+ffvy22+/AdC8eXNeffVVrr/+el599VWmT5/O8uXLWbx4MePGjcNgMFR5n1GjRjFq1Cjv6N5T3/8Pc5dO6JtFN2R3fE5VVUrXb8Syeg3BA/oR1KtHYzepSZFVUUKImvB58JOSksLSpUtJSkoiJyeHOXPmMGDAAHbt2uXN+4mJqbwfUkxMDEeOHPF+PWLECPLy8rBYLERHV/1Dc+bMmbz99tu8//77TJgwocbtdDgcPqlAeuqeF1p105KSEo4fP47JZCIwMJDy8nImTJjAG2+8wbp167j//vsBuOGGG1AU5Zz9M5lM3Hvvvdx77710++drDdGFWjm12ivgtGrN1VGdLmyHj1D8wxoqDhwk+k+XEjH6Gs7z8gZT0375m+2THqjyuMPhYNWqVRfcv69zuVA/N6pzsfSpQdsvIz8+4/Pg58orr/T+f3JyMv3796dt27a8++673gRl5Q8fyKqqnnEsICDgrIEPQHR0NA8//DBPPPEEY8eOrXE7V65cidlsrvF152vVqlU+u7evLF++vMrj9913n/f/a9Ov5xLa1rpNDeWplm3O/+Q2STBkqO8aU49q1C8/kpaWVu37F+K/r/NxMfbrQu+T1VpNfbF6Jjk/vtPgWZmBgYEkJydz4MABrr32WgCys7MrFcfLzc09YzTofEybNo1FixaxaNGiGl87bNiwsy7XrotTv5kOHTr0ohqWr2u//HX0J0BReKplG548egibqqI6Pbu2l23eSvn+A6gnf+tT9DoCWrXCmNQOU1J79DExnnwmP/XHfjWms43i1Ib8+7pwXCx9slgsjd0EUQ8aPPix2Wzs2bOHQYMG0bp1a2JjY1m1ahU9enhyJOx2Oz/88APPP/98je8dFBTErFmzmD17NqNGjarRtXq93qf/IH19/8ZS237teWBajbe/OB9uhwNHbh5asxldeFit71OalU3e2nWUbt6Ku6wMQ4vmBP5pEIa4WPQxzdCFh3v36VIBO5y1EKM/salqgwU/DZlsLP++LhwXep8atO0y7eUzPg9+Hn74YUaNGkVCQgK5ubnMmTMHi8XCrbfeiqIoTJ06lblz59K+fXvat2/P3LlzMZvN3HzzzbV63t13380rr7zC8uXLSUlJqefeCH/iKinh2LyXcJeXowkIQGM24zxxAtxuAEIGpRI5ZnSt7n381YXYNRqC+vYmuG9vDPFx576oiZIVVUL4hqKqKHX4ZaUu117sfB78HDt2jHHjxpGfn090dDT9+vVj/fr1tGrVCoBHHnmE8vJy7rvvPm+Rw5UrV3pr/NSUXq/nmWeeqXXwJBrO+Wx+qqoqqsOBporVZPasbNxlZQT16YU+Jga3tQxdVBSGuFhy31mGIy+v1m3TRYQTM/V+FJ3U6zmdBDpCiIuBzz/ZV6xYUe37iqIwe/ZsZs+eXav7p6enn3Fs3LhxjBs3rlb3Ew2rugDIbbNx5JEZoCi0nD0DXVhYpfeNbVqjiwjHVVpK1PibKiXJhwxMpWjVtziLitGFhda4Xc78Ao4+Mw9XUTFBfXsTNe5G7zRXUyBBjhB+oJGmvRYtWsSLL75IVlYWXbp0YcGCBQwaNOis5y9cuJB//OMfpKenk5CQwIwZM7jlllsqnbNgwQIWL15MRkYGUVFRjBkzhnnz5mE0GmvXyDqSX2uF36o4nA6AxmSschsMRacj8vpryXnzbUp+3kDIadtgBA/oh+WndWS+OB9tYCCKVos+JgZ9sygUvR5Fq0UTFIghJgZ9TDM0RiOqy0XF4Qw4uRrNVeQpplm+Zx9+t3a9nkiQI4T/aozVXh9++CFTp05l0aJFpKam8vrrr3PllVeye/duEhISzjh/8eLFTJ8+nTfffJM+ffqwceNG7rrrLsLDw725t++//z6PPfYYS5YsYcCAAezfv5+JEycC8Morr9S+g3UgwY9odGcb/TF37ECrF54FVfVOPzly87Du3YehWTOMSe0wd+1CUP8UCj/+FEN8HMZEz3SqNiiQuPvvxfLjT6AoqA4HjuwcKg4dRnU6UV0u1IoK77O0oaG4y60YtToYMpSI0dfgjonGENPsotqkVIoBCnEBaYSRn/nz53PHHXdw5513Ap4Rm2+++YbFixczb968M85ftmwZ99xzj7fETJs2bVi/fj3PP/+8N/j5+eefSU1N9aajJCYmMm7cODZu3FjLjtWdBD/CL5wtANIEBHj/32Up4dizv68CDL/6KsKGDCZy9NU4snPI+edbNJ/+N3ShnpIF+ugoIq+75qzPdNtsOHLzsGfn4MjJRWs2EdrOM+oT3K9Poy8Jr4s/jug4HI5z1soRQlyc/rg8PyAggIDTPltPsdvtbN68mccee6zS8WHDhrFu3boq722z2c6YujKZTGzcuBGHw4Fer2fgwIG89957bNy4kb59+3Lo0CHS0tK49dZb69iz2pPgR1w4dFoMLZqD240jLx/tyaKUmoAAosffxLE5z+HIzvYGP+eiCQggoGULAlq28B67ECsgy9SVEBen+pr2atmyZaXjTz75ZJV5tvn5+bhcrip3XTi1I8MfDR8+nLfeeotrr72Wnj17snnzZpYsWYLD4SA/P5+4uDhuuukm8vLyGDhwIKqq4nQ6uffee88IshqSBD/Cb5xr9ZfWbKb53x6s8j2NyfObh9tu90nb/IkEO0I0EfU07XX06NFKRXyrGvU53fnsunDKrFmzyM7Opl+/fqiqSkxMDBMnTuSFF15Aq9UCsHr1ap599lkWLVpESkoKBw8eZMqUKcTFxTFr1qw6dLD2JPgRfuV8lr9XRTm5FF61XVzBjwQ6Qoi6CgkJOa8dDKKiotBqtWeM8lS364LJZGLJkiW8/vrr5OTkEBcXxxtvvEFwcDBRUVGAJ0CaMGGCN48oOTmZsrIy7r77bmbMmNEom5FL8CMaxZdffsmKFSvIyMggMzOTFi1a0K9fP6666qpa3U/R60FRcNts9dzShiOBjhDidA292stgMNCrVy9WrVrF6NG/F4hdtWoV11xz9vxJ8NTYa9HCk0KwYsUKRo4c6Q1qrFbrGQGOVqv11HFrpNxKCX6ET5SWlrJjxw527NiB0+kkLCwMm83G/v37+eSTT9i7dy89evSgU6dO9O3bl8OHD7Ns2TKef/55XnnlFf6ucZ3zGY7CQtzlFQQ0j0dRFAxxsVjWrCXwkuQql8b7Gwl2hBDVaoTVXtOmTWPChAn07t2b/v3788Ybb5CRkcGkSZMAmD59OpmZmSxduhSA/fv3s3HjRlJSUjhx4gTz589n586dvPvuu957jho1ivnz59OjRw/vtNesWbO4+uqrvVNjDU2CH1Ev8vLyWLZsGevXr2fbtm0cPHgQVVXRarUoioLT6QQgNjaWK664ggULFjBs2LBK88iqqjJ9+nQefPBBHnnkEf4dF4WzqAjL6h+pOPib5ySNxlNzR1WxH8sEVcXYri2aQDNumx1nQQEZM2YTe989mDq0b4xvRZUk0BFCXAjGjh1LQUEBTz/9NFlZWXTt2pW0tDTvrgxZWVlkZGR4z3e5XLz88svs27cPvV7P4MGDWbduHYmJid5zZs6ciaIozJw5k8zMTKKjoxk1ahTPPvtsQ3fPS4IfUSdut5vJkyfz1ltvodFo6NevHyNGjKB79+5ccskldO7cGYPBgNVqRafTVZtopygKzz33HHFxcTz44INoIyNwFp5AYwzA3C3ZU+tHVVHdbkp/3oA2JJjAHt2xZ2ai2mwY4mLQRYSjOhzooiMb8LtwJgl2hBD1oS7TXrV13333cd9991X53jvvvFPp606dOrF169Zq76fT6XjyySd58skn66uJdSbBj6iT7777jkWLFvHUU08xefJkIiOrDjoCAwPP+55Tpkyhd+/ezJ8/nx8UN0EpfSrV+wEIHzEcBQVtSO32gKtvEuwIIeqdqnpedbleVEmCH1EnFSerJH/33Xe89957KIpCQkICAwcO5PHHH691NeHU1FRSU1MBqlz9pTuPlQu+JMGOEMLXGmN7i6ZCgh9RJyNGjODxxx/n5ZdfxnZypdX+/fv59ttvCQsLY8qUKY3cwvohwY4QQlw8JPgRdaLRaHj22We56667aN26tff4pEmTuO222+rlGbWt/VMXsgeWEKLRNdKu7k2BBD+izvLz87njjjto27Ytv/3mWZU1dOjQ8yqq5S9OjezIHlhCCH+huD2vulwvqibBj6izDz/8kO+++8779ZdffsmIESPq9Rn1Pfoj01hCCNF0SfAj6uyee+6hffv2fP3116xYsYKZM2cyZMgQDAYD6enpqKpKREQEYWFhdXpOXQIgCXaEEBccmfbyGQl+RJ3pdDqGDRvGsGHDGDt2LJdddhnt27fHbreTk5PjPS8hIYGUlBTvq1+/fuh0vvsrKAGPEOJCJqu9fEeCH1GvUlJSWLt2LR988AFGo5HU1FSMRiM5OTls2bKFDRs2MGvWLMrLy2nevDnt2rWjT58+hISEMGnSJKKjo6u9f3WjPxLsCCGEOB8S/JxksVh8cl+Hw4HVasVisVxUq4eq61dSUhKzZ88+45qRI0d6r92wYQPvvfcexcXFLFmyhNLSUhYvXszIkSOxWCwcOHCAhx56yHvN6bZNvItu/3yN7ZMeqHS8Pv4Mm+Kf14VM+nXhuFj65KufFVWSIoc+o6iNtaWqn6ioqKB169ZkZ2c3dlOEEEJcAGJjYzl8+DBGo9En97dYLISGhpIy6hl0+to/w+moYMPnsyguLr6gVt82hCY/8mM0Gjl8+DB2u72xmyKEEOICYDAYfBb4iIbR5IMf8ARA8hdZCCGEX5HVXj4jwY8QQgjhh2S1l+9I8COEEEL4I0l49hlNYzdACCGEEKIhyciPEEII4Ydk2st3JPgRQggh/JEkPPuMTHsJIYQQokmRkR8hhBDCD8m0l+9I8COEEEL4I7fqedXlelElmfYSQgghRJMiIz9CCCGEP5KEZ5+R4EcIIYTwQwp1zPmpt5ZcfGTaSwghhBBNioz8CCGEEP5ItrfwGQl+hBBCCD8kS919R4IfIYQQwh9JwrPPSM6PEEIIIZoUGfkRQggh/JCiqih1yNupy7UXOwl+hBBCCH/kPvmqy/WiSjLtJYQQQogmRUZ+hBBCCD8k016+I8GPEEII4Y9ktZfPyLSXEEIIIZoUGfkRQggh/JFUePYZCX6EEEIIPyQVnn1Hpr2EEEII0aTIyI8QQgjhj2Tay2ck+BFCCCH8kOL2vOpyvaiaBD9CCCGEP5KRH5+RnB8hhBBCNCky8iOEEEL4Iyly6DMS/AghhBB+SLa38B2Z9hJCCCFEkyIjP0IIIYQ/koRnn5HgRwghhPBHKlCX5eoS+5yVTHsJIYQQokmRkR8hhBDCD0nCs+9I8COEEEL4I5U65vzUW0suOjLtJYQQQogmRUZ+hBBCCH8kq718RoIfIYQQwh+5AaWO14sqSfAjhBBC+CFJePYdyfkRQgghRJMiIz9CCCGEP5KcH5+R4EcIIYTwRxL8+IxMewkhhBCiSZGRHyGEEMIfyciPz0jwI4QQQvgjWeruMzLtJYQQQogmRUZ+hBBCCD8kdX58R4IfIYQQwh9Jzo/PyLSXEEIIIbwWLVpE69atMRqN9OrVix9//LHa8xcuXEinTp0wmUx06NCBpUuXnnFOUVERkydPJi4uDqPRSKdOnUhLS/NVF85JRn6EEEIIf+RWQanD6I275td++OGHTJ06lUWLFpGamsrrr7/OlVdeye7du0lISDjj/MWLFzN9+nTefPNN+vTpw8aNG7nrrrsIDw9n1KhRANjtdoYOHUqzZs346KOPaNGiBUePHiU4OLj2fasjRVVlXEwIIYTwFxaLhdDQUP7cZgo6bUCt7+N02fj20KsUFxcTEhJyXtekpKTQs2dPFi9e7D3WqVMnrr32WubNm3fG+QMGDCA1NZUXX3zRe2zq1Kls2rSJtWvXAvDPf/6TF198kb1796LX62vdn/ok015CCCGEX1J/z/upzQvP2IbFYqn0stlsVT7NbrezefNmhg0bVun4sGHDWLduXZXX2Gw2jEZjpWMmk4mNGzficDgA+Oyzz+jfvz+TJ08mJiaGrl27MnfuXFwuVx2/P7UnwY8QQghxEWvZsiWhoaHeV1UjOAD5+fm4XC5iYmIqHY+JiSE7O7vKa4YPH85bb73F5s2bUVWVTZs2sWTJEhwOB/n5+QAcOnSIjz76CJfLRVpaGjNnzuTll1/m2Wefrd+O1oDk/AghhBD+qJ5Wex09erTStFdAQPVTaYpSubKiqqpnHDtl1qxZZGdn069fP1RVJSYmhokTJ/LCCy+g1WoBcLvdNGvWjDfeeAOtVkuvXr04fvw4L774Ik888UTt+1cHMvIjhBBC+CO3WvcXEBISUul1tuAnKioKrVZ7xihPbm7uGaNBp5hMJpYsWYLVaiU9PZ2MjAwSExMJDg4mKioKgLi4OJKSkrzBEHjyiLKzs7Hb7fXxnaoxCX6EEEIIgcFgoFevXqxatarS8VWrVjFgwIBqr9Xr9bRo0QKtVsuKFSsYOXIkGo0nxEhNTeXgwYO43b/vt7F//37i4uIwGAz135HzIMGPEEII4Y9Ud91fNTRt2jTeeustlixZwp49e3jwwQfJyMhg0qRJAEyfPp1bbrnFe/7+/ft57733OHDgABs3buSmm25i586dzJ0713vOvffeS0FBAVOmTGH//v18+eWXzJ07l8mTJ9f9e1RLkvMjhBBC+KNGqPA8duxYCgoKePrpp8nKyqJr166kpaXRqlUrALKyssjIyPCe73K5ePnll9m3bx96vZ7Bgwezbt06EhMTvee0bNmSlStX8uCDD9KtWzeaN2/OlClTePTRR2vftzqSOj9CCCGEH/HW+Wl5LzpNHer8uG18e3Rxjer8NBUy8iOEEEL4I/fvtXpqf72oigQ/QgghhD+SjU19RhKehRBCCNGkyMiPEEII4Y9U6jjyU28tuehI8COEEEL4I5n28hkJfoQQQgh/5HYDNa/VU/l6URXJ+RFCCCFEkyIjP0IIIYQ/kmkvn5HgRwghhPBHEvz4jEx7CSGEEKJJkZEfIYQQwh9JhWefkeBHCCGE8EOq6katxc7sp18vqibTXkIIIYRoUmTkRwghhPBHqlq3qStJeD4rCX6EEEIIf6TWMedHgp+zkmkvIYQQQjQpMvIjhBBC+CO3G5Q6JC1LwvNZSfAjhBBC+COZ9vIZCX6EEEIIP6S63ah1GPmRpe5nJzk/QgghhGhSZORHCCGE8Ecy7eUzEvwIIYQQ/sitgiLBjy/ItJcQQgghmhQZ+RFCCCH8kaoCdVnqLiM/ZyPBjxBCCOGHVLeKWodpL1WCn7OSaS8hhBBCNCky8iOEEEL4I9VN3aa9pM7P2UjwI4QQQvghmfbyHZn2EkIIIUSTIiM/QgghhB9yqrY6TV05cdRjay4uEvwIIYQQfsRgMBAbG8va7LQ63ys2NhaDwVAPrbq4KKpMCgohhBB+paKiArvdXuf7GAwGjEZjPbTo4iLBjxBCCCGaFEl4FkIIIUSTIsGPEEIIIZoUCX6EEEII0aRI8COEEEKIJkWCHyGEEEI0KRL8CCGEEKJJkeBHCCGEEE3K/wNAwg6Bdws4XwAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(subplot_kw=dict(projection=ccrs.PlateCarree()))\n",
+    "da_grid = xr.ones_like(ds_danra_hl.isel(time=0, altitude=0).t)\n",
+    "da_grid.name = \"grid\"\n",
+    "da_grid.attrs = {}\n",
+    "\n",
+    "da_grid.plot(ax=ax, x=\"lon\", y=\"lat\")\n",
+    "\n",
+    "ax.gridlines(draw_labels=[\"top\", \"left\"])\n",
+    "ax.coastlines()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def create_mask(ds, bbox):\n",
+    "    da_mask = np.logical_and(\n",
+    "        np.logical_and(ds.lon > bbox[0], ds.lon < bbox[1]),\n",
+    "        np.logical_and(ds.lat > bbox[2], ds.lat < bbox[3]),\n",
+    "    )\n",
+    "    return da_mask\n",
+    "\n",
+    "bbox = [8, 16, 54, 58] # W, E, S, N"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<cartopy.mpl.feature_artist.FeatureArtist at 0x7f31c93b7c10>"
+      ]
+     },
+     "execution_count": 18,
+     "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": [
+    "da_mask = create_mask(ds=ds_danra_hl, bbox=bbox)\n",
+    "fig, ax = plt.subplots(figsize=(10, 4), subplot_kw=dict(projection=ccrs.PlateCarree()))\n",
+    "da_mask.plot(ax=ax, x=\"lon\", y=\"lat\", transform=ccrs.PlateCarree())\n",
+    "ax.gridlines(draw_labels=[\"top\", \"left\"])\n",
+    "ax.coastlines()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "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",
+       "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n",
+       "<path d=\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
+       "<path d=\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
+       "</symbol>\n",
+       "<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n",
+       "<path d=\"M28.681 7.159c-0.694-0.947-1.662-2.053-2.724-3.116s-2.169-2.030-3.116-2.724c-1.612-1.182-2.393-1.319-2.841-1.319h-15.5c-1.378 0-2.5 1.121-2.5 2.5v27c0 1.378 1.122 2.5 2.5 2.5h23c1.378 0 2.5-1.122 2.5-2.5v-19.5c0-0.448-0.137-1.23-1.319-2.841zM24.543 5.457c0.959 0.959 1.712 1.825 2.268 2.543h-4.811v-4.811c0.718 0.556 1.584 1.309 2.543 2.268zM28 29.5c0 0.271-0.229 0.5-0.5 0.5h-23c-0.271 0-0.5-0.229-0.5-0.5v-27c0-0.271 0.229-0.5 0.5-0.5 0 0 15.499-0 15.5 0v7c0 0.552 0.448 1 1 1h7v19.5z\"></path>\n",
+       "<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.DataArray &#x27;10m_u_component_of_wind&#x27; (lat: 721, lon: 1440)&gt;\n",
+       "dask.array&lt;getitem, shape=(721, 1440), dtype=float32, chunksize=(721, 1440), chunktype=numpy.ndarray&gt;\n",
+       "Coordinates:\n",
+       "  * lat      (lat) float32 90.0 89.75 89.5 89.25 ... -89.25 -89.5 -89.75 -90.0\n",
+       "  * lon      (lon) float32 0.0 0.25 0.5 0.75 1.0 ... 359.0 359.2 359.5 359.8\n",
+       "    time     datetime64[ns] 1959-01-01\n",
+       "Attributes:\n",
+       "    long_name:   10 metre U wind component\n",
+       "    short_name:  u10\n",
+       "    units:       m s**-1</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'10m_u_component_of_wind'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>lat</span>: 721</li><li><span class='xr-has-index'>lon</span>: 1440</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-4aa984dd-181f-40cb-89fc-71dbd2682aea' class='xr-array-in' type='checkbox' checked><label for='section-4aa984dd-181f-40cb-89fc-71dbd2682aea' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>dask.array&lt;chunksize=(721, 1440), meta=np.ndarray&gt;</span></div><div class='xr-array-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> 3.96 MiB </td>\n",
+       "                        <td> 3.96 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (721, 1440) </td>\n",
+       "                        <td> (721, 1440) </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\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"110\" 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=\"60\" x2=\"120\" y2=\"60\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"60\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"60\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,60.083333333333336 0.0,60.083333333333336\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"80.083333\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1440</text>\n",
+       "  <text x=\"140.000000\" y=\"30.041667\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,30.041667)\">721</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></div></li><li class='xr-section-item'><input id='section-f14248a5-e5cf-473e-b5cc-a052f741cfa1' class='xr-section-summary-in' type='checkbox'  checked><label for='section-f14248a5-e5cf-473e-b5cc-a052f741cfa1' class='xr-section-summary' >Coordinates: <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-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>90.0 89.75 89.5 ... -89.75 -90.0</div><input id='attrs-89808631-0515-429c-9a10-2c4f37210b65' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-89808631-0515-429c-9a10-2c4f37210b65' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-002c5b8a-f722-43ac-b5c3-52a0bd9f10d1' class='xr-var-data-in' type='checkbox'><label for='data-002c5b8a-f722-43ac-b5c3-52a0bd9f10d1' title='Show/Hide data 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>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><pre>array([ 90.  ,  89.75,  89.5 , ..., -89.5 , -89.75, -90.  ], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0 0.25 0.5 ... 359.2 359.5 359.8</div><input id='attrs-e2a9a850-bfbe-4841-aef1-118b92bb74f7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e2a9a850-bfbe-4841-aef1-118b92bb74f7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e057e02d-6392-4755-87af-f74b67833112' class='xr-var-data-in' type='checkbox'><label for='data-e057e02d-6392-4755-87af-f74b67833112' title='Show/Hide data 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>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>array([0.0000e+00, 2.5000e-01, 5.0000e-01, ..., 3.5925e+02, 3.5950e+02,\n",
+       "       3.5975e+02], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1959-01-01</div><input id='attrs-c29e1b3c-7ff5-4713-adb3-33b1eeb61fd5' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c29e1b3c-7ff5-4713-adb3-33b1eeb61fd5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c492521b-cc2d-4d92-9b28-44725225202e' class='xr-var-data-in' type='checkbox'><label for='data-c492521b-cc2d-4d92-9b28-44725225202e' title='Show/Hide data 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;, dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-9eacec1e-21a9-4f12-a46c-6079cb108926' class='xr-section-summary-in' type='checkbox'  ><label for='section-9eacec1e-21a9-4f12-a46c-6079cb108926' 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>lat</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-a0cd351f-dff3-4d91-a2f7-039c53ac6100' class='xr-index-data-in' type='checkbox'/><label for='index-a0cd351f-dff3-4d91-a2f7-039c53ac6100' 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([  90.0,  89.75,   89.5,  89.25,   89.0,  88.75,   88.5,  88.25,   88.0,\n",
+       "        87.75,\n",
+       "       ...\n",
+       "       -87.75,  -88.0, -88.25,  -88.5, -88.75,  -89.0, -89.25,  -89.5, -89.75,\n",
+       "        -90.0],\n",
+       "      dtype=&#x27;float32&#x27;, name=&#x27;lat&#x27;, length=721))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lon</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-fcccfc2a-88eb-477e-812f-09e5a8ec6bba' class='xr-index-data-in' type='checkbox'/><label for='index-fcccfc2a-88eb-477e-812f-09e5a8ec6bba' 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,   0.25,    0.5,   0.75,    1.0,   1.25,    1.5,   1.75,    2.0,\n",
+       "         2.25,\n",
+       "       ...\n",
+       "        357.5, 357.75,  358.0, 358.25,  358.5, 358.75,  359.0, 359.25,  359.5,\n",
+       "       359.75],\n",
+       "      dtype=&#x27;float32&#x27;, name=&#x27;lon&#x27;, length=1440))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-447b5a33-26df-4ea6-8434-26483aa5ce91' class='xr-section-summary-in' type='checkbox'  checked><label for='section-447b5a33-26df-4ea6-8434-26483aa5ce91' class='xr-section-summary' >Attributes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><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></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.DataArray '10m_u_component_of_wind' (lat: 721, lon: 1440)>\n",
+       "dask.array<getitem, shape=(721, 1440), dtype=float32, chunksize=(721, 1440), chunktype=numpy.ndarray>\n",
+       "Coordinates:\n",
+       "  * lat      (lat) float32 90.0 89.75 89.5 89.25 ... -89.25 -89.5 -89.75 -90.0\n",
+       "  * lon      (lon) float32 0.0 0.25 0.5 0.75 1.0 ... 359.0 359.2 359.5 359.8\n",
+       "    time     datetime64[ns] 1959-01-01\n",
+       "Attributes:\n",
+       "    long_name:   10 metre U wind component\n",
+       "    short_name:  u10\n",
+       "    units:       m s**-1"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "da = ds_era5['10m_u_component_of_wind'].isel(time=0)\n",
+    "da"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<cartopy.mpl.feature_artist.FeatureArtist at 0x7f31c97d5420>"
+      ]
+     },
+     "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(figsize=(10, 4), subplot_kw=dict(projection=ccrs.PlateCarree()))\n",
+    "da.plot(ax=ax, x=\"lon\", y=\"lat\", transform=ccrs.PlateCarree())\n",
+    "ax.gridlines(draw_labels=[\"top\", \"left\"])\n",
+    "ax.coastlines()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "da_mask_era5 = create_mask(ds=ds_era5, bbox=bbox)\n",
+    "ds_era5"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def create_lateral_boundary_mask(ds, bbox, width):\n",
+    "    da_mask_inner = create_mask(ds=ds, bbox=bbox)\n",
+    "    da_mask_outer = np.logical_and(\n",
+    "        np.logical_and(ds.lon > bbox[0] - width, ds.lon < bbox[1] + width),\n",
+    "        np.logical_and(ds.lat > bbox[2] - width, ds.lat < bbox[3] + width),\n",
+    "    )\n",
+    "    da_mask_edge = da_mask_outer ^ da_mask_inner\n",
+    "    return da_mask_edge"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "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",
+       "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n",
+       "<path d=\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
+       "<path d=\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
+       "</symbol>\n",
+       "<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n",
+       "<path d=\"M28.681 7.159c-0.694-0.947-1.662-2.053-2.724-3.116s-2.169-2.030-3.116-2.724c-1.612-1.182-2.393-1.319-2.841-1.319h-15.5c-1.378 0-2.5 1.121-2.5 2.5v27c0 1.378 1.122 2.5 2.5 2.5h23c1.378 0 2.5-1.122 2.5-2.5v-19.5c0-0.448-0.137-1.23-1.319-2.841zM24.543 5.457c0.959 0.959 1.712 1.825 2.268 2.543h-4.811v-4.811c0.718 0.556 1.584 1.309 2.543 2.268zM28 29.5c0 0.271-0.229 0.5-0.5 0.5h-23c-0.271 0-0.5-0.229-0.5-0.5v-27c0-0.271 0.229-0.5 0.5-0.5 0 0 15.499-0 15.5 0v7c0 0.552 0.448 1 1 1h7v19.5z\"></path>\n",
+       "<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.DataArray &#x27;lon&#x27; (lon: 1440, lat: 721)&gt;\n",
+       "array([[False, False, False, ..., False, False, False],\n",
+       "       [False, False, False, ..., False, False, False],\n",
+       "       [False, False, False, ..., False, False, False],\n",
+       "       ...,\n",
+       "       [False, False, False, ..., False, False, False],\n",
+       "       [False, False, False, ..., False, False, False],\n",
+       "       [False, False, False, ..., False, False, False]])\n",
+       "Coordinates:\n",
+       "  * lon      (lon) float32 0.0 0.25 0.5 0.75 1.0 ... 359.0 359.2 359.5 359.8\n",
+       "  * lat      (lat) float32 90.0 89.75 89.5 89.25 ... -89.25 -89.5 -89.75 -90.0</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'lon'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>lon</span>: 1440</li><li><span class='xr-has-index'>lat</span>: 721</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-86c44faa-eb77-4d39-a1a7-52a2619bdb38' class='xr-array-in' type='checkbox' checked><label for='section-86c44faa-eb77-4d39-a1a7-52a2619bdb38' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>False False False False False False ... False False False False False</span></div><div class='xr-array-data'><pre>array([[False, False, False, ..., False, False, False],\n",
+       "       [False, False, False, ..., False, False, False],\n",
+       "       [False, False, False, ..., False, False, False],\n",
+       "       ...,\n",
+       "       [False, False, False, ..., False, False, False],\n",
+       "       [False, False, False, ..., False, False, False],\n",
+       "       [False, False, False, ..., False, False, False]])</pre></div></div></li><li class='xr-section-item'><input id='section-3bebbcc2-5053-4851-93bc-707431c34881' class='xr-section-summary-in' type='checkbox'  checked><label for='section-3bebbcc2-5053-4851-93bc-707431c34881' class='xr-section-summary' >Coordinates: <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-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0 0.25 0.5 ... 359.2 359.5 359.8</div><input id='attrs-133e8fa3-5145-4590-a294-9464de67715c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-133e8fa3-5145-4590-a294-9464de67715c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9815cf23-9996-4786-9aa3-0fb73b6c92c4' class='xr-var-data-in' type='checkbox'><label for='data-9815cf23-9996-4786-9aa3-0fb73b6c92c4' title='Show/Hide data 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>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>array([0.0000e+00, 2.5000e-01, 5.0000e-01, ..., 3.5925e+02, 3.5950e+02,\n",
+       "       3.5975e+02], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>90.0 89.75 89.5 ... -89.75 -90.0</div><input id='attrs-68371d71-75a2-461c-be63-41e7527db41f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-68371d71-75a2-461c-be63-41e7527db41f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-058cce2a-be26-4628-8686-3b3516499534' class='xr-var-data-in' type='checkbox'><label for='data-058cce2a-be26-4628-8686-3b3516499534' title='Show/Hide data 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>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><pre>array([ 90.  ,  89.75,  89.5 , ..., -89.5 , -89.75, -90.  ], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-3e464d27-15f3-4d75-b0ef-339ce9ad315d' class='xr-section-summary-in' type='checkbox'  ><label for='section-3e464d27-15f3-4d75-b0ef-339ce9ad315d' 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>lon</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-0b0d3664-b54f-4dee-beea-614fdd57bed5' class='xr-index-data-in' type='checkbox'/><label for='index-0b0d3664-b54f-4dee-beea-614fdd57bed5' 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,   0.25,    0.5,   0.75,    1.0,   1.25,    1.5,   1.75,    2.0,\n",
+       "         2.25,\n",
+       "       ...\n",
+       "        357.5, 357.75,  358.0, 358.25,  358.5, 358.75,  359.0, 359.25,  359.5,\n",
+       "       359.75],\n",
+       "      dtype=&#x27;float32&#x27;, name=&#x27;lon&#x27;, length=1440))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lat</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-5fac22d8-1bb9-40bf-90b8-01ead4393269' class='xr-index-data-in' type='checkbox'/><label for='index-5fac22d8-1bb9-40bf-90b8-01ead4393269' 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([  90.0,  89.75,   89.5,  89.25,   89.0,  88.75,   88.5,  88.25,   88.0,\n",
+       "        87.75,\n",
+       "       ...\n",
+       "       -87.75,  -88.0, -88.25,  -88.5, -88.75,  -89.0, -89.25,  -89.5, -89.75,\n",
+       "        -90.0],\n",
+       "      dtype=&#x27;float32&#x27;, name=&#x27;lat&#x27;, length=721))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-3e4b95c0-fa22-434b-bbb8-a1062e8c78f4' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-3e4b95c0-fa22-434b-bbb8-a1062e8c78f4' 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.DataArray 'lon' (lon: 1440, lat: 721)>\n",
+       "array([[False, False, False, ..., False, False, False],\n",
+       "       [False, False, False, ..., False, False, False],\n",
+       "       [False, False, False, ..., False, False, False],\n",
+       "       ...,\n",
+       "       [False, False, False, ..., False, False, False],\n",
+       "       [False, False, False, ..., False, False, False],\n",
+       "       [False, False, False, ..., False, False, False]])\n",
+       "Coordinates:\n",
+       "  * lon      (lon) float32 0.0 0.25 0.5 0.75 1.0 ... 359.0 359.2 359.5 359.8\n",
+       "  * lat      (lat) float32 90.0 89.75 89.5 89.25 ... -89.25 -89.5 -89.75 -90.0"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "da_mask_era5 = create_lateral_boundary_mask(ds=ds_era5, bbox=bbox, width=1.0)\n",
+    "da_mask_era5"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<cartopy.mpl.feature_artist.FeatureArtist at 0x7f31c81ac460>"
+      ]
+     },
+     "execution_count": 28,
+     "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(figsize=(10, 4), subplot_kw=dict(projection=ccrs.PlateCarree()))\n",
+    "da_mask_era5.plot(ax=ax, x=\"lon\", y=\"lat\", transform=ccrs.PlateCarree())\n",
+    "ax.gridlines(draw_labels=[\"top\", \"left\"])\n",
+    "ax.coastlines()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "da_mask_danra = create_mask(ds=ds_danra_hl, bbox=bbox)\n",
+    "ds_era5_edge = ds_era5.where(da_mask_era5, drop=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "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",
+       "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n",
+       "<path d=\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
+       "<path d=\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
+       "</symbol>\n",
+       "<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n",
+       "<path d=\"M28.681 7.159c-0.694-0.947-1.662-2.053-2.724-3.116s-2.169-2.030-3.116-2.724c-1.612-1.182-2.393-1.319-2.841-1.319h-15.5c-1.378 0-2.5 1.121-2.5 2.5v27c0 1.378 1.122 2.5 2.5 2.5h23c1.378 0 2.5-1.122 2.5-2.5v-19.5c0-0.448-0.137-1.23-1.319-2.841zM24.543 5.457c0.959 0.959 1.712 1.825 2.268 2.543h-4.811v-4.811c0.718 0.556 1.584 1.309 2.543 2.268zM28 29.5c0 0.271-0.229 0.5-0.5 0.5h-23c-0.271 0-0.5-0.229-0.5-0.5v-27c0-0.271 0.229-0.5 0.5-0.5 0 0 15.499-0 15.5 0v7c0 0.552 0.448 1 1 1h7v19.5z\"></path>\n",
+       "<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.DataArray &#x27;t&#x27; (altitude: 1, y: 211, x: 229)&gt;\n",
+       "dask.array&lt;where, shape=(1, 211, 229), dtype=float64, chunksize=(1, 116, 152), chunktype=numpy.ndarray&gt;\n",
+       "Coordinates:\n",
+       "  * altitude  (altitude) int64 100\n",
+       "    lat       (y, x) float64 53.16 53.17 53.17 53.18 ... 58.76 58.76 58.76 58.77\n",
+       "    lon       (y, x) float64 8.371 8.407 8.443 8.48 ... 15.66 15.71 15.75 15.79\n",
+       "    time      datetime64[ns] 1990-09-01\n",
+       "  * x         (x) float64 -1.099e+06 -1.097e+06 ... -5.317e+05 -5.292e+05\n",
+       "  * y         (y) float64 -2.595e+05 -2.57e+05 -2.545e+05 ... 2.63e+05 2.655e+05\n",
+       "Attributes:\n",
+       "    level_type:   heightAboveGround\n",
+       "    long_name:    Temperature\n",
+       "    shortName:    t\n",
+       "    stepType:     instant\n",
+       "    stepUnits:    1\n",
+       "    typeOfLevel:  heightAboveGround\n",
+       "    units:        K</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'t'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>altitude</span>: 1</li><li><span class='xr-has-index'>y</span>: 211</li><li><span class='xr-has-index'>x</span>: 229</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-285f036a-27fa-49b2-a665-ad7ec80214c1' class='xr-array-in' type='checkbox' checked><label for='section-285f036a-27fa-49b2-a665-ad7ec80214c1' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>dask.array&lt;chunksize=(1, 116, 152), meta=np.ndarray&gt;</span></div><div class='xr-array-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> 377.49 kiB </td>\n",
+       "                        <td> 137.75 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (1, 211, 229) </td>\n",
+       "                        <td> (1, 116, 152) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 4 chunks in 8 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=\"175\" 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=\"60\" x2=\"24\" y2=\"75\" />\n",
+       "  <line x1=\"10\" y1=\"110\" x2=\"24\" y2=\"125\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"110\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"125\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"10.0,0.0 24.9485979497544,14.948597949754403 24.9485979497544,125.51628353927404 10.0,110.56768558951964\" 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=\"89\" y1=\"0\" x2=\"104\" y2=\"14\" />\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=\"75\" x2=\"144\" y2=\"75\" />\n",
+       "  <line x1=\"24\" y1=\"125\" x2=\"144\" y2=\"125\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"125\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"104\" y1=\"14\" x2=\"104\" y2=\"125\" />\n",
+       "  <line x1=\"144\" y1=\"14\" x2=\"144\" y2=\"125\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"24.9485979497544,14.948597949754403 144.9485979497544,14.948597949754403 144.9485979497544,125.51628353927404 24.9485979497544,125.51628353927404\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"84.948598\" y=\"145.516284\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >229</text>\n",
+       "  <text x=\"164.948598\" y=\"70.232441\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,164.948598,70.232441)\">211</text>\n",
+       "  <text x=\"7.474299\" y=\"138.041985\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,7.474299,138.041985)\">1</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></div></li><li class='xr-section-item'><input id='section-64b5a8f8-f52d-45ad-950d-d1332b5a56a7' class='xr-section-summary-in' type='checkbox'  checked><label for='section-64b5a8f8-f52d-45ad-950d-d1332b5a56a7' 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-e34f0ad5-8463-4432-9be8-03e441bc2436' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e34f0ad5-8463-4432-9be8-03e441bc2436' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-778102f6-2278-4d6e-a62c-381980bed34e' class='xr-var-data-in' type='checkbox'><label for='data-778102f6-2278-4d6e-a62c-381980bed34e' title='Show/Hide data 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'>53.16 53.17 53.17 ... 58.76 58.77</div><input id='attrs-0e9da8c5-8899-4c9a-ab09-6753f9c6d1ec' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0e9da8c5-8899-4c9a-ab09-6753f9c6d1ec' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bb22b4bd-0afc-4dc2-878c-aa311fe32dc1' class='xr-var-data-in' type='checkbox'><label for='data-bb22b4bd-0afc-4dc2-878c-aa311fe32dc1' title='Show/Hide data 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'><pre>array([[53.16115242, 53.16654032, 53.17191669, ..., 54.07745658,\n",
+       "        54.08013375, 54.08279855],\n",
+       "       [53.18294874, 53.18833963, 53.19371897, ..., 54.09976837,\n",
+       "        54.10244707, 54.10511339],\n",
+       "       [53.20474484, 53.21013871, 53.21552103, ..., 54.12208036,\n",
+       "        54.12476059, 54.12742844],\n",
+       "       ...,\n",
+       "       [57.68372932, 57.68979277, 57.69584341, ..., 58.71733907,\n",
+       "        58.72036629, 58.72337957],\n",
+       "       [57.7053864 , 57.7114534 , 57.71750758, ..., 58.73961438,\n",
+       "        58.74264346, 58.74565859],\n",
+       "       [57.72704232, 57.73311287, 57.73917059, ..., 58.76188913,\n",
+       "        58.76492006, 58.76793703]])</pre></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'>8.371 8.407 8.443 ... 15.75 15.79</div><input id='attrs-4be101de-a228-45af-8112-d2994d789df7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4be101de-a228-45af-8112-d2994d789df7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-943f10c7-2149-463f-81da-6832577b05cf' class='xr-var-data-in' type='checkbox'><label for='data-943f10c7-2149-463f-81da-6832577b05cf' title='Show/Hide data 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'><pre>array([[ 8.37056599,  8.40692442,  8.44329237, ..., 16.7949401 ,\n",
+       "        16.83297233, 16.87100962],\n",
+       "       [ 8.36156527,  8.39794181,  8.43432789, ..., 16.79036385,\n",
+       "        16.82841689, 16.86647499],\n",
+       "       [ 8.35255499,  8.38894966,  8.42535389, ..., 16.78578251,\n",
+       "        16.82385638, 16.86193532],\n",
+       "       ...,\n",
+       "       [ 6.26808054,  6.30859971,  6.34913228, ..., 15.71960165,\n",
+       "        15.76250401, 15.80541365],\n",
+       "       [ 6.25671963,  6.29726089,  6.33781557, ..., 15.71375468,\n",
+       "        15.75668341, 15.79961944],\n",
+       "       [ 6.24534529,  6.28590865,  6.32648546, ..., 15.70790038,\n",
+       "        15.75085552, 15.79381796]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1990-09-01</div><input id='attrs-fe9ae24b-2e7a-41de-9319-83e4d2bed6ee' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-fe9ae24b-2e7a-41de-9319-83e4d2bed6ee' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-db556844-2727-49b1-916c-cc23b394fbf2' class='xr-var-data-in' type='checkbox'><label for='data-db556844-2727-49b1-916c-cc23b394fbf2' title='Show/Hide data 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;, 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'>-1.099e+06 ... -5.292e+05</div><input id='attrs-709f9d2e-1182-49c3-a77b-ad8857ec54ff' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-709f9d2e-1182-49c3-a77b-ad8857ec54ff' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2291817f-abf4-4635-8c46-7da612ac60bc' class='xr-var-data-in' type='checkbox'><label for='data-2291817f-abf4-4635-8c46-7da612ac60bc' title='Show/Hide data 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([-1099248.193226, -1096748.193226, -1094248.193226, ...,  -534248.193226,\n",
+       "        -531748.193226,  -529248.193226])</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'>-2.595e+05 -2.57e+05 ... 2.655e+05</div><input id='attrs-36e81b4d-eaa9-488a-9e02-d5c727abfac8' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-36e81b4d-eaa9-488a-9e02-d5c727abfac8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a4b99a90-e3b7-47bf-989b-496d1e3d5e39' class='xr-var-data-in' type='checkbox'><label for='data-a4b99a90-e3b7-47bf-989b-496d1e3d5e39' title='Show/Hide data 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([-259541.209133, -257041.209133, -254541.209133, ...,  260458.790867,\n",
+       "        262958.790867,  265458.790867])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-2f5d55eb-f257-4e55-9f14-ddd0ad49b708' class='xr-section-summary-in' type='checkbox'  ><label for='section-2f5d55eb-f257-4e55-9f14-ddd0ad49b708' 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>altitude</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-63690676-b965-45f6-a3e2-ca3db6b47fac' class='xr-index-data-in' type='checkbox'/><label for='index-63690676-b965-45f6-a3e2-ca3db6b47fac' 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>x</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-694ac1a3-13ee-4fa8-b326-6bdb3c069fa1' class='xr-index-data-in' type='checkbox'/><label for='index-694ac1a3-13ee-4fa8-b326-6bdb3c069fa1' 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([-1099248.1932260487, -1096748.1932260487, -1094248.1932260487,\n",
+       "       -1091748.1932260487, -1089248.1932260487, -1086748.1932260487,\n",
+       "       -1084248.1932260487, -1081748.1932260487, -1079248.1932260487,\n",
+       "       -1076748.1932260487,\n",
+       "       ...\n",
+       "        -551748.1932260487,  -549248.1932260487,  -546748.1932260487,\n",
+       "        -544248.1932260487,  -541748.1932260487,  -539248.1932260487,\n",
+       "        -536748.1932260487,  -534248.1932260487,  -531748.1932260487,\n",
+       "        -529248.1932260487],\n",
+       "      dtype=&#x27;float64&#x27;, name=&#x27;x&#x27;, length=229))</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-181549f1-fcd1-42ec-975a-808148475a8a' class='xr-index-data-in' type='checkbox'/><label for='index-181549f1-fcd1-42ec-975a-808148475a8a' 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([-259541.2091329489, -257041.2091329489, -254541.2091329489,\n",
+       "       -252041.2091329489, -249541.2091329489, -247041.2091329489,\n",
+       "       -244541.2091329489, -242041.2091329489, -239541.2091329489,\n",
+       "       -237041.2091329489,\n",
+       "       ...\n",
+       "        242958.7908670511,  245458.7908670511,  247958.7908670511,\n",
+       "        250458.7908670511,  252958.7908670511,  255458.7908670511,\n",
+       "        257958.7908670511,  260458.7908670511,  262958.7908670511,\n",
+       "        265458.7908670511],\n",
+       "      dtype=&#x27;float64&#x27;, name=&#x27;y&#x27;, length=211))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-7eb20c7a-ae8b-48ba-b493-06d53778d023' class='xr-section-summary-in' type='checkbox'  checked><label for='section-7eb20c7a-ae8b-48ba-b493-06d53778d023' class='xr-section-summary' >Attributes: <span>(7)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><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></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.DataArray 't' (altitude: 1, y: 211, x: 229)>\n",
+       "dask.array<where, shape=(1, 211, 229), dtype=float64, chunksize=(1, 116, 152), chunktype=numpy.ndarray>\n",
+       "Coordinates:\n",
+       "  * altitude  (altitude) int64 100\n",
+       "    lat       (y, x) float64 53.16 53.17 53.17 53.18 ... 58.76 58.76 58.76 58.77\n",
+       "    lon       (y, x) float64 8.371 8.407 8.443 8.48 ... 15.66 15.71 15.75 15.79\n",
+       "    time      datetime64[ns] 1990-09-01\n",
+       "  * x         (x) float64 -1.099e+06 -1.097e+06 ... -5.317e+05 -5.292e+05\n",
+       "  * y         (y) float64 -2.595e+05 -2.57e+05 -2.545e+05 ... 2.63e+05 2.655e+05\n",
+       "Attributes:\n",
+       "    level_type:   heightAboveGround\n",
+       "    long_name:    Temperature\n",
+       "    shortName:    t\n",
+       "    stepType:     instant\n",
+       "    stepUnits:    1\n",
+       "    typeOfLevel:  heightAboveGround\n",
+       "    units:        K"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "da_danra = ds_danra_hl.isel(time=0).where(da_mask.compute(), drop=True).t\n",
+    "da_danra"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "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",
+       "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n",
+       "<path d=\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
+       "<path d=\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
+       "</symbol>\n",
+       "<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n",
+       "<path d=\"M28.681 7.159c-0.694-0.947-1.662-2.053-2.724-3.116s-2.169-2.030-3.116-2.724c-1.612-1.182-2.393-1.319-2.841-1.319h-15.5c-1.378 0-2.5 1.121-2.5 2.5v27c0 1.378 1.122 2.5 2.5 2.5h23c1.378 0 2.5-1.122 2.5-2.5v-19.5c0-0.448-0.137-1.23-1.319-2.841zM24.543 5.457c0.959 0.959 1.712 1.825 2.268 2.543h-4.811v-4.811c0.718 0.556 1.584 1.309 2.543 2.268zM28 29.5c0 0.271-0.229 0.5-0.5 0.5h-23c-0.271 0-0.5-0.229-0.5-0.5v-27c0-0.271 0.229-0.5 0.5-0.5 0 0 15.499-0 15.5 0v7c0 0.552 0.448 1 1 1h7v19.5z\"></path>\n",
+       "<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.DataArray &#x27;2m_temperature&#x27; (lat: 23, lon: 39)&gt;\n",
+       "dask.array&lt;getitem, shape=(23, 39), dtype=float32, chunksize=(23, 39), chunktype=numpy.ndarray&gt;\n",
+       "Coordinates:\n",
+       "  * lat      (lat) float32 58.75 58.5 58.25 58.0 57.75 ... 54.0 53.75 53.5 53.25\n",
+       "  * lon      (lon) float32 7.25 7.5 7.75 8.0 8.25 ... 16.0 16.25 16.5 16.75\n",
+       "    time     datetime64[ns] 1990-09-01\n",
+       "Attributes:\n",
+       "    long_name:   2 metre temperature\n",
+       "    short_name:  t2m\n",
+       "    units:       K</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'2m_temperature'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>lat</span>: 23</li><li><span class='xr-has-index'>lon</span>: 39</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-df971509-d9ce-486a-acd0-8e441ef30856' class='xr-array-in' type='checkbox' checked><label for='section-df971509-d9ce-486a-acd0-8e441ef30856' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>dask.array&lt;chunksize=(23, 39), meta=np.ndarray&gt;</span></div><div class='xr-array-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> 3.50 kiB </td>\n",
+       "                        <td> 3.50 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (23, 39) </td>\n",
+       "                        <td> (23, 39) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 7 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",
+       "        <svg width=\"170\" height=\"120\" 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=\"70\" x2=\"120\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,70.76923076923077 0.0,70.76923076923077\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"90.769231\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >39</text>\n",
+       "  <text x=\"140.000000\" y=\"35.384615\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,35.384615)\">23</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></div></li><li class='xr-section-item'><input id='section-2b8e74e0-0de8-409b-bdb1-963f1e6a2c7d' class='xr-section-summary-in' type='checkbox'  checked><label for='section-2b8e74e0-0de8-409b-bdb1-963f1e6a2c7d' class='xr-section-summary' >Coordinates: <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-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>58.75 58.5 58.25 ... 53.5 53.25</div><input id='attrs-d2464dbf-0a0b-49ec-b3ea-ab6d6bc1b06e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d2464dbf-0a0b-49ec-b3ea-ab6d6bc1b06e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cb25b39c-64d2-490e-b81e-2ef1600affa2' class='xr-var-data-in' type='checkbox'><label for='data-cb25b39c-64d2-490e-b81e-2ef1600affa2' title='Show/Hide data 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>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><pre>array([58.75, 58.5 , 58.25, 58.  , 57.75, 57.5 , 57.25, 57.  , 56.75, 56.5 ,\n",
+       "       56.25, 56.  , 55.75, 55.5 , 55.25, 55.  , 54.75, 54.5 , 54.25, 54.  ,\n",
+       "       53.75, 53.5 , 53.25], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>7.25 7.5 7.75 ... 16.25 16.5 16.75</div><input id='attrs-f8c09cb8-5910-48b6-9879-823c5a99de3d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f8c09cb8-5910-48b6-9879-823c5a99de3d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9ed9e79e-4f7c-412e-b37c-3bcce4697433' class='xr-var-data-in' type='checkbox'><label for='data-9ed9e79e-4f7c-412e-b37c-3bcce4697433' title='Show/Hide data 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>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>array([ 7.25,  7.5 ,  7.75,  8.  ,  8.25,  8.5 ,  8.75,  9.  ,  9.25,  9.5 ,\n",
+       "        9.75, 10.  , 10.25, 10.5 , 10.75, 11.  , 11.25, 11.5 , 11.75, 12.  ,\n",
+       "       12.25, 12.5 , 12.75, 13.  , 13.25, 13.5 , 13.75, 14.  , 14.25, 14.5 ,\n",
+       "       14.75, 15.  , 15.25, 15.5 , 15.75, 16.  , 16.25, 16.5 , 16.75],\n",
+       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1990-09-01</div><input id='attrs-483bb870-5e8a-4562-b304-c7babd6fa741' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-483bb870-5e8a-4562-b304-c7babd6fa741' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5e883eb2-2cd3-4386-9c60-acc1c59d55b0' class='xr-var-data-in' type='checkbox'><label for='data-5e883eb2-2cd3-4386-9c60-acc1c59d55b0' title='Show/Hide data 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;, dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-aceb82f8-e260-4095-9b42-803c02771361' class='xr-section-summary-in' type='checkbox'  ><label for='section-aceb82f8-e260-4095-9b42-803c02771361' 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>lat</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-1f759d01-5ae3-4367-9ea8-f7ccc921afd4' class='xr-index-data-in' type='checkbox'/><label for='index-1f759d01-5ae3-4367-9ea8-f7ccc921afd4' 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([58.75,  58.5, 58.25,  58.0, 57.75,  57.5, 57.25,  57.0, 56.75,  56.5,\n",
+       "       56.25,  56.0, 55.75,  55.5, 55.25,  55.0, 54.75,  54.5, 54.25,  54.0,\n",
+       "       53.75,  53.5, 53.25],\n",
+       "      dtype=&#x27;float32&#x27;, name=&#x27;lat&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lon</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-c28282c8-e48f-42c2-9e87-634f3881c148' class='xr-index-data-in' type='checkbox'/><label for='index-c28282c8-e48f-42c2-9e87-634f3881c148' 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([ 7.25,   7.5,  7.75,   8.0,  8.25,   8.5,  8.75,   9.0,  9.25,   9.5,\n",
+       "        9.75,  10.0, 10.25,  10.5, 10.75,  11.0, 11.25,  11.5, 11.75,  12.0,\n",
+       "       12.25,  12.5, 12.75,  13.0, 13.25,  13.5, 13.75,  14.0, 14.25,  14.5,\n",
+       "       14.75,  15.0, 15.25,  15.5, 15.75,  16.0, 16.25,  16.5, 16.75],\n",
+       "      dtype=&#x27;float32&#x27;, name=&#x27;lon&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-b1909003-5d93-4385-bfed-a59810db9f6a' class='xr-section-summary-in' type='checkbox'  checked><label for='section-b1909003-5d93-4385-bfed-a59810db9f6a' class='xr-section-summary' >Attributes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><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></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.DataArray '2m_temperature' (lat: 23, lon: 39)>\n",
+       "dask.array<getitem, shape=(23, 39), dtype=float32, chunksize=(23, 39), chunktype=numpy.ndarray>\n",
+       "Coordinates:\n",
+       "  * lat      (lat) float32 58.75 58.5 58.25 58.0 57.75 ... 54.0 53.75 53.5 53.25\n",
+       "  * lon      (lon) float32 7.25 7.5 7.75 8.0 8.25 ... 16.0 16.25 16.5 16.75\n",
+       "    time     datetime64[ns] 1990-09-01\n",
+       "Attributes:\n",
+       "    long_name:   2 metre temperature\n",
+       "    short_name:  t2m\n",
+       "    units:       K"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "da_era5 = ds_era5_edge.sel(time=da_danra.time, method=\"nearest\")[\"2m_temperature\"]\n",
+    "da_era5"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<cartopy.mpl.feature_artist.FeatureArtist at 0x7f319826fc40>"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 1000x400 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(figsize=(10, 4), subplot_kw=dict(projection=ccrs.PlateCarree()))\n",
+    "da_danra.plot(ax=ax, x=\"lon\", y=\"lat\", transform=ccrs.PlateCarree())\n",
+    "da_era5.plot(ax=ax, x=\"lon\", y=\"lat\", transform=ccrs.PlateCarree())\n",
+    "ax.gridlines(draw_labels=[\"top\", \"left\"])\n",
+    "ax.coastlines()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "base",
+   "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": "3.10.13"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

From 3347ade79369938df3334b8c3e2d79789315a110 Mon Sep 17 00:00:00 2001
From: Leif Denby <lcd@dmi.dk>
Date: Tue, 12 Mar 2024 14:24:26 +0000
Subject: [PATCH 3/4] increase margin around denmark

---
 notebooks/lateral-boundaries.ipynb | 4018 +++++++++++++++++++++++++---
 1 file changed, 3628 insertions(+), 390 deletions(-)

diff --git a/notebooks/lateral-boundaries.ipynb b/notebooks/lateral-boundaries.ipynb
index 6ea34fa..b1eb1bf 100644
--- a/notebooks/lateral-boundaries.ipynb
+++ b/notebooks/lateral-boundaries.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -14,7 +14,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -24,7 +24,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
@@ -414,14 +414,14 @@
        "    type_of_low_vegetation                            (lat, lon) float32 dask.array&lt;chunksize=(721, 1440), meta=np.ndarray&gt;\n",
        "    u_component_of_wind                               (time, level, lat, lon) float32 dask.array&lt;chunksize=(48, 37, 721, 1440), meta=np.ndarray&gt;\n",
        "    v_component_of_wind                               (time, level, lat, lon) float32 dask.array&lt;chunksize=(48, 37, 721, 1440), meta=np.ndarray&gt;\n",
-       "    vertical_velocity                                 (time, level, lat, lon) float32 dask.array&lt;chunksize=(48, 37, 721, 1440), 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-206a4751-ac04-45b0-9f6b-0c6bf3cecc33' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-206a4751-ac04-45b0-9f6b-0c6bf3cecc33' 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>: 552264</li><li><span class='xr-has-index'>lat</span>: 721</li><li><span class='xr-has-index'>lon</span>: 1440</li><li><span class='xr-has-index'>level</span>: 37</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-6a3714e4-1d93-4b46-8fb5-71c61bfccdb7' class='xr-section-summary-in' type='checkbox'  checked><label for='section-6a3714e4-1d93-4b46-8fb5-71c61bfccdb7' 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'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>90.0 89.75 89.5 ... -89.75 -90.0</div><input id='attrs-f0fd45fd-428f-4149-9b11-380de3acebd6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f0fd45fd-428f-4149-9b11-380de3acebd6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-868c3dd6-5a8f-438a-8cd2-4497813fcdf5' class='xr-var-data-in' type='checkbox'><label for='data-868c3dd6-5a8f-438a-8cd2-4497813fcdf5' title='Show/Hide data 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>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><pre>array([ 90.  ,  89.75,  89.5 , ..., -89.5 , -89.75, -90.  ], dtype=float32)</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'>1 2 3 5 7 ... 900 925 950 975 1000</div><input id='attrs-d11a6d82-c339-41bc-bf76-0dfe49667866' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d11a6d82-c339-41bc-bf76-0dfe49667866' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a665a74f-8a44-4999-b655-9296db0689f8' class='xr-var-data-in' type='checkbox'><label for='data-a665a74f-8a44-4999-b655-9296db0689f8' title='Show/Hide data 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([   1,    2,    3,    5,    7,   10,   20,   30,   50,   70,  100,  125,\n",
+       "    vertical_velocity                                 (time, level, lat, lon) float32 dask.array&lt;chunksize=(48, 37, 721, 1440), 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-5db8186f-fde8-439c-a052-1ffd71cdd376' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-5db8186f-fde8-439c-a052-1ffd71cdd376' 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>: 552264</li><li><span class='xr-has-index'>lat</span>: 721</li><li><span class='xr-has-index'>lon</span>: 1440</li><li><span class='xr-has-index'>level</span>: 37</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-14ad6095-c552-4aa2-b0eb-5e8ccb8f9463' class='xr-section-summary-in' type='checkbox'  checked><label for='section-14ad6095-c552-4aa2-b0eb-5e8ccb8f9463' 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'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>90.0 89.75 89.5 ... -89.75 -90.0</div><input id='attrs-f55bfdbc-9eb5-4be2-985f-ec4f818d7251' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f55bfdbc-9eb5-4be2-985f-ec4f818d7251' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b33150fb-9436-41e3-8e42-63ee1e21a889' class='xr-var-data-in' type='checkbox'><label for='data-b33150fb-9436-41e3-8e42-63ee1e21a889' title='Show/Hide data 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>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><pre>array([ 90.  ,  89.75,  89.5 , ..., -89.5 , -89.75, -90.  ], dtype=float32)</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'>1 2 3 5 7 ... 900 925 950 975 1000</div><input id='attrs-4927554d-9c1b-4f28-8c43-6cd614eab5b7' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4927554d-9c1b-4f28-8c43-6cd614eab5b7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-78c78554-e1b8-4fa0-a91a-4b560512462b' class='xr-var-data-in' type='checkbox'><label for='data-78c78554-e1b8-4fa0-a91a-4b560512462b' title='Show/Hide data 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([   1,    2,    3,    5,    7,   10,   20,   30,   50,   70,  100,  125,\n",
        "        150,  175,  200,  225,  250,  300,  350,  400,  450,  500,  550,  600,\n",
        "        650,  700,  750,  775,  800,  825,  850,  875,  900,  925,  950,  975,\n",
-       "       1000])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0 0.25 0.5 ... 359.2 359.5 359.8</div><input id='attrs-20421289-44ee-49c7-ba05-31d016472533' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-20421289-44ee-49c7-ba05-31d016472533' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fdd7ab6e-9979-4a10-99f7-8f8a1943d18b' class='xr-var-data-in' type='checkbox'><label for='data-fdd7ab6e-9979-4a10-99f7-8f8a1943d18b' title='Show/Hide data 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>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>array([0.0000e+00, 2.5000e-01, 5.0000e-01, ..., 3.5925e+02, 3.5950e+02,\n",
-       "       3.5975e+02], dtype=float32)</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 ... 2021-12-31T23:00:00</div><input id='attrs-5542f7f1-2133-413a-ab87-fe7778ca9c7c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5542f7f1-2133-413a-ab87-fe7778ca9c7c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-130f35b6-e038-42c9-b455-9d8fedd18507' class='xr-var-data-in' type='checkbox'><label for='data-130f35b6-e038-42c9-b455-9d8fedd18507' title='Show/Hide data 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;1959-01-01T01:00:00.000000000&#x27;,\n",
+       "       1000])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0 0.25 0.5 ... 359.2 359.5 359.8</div><input id='attrs-e7cd8a6d-cf0f-453d-a057-83ec7b8686aa' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e7cd8a6d-cf0f-453d-a057-83ec7b8686aa' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-102ef2db-621a-4588-b9d0-f00805732be9' class='xr-var-data-in' type='checkbox'><label for='data-102ef2db-621a-4588-b9d0-f00805732be9' title='Show/Hide data 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>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>array([0.0000e+00, 2.5000e-01, 5.0000e-01, ..., 3.5925e+02, 3.5950e+02,\n",
+       "       3.5975e+02], dtype=float32)</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 ... 2021-12-31T23:00:00</div><input id='attrs-01139fd9-69a0-450d-939b-3f5a90949b3d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-01139fd9-69a0-450d-939b-3f5a90949b3d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-510b1377-703d-435b-a244-d1f24a6e7e97' class='xr-var-data-in' type='checkbox'><label for='data-510b1377-703d-435b-a244-d1f24a6e7e97' title='Show/Hide data 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;1959-01-01T01:00:00.000000000&#x27;,\n",
        "       &#x27;1959-01-01T02:00:00.000000000&#x27;, ..., &#x27;2021-12-31T21:00:00.000000000&#x27;,\n",
        "       &#x27;2021-12-31T22:00:00.000000000&#x27;, &#x27;2021-12-31T23:00:00.000000000&#x27;],\n",
-       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-41349960-85f9-4a26-b97b-2bd59fd21d5d' class='xr-section-summary-in' type='checkbox'  ><label for='section-41349960-85f9-4a26-b97b-2bd59fd21d5d' class='xr-section-summary' >Data variables: <span>(31)</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, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(48, 721, 1440), meta=np.ndarray&gt;</div><input id='attrs-31602b59-cdd8-4783-b927-a732b95700a8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-31602b59-cdd8-4783-b927-a732b95700a8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-179d0791-cb77-44c1-adb3-a1637e9e44cf' class='xr-var-data-in' type='checkbox'><label for='data-179d0791-cb77-44c1-adb3-a1637e9e44cf' title='Show/Hide data 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-0f0aa3d3-47cc-4c84-8295-466644c240e4' class='xr-section-summary-in' type='checkbox'  ><label for='section-0f0aa3d3-47cc-4c84-8295-466644c240e4' class='xr-section-summary' >Data variables: <span>(31)</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, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(48, 721, 1440), meta=np.ndarray&gt;</div><input id='attrs-db362348-702c-4eae-b139-4ab156acc9c0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-db362348-702c-4eae-b139-4ab156acc9c0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e6e0ccd3-857a-494e-a662-5449bf683ffb' class='xr-var-data-in' type='checkbox'><label for='data-e6e0ccd3-857a-494e-a662-5449bf683ffb' title='Show/Hide data 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",
@@ -535,7 +535,7 @@
        "</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, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(48, 721, 1440), meta=np.ndarray&gt;</div><input id='attrs-994f102c-626e-48dd-940e-37800cebdb8e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-994f102c-626e-48dd-940e-37800cebdb8e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-acf32e54-070e-4ed8-b7ae-4421809f5aad' class='xr-var-data-in' type='checkbox'><label for='data-acf32e54-070e-4ed8-b7ae-4421809f5aad' title='Show/Hide data 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, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(48, 721, 1440), meta=np.ndarray&gt;</div><input id='attrs-a437f1d6-99d4-4d8d-8a6d-da9fd85c3dd5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a437f1d6-99d4-4d8d-8a6d-da9fd85c3dd5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8fc31981-e8d0-4946-9904-3383d7e7804c' class='xr-var-data-in' type='checkbox'><label for='data-8fc31981-e8d0-4946-9904-3383d7e7804c' title='Show/Hide data 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",
@@ -649,7 +649,7 @@
        "</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, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(48, 721, 1440), meta=np.ndarray&gt;</div><input id='attrs-67f81c8b-e7ca-4ce7-b9ba-2768ec787f2b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-67f81c8b-e7ca-4ce7-b9ba-2768ec787f2b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-460dd4af-da8e-4cd2-81fb-80003922e1c2' class='xr-var-data-in' type='checkbox'><label for='data-460dd4af-da8e-4cd2-81fb-80003922e1c2' title='Show/Hide data 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, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(48, 721, 1440), meta=np.ndarray&gt;</div><input id='attrs-bc3aa21e-7d78-40fe-9759-1d4f834e5b51' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bc3aa21e-7d78-40fe-9759-1d4f834e5b51' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5675c5e5-a841-4ff0-8431-9d192647c8d7' class='xr-var-data-in' type='checkbox'><label for='data-5675c5e5-a841-4ff0-8431-9d192647c8d7' title='Show/Hide data 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",
@@ -763,7 +763,7 @@
        "</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'>(lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(721, 1440), meta=np.ndarray&gt;</div><input id='attrs-36850b55-9706-4cba-b246-4da927387d94' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-36850b55-9706-4cba-b246-4da927387d94' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3a0fe18e-36e5-4773-819b-46131f06beea' class='xr-var-data-in' type='checkbox'><label for='data-3a0fe18e-36e5-4773-819b-46131f06beea' title='Show/Hide data 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'>(lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(721, 1440), meta=np.ndarray&gt;</div><input id='attrs-20d9c748-65cf-47a2-aa7a-69456b2cc853' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-20d9c748-65cf-47a2-aa7a-69456b2cc853' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d2cdc768-3140-419a-bc7f-c1b5791ebf60' class='xr-var-data-in' type='checkbox'><label for='data-d2cdc768-3140-419a-bc7f-c1b5791ebf60' title='Show/Hide data 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",
@@ -818,7 +818,7 @@
        "</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'>(lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(721, 1440), meta=np.ndarray&gt;</div><input id='attrs-1efc08d9-59ac-4750-89e5-eb6f5e724de9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1efc08d9-59ac-4750-89e5-eb6f5e724de9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-154c4280-7319-4eb5-9e73-d555534bdfa9' class='xr-var-data-in' type='checkbox'><label for='data-154c4280-7319-4eb5-9e73-d555534bdfa9' title='Show/Hide data 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'>(lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(721, 1440), meta=np.ndarray&gt;</div><input id='attrs-c44b65a7-1617-41f3-8fc4-1c3e8b889594' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c44b65a7-1617-41f3-8fc4-1c3e8b889594' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d60b0ad5-7165-4f57-91cf-495b1251c24b' class='xr-var-data-in' type='checkbox'><label for='data-d60b0ad5-7165-4f57-91cf-495b1251c24b' title='Show/Hide data 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",
@@ -873,7 +873,7 @@
        "</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, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(48, 37, 721, 1440), meta=np.ndarray&gt;</div><input id='attrs-6984f5db-e409-461f-a12a-695562a01c88' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6984f5db-e409-461f-a12a-695562a01c88' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-659c9f36-2015-4da0-8d91-ea20b7e49648' class='xr-var-data-in' type='checkbox'><label for='data-659c9f36-2015-4da0-8d91-ea20b7e49648' title='Show/Hide data 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, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(48, 37, 721, 1440), meta=np.ndarray&gt;</div><input id='attrs-f3f1d8ef-0f3f-4d78-9e31-60f30b870345' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f3f1d8ef-0f3f-4d78-9e31-60f30b870345' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3f649923-60de-4173-9434-e9d8635e0671' class='xr-var-data-in' type='checkbox'><label for='data-3f649923-60de-4173-9434-e9d8635e0671' title='Show/Hide data 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",
@@ -998,7 +998,7 @@
        "</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'>(lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(721, 1440), meta=np.ndarray&gt;</div><input id='attrs-ba884afd-aa87-424a-81f4-c5a9c8243a2f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ba884afd-aa87-424a-81f4-c5a9c8243a2f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-25b63f77-0bde-4d69-b814-d7fe25cdb87f' class='xr-var-data-in' type='checkbox'><label for='data-25b63f77-0bde-4d69-b814-d7fe25cdb87f' title='Show/Hide data 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'>(lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(721, 1440), meta=np.ndarray&gt;</div><input id='attrs-b501855c-0a09-433d-bd6b-c22e3f39de37' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b501855c-0a09-433d-bd6b-c22e3f39de37' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-848db9fa-9e87-4eae-9c97-f66f9322b252' class='xr-var-data-in' type='checkbox'><label for='data-848db9fa-9e87-4eae-9c97-f66f9322b252' title='Show/Hide data 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",
@@ -1053,7 +1053,7 @@
        "</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'>(lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(721, 1440), meta=np.ndarray&gt;</div><input id='attrs-16347a3e-0aca-46d1-96de-d8441108ce67' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-16347a3e-0aca-46d1-96de-d8441108ce67' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1306ab1a-e8e4-4687-8a1e-c2180a37cd6a' class='xr-var-data-in' type='checkbox'><label for='data-1306ab1a-e8e4-4687-8a1e-c2180a37cd6a' title='Show/Hide data 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'>(lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(721, 1440), meta=np.ndarray&gt;</div><input id='attrs-b2418443-bf37-4920-8d4e-1936492109cb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b2418443-bf37-4920-8d4e-1936492109cb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d96bc3ed-aefb-4318-bafe-0c10d30b120f' class='xr-var-data-in' type='checkbox'><label for='data-d96bc3ed-aefb-4318-bafe-0c10d30b120f' title='Show/Hide data 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",
@@ -1108,7 +1108,7 @@
        "</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'>(lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(721, 1440), meta=np.ndarray&gt;</div><input id='attrs-0badc767-8f97-4b4c-98e5-23b6899bb88d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0badc767-8f97-4b4c-98e5-23b6899bb88d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e120a213-7bae-4075-848f-86f8a308b6c3' class='xr-var-data-in' type='checkbox'><label for='data-e120a213-7bae-4075-848f-86f8a308b6c3' title='Show/Hide data 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'>(lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(721, 1440), meta=np.ndarray&gt;</div><input id='attrs-c426c894-862a-4c27-91de-b10d05673c93' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c426c894-862a-4c27-91de-b10d05673c93' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8aa9cee8-b242-4135-a8fc-09c78d3b1223' class='xr-var-data-in' type='checkbox'><label for='data-8aa9cee8-b242-4135-a8fc-09c78d3b1223' title='Show/Hide data 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",
@@ -1163,7 +1163,7 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lake_depth</span></div><div class='xr-var-dims'>(lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(721, 1440), meta=np.ndarray&gt;</div><input id='attrs-3a9dcd3b-71df-44db-ba6a-9d07d9aa1f64' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3a9dcd3b-71df-44db-ba6a-9d07d9aa1f64' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-999ec503-a0c5-472c-8c0f-d93450abe716' class='xr-var-data-in' type='checkbox'><label for='data-999ec503-a0c5-472c-8c0f-d93450abe716' title='Show/Hide data 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 total depth</dd><dt><span>short_name :</span></dt><dd>dl</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>lake_depth</span></div><div class='xr-var-dims'>(lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(721, 1440), meta=np.ndarray&gt;</div><input id='attrs-2ed810c4-bf0a-4ae0-975f-3544f8acea0a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2ed810c4-bf0a-4ae0-975f-3544f8acea0a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d3014ea3-1da1-4afe-9db8-9aa3a83289a0' class='xr-var-data-in' type='checkbox'><label for='data-d3014ea3-1da1-4afe-9db8-9aa3a83289a0' title='Show/Hide data 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 total depth</dd><dt><span>short_name :</span></dt><dd>dl</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",
@@ -1218,7 +1218,7 @@
        "</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'>(lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(721, 1440), meta=np.ndarray&gt;</div><input id='attrs-0e0507a1-0618-4627-bf2b-d963fc3ef483' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0e0507a1-0618-4627-bf2b-d963fc3ef483' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-49c23b87-ef79-4bf6-a8a5-47341eabe8e2' class='xr-var-data-in' type='checkbox'><label for='data-49c23b87-ef79-4bf6-a8a5-47341eabe8e2' title='Show/Hide data 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'>(lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(721, 1440), meta=np.ndarray&gt;</div><input id='attrs-6dc9dcf3-2ed3-492e-b29e-988c7307b3e1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6dc9dcf3-2ed3-492e-b29e-988c7307b3e1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-33b2b2b8-cb23-4646-81f3-762e1af03e2f' class='xr-var-data-in' type='checkbox'><label for='data-33b2b2b8-cb23-4646-81f3-762e1af03e2f' title='Show/Hide data 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",
@@ -1273,7 +1273,7 @@
        "</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'>(lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(721, 1440), meta=np.ndarray&gt;</div><input id='attrs-dc317855-af06-4109-b622-9e1a61148ee6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-dc317855-af06-4109-b622-9e1a61148ee6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6b487db2-1eb2-4b10-955b-edff37344fd6' class='xr-var-data-in' type='checkbox'><label for='data-6b487db2-1eb2-4b10-955b-edff37344fd6' title='Show/Hide data 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'>(lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(721, 1440), meta=np.ndarray&gt;</div><input id='attrs-99e2ce1b-0fe3-4aa7-9441-5e86822414c9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-99e2ce1b-0fe3-4aa7-9441-5e86822414c9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f2aa8f37-c435-4548-9e99-0e9c29050c5b' class='xr-var-data-in' type='checkbox'><label for='data-f2aa8f37-c435-4548-9e99-0e9c29050c5b' title='Show/Hide data 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",
@@ -1328,7 +1328,7 @@
        "</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, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(48, 721, 1440), meta=np.ndarray&gt;</div><input id='attrs-fc72efee-846d-4bcd-96ed-e527a1b67f08' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fc72efee-846d-4bcd-96ed-e527a1b67f08' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e6aa3d5a-a62a-45be-9471-383357dfbb5d' class='xr-var-data-in' type='checkbox'><label for='data-e6aa3d5a-a62a-45be-9471-383357dfbb5d' title='Show/Hide data 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, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(48, 721, 1440), meta=np.ndarray&gt;</div><input id='attrs-bcc12373-ac18-499b-b03d-95b51e6a5399' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bcc12373-ac18-499b-b03d-95b51e6a5399' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-64946950-a7a6-4278-acad-1f9310bb8a8e' class='xr-var-data-in' type='checkbox'><label for='data-64946950-a7a6-4278-acad-1f9310bb8a8e' title='Show/Hide data 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",
@@ -1442,7 +1442,7 @@
        "</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, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(48, 721, 1440), meta=np.ndarray&gt;</div><input id='attrs-471fdb4d-9be7-4818-a7f4-d3e502b3b967' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-471fdb4d-9be7-4818-a7f4-d3e502b3b967' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-aab37796-267f-4fd2-bae9-f331589c8c74' class='xr-var-data-in' type='checkbox'><label for='data-aab37796-267f-4fd2-bae9-f331589c8c74' title='Show/Hide data 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, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(48, 721, 1440), meta=np.ndarray&gt;</div><input id='attrs-10184392-f7ea-43b9-9ec4-72507cb19b59' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-10184392-f7ea-43b9-9ec4-72507cb19b59' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1d4fc37d-f50c-4cc1-ae25-cc561b68548e' class='xr-var-data-in' type='checkbox'><label for='data-1d4fc37d-f50c-4cc1-ae25-cc561b68548e' title='Show/Hide data 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",
@@ -1556,7 +1556,7 @@
        "</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, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(48, 721, 1440), meta=np.ndarray&gt;</div><input id='attrs-0e2ee9d9-f829-4ff9-bb4a-0ad9e8fe08c7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0e2ee9d9-f829-4ff9-bb4a-0ad9e8fe08c7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dcf82163-cc05-4a40-928a-d0bb13187191' class='xr-var-data-in' type='checkbox'><label for='data-dcf82163-cc05-4a40-928a-d0bb13187191' title='Show/Hide data 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, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(48, 721, 1440), meta=np.ndarray&gt;</div><input id='attrs-346bf32e-3efa-4b01-a786-996f9acbf7da' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-346bf32e-3efa-4b01-a786-996f9acbf7da' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a91023c7-4106-4878-8b70-dd613bc632cd' class='xr-var-data-in' type='checkbox'><label for='data-a91023c7-4106-4878-8b70-dd613bc632cd' title='Show/Hide data 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",
@@ -1670,7 +1670,7 @@
        "</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'>(lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(721, 1440), meta=np.ndarray&gt;</div><input id='attrs-74e14e42-abd8-4be9-af68-654c8017afae' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-74e14e42-abd8-4be9-af68-654c8017afae' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7519ac24-aa6d-4058-b39b-492ff5b209d1' class='xr-var-data-in' type='checkbox'><label for='data-7519ac24-aa6d-4058-b39b-492ff5b209d1' title='Show/Hide data 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'>(lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(721, 1440), meta=np.ndarray&gt;</div><input id='attrs-f2cd0e12-ad3f-421a-9520-b600235c5c73' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f2cd0e12-ad3f-421a-9520-b600235c5c73' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-edf79e85-2b1e-49e1-825e-c2187f55ed1a' class='xr-var-data-in' type='checkbox'><label for='data-edf79e85-2b1e-49e1-825e-c2187f55ed1a' title='Show/Hide data 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",
@@ -1725,7 +1725,7 @@
        "</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'>(lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(721, 1440), meta=np.ndarray&gt;</div><input id='attrs-2206ef6b-1428-4272-811a-5526a00cfeae' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2206ef6b-1428-4272-811a-5526a00cfeae' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-60d9904b-cdbe-442c-80ee-7be177449290' class='xr-var-data-in' type='checkbox'><label for='data-60d9904b-cdbe-442c-80ee-7be177449290' title='Show/Hide data 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'>(lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(721, 1440), meta=np.ndarray&gt;</div><input id='attrs-9ecc4a84-015c-41d1-b090-701c9a0f648a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9ecc4a84-015c-41d1-b090-701c9a0f648a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3f7192c0-6244-4ba1-8eed-b1237c8f995a' class='xr-var-data-in' type='checkbox'><label for='data-3f7192c0-6244-4ba1-8eed-b1237c8f995a' title='Show/Hide data 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",
@@ -1780,7 +1780,7 @@
        "</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, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(48, 37, 721, 1440), meta=np.ndarray&gt;</div><input id='attrs-d1b233f0-b34d-4a36-97be-3f7aff50d919' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d1b233f0-b34d-4a36-97be-3f7aff50d919' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9df61de0-bc0a-4e8f-8caf-ba421d9d6a37' class='xr-var-data-in' type='checkbox'><label for='data-9df61de0-bc0a-4e8f-8caf-ba421d9d6a37' title='Show/Hide data 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, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(48, 37, 721, 1440), meta=np.ndarray&gt;</div><input id='attrs-7169a4e5-2f96-4095-a95f-dc3893b268dc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7169a4e5-2f96-4095-a95f-dc3893b268dc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4a0cba6c-f07d-48ee-b8e7-24cd340da4da' class='xr-var-data-in' type='checkbox'><label for='data-4a0cba6c-f07d-48ee-b8e7-24cd340da4da' title='Show/Hide data 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",
@@ -1905,7 +1905,7 @@
        "</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'>(lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(721, 1440), meta=np.ndarray&gt;</div><input id='attrs-8199fee6-73ee-463a-ada8-2bae5a8a6ee0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8199fee6-73ee-463a-ada8-2bae5a8a6ee0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f2ceae66-3268-4a7d-82c1-401f396029ee' class='xr-var-data-in' type='checkbox'><label for='data-f2ceae66-3268-4a7d-82c1-401f396029ee' title='Show/Hide data 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'>(lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(721, 1440), meta=np.ndarray&gt;</div><input id='attrs-5e4dcc62-4dbc-44ff-a22d-5643a6de490f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5e4dcc62-4dbc-44ff-a22d-5643a6de490f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8713197a-8316-47e0-99ba-0ae3569c5b7b' class='xr-var-data-in' type='checkbox'><label for='data-8713197a-8316-47e0-99ba-0ae3569c5b7b' title='Show/Hide data 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",
@@ -1960,7 +1960,7 @@
        "</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'>(lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(721, 1440), meta=np.ndarray&gt;</div><input id='attrs-48cce45f-0932-4801-aa90-2bd58ceafc3a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-48cce45f-0932-4801-aa90-2bd58ceafc3a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-689ea885-232a-4ed6-a8d3-a4ec52caf23d' class='xr-var-data-in' type='checkbox'><label for='data-689ea885-232a-4ed6-a8d3-a4ec52caf23d' title='Show/Hide data 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'>(lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(721, 1440), meta=np.ndarray&gt;</div><input id='attrs-6fe5d13e-18e3-4373-b233-83ce59626539' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6fe5d13e-18e3-4373-b233-83ce59626539' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-aa62688e-f261-46f1-a956-e6b480a80c31' class='xr-var-data-in' type='checkbox'><label for='data-aa62688e-f261-46f1-a956-e6b480a80c31' title='Show/Hide data 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",
@@ -2015,7 +2015,7 @@
        "</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, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(48, 721, 1440), meta=np.ndarray&gt;</div><input id='attrs-be5fc87c-9971-48ec-8d5d-c9cc7d60f386' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-be5fc87c-9971-48ec-8d5d-c9cc7d60f386' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0816fae2-5cdf-4123-96b3-0c56af6d4013' class='xr-var-data-in' type='checkbox'><label for='data-0816fae2-5cdf-4123-96b3-0c56af6d4013' title='Show/Hide data 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, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(48, 721, 1440), meta=np.ndarray&gt;</div><input id='attrs-2da18f76-dabb-4a94-a465-90a4c2bdc691' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2da18f76-dabb-4a94-a465-90a4c2bdc691' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f9758ceb-ad53-493d-a7d6-4ba18c79e677' class='xr-var-data-in' type='checkbox'><label for='data-f9758ceb-ad53-493d-a7d6-4ba18c79e677' title='Show/Hide data 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",
@@ -2129,7 +2129,7 @@
        "</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, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(48, 37, 721, 1440), meta=np.ndarray&gt;</div><input id='attrs-d18edfb3-cbdb-4919-b8a6-42d506f65107' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d18edfb3-cbdb-4919-b8a6-42d506f65107' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f7b65e0e-e895-4e4b-8126-58acf5a8336e' class='xr-var-data-in' type='checkbox'><label for='data-f7b65e0e-e895-4e4b-8126-58acf5a8336e' title='Show/Hide data 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, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(48, 37, 721, 1440), meta=np.ndarray&gt;</div><input id='attrs-4afdbf7a-4f92-4959-999c-b0400c250795' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4afdbf7a-4f92-4959-999c-b0400c250795' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-19d9d13b-b825-4fc7-8d8c-8492932c3db2' class='xr-var-data-in' type='checkbox'><label for='data-19d9d13b-b825-4fc7-8d8c-8492932c3db2' title='Show/Hide data 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",
@@ -2254,7 +2254,7 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>toa_incident_solar_radiation</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(48, 721, 1440), meta=np.ndarray&gt;</div><input id='attrs-7562f572-cff6-44ac-93f2-2d5d20253b95' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7562f572-cff6-44ac-93f2-2d5d20253b95' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bb17843d-9fa0-4426-9912-cc3e0d09d345' class='xr-var-data-in' type='checkbox'><label for='data-bb17843d-9fa0-4426-9912-cc3e0d09d345' title='Show/Hide data 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>TOA incident solar radiation</dd><dt><span>short_name :</span></dt><dd>tisr</dd><dt><span>units :</span></dt><dd>J 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>toa_incident_solar_radiation</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(48, 721, 1440), meta=np.ndarray&gt;</div><input id='attrs-61097628-db1e-4765-9c5e-7a5fe53f94f0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-61097628-db1e-4765-9c5e-7a5fe53f94f0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2f2ea25a-e7a6-4206-bc6e-fc18a6bb5c2b' class='xr-var-data-in' type='checkbox'><label for='data-2f2ea25a-e7a6-4206-bc6e-fc18a6bb5c2b' title='Show/Hide data 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>TOA incident solar radiation</dd><dt><span>short_name :</span></dt><dd>tisr</dd><dt><span>units :</span></dt><dd>J m**-2</dd></dl></div><div class='xr-var-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -2368,7 +2368,7 @@
        "</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, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(48, 721, 1440), meta=np.ndarray&gt;</div><input id='attrs-0de97b9d-9746-4154-ae13-3bef109e432d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0de97b9d-9746-4154-ae13-3bef109e432d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5594b978-b02a-4e0a-a847-563b4a7a4ada' class='xr-var-data-in' type='checkbox'><label for='data-5594b978-b02a-4e0a-a847-563b4a7a4ada' title='Show/Hide data 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, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(48, 721, 1440), meta=np.ndarray&gt;</div><input id='attrs-083f7f72-5521-4506-9607-1d698caa92b0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-083f7f72-5521-4506-9607-1d698caa92b0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6ad9d991-7263-4e60-9f24-51658b069626' class='xr-var-data-in' type='checkbox'><label for='data-6ad9d991-7263-4e60-9f24-51658b069626' title='Show/Hide data 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",
@@ -2482,7 +2482,7 @@
        "</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, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(48, 721, 1440), meta=np.ndarray&gt;</div><input id='attrs-011a7c38-99f2-4a09-b600-3667543b3b63' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-011a7c38-99f2-4a09-b600-3667543b3b63' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5e7c958b-b185-4d2f-8635-3a3929513a55' class='xr-var-data-in' type='checkbox'><label for='data-5e7c958b-b185-4d2f-8635-3a3929513a55' title='Show/Hide data 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, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(48, 721, 1440), meta=np.ndarray&gt;</div><input id='attrs-baa68107-da89-45f3-bc9c-f712f677ac77' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-baa68107-da89-45f3-bc9c-f712f677ac77' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-393ee751-3ffa-476c-a26f-8f24920f8bb8' class='xr-var-data-in' type='checkbox'><label for='data-393ee751-3ffa-476c-a26f-8f24920f8bb8' title='Show/Hide data 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",
@@ -2596,7 +2596,7 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>total_precipitation</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(48, 721, 1440), meta=np.ndarray&gt;</div><input id='attrs-ede30d0d-6add-4986-b8c2-e7a59e9ddf35' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ede30d0d-6add-4986-b8c2-e7a59e9ddf35' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-548297b6-8eed-46c0-bcb8-b2f6330c4b54' class='xr-var-data-in' type='checkbox'><label for='data-548297b6-8eed-46c0-bcb8-b2f6330c4b54' title='Show/Hide data 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</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(48, 721, 1440), meta=np.ndarray&gt;</div><input id='attrs-96b0b7ac-7779-4f05-94a0-197f3496572f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-96b0b7ac-7779-4f05-94a0-197f3496572f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6ae14259-c9e9-4350-a7c7-3c2a91f94c49' class='xr-var-data-in' type='checkbox'><label for='data-6ae14259-c9e9-4350-a7c7-3c2a91f94c49' title='Show/Hide data 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",
@@ -2710,7 +2710,7 @@
        "</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'>(lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(721, 1440), meta=np.ndarray&gt;</div><input id='attrs-deb29833-8bfb-450a-8d7a-f6ad60b7ac28' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-deb29833-8bfb-450a-8d7a-f6ad60b7ac28' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ad63722b-669f-4910-8b5a-81eb8797d9fe' class='xr-var-data-in' type='checkbox'><label for='data-ad63722b-669f-4910-8b5a-81eb8797d9fe' title='Show/Hide data 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'>(lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(721, 1440), meta=np.ndarray&gt;</div><input id='attrs-753bccbc-6e8a-4715-9871-336315dae073' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-753bccbc-6e8a-4715-9871-336315dae073' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c05a86d5-58e7-4135-8003-429374ffc9a5' class='xr-var-data-in' type='checkbox'><label for='data-c05a86d5-58e7-4135-8003-429374ffc9a5' title='Show/Hide data 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",
@@ -2765,7 +2765,7 @@
        "</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'>(lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(721, 1440), meta=np.ndarray&gt;</div><input id='attrs-cdb2b364-7e62-4a38-a9e4-dd3d81953881' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cdb2b364-7e62-4a38-a9e4-dd3d81953881' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4d3edc6a-ff7a-4343-bdb1-70bab6e901ec' class='xr-var-data-in' type='checkbox'><label for='data-4d3edc6a-ff7a-4343-bdb1-70bab6e901ec' title='Show/Hide data 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'>(lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(721, 1440), meta=np.ndarray&gt;</div><input id='attrs-74a35f47-6dbe-46d6-b584-93fbe8ca7560' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-74a35f47-6dbe-46d6-b584-93fbe8ca7560' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-63c5f5bb-1e16-4f3f-8b10-bb32b9458bd4' class='xr-var-data-in' type='checkbox'><label for='data-63c5f5bb-1e16-4f3f-8b10-bb32b9458bd4' title='Show/Hide data 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",
@@ -2820,7 +2820,7 @@
        "</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, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(48, 37, 721, 1440), meta=np.ndarray&gt;</div><input id='attrs-beee5f6c-940b-4ff5-9c01-04717026a30d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-beee5f6c-940b-4ff5-9c01-04717026a30d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-95c0e34b-c847-49c6-92a2-83ee74a124c4' class='xr-var-data-in' type='checkbox'><label for='data-95c0e34b-c847-49c6-92a2-83ee74a124c4' title='Show/Hide data 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, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(48, 37, 721, 1440), meta=np.ndarray&gt;</div><input id='attrs-920db474-0952-476a-a406-e33674e425d3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-920db474-0952-476a-a406-e33674e425d3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b22b23f6-e3af-4ec0-8709-0dc2eaa6bb11' class='xr-var-data-in' type='checkbox'><label for='data-b22b23f6-e3af-4ec0-8709-0dc2eaa6bb11' title='Show/Hide data 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",
@@ -2945,7 +2945,7 @@
        "</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, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(48, 37, 721, 1440), meta=np.ndarray&gt;</div><input id='attrs-5206f8df-5785-43ad-b69e-e5cc21e8f94b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5206f8df-5785-43ad-b69e-e5cc21e8f94b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a3f80f68-c51a-4af6-a922-b9ef9b77eda1' class='xr-var-data-in' type='checkbox'><label for='data-a3f80f68-c51a-4af6-a922-b9ef9b77eda1' title='Show/Hide data 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, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(48, 37, 721, 1440), meta=np.ndarray&gt;</div><input id='attrs-d88134ca-71b2-485e-b673-5016cc9af0b9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d88134ca-71b2-485e-b673-5016cc9af0b9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8182bf23-ecad-4cd9-87be-488ecbd73172' class='xr-var-data-in' type='checkbox'><label for='data-8182bf23-ecad-4cd9-87be-488ecbd73172' title='Show/Hide data 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",
@@ -3070,7 +3070,7 @@
        "</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, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(48, 37, 721, 1440), meta=np.ndarray&gt;</div><input id='attrs-ba4f1c39-6d4e-43dd-99a2-af6dc8deb50b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ba4f1c39-6d4e-43dd-99a2-af6dc8deb50b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2733907a-b3c9-4156-a77b-bf7f0e25a058' class='xr-var-data-in' type='checkbox'><label for='data-2733907a-b3c9-4156-a77b-bf7f0e25a058' title='Show/Hide data 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, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(48, 37, 721, 1440), meta=np.ndarray&gt;</div><input id='attrs-5a6677e8-16c1-4caa-8826-b3f5fc483d0e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5a6677e8-16c1-4caa-8826-b3f5fc483d0e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-799a1a0c-84c8-45cc-872f-e66639ea1109' class='xr-var-data-in' type='checkbox'><label for='data-799a1a0c-84c8-45cc-872f-e66639ea1109' title='Show/Hide data 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",
@@ -3195,21 +3195,21 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-06e9e1f8-4bd4-43e5-b55e-16634b5fe2da' class='xr-section-summary-in' type='checkbox'  ><label for='section-06e9e1f8-4bd4-43e5-b55e-16634b5fe2da' 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>lat</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-1f57c8de-37c9-4744-a5cb-f00aef966008' class='xr-index-data-in' type='checkbox'/><label for='index-1f57c8de-37c9-4744-a5cb-f00aef966008' 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([  90.0,  89.75,   89.5,  89.25,   89.0,  88.75,   88.5,  88.25,   88.0,\n",
+       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-dbab1161-67fa-43a8-9f59-1eed165fe837' class='xr-section-summary-in' type='checkbox'  ><label for='section-dbab1161-67fa-43a8-9f59-1eed165fe837' 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>lat</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-2dca73c0-49e0-42d4-92bf-584bbcf5a7cf' class='xr-index-data-in' type='checkbox'/><label for='index-2dca73c0-49e0-42d4-92bf-584bbcf5a7cf' 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([  90.0,  89.75,   89.5,  89.25,   89.0,  88.75,   88.5,  88.25,   88.0,\n",
        "        87.75,\n",
        "       ...\n",
        "       -87.75,  -88.0, -88.25,  -88.5, -88.75,  -89.0, -89.25,  -89.5, -89.75,\n",
        "        -90.0],\n",
-       "      dtype=&#x27;float32&#x27;, name=&#x27;lat&#x27;, length=721))</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-afe045bc-a9e4-4af5-9de4-f679c41e7179' class='xr-index-data-in' type='checkbox'/><label for='index-afe045bc-a9e4-4af5-9de4-f679c41e7179' 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([   1,    2,    3,    5,    7,   10,   20,   30,   50,   70,  100,  125,\n",
+       "      dtype=&#x27;float32&#x27;, name=&#x27;lat&#x27;, length=721))</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-dbb629e3-2b6b-4399-958d-7ae619833e50' class='xr-index-data-in' type='checkbox'/><label for='index-dbb629e3-2b6b-4399-958d-7ae619833e50' 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([   1,    2,    3,    5,    7,   10,   20,   30,   50,   70,  100,  125,\n",
        "        150,  175,  200,  225,  250,  300,  350,  400,  450,  500,  550,  600,\n",
        "        650,  700,  750,  775,  800,  825,  850,  875,  900,  925,  950,  975,\n",
        "       1000],\n",
-       "      dtype=&#x27;int64&#x27;, name=&#x27;level&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lon</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-5b839066-5474-42c7-bf0f-2a11d49ca844' class='xr-index-data-in' type='checkbox'/><label for='index-5b839066-5474-42c7-bf0f-2a11d49ca844' 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,   0.25,    0.5,   0.75,    1.0,   1.25,    1.5,   1.75,    2.0,\n",
+       "      dtype=&#x27;int64&#x27;, name=&#x27;level&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lon</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-613913aa-a6eb-4d8e-b57b-b30a6f063361' class='xr-index-data-in' type='checkbox'/><label for='index-613913aa-a6eb-4d8e-b57b-b30a6f063361' 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,   0.25,    0.5,   0.75,    1.0,   1.25,    1.5,   1.75,    2.0,\n",
        "         2.25,\n",
        "       ...\n",
        "        357.5, 357.75,  358.0, 358.25,  358.5, 358.75,  359.0, 359.25,  359.5,\n",
        "       359.75],\n",
-       "      dtype=&#x27;float32&#x27;, name=&#x27;lon&#x27;, length=1440))</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-49e0d522-320b-4be0-9f8a-467caab9f043' class='xr-index-data-in' type='checkbox'/><label for='index-49e0d522-320b-4be0-9f8a-467caab9f043' 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 01:00:00&#x27;,\n",
+       "      dtype=&#x27;float32&#x27;, name=&#x27;lon&#x27;, length=1440))</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-cae47089-77f5-45e1-8135-24057edb81ba' class='xr-index-data-in' type='checkbox'/><label for='index-cae47089-77f5-45e1-8135-24057edb81ba' 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 01:00:00&#x27;,\n",
        "               &#x27;1959-01-01 02:00:00&#x27;, &#x27;1959-01-01 03:00:00&#x27;,\n",
        "               &#x27;1959-01-01 04:00:00&#x27;, &#x27;1959-01-01 05:00:00&#x27;,\n",
        "               &#x27;1959-01-01 06:00:00&#x27;, &#x27;1959-01-01 07:00:00&#x27;,\n",
@@ -3220,7 +3220,7 @@
        "               &#x27;2021-12-31 18:00:00&#x27;, &#x27;2021-12-31 19:00:00&#x27;,\n",
        "               &#x27;2021-12-31 20:00:00&#x27;, &#x27;2021-12-31 21:00:00&#x27;,\n",
        "               &#x27;2021-12-31 22:00:00&#x27;, &#x27;2021-12-31 23:00:00&#x27;],\n",
-       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=552264, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-a7e4043c-be5a-4acb-a134-8bbd620117d1' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-a7e4043c-be5a-4acb-a134-8bbd620117d1' 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=552264, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-3ac639c2-10c0-4383-b9af-6fd1d64d3fb5' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-3ac639c2-10c0-4383-b9af-6fd1d64d3fb5' 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>\n",
@@ -3247,7 +3247,7 @@
        "    vertical_velocity                                 (time, level, lat, lon) float32 dask.array<chunksize=(48, 37, 721, 1440), meta=np.ndarray>"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3260,7 +3260,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -3644,7 +3644,7 @@
        "    u         (altitude, time, y, x) float64 dask.array&lt;chunksize=(1, 256, 256, 256), meta=np.ndarray&gt;\n",
        "    v         (altitude, time, y, x) float64 dask.array&lt;chunksize=(1, 256, 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-5846b125-12ae-489b-a62b-04bc317145f9' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-5846b125-12ae-489b-a62b-04bc317145f9' 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>: 29120</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-6550e345-ea9b-4543-b4fe-b6d0a93ad17f' class='xr-section-summary-in' type='checkbox'  checked><label for='section-6550e345-ea9b-4543-b4fe-b6d0a93ad17f' 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-b37b5d13-8518-4bef-8d8b-56809524458d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b37b5d13-8518-4bef-8d8b-56809524458d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8d53701c-019b-49a5-9f08-dfe2a35094a2' class='xr-var-data-in' type='checkbox'><label for='data-8d53701c-019b-49a5-9f08-dfe2a35094a2' title='Show/Hide data 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-cbb66280-34c1-4a5e-abce-95c8f79c20a6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cbb66280-34c1-4a5e-abce-95c8f79c20a6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ffad41bf-73e8-47c0-9707-da748bf94d54' class='xr-var-data-in' type='checkbox'><label for='data-ffad41bf-73e8-47c0-9707-da748bf94d54' title='Show/Hide data 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-7c59c729-bb1d-49fd-9ecb-0c1a12eda14a' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-7c59c729-bb1d-49fd-9ecb-0c1a12eda14a' 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>: 29120</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-a2336098-b219-4638-a1c9-b3e237bb3328' class='xr-section-summary-in' type='checkbox'  checked><label for='section-a2336098-b219-4638-a1c9-b3e237bb3328' 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-b1327fc3-315e-45dc-bf9d-fc67bc171f73' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b1327fc3-315e-45dc-bf9d-fc67bc171f73' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f985c56a-d992-4d32-8fd2-b71252774fc7' class='xr-var-data-in' type='checkbox'><label for='data-f985c56a-d992-4d32-8fd2-b71252774fc7' title='Show/Hide data 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-4c796771-b980-4032-89b6-0c002fcf1947' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4c796771-b980-4032-89b6-0c002fcf1947' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d8c2bc44-bbad-40ec-9399-8e4ffdf5a96f' class='xr-var-data-in' type='checkbox'><label for='data-d8c2bc44-bbad-40ec-9399-8e4ffdf5a96f' title='Show/Hide data 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",
@@ -3704,7 +3704,7 @@
        "</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-a948e48a-e5cc-41bf-9482-ab720498fd67' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a948e48a-e5cc-41bf-9482-ab720498fd67' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e8ec945a-4472-47e3-a21e-953bac2bc3e3' class='xr-var-data-in' type='checkbox'><label for='data-e8ec945a-4472-47e3-a21e-953bac2bc3e3' title='Show/Hide data 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-e8d04ef2-440d-435f-8c40-3114b5f5e9cc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e8d04ef2-440d-435f-8c40-3114b5f5e9cc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-359362ab-b788-4080-b74d-87113f2ed3a1' class='xr-var-data-in' type='checkbox'><label for='data-359362ab-b788-4080-b74d-87113f2ed3a1' title='Show/Hide data 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",
@@ -3764,12 +3764,12 @@
        "</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 ... 2000-08-18T21:00:00</div><input id='attrs-31973714-a226-4274-97a2-f30a25829639' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-31973714-a226-4274-97a2-f30a25829639' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-07294a84-7dfb-4759-8ab8-d1462ee0d6d1' class='xr-var-data-in' type='checkbox'><label for='data-07294a84-7dfb-4759-8ab8-d1462ee0d6d1' title='Show/Hide data 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 ... 2000-08-18T21:00:00</div><input id='attrs-dcb56b9b-3849-4250-b774-1d8de2d7ade1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-dcb56b9b-3849-4250-b774-1d8de2d7ade1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0bb7c06a-74ac-490c-955f-639aa3f73098' class='xr-var-data-in' type='checkbox'><label for='data-0bb7c06a-74ac-490c-955f-639aa3f73098' title='Show/Hide data 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;2000-08-18T15:00:00.000000000&#x27;,\n",
        "       &#x27;2000-08-18T18:00:00.000000000&#x27;, &#x27;2000-08-18T21:00:00.000000000&#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-8fb89613-3129-45db-9000-96506a62e31a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8fb89613-3129-45db-9000-96506a62e31a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5c8cb316-53c7-4ba9-a8df-10c38266969e' class='xr-var-data-in' type='checkbox'><label for='data-5c8cb316-53c7-4ba9-a8df-10c38266969e' title='Show/Hide data 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-14e6ef26-fd32-4494-aa4e-406461b10acf' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-14e6ef26-fd32-4494-aa4e-406461b10acf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-71b2cafb-eb92-49e0-b944-359cef7808cc' class='xr-var-data-in' type='checkbox'><label for='data-71b2cafb-eb92-49e0-b944-359cef7808cc' title='Show/Hide data 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-f92f6e56-7548-4b6d-9841-64c790d8d52d' class='xr-section-summary-in' type='checkbox'  checked><label for='section-f92f6e56-7548-4b6d-9841-64c790d8d52d' 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, 256, 256, 256), meta=np.ndarray&gt;</div><input id='attrs-e0c5ddb7-5e47-4727-9e4e-c35bf61fd97f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e0c5ddb7-5e47-4727-9e4e-c35bf61fd97f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-62ab3efd-6144-4066-917d-1bd03e097140' class='xr-var-data-in' type='checkbox'><label for='data-62ab3efd-6144-4066-917d-1bd03e097140' title='Show/Hide data 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-20098bf8-e960-4eac-a4e7-b802087599a2' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-20098bf8-e960-4eac-a4e7-b802087599a2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-17607928-ab70-4fc6-8b53-1ea8db9cee01' class='xr-var-data-in' type='checkbox'><label for='data-17607928-ab70-4fc6-8b53-1ea8db9cee01' title='Show/Hide data 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-0832ce05-8a7f-4d56-8ea0-53126f2a43ba' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0832ce05-8a7f-4d56-8ea0-53126f2a43ba' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dcc4d401-eea9-4572-9bea-c50995ec67b8' class='xr-var-data-in' type='checkbox'><label for='data-dcc4d401-eea9-4572-9bea-c50995ec67b8' title='Show/Hide data 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-ff686a17-9f69-4f23-a3fa-28a606d58735' class='xr-section-summary-in' type='checkbox'  checked><label for='section-ff686a17-9f69-4f23-a3fa-28a606d58735' 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, 256, 256, 256), meta=np.ndarray&gt;</div><input id='attrs-bc6937d1-00ad-4a46-8943-01500e736444' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bc6937d1-00ad-4a46-8943-01500e736444' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-98aab036-987b-4777-a3d9-a0609432cfe3' class='xr-var-data-in' type='checkbox'><label for='data-98aab036-987b-4777-a3d9-a0609432cfe3' title='Show/Hide data 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",
@@ -3935,7 +3935,7 @@
        "</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, 256, 256, 256), meta=np.ndarray&gt;</div><input id='attrs-98802385-e8e8-42c2-9336-a706434ed9dd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-98802385-e8e8-42c2-9336-a706434ed9dd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-27e4c0e3-83af-4850-a101-aed81d14034a' class='xr-var-data-in' type='checkbox'><label for='data-27e4c0e3-83af-4850-a101-aed81d14034a' title='Show/Hide data 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, 256, 256, 256), meta=np.ndarray&gt;</div><input id='attrs-8bb9dd5e-dafc-4658-8f8e-460eb2695e52' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8bb9dd5e-dafc-4658-8f8e-460eb2695e52' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e8e81acf-48cf-40f4-89c7-5ddcd7662ae1' class='xr-var-data-in' type='checkbox'><label for='data-e8e81acf-48cf-40f4-89c7-5ddcd7662ae1' title='Show/Hide data 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",
@@ -4101,7 +4101,7 @@
        "</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, 256, 256, 256), meta=np.ndarray&gt;</div><input id='attrs-3ffe8b36-87dd-45f6-b6c2-1f85984b7fed' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3ffe8b36-87dd-45f6-b6c2-1f85984b7fed' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5bd5872a-429b-4b6f-bf6c-fd560fa73a5f' class='xr-var-data-in' type='checkbox'><label for='data-5bd5872a-429b-4b6f-bf6c-fd560fa73a5f' title='Show/Hide data 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, 256, 256, 256), meta=np.ndarray&gt;</div><input id='attrs-f6d0a67f-8678-4f7b-bc79-332e2313a878' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f6d0a67f-8678-4f7b-bc79-332e2313a878' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d3d3fa06-a3a3-432d-ae10-61e796cf75ae' class='xr-var-data-in' type='checkbox'><label for='data-d3d3fa06-a3a3-432d-ae10-61e796cf75ae' title='Show/Hide data 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",
@@ -4267,7 +4267,7 @@
        "</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, 256, 256, 256), meta=np.ndarray&gt;</div><input id='attrs-efd13aa6-cd01-45fd-b82b-11f779322544' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-efd13aa6-cd01-45fd-b82b-11f779322544' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-679c2532-24f6-4d59-9d2e-c7a67a9e331f' class='xr-var-data-in' type='checkbox'><label for='data-679c2532-24f6-4d59-9d2e-c7a67a9e331f' title='Show/Hide data 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, 256, 256, 256), meta=np.ndarray&gt;</div><input id='attrs-b5e4bd38-125e-4ec5-9eda-8d045ad23bc1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b5e4bd38-125e-4ec5-9eda-8d045ad23bc1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-432d4fc9-947a-4b3d-ad52-a18866be6844' class='xr-var-data-in' type='checkbox'><label for='data-432d4fc9-947a-4b3d-ad52-a18866be6844' title='Show/Hide data 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",
@@ -4433,7 +4433,7 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-0c400b99-fc0f-42e0-ab41-f5c32f00533f' class='xr-section-summary-in' type='checkbox'  ><label for='section-0c400b99-fc0f-42e0-ab41-f5c32f00533f' 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-3b5db321-f42d-4301-a3a0-17034a88748c' class='xr-index-data-in' type='checkbox'/><label for='index-3b5db321-f42d-4301-a3a0-17034a88748c' 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-3166046d-e372-41d4-8fee-47fe306ebdb8' class='xr-index-data-in' type='checkbox'/><label for='index-3166046d-e372-41d4-8fee-47fe306ebdb8' 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-436ab98e-5b21-4d87-a651-8a34fad7a5aa' class='xr-section-summary-in' type='checkbox'  ><label for='section-436ab98e-5b21-4d87-a651-8a34fad7a5aa' 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-45e7fc34-e218-4bbb-a596-3605ae4a93c3' class='xr-index-data-in' type='checkbox'/><label for='index-45e7fc34-e218-4bbb-a596-3605ae4a93c3' 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-dc494760-b483-4127-85a4-749958260e1f' class='xr-index-data-in' type='checkbox'/><label for='index-dc494760-b483-4127-85a4-749958260e1f' 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",
@@ -4444,7 +4444,7 @@
        "               &#x27;2000-08-18 06:00:00&#x27;, &#x27;2000-08-18 09:00:00&#x27;,\n",
        "               &#x27;2000-08-18 12:00:00&#x27;, &#x27;2000-08-18 15:00:00&#x27;,\n",
        "               &#x27;2000-08-18 18:00:00&#x27;, &#x27;2000-08-18 21:00:00&#x27;],\n",
-       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=29120, 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-94607c26-5fd5-499d-a2b9-f14eafc3d21d' class='xr-index-data-in' type='checkbox'/><label for='index-94607c26-5fd5-499d-a2b9-f14eafc3d21d' 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;, length=29120, 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-cec30265-04f6-407a-bbac-946713528845' class='xr-index-data-in' type='checkbox'/><label for='index-cec30265-04f6-407a-bbac-946713528845' 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",
@@ -4453,7 +4453,7 @@
        "        -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-bd2e971b-ecc3-4a23-b0e7-03e27aa143ab' class='xr-index-data-in' type='checkbox'/><label for='index-bd2e971b-ecc3-4a23-b0e7-03e27aa143ab' 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-072b8474-f646-4eea-a468-21af3c3aa95c' class='xr-index-data-in' type='checkbox'/><label for='index-072b8474-f646-4eea-a468-21af3c3aa95c' 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",
@@ -4462,7 +4462,7 @@
        "        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-1e050aec-2b84-4e51-b81d-828a5fd823bb' class='xr-section-summary-in' type='checkbox'  checked><label for='section-1e050aec-2b84-4e51-b81d-828a5fd823bb' 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-973d8c4c-c7e4-407f-a20c-489a56071538' class='xr-section-summary-in' type='checkbox'  checked><label for='section-973d8c4c-c7e4-407f-a20c-489a56071538' 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>\n",
@@ -4483,7 +4483,7 @@
        "    description:  All prognostic variables for 10-year period on reduced levels"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4495,16 +4495,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<cartopy.mpl.feature_artist.FeatureArtist at 0x7f31c97dad70>"
+       "<cartopy.mpl.feature_artist.FeatureArtist at 0x7fa0c4d8f0a0>"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -4533,7 +4533,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -4544,27 +4544,27 @@
     "    )\n",
     "    return da_mask\n",
     "\n",
-    "bbox = [8, 16, 54, 58] # W, E, S, N"
+    "bbox = [6, 18, 52, 60] # W, E, S, N"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<cartopy.mpl.feature_artist.FeatureArtist at 0x7f31c93b7c10>"
+       "<cartopy.mpl.feature_artist.FeatureArtist at 0x7fa0bc496ce0>"
       ]
      },
-     "execution_count": 18,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": "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",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 1000x400 with 2 Axes>"
       ]
@@ -4583,7 +4583,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
@@ -4961,7 +4961,7 @@
        "Attributes:\n",
        "    long_name:   10 metre U wind component\n",
        "    short_name:  u10\n",
-       "    units:       m s**-1</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'10m_u_component_of_wind'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>lat</span>: 721</li><li><span class='xr-has-index'>lon</span>: 1440</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-4aa984dd-181f-40cb-89fc-71dbd2682aea' class='xr-array-in' type='checkbox' checked><label for='section-4aa984dd-181f-40cb-89fc-71dbd2682aea' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>dask.array&lt;chunksize=(721, 1440), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>\n",
+       "    units:       m s**-1</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'10m_u_component_of_wind'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>lat</span>: 721</li><li><span class='xr-has-index'>lon</span>: 1440</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-734eb952-d5ef-4716-adef-ec776163c8e9' class='xr-array-in' type='checkbox' checked><label for='section-734eb952-d5ef-4716-adef-ec776163c8e9' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>dask.array&lt;chunksize=(721, 1440), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -5016,18 +5016,18 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></div></li><li class='xr-section-item'><input id='section-f14248a5-e5cf-473e-b5cc-a052f741cfa1' class='xr-section-summary-in' type='checkbox'  checked><label for='section-f14248a5-e5cf-473e-b5cc-a052f741cfa1' class='xr-section-summary' >Coordinates: <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-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>90.0 89.75 89.5 ... -89.75 -90.0</div><input id='attrs-89808631-0515-429c-9a10-2c4f37210b65' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-89808631-0515-429c-9a10-2c4f37210b65' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-002c5b8a-f722-43ac-b5c3-52a0bd9f10d1' class='xr-var-data-in' type='checkbox'><label for='data-002c5b8a-f722-43ac-b5c3-52a0bd9f10d1' title='Show/Hide data 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>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><pre>array([ 90.  ,  89.75,  89.5 , ..., -89.5 , -89.75, -90.  ], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0 0.25 0.5 ... 359.2 359.5 359.8</div><input id='attrs-e2a9a850-bfbe-4841-aef1-118b92bb74f7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e2a9a850-bfbe-4841-aef1-118b92bb74f7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e057e02d-6392-4755-87af-f74b67833112' class='xr-var-data-in' type='checkbox'><label for='data-e057e02d-6392-4755-87af-f74b67833112' title='Show/Hide data 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>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>array([0.0000e+00, 2.5000e-01, 5.0000e-01, ..., 3.5925e+02, 3.5950e+02,\n",
-       "       3.5975e+02], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1959-01-01</div><input id='attrs-c29e1b3c-7ff5-4713-adb3-33b1eeb61fd5' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c29e1b3c-7ff5-4713-adb3-33b1eeb61fd5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c492521b-cc2d-4d92-9b28-44725225202e' class='xr-var-data-in' type='checkbox'><label for='data-c492521b-cc2d-4d92-9b28-44725225202e' title='Show/Hide data 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;, dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-9eacec1e-21a9-4f12-a46c-6079cb108926' class='xr-section-summary-in' type='checkbox'  ><label for='section-9eacec1e-21a9-4f12-a46c-6079cb108926' 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>lat</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-a0cd351f-dff3-4d91-a2f7-039c53ac6100' class='xr-index-data-in' type='checkbox'/><label for='index-a0cd351f-dff3-4d91-a2f7-039c53ac6100' 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([  90.0,  89.75,   89.5,  89.25,   89.0,  88.75,   88.5,  88.25,   88.0,\n",
+       "</table></div></div></li><li class='xr-section-item'><input id='section-7f5817fb-ee6e-4fec-b589-b0ef4b310f1c' class='xr-section-summary-in' type='checkbox'  checked><label for='section-7f5817fb-ee6e-4fec-b589-b0ef4b310f1c' class='xr-section-summary' >Coordinates: <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-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>90.0 89.75 89.5 ... -89.75 -90.0</div><input id='attrs-aa15dcee-92fa-4c62-98e8-84f3ca6e1a57' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-aa15dcee-92fa-4c62-98e8-84f3ca6e1a57' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-98e97c96-96ec-4b3f-87ac-020e0d700b01' class='xr-var-data-in' type='checkbox'><label for='data-98e97c96-96ec-4b3f-87ac-020e0d700b01' title='Show/Hide data 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>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><pre>array([ 90.  ,  89.75,  89.5 , ..., -89.5 , -89.75, -90.  ], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0 0.25 0.5 ... 359.2 359.5 359.8</div><input id='attrs-b8f01150-c3e3-48fa-9109-7cdc84f53c30' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b8f01150-c3e3-48fa-9109-7cdc84f53c30' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f8bf858f-c089-4c72-bf87-3482cb68cb3e' class='xr-var-data-in' type='checkbox'><label for='data-f8bf858f-c089-4c72-bf87-3482cb68cb3e' title='Show/Hide data 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>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>array([0.0000e+00, 2.5000e-01, 5.0000e-01, ..., 3.5925e+02, 3.5950e+02,\n",
+       "       3.5975e+02], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1959-01-01</div><input id='attrs-59c7160e-83a9-4d67-a572-f6a3a76a93ce' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-59c7160e-83a9-4d67-a572-f6a3a76a93ce' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-42a05e25-62ad-4712-93e5-2e46105fc8c5' class='xr-var-data-in' type='checkbox'><label for='data-42a05e25-62ad-4712-93e5-2e46105fc8c5' title='Show/Hide data 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;, dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-b757ed62-3948-4906-9202-98dab0ae789c' class='xr-section-summary-in' type='checkbox'  ><label for='section-b757ed62-3948-4906-9202-98dab0ae789c' 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>lat</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-be300539-bfaa-48bb-8f07-b481b2a71f72' class='xr-index-data-in' type='checkbox'/><label for='index-be300539-bfaa-48bb-8f07-b481b2a71f72' 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([  90.0,  89.75,   89.5,  89.25,   89.0,  88.75,   88.5,  88.25,   88.0,\n",
        "        87.75,\n",
        "       ...\n",
        "       -87.75,  -88.0, -88.25,  -88.5, -88.75,  -89.0, -89.25,  -89.5, -89.75,\n",
        "        -90.0],\n",
-       "      dtype=&#x27;float32&#x27;, name=&#x27;lat&#x27;, length=721))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lon</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-fcccfc2a-88eb-477e-812f-09e5a8ec6bba' class='xr-index-data-in' type='checkbox'/><label for='index-fcccfc2a-88eb-477e-812f-09e5a8ec6bba' 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,   0.25,    0.5,   0.75,    1.0,   1.25,    1.5,   1.75,    2.0,\n",
+       "      dtype=&#x27;float32&#x27;, name=&#x27;lat&#x27;, length=721))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lon</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-2e5bf13c-07a6-467a-bf05-343b9d271a1f' class='xr-index-data-in' type='checkbox'/><label for='index-2e5bf13c-07a6-467a-bf05-343b9d271a1f' 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,   0.25,    0.5,   0.75,    1.0,   1.25,    1.5,   1.75,    2.0,\n",
        "         2.25,\n",
        "       ...\n",
        "        357.5, 357.75,  358.0, 358.25,  358.5, 358.75,  359.0, 359.25,  359.5,\n",
        "       359.75],\n",
-       "      dtype=&#x27;float32&#x27;, name=&#x27;lon&#x27;, length=1440))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-447b5a33-26df-4ea6-8434-26483aa5ce91' class='xr-section-summary-in' type='checkbox'  checked><label for='section-447b5a33-26df-4ea6-8434-26483aa5ce91' class='xr-section-summary' >Attributes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><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></li></ul></div></div>"
+       "      dtype=&#x27;float32&#x27;, name=&#x27;lon&#x27;, length=1440))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-c8703966-112e-427c-b8b5-65c4e32e493e' class='xr-section-summary-in' type='checkbox'  checked><label for='section-c8703966-112e-427c-b8b5-65c4e32e493e' class='xr-section-summary' >Attributes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><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></li></ul></div></div>"
       ],
       "text/plain": [
        "<xarray.DataArray '10m_u_component_of_wind' (lat: 721, lon: 1440)>\n",
@@ -5042,7 +5042,7 @@
        "    units:       m s**-1"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5054,16 +5054,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<cartopy.mpl.feature_artist.FeatureArtist at 0x7f31c97d5420>"
+       "<cartopy.mpl.feature_artist.FeatureArtist at 0x7fa0bc12a8f0>"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -5087,33 +5087,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "da_mask_era5 = create_mask(ds=ds_era5, bbox=bbox)\n",
-    "ds_era5"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def create_lateral_boundary_mask(ds, bbox, width):\n",
-    "    da_mask_inner = create_mask(ds=ds, bbox=bbox)\n",
-    "    da_mask_outer = np.logical_and(\n",
-    "        np.logical_and(ds.lon > bbox[0] - width, ds.lon < bbox[1] + width),\n",
-    "        np.logical_and(ds.lat > bbox[2] - width, ds.lat < bbox[3] + width),\n",
-    "    )\n",
-    "    da_mask_edge = da_mask_outer ^ da_mask_inner\n",
-    "    return da_mask_edge"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
@@ -5482,170 +5456,3421 @@
        "  stroke: currentColor;\n",
        "  fill: currentColor;\n",
        "}\n",
-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;lon&#x27; (lon: 1440, lat: 721)&gt;\n",
-       "array([[False, False, False, ..., False, False, False],\n",
-       "       [False, False, False, ..., False, False, False],\n",
-       "       [False, False, False, ..., False, False, False],\n",
-       "       ...,\n",
-       "       [False, False, False, ..., False, False, False],\n",
-       "       [False, False, False, ..., False, False, False],\n",
-       "       [False, False, False, ..., False, False, False]])\n",
-       "Coordinates:\n",
-       "  * lon      (lon) float32 0.0 0.25 0.5 0.75 1.0 ... 359.0 359.2 359.5 359.8\n",
-       "  * lat      (lat) float32 90.0 89.75 89.5 89.25 ... -89.25 -89.5 -89.75 -90.0</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'lon'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>lon</span>: 1440</li><li><span class='xr-has-index'>lat</span>: 721</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-86c44faa-eb77-4d39-a1a7-52a2619bdb38' class='xr-array-in' type='checkbox' checked><label for='section-86c44faa-eb77-4d39-a1a7-52a2619bdb38' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>False False False False False False ... False False False False False</span></div><div class='xr-array-data'><pre>array([[False, False, False, ..., False, False, False],\n",
-       "       [False, False, False, ..., False, False, False],\n",
-       "       [False, False, False, ..., False, False, False],\n",
-       "       ...,\n",
-       "       [False, False, False, ..., False, False, False],\n",
-       "       [False, False, False, ..., False, False, False],\n",
-       "       [False, False, False, ..., False, False, False]])</pre></div></div></li><li class='xr-section-item'><input id='section-3bebbcc2-5053-4851-93bc-707431c34881' class='xr-section-summary-in' type='checkbox'  checked><label for='section-3bebbcc2-5053-4851-93bc-707431c34881' class='xr-section-summary' >Coordinates: <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-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0 0.25 0.5 ... 359.2 359.5 359.8</div><input id='attrs-133e8fa3-5145-4590-a294-9464de67715c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-133e8fa3-5145-4590-a294-9464de67715c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9815cf23-9996-4786-9aa3-0fb73b6c92c4' class='xr-var-data-in' type='checkbox'><label for='data-9815cf23-9996-4786-9aa3-0fb73b6c92c4' title='Show/Hide data 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>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>array([0.0000e+00, 2.5000e-01, 5.0000e-01, ..., 3.5925e+02, 3.5950e+02,\n",
-       "       3.5975e+02], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>90.0 89.75 89.5 ... -89.75 -90.0</div><input id='attrs-68371d71-75a2-461c-be63-41e7527db41f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-68371d71-75a2-461c-be63-41e7527db41f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-058cce2a-be26-4628-8686-3b3516499534' class='xr-var-data-in' type='checkbox'><label for='data-058cce2a-be26-4628-8686-3b3516499534' title='Show/Hide data 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>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><pre>array([ 90.  ,  89.75,  89.5 , ..., -89.5 , -89.75, -90.  ], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-3e464d27-15f3-4d75-b0ef-339ce9ad315d' class='xr-section-summary-in' type='checkbox'  ><label for='section-3e464d27-15f3-4d75-b0ef-339ce9ad315d' 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>lon</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-0b0d3664-b54f-4dee-beea-614fdd57bed5' class='xr-index-data-in' type='checkbox'/><label for='index-0b0d3664-b54f-4dee-beea-614fdd57bed5' 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,   0.25,    0.5,   0.75,    1.0,   1.25,    1.5,   1.75,    2.0,\n",
-       "         2.25,\n",
-       "       ...\n",
-       "        357.5, 357.75,  358.0, 358.25,  358.5, 358.75,  359.0, 359.25,  359.5,\n",
-       "       359.75],\n",
-       "      dtype=&#x27;float32&#x27;, name=&#x27;lon&#x27;, length=1440))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lat</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-5fac22d8-1bb9-40bf-90b8-01ead4393269' class='xr-index-data-in' type='checkbox'/><label for='index-5fac22d8-1bb9-40bf-90b8-01ead4393269' 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([  90.0,  89.75,   89.5,  89.25,   89.0,  88.75,   88.5,  88.25,   88.0,\n",
-       "        87.75,\n",
-       "       ...\n",
-       "       -87.75,  -88.0, -88.25,  -88.5, -88.75,  -89.0, -89.25,  -89.5, -89.75,\n",
-       "        -90.0],\n",
-       "      dtype=&#x27;float32&#x27;, name=&#x27;lat&#x27;, length=721))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-3e4b95c0-fa22-434b-bbb8-a1062e8c78f4' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-3e4b95c0-fa22-434b-bbb8-a1062e8c78f4' 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.DataArray 'lon' (lon: 1440, lat: 721)>\n",
-       "array([[False, False, False, ..., False, False, False],\n",
-       "       [False, False, False, ..., False, False, False],\n",
-       "       [False, False, False, ..., False, False, False],\n",
-       "       ...,\n",
-       "       [False, False, False, ..., False, False, False],\n",
-       "       [False, False, False, ..., False, False, False],\n",
-       "       [False, False, False, ..., False, False, False]])\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
+       "Dimensions:                                           (time: 552264, lat: 721,\n",
+       "                                                       lon: 1440, level: 37)\n",
        "Coordinates:\n",
-       "  * lon      (lon) float32 0.0 0.25 0.5 0.75 1.0 ... 359.0 359.2 359.5 359.8\n",
-       "  * lat      (lat) float32 90.0 89.75 89.5 89.25 ... -89.25 -89.5 -89.75 -90.0"
-      ]
-     },
-     "execution_count": 27,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "da_mask_era5 = create_lateral_boundary_mask(ds=ds_era5, bbox=bbox, width=1.0)\n",
-    "da_mask_era5"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<cartopy.mpl.feature_artist.FeatureArtist at 0x7f31c81ac460>"
-      ]
-     },
-     "execution_count": 28,
-     "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(figsize=(10, 4), subplot_kw=dict(projection=ccrs.PlateCarree()))\n",
-    "da_mask_era5.plot(ax=ax, x=\"lon\", y=\"lat\", transform=ccrs.PlateCarree())\n",
-    "ax.gridlines(draw_labels=[\"top\", \"left\"])\n",
-    "ax.coastlines()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "da_mask_danra = create_mask(ds=ds_danra_hl, bbox=bbox)\n",
-    "ds_era5_edge = ds_era5.where(da_mask_era5, drop=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {},
-   "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",
-       "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n",
-       "<path d=\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
-       "<path d=\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
-       "</symbol>\n",
-       "<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n",
-       "<path d=\"M28.681 7.159c-0.694-0.947-1.662-2.053-2.724-3.116s-2.169-2.030-3.116-2.724c-1.612-1.182-2.393-1.319-2.841-1.319h-15.5c-1.378 0-2.5 1.121-2.5 2.5v27c0 1.378 1.122 2.5 2.5 2.5h23c1.378 0 2.5-1.122 2.5-2.5v-19.5c0-0.448-0.137-1.23-1.319-2.841zM24.543 5.457c0.959 0.959 1.712 1.825 2.268 2.543h-4.811v-4.811c0.718 0.556 1.584 1.309 2.543 2.268zM28 29.5c0 0.271-0.229 0.5-0.5 0.5h-23c-0.271 0-0.5-0.229-0.5-0.5v-27c0-0.271 0.229-0.5 0.5-0.5 0 0 15.499-0 15.5 0v7c0 0.552 0.448 1 1 1h7v19.5z\"></path>\n",
-       "<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",
+       "  * lat                                               (lat) float32 90.0 ... ...\n",
+       "  * level                                             (level) int64 1 2 ... 1000\n",
+       "  * lon                                               (lon) float32 0.0 ... 3...\n",
+       "  * time                                              (time) datetime64[ns] 1...\n",
+       "Data variables: (12/31)\n",
+       "    10m_u_component_of_wind                           (time, lat, lon) float32 dask.array&lt;chunksize=(48, 721, 1440), meta=np.ndarray&gt;\n",
+       "    10m_v_component_of_wind                           (time, lat, lon) float32 dask.array&lt;chunksize=(48, 721, 1440), meta=np.ndarray&gt;\n",
+       "    2m_temperature                                    (time, lat, lon) float32 dask.array&lt;chunksize=(48, 721, 1440), meta=np.ndarray&gt;\n",
+       "    angle_of_sub_gridscale_orography                  (lat, lon) float32 dask.array&lt;chunksize=(721, 1440), meta=np.ndarray&gt;\n",
+       "    anisotropy_of_sub_gridscale_orography             (lat, lon) float32 dask.array&lt;chunksize=(721, 1440), meta=np.ndarray&gt;\n",
+       "    geopotential                                      (time, level, lat, lon) float32 dask.array&lt;chunksize=(48, 37, 721, 1440), meta=np.ndarray&gt;\n",
+       "    ...                                                ...\n",
+       "    total_precipitation                               (time, lat, lon) float32 dask.array&lt;chunksize=(48, 721, 1440), meta=np.ndarray&gt;\n",
+       "    type_of_high_vegetation                           (lat, lon) float32 dask.array&lt;chunksize=(721, 1440), meta=np.ndarray&gt;\n",
+       "    type_of_low_vegetation                            (lat, lon) float32 dask.array&lt;chunksize=(721, 1440), meta=np.ndarray&gt;\n",
+       "    u_component_of_wind                               (time, level, lat, lon) float32 dask.array&lt;chunksize=(48, 37, 721, 1440), meta=np.ndarray&gt;\n",
+       "    v_component_of_wind                               (time, level, lat, lon) float32 dask.array&lt;chunksize=(48, 37, 721, 1440), meta=np.ndarray&gt;\n",
+       "    vertical_velocity                                 (time, level, lat, lon) float32 dask.array&lt;chunksize=(48, 37, 721, 1440), 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-f8aa4f35-1c5b-46f1-a248-f216d7d7d91d' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-f8aa4f35-1c5b-46f1-a248-f216d7d7d91d' 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>: 552264</li><li><span class='xr-has-index'>lat</span>: 721</li><li><span class='xr-has-index'>lon</span>: 1440</li><li><span class='xr-has-index'>level</span>: 37</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-e3fa4438-f84b-4bbe-b309-9244f3cbea1f' class='xr-section-summary-in' type='checkbox'  checked><label for='section-e3fa4438-f84b-4bbe-b309-9244f3cbea1f' 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'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>90.0 89.75 89.5 ... -89.75 -90.0</div><input id='attrs-37029ea8-4c14-46cd-95d7-2ef190158d33' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-37029ea8-4c14-46cd-95d7-2ef190158d33' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0d45eb4e-18d7-4f74-9d83-e15388c44a34' class='xr-var-data-in' type='checkbox'><label for='data-0d45eb4e-18d7-4f74-9d83-e15388c44a34' title='Show/Hide data 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>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><pre>array([ 90.  ,  89.75,  89.5 , ..., -89.5 , -89.75, -90.  ], dtype=float32)</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'>1 2 3 5 7 ... 900 925 950 975 1000</div><input id='attrs-9be53e88-2cb3-4e83-a1b8-32af4d8c6b26' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9be53e88-2cb3-4e83-a1b8-32af4d8c6b26' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-acb950fb-3ee3-4a82-85d9-259a1d8639fc' class='xr-var-data-in' type='checkbox'><label for='data-acb950fb-3ee3-4a82-85d9-259a1d8639fc' title='Show/Hide data 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([   1,    2,    3,    5,    7,   10,   20,   30,   50,   70,  100,  125,\n",
+       "        150,  175,  200,  225,  250,  300,  350,  400,  450,  500,  550,  600,\n",
+       "        650,  700,  750,  775,  800,  825,  850,  875,  900,  925,  950,  975,\n",
+       "       1000])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0 0.25 0.5 ... 359.2 359.5 359.8</div><input id='attrs-40c69dea-ac47-46b1-a5a6-d6f7519d2555' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-40c69dea-ac47-46b1-a5a6-d6f7519d2555' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fdf0f9d7-f62f-4dfe-9cd3-ffab5ce14de5' class='xr-var-data-in' type='checkbox'><label for='data-fdf0f9d7-f62f-4dfe-9cd3-ffab5ce14de5' title='Show/Hide data 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>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>array([0.0000e+00, 2.5000e-01, 5.0000e-01, ..., 3.5925e+02, 3.5950e+02,\n",
+       "       3.5975e+02], dtype=float32)</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 ... 2021-12-31T23:00:00</div><input id='attrs-1897f33d-1ec8-424a-8e56-bf316162f68a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1897f33d-1ec8-424a-8e56-bf316162f68a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-34e1bd1b-e1b3-47ad-aed1-73a8aa3bb91e' class='xr-var-data-in' type='checkbox'><label for='data-34e1bd1b-e1b3-47ad-aed1-73a8aa3bb91e' title='Show/Hide data 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;1959-01-01T01:00:00.000000000&#x27;,\n",
+       "       &#x27;1959-01-01T02:00:00.000000000&#x27;, ..., &#x27;2021-12-31T21:00:00.000000000&#x27;,\n",
+       "       &#x27;2021-12-31T22:00:00.000000000&#x27;, &#x27;2021-12-31T23:00:00.000000000&#x27;],\n",
+       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-811fb0f2-cae1-44e2-a835-0fd9186c2844' class='xr-section-summary-in' type='checkbox'  ><label for='section-811fb0f2-cae1-44e2-a835-0fd9186c2844' class='xr-section-summary' >Data variables: <span>(31)</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, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(48, 721, 1440), meta=np.ndarray&gt;</div><input id='attrs-a17e960b-eaa0-4d50-a452-659601bef32a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a17e960b-eaa0-4d50-a452-659601bef32a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f45fcccc-0750-4415-85fe-57e1ee39f769' class='xr-var-data-in' type='checkbox'><label for='data-f45fcccc-0750-4415-85fe-57e1ee39f769' title='Show/Hide data 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> 2.09 TiB </td>\n",
+       "                        <td> 190.11 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (552264, 721, 1440) </td>\n",
+       "                        <td> (48, 721, 1440) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 11506 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",
+       "        <svg width=\"156\" height=\"146\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
        "\n",
-       ".xr-text-repr-fallback {\n",
-       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
-       "  display: none;\n",
-       "}\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"10\" y1=\"25\" x2=\"80\" y2=\"96\" style=\"stroke-width:2\" />\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",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"13\" y1=\"3\" x2=\"13\" y2=\"29\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"17\" y2=\"32\" />\n",
+       "  <line x1=\"21\" y1=\"11\" x2=\"21\" y2=\"36\" />\n",
+       "  <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=\"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=\"69\" />\n",
+       "  <line x1=\"58\" y1=\"48\" x2=\"58\" y2=\"73\" />\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",
+       "  <line x1=\"76\" y1=\"66\" x2=\"76\" y2=\"92\" />\n",
+       "  <line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"96\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"10.0,0.0 80.58823529411765,70.58823529411765 80.58823529411765,96.00085180870013 10.0,25.41261651458249\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"35\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"13\" y1=\"3\" x2=\"39\" y2=\"3\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"42\" y2=\"7\" />\n",
+       "  <line x1=\"21\" y1=\"11\" x2=\"46\" y2=\"11\" />\n",
+       "  <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=\"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=\"79\" y2=\"44\" />\n",
+       "  <line x1=\"58\" y1=\"48\" x2=\"83\" y2=\"48\" />\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",
+       "  <line x1=\"76\" y1=\"66\" x2=\"102\" y2=\"66\" />\n",
+       "  <line x1=\"80\" y1=\"70\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"35\" y1=\"0\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"10.0,0.0 35.41261651458249,0.0 106.00085180870013,70.58823529411765 80.58823529411765,70.58823529411765\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"80\" y1=\"70\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"80\" y1=\"96\" x2=\"106\" y2=\"96\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"96\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"106\" y1=\"70\" x2=\"106\" y2=\"96\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"80.58823529411765,70.58823529411765 106.00085180870013,70.58823529411765 106.00085180870013,96.00085180870013 80.58823529411765,96.00085180870013\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"93.294544\" y=\"116.000852\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1440</text>\n",
+       "  <text x=\"126.000852\" y=\"83.294544\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,126.000852,83.294544)\">721</text>\n",
+       "  <text x=\"35.294118\" y=\"80.706734\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,35.294118,80.706734)\">552264</text>\n",
+       "</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, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(48, 721, 1440), meta=np.ndarray&gt;</div><input id='attrs-0758984a-ea2f-45b7-8b28-816e0f230e16' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0758984a-ea2f-45b7-8b28-816e0f230e16' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2e9faeba-0a27-46e6-b0cf-ef25e911c9a7' class='xr-var-data-in' type='checkbox'><label for='data-2e9faeba-0a27-46e6-b0cf-ef25e911c9a7' title='Show/Hide data 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> 2.09 TiB </td>\n",
+       "                        <td> 190.11 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (552264, 721, 1440) </td>\n",
+       "                        <td> (48, 721, 1440) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 11506 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",
+       "        <svg width=\"156\" height=\"146\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"10\" y1=\"25\" x2=\"80\" y2=\"96\" 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=\"13\" y1=\"3\" x2=\"13\" y2=\"29\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"17\" y2=\"32\" />\n",
+       "  <line x1=\"21\" y1=\"11\" x2=\"21\" y2=\"36\" />\n",
+       "  <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=\"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=\"69\" />\n",
+       "  <line x1=\"58\" y1=\"48\" x2=\"58\" y2=\"73\" />\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",
+       "  <line x1=\"76\" y1=\"66\" x2=\"76\" y2=\"92\" />\n",
+       "  <line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"96\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"10.0,0.0 80.58823529411765,70.58823529411765 80.58823529411765,96.00085180870013 10.0,25.41261651458249\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"35\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"13\" y1=\"3\" x2=\"39\" y2=\"3\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"42\" y2=\"7\" />\n",
+       "  <line x1=\"21\" y1=\"11\" x2=\"46\" y2=\"11\" />\n",
+       "  <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=\"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=\"79\" y2=\"44\" />\n",
+       "  <line x1=\"58\" y1=\"48\" x2=\"83\" y2=\"48\" />\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",
+       "  <line x1=\"76\" y1=\"66\" x2=\"102\" y2=\"66\" />\n",
+       "  <line x1=\"80\" y1=\"70\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"35\" y1=\"0\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"10.0,0.0 35.41261651458249,0.0 106.00085180870013,70.58823529411765 80.58823529411765,70.58823529411765\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"80\" y1=\"70\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"80\" y1=\"96\" x2=\"106\" y2=\"96\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"96\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"106\" y1=\"70\" x2=\"106\" y2=\"96\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"80.58823529411765,70.58823529411765 106.00085180870013,70.58823529411765 106.00085180870013,96.00085180870013 80.58823529411765,96.00085180870013\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"93.294544\" y=\"116.000852\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1440</text>\n",
+       "  <text x=\"126.000852\" y=\"83.294544\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,126.000852,83.294544)\">721</text>\n",
+       "  <text x=\"35.294118\" y=\"80.706734\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,35.294118,80.706734)\">552264</text>\n",
+       "</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, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(48, 721, 1440), meta=np.ndarray&gt;</div><input id='attrs-955033be-734d-4c39-84bf-0e917e74fa98' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-955033be-734d-4c39-84bf-0e917e74fa98' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2ecdf10f-3dc4-47f1-8e93-4c14db778fc6' class='xr-var-data-in' type='checkbox'><label for='data-2ecdf10f-3dc4-47f1-8e93-4c14db778fc6' title='Show/Hide data 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",
+       "                <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> 2.09 TiB </td>\n",
+       "                        <td> 190.11 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (552264, 721, 1440) </td>\n",
+       "                        <td> (48, 721, 1440) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 11506 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",
+       "        <svg width=\"156\" height=\"146\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"10\" y1=\"25\" x2=\"80\" y2=\"96\" 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=\"13\" y1=\"3\" x2=\"13\" y2=\"29\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"17\" y2=\"32\" />\n",
+       "  <line x1=\"21\" y1=\"11\" x2=\"21\" y2=\"36\" />\n",
+       "  <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=\"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=\"69\" />\n",
+       "  <line x1=\"58\" y1=\"48\" x2=\"58\" y2=\"73\" />\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",
+       "  <line x1=\"76\" y1=\"66\" x2=\"76\" y2=\"92\" />\n",
+       "  <line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"96\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"10.0,0.0 80.58823529411765,70.58823529411765 80.58823529411765,96.00085180870013 10.0,25.41261651458249\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"35\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"13\" y1=\"3\" x2=\"39\" y2=\"3\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"42\" y2=\"7\" />\n",
+       "  <line x1=\"21\" y1=\"11\" x2=\"46\" y2=\"11\" />\n",
+       "  <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=\"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=\"79\" y2=\"44\" />\n",
+       "  <line x1=\"58\" y1=\"48\" x2=\"83\" y2=\"48\" />\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",
+       "  <line x1=\"76\" y1=\"66\" x2=\"102\" y2=\"66\" />\n",
+       "  <line x1=\"80\" y1=\"70\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"35\" y1=\"0\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"10.0,0.0 35.41261651458249,0.0 106.00085180870013,70.58823529411765 80.58823529411765,70.58823529411765\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"80\" y1=\"70\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"80\" y1=\"96\" x2=\"106\" y2=\"96\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"96\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"106\" y1=\"70\" x2=\"106\" y2=\"96\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"80.58823529411765,70.58823529411765 106.00085180870013,70.58823529411765 106.00085180870013,96.00085180870013 80.58823529411765,96.00085180870013\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"93.294544\" y=\"116.000852\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1440</text>\n",
+       "  <text x=\"126.000852\" y=\"83.294544\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,126.000852,83.294544)\">721</text>\n",
+       "  <text x=\"35.294118\" y=\"80.706734\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,35.294118,80.706734)\">552264</text>\n",
+       "</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'>(lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(721, 1440), meta=np.ndarray&gt;</div><input id='attrs-e01c515c-d04a-4f2e-9459-0b8ad235adf9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e01c515c-d04a-4f2e-9459-0b8ad235adf9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-58a97b6e-9f3d-45a1-8f0a-46a7c073e186' class='xr-var-data-in' type='checkbox'><label for='data-58a97b6e-9f3d-45a1-8f0a-46a7c073e186' title='Show/Hide data 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> 3.96 MiB </td>\n",
+       "                        <td> 3.96 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (721, 1440) </td>\n",
+       "                        <td> (721, 1440) </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",
+       "        <svg width=\"170\" height=\"110\" 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=\"60\" x2=\"120\" y2=\"60\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"60\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"60\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,60.083333333333336 0.0,60.083333333333336\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"80.083333\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1440</text>\n",
+       "  <text x=\"140.000000\" y=\"30.041667\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,30.041667)\">721</text>\n",
+       "</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'>(lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(721, 1440), meta=np.ndarray&gt;</div><input id='attrs-9102d880-993e-497c-a149-95c537c1536e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9102d880-993e-497c-a149-95c537c1536e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e145ef98-e0ce-441a-9edf-acb9eb839bd9' class='xr-var-data-in' type='checkbox'><label for='data-e145ef98-e0ce-441a-9edf-acb9eb839bd9' title='Show/Hide data 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> 3.96 MiB </td>\n",
+       "                        <td> 3.96 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (721, 1440) </td>\n",
+       "                        <td> (721, 1440) </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",
+       "        <svg width=\"170\" height=\"110\" 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=\"60\" x2=\"120\" y2=\"60\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"60\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"60\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,60.083333333333336 0.0,60.083333333333336\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"80.083333\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1440</text>\n",
+       "  <text x=\"140.000000\" y=\"30.041667\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,30.041667)\">721</text>\n",
+       "</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, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(48, 37, 721, 1440), meta=np.ndarray&gt;</div><input id='attrs-87dd7bfa-5b7f-47a2-9f33-813283b491e9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-87dd7bfa-5b7f-47a2-9f33-813283b491e9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9ee5734d-3e7c-4b9c-a8ab-dba14f275948' class='xr-var-data-in' type='checkbox'><label for='data-9ee5734d-3e7c-4b9c-a8ab-dba14f275948' title='Show/Hide data 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",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 77.18 TiB </td>\n",
+       "                        <td> 6.87 GiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (552264, 37, 721, 1440) </td>\n",
+       "                        <td> (48, 37, 721, 1440) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 11506 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",
+       "        <svg width=\"470\" height=\"90\" 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=\"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=\"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=\"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=\"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=\"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=\"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=\"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=\"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",
+       "  <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:#8B4903A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >552264</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",
+       "\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"190\" y1=\"0\" x2=\"204\" y2=\"14\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"190\" y1=\"25\" x2=\"204\" y2=\"40\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"190\" y1=\"0\" x2=\"190\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"204\" y1=\"14\" x2=\"204\" y2=\"40\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"190.0,0.0 204.9485979497544,14.948597949754403 204.9485979497544,40.36121446433689 190.0,25.41261651458249\" 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",
+       "  <line x1=\"215\" y1=\"0\" x2=\"230\" y2=\"14\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"190.0,0.0 215.41261651458248,0.0 230.36121446433688,14.948597949754403 204.9485979497544,14.948597949754403\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\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",
+       "  <polygon points=\"204.9485979497544,14.948597949754403 230.36121446433688,14.948597949754403 230.36121446433688,40.36121446433689 204.9485979497544,40.36121446433689\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"217.654906\" y=\"60.361214\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1440</text>\n",
+       "  <text x=\"250.361214\" y=\"27.654906\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,250.361214,27.654906)\">721</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)\">37</text>\n",
+       "</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'>(lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(721, 1440), meta=np.ndarray&gt;</div><input id='attrs-47cecb2c-cd75-468c-aa99-c4828d180ee4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-47cecb2c-cd75-468c-aa99-c4828d180ee4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-036c7c14-d23e-485c-aa18-b60db13d934a' class='xr-var-data-in' type='checkbox'><label for='data-036c7c14-d23e-485c-aa18-b60db13d934a' title='Show/Hide data 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",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 3.96 MiB </td>\n",
+       "                        <td> 3.96 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (721, 1440) </td>\n",
+       "                        <td> (721, 1440) </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",
+       "        <svg width=\"170\" height=\"110\" 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=\"60\" x2=\"120\" y2=\"60\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"60\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"60\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,60.083333333333336 0.0,60.083333333333336\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"80.083333\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1440</text>\n",
+       "  <text x=\"140.000000\" y=\"30.041667\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,30.041667)\">721</text>\n",
+       "</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'>(lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(721, 1440), meta=np.ndarray&gt;</div><input id='attrs-3538b06f-5f15-446c-9ff6-f5b9ac903ab1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3538b06f-5f15-446c-9ff6-f5b9ac903ab1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3abe1c0b-9b41-4f72-ac4e-30cdffb9e8db' class='xr-var-data-in' type='checkbox'><label for='data-3abe1c0b-9b41-4f72-ac4e-30cdffb9e8db' title='Show/Hide data 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",
+       "                <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> 3.96 MiB </td>\n",
+       "                        <td> 3.96 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (721, 1440) </td>\n",
+       "                        <td> (721, 1440) </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",
+       "        <svg width=\"170\" height=\"110\" 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=\"60\" x2=\"120\" y2=\"60\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"60\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"60\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,60.083333333333336 0.0,60.083333333333336\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"80.083333\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1440</text>\n",
+       "  <text x=\"140.000000\" y=\"30.041667\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,30.041667)\">721</text>\n",
+       "</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'>(lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(721, 1440), meta=np.ndarray&gt;</div><input id='attrs-f9fedb2e-3403-4fdd-97bc-78a53bd8f9ef' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f9fedb2e-3403-4fdd-97bc-78a53bd8f9ef' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4dd57b3f-ecc8-4d6d-bc7b-9fed8530ba02' class='xr-var-data-in' type='checkbox'><label for='data-4dd57b3f-ecc8-4d6d-bc7b-9fed8530ba02' title='Show/Hide data 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> 3.96 MiB </td>\n",
+       "                        <td> 3.96 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (721, 1440) </td>\n",
+       "                        <td> (721, 1440) </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",
+       "        <svg width=\"170\" height=\"110\" 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=\"60\" x2=\"120\" y2=\"60\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"60\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"60\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,60.083333333333336 0.0,60.083333333333336\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"80.083333\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1440</text>\n",
+       "  <text x=\"140.000000\" y=\"30.041667\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,30.041667)\">721</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lake_depth</span></div><div class='xr-var-dims'>(lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(721, 1440), meta=np.ndarray&gt;</div><input id='attrs-d9897ec7-9fe3-4498-8621-d0329cd67b8a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d9897ec7-9fe3-4498-8621-d0329cd67b8a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1cb5f1ca-58d4-4a68-ae42-c26c88371a39' class='xr-var-data-in' type='checkbox'><label for='data-1cb5f1ca-58d4-4a68-ae42-c26c88371a39' title='Show/Hide data 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 total depth</dd><dt><span>short_name :</span></dt><dd>dl</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> 3.96 MiB </td>\n",
+       "                        <td> 3.96 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (721, 1440) </td>\n",
+       "                        <td> (721, 1440) </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",
+       "        <svg width=\"170\" height=\"110\" 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=\"60\" x2=\"120\" y2=\"60\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"60\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"60\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,60.083333333333336 0.0,60.083333333333336\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"80.083333\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1440</text>\n",
+       "  <text x=\"140.000000\" y=\"30.041667\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,30.041667)\">721</text>\n",
+       "</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'>(lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(721, 1440), meta=np.ndarray&gt;</div><input id='attrs-ca6c2885-8540-4644-ad26-bb8a17a156a7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ca6c2885-8540-4644-ad26-bb8a17a156a7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-523d0dd7-774f-4e95-a663-fc88129e5be8' class='xr-var-data-in' type='checkbox'><label for='data-523d0dd7-774f-4e95-a663-fc88129e5be8' title='Show/Hide data 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> 3.96 MiB </td>\n",
+       "                        <td> 3.96 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (721, 1440) </td>\n",
+       "                        <td> (721, 1440) </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",
+       "        <svg width=\"170\" height=\"110\" 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=\"60\" x2=\"120\" y2=\"60\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"60\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"60\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,60.083333333333336 0.0,60.083333333333336\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"80.083333\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1440</text>\n",
+       "  <text x=\"140.000000\" y=\"30.041667\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,30.041667)\">721</text>\n",
+       "</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'>(lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(721, 1440), meta=np.ndarray&gt;</div><input id='attrs-28b31f97-1304-4543-8dc5-eea2b4dec7fc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-28b31f97-1304-4543-8dc5-eea2b4dec7fc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6d16c2de-9384-48a3-8e6e-859ca2b246c0' class='xr-var-data-in' type='checkbox'><label for='data-6d16c2de-9384-48a3-8e6e-859ca2b246c0' title='Show/Hide data 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",
+       "                <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> 3.96 MiB </td>\n",
+       "                        <td> 3.96 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (721, 1440) </td>\n",
+       "                        <td> (721, 1440) </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",
+       "        <svg width=\"170\" height=\"110\" 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=\"60\" x2=\"120\" y2=\"60\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"60\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"60\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,60.083333333333336 0.0,60.083333333333336\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"80.083333\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1440</text>\n",
+       "  <text x=\"140.000000\" y=\"30.041667\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,30.041667)\">721</text>\n",
+       "</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, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(48, 721, 1440), meta=np.ndarray&gt;</div><input id='attrs-4704f255-2155-48ce-adf0-ce47a6541a5b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4704f255-2155-48ce-adf0-ce47a6541a5b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f3b21878-c634-4d28-a9bc-e7d727e2818f' class='xr-var-data-in' type='checkbox'><label for='data-f3b21878-c634-4d28-a9bc-e7d727e2818f' title='Show/Hide data 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",
+       "                        <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> 2.09 TiB </td>\n",
+       "                        <td> 190.11 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (552264, 721, 1440) </td>\n",
+       "                        <td> (48, 721, 1440) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 11506 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",
+       "        <svg width=\"156\" height=\"146\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"10\" y1=\"25\" x2=\"80\" y2=\"96\" 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=\"13\" y1=\"3\" x2=\"13\" y2=\"29\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"17\" y2=\"32\" />\n",
+       "  <line x1=\"21\" y1=\"11\" x2=\"21\" y2=\"36\" />\n",
+       "  <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=\"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=\"69\" />\n",
+       "  <line x1=\"58\" y1=\"48\" x2=\"58\" y2=\"73\" />\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",
+       "  <line x1=\"76\" y1=\"66\" x2=\"76\" y2=\"92\" />\n",
+       "  <line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"96\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"10.0,0.0 80.58823529411765,70.58823529411765 80.58823529411765,96.00085180870013 10.0,25.41261651458249\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"35\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"13\" y1=\"3\" x2=\"39\" y2=\"3\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"42\" y2=\"7\" />\n",
+       "  <line x1=\"21\" y1=\"11\" x2=\"46\" y2=\"11\" />\n",
+       "  <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=\"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=\"79\" y2=\"44\" />\n",
+       "  <line x1=\"58\" y1=\"48\" x2=\"83\" y2=\"48\" />\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",
+       "  <line x1=\"76\" y1=\"66\" x2=\"102\" y2=\"66\" />\n",
+       "  <line x1=\"80\" y1=\"70\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"35\" y1=\"0\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"10.0,0.0 35.41261651458249,0.0 106.00085180870013,70.58823529411765 80.58823529411765,70.58823529411765\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"80\" y1=\"70\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"80\" y1=\"96\" x2=\"106\" y2=\"96\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"96\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"106\" y1=\"70\" x2=\"106\" y2=\"96\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"80.58823529411765,70.58823529411765 106.00085180870013,70.58823529411765 106.00085180870013,96.00085180870013 80.58823529411765,96.00085180870013\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"93.294544\" y=\"116.000852\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1440</text>\n",
+       "  <text x=\"126.000852\" y=\"83.294544\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,126.000852,83.294544)\">721</text>\n",
+       "  <text x=\"35.294118\" y=\"80.706734\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,35.294118,80.706734)\">552264</text>\n",
+       "</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, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(48, 721, 1440), meta=np.ndarray&gt;</div><input id='attrs-27b76562-37aa-48f9-af78-245270fac9cb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-27b76562-37aa-48f9-af78-245270fac9cb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c53cee64-5576-4d2c-9bdc-607da13f7f48' class='xr-var-data-in' type='checkbox'><label for='data-c53cee64-5576-4d2c-9bdc-607da13f7f48' title='Show/Hide data 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",
+       "                <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> 2.09 TiB </td>\n",
+       "                        <td> 190.11 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (552264, 721, 1440) </td>\n",
+       "                        <td> (48, 721, 1440) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 11506 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",
+       "        <svg width=\"156\" height=\"146\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"10\" y1=\"25\" x2=\"80\" y2=\"96\" 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=\"13\" y1=\"3\" x2=\"13\" y2=\"29\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"17\" y2=\"32\" />\n",
+       "  <line x1=\"21\" y1=\"11\" x2=\"21\" y2=\"36\" />\n",
+       "  <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=\"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=\"69\" />\n",
+       "  <line x1=\"58\" y1=\"48\" x2=\"58\" y2=\"73\" />\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",
+       "  <line x1=\"76\" y1=\"66\" x2=\"76\" y2=\"92\" />\n",
+       "  <line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"96\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"10.0,0.0 80.58823529411765,70.58823529411765 80.58823529411765,96.00085180870013 10.0,25.41261651458249\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"35\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"13\" y1=\"3\" x2=\"39\" y2=\"3\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"42\" y2=\"7\" />\n",
+       "  <line x1=\"21\" y1=\"11\" x2=\"46\" y2=\"11\" />\n",
+       "  <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=\"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=\"79\" y2=\"44\" />\n",
+       "  <line x1=\"58\" y1=\"48\" x2=\"83\" y2=\"48\" />\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",
+       "  <line x1=\"76\" y1=\"66\" x2=\"102\" y2=\"66\" />\n",
+       "  <line x1=\"80\" y1=\"70\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"35\" y1=\"0\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"10.0,0.0 35.41261651458249,0.0 106.00085180870013,70.58823529411765 80.58823529411765,70.58823529411765\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"80\" y1=\"70\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"80\" y1=\"96\" x2=\"106\" y2=\"96\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"96\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"106\" y1=\"70\" x2=\"106\" y2=\"96\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"80.58823529411765,70.58823529411765 106.00085180870013,70.58823529411765 106.00085180870013,96.00085180870013 80.58823529411765,96.00085180870013\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"93.294544\" y=\"116.000852\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1440</text>\n",
+       "  <text x=\"126.000852\" y=\"83.294544\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,126.000852,83.294544)\">721</text>\n",
+       "  <text x=\"35.294118\" y=\"80.706734\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,35.294118,80.706734)\">552264</text>\n",
+       "</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, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(48, 721, 1440), meta=np.ndarray&gt;</div><input id='attrs-17137a25-a417-40fe-9165-31b778de09c8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-17137a25-a417-40fe-9165-31b778de09c8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-53f21aa8-1ad9-4aba-8819-82f45b494836' class='xr-var-data-in' type='checkbox'><label for='data-53f21aa8-1ad9-4aba-8819-82f45b494836' title='Show/Hide data 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",
+       "                <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> 2.09 TiB </td>\n",
+       "                        <td> 190.11 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (552264, 721, 1440) </td>\n",
+       "                        <td> (48, 721, 1440) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 11506 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",
+       "        <svg width=\"156\" height=\"146\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"10\" y1=\"25\" x2=\"80\" y2=\"96\" 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=\"13\" y1=\"3\" x2=\"13\" y2=\"29\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"17\" y2=\"32\" />\n",
+       "  <line x1=\"21\" y1=\"11\" x2=\"21\" y2=\"36\" />\n",
+       "  <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=\"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=\"69\" />\n",
+       "  <line x1=\"58\" y1=\"48\" x2=\"58\" y2=\"73\" />\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",
+       "  <line x1=\"76\" y1=\"66\" x2=\"76\" y2=\"92\" />\n",
+       "  <line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"96\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"10.0,0.0 80.58823529411765,70.58823529411765 80.58823529411765,96.00085180870013 10.0,25.41261651458249\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"35\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"13\" y1=\"3\" x2=\"39\" y2=\"3\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"42\" y2=\"7\" />\n",
+       "  <line x1=\"21\" y1=\"11\" x2=\"46\" y2=\"11\" />\n",
+       "  <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=\"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=\"79\" y2=\"44\" />\n",
+       "  <line x1=\"58\" y1=\"48\" x2=\"83\" y2=\"48\" />\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",
+       "  <line x1=\"76\" y1=\"66\" x2=\"102\" y2=\"66\" />\n",
+       "  <line x1=\"80\" y1=\"70\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"35\" y1=\"0\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"10.0,0.0 35.41261651458249,0.0 106.00085180870013,70.58823529411765 80.58823529411765,70.58823529411765\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"80\" y1=\"70\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"80\" y1=\"96\" x2=\"106\" y2=\"96\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"96\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"106\" y1=\"70\" x2=\"106\" y2=\"96\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"80.58823529411765,70.58823529411765 106.00085180870013,70.58823529411765 106.00085180870013,96.00085180870013 80.58823529411765,96.00085180870013\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"93.294544\" y=\"116.000852\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1440</text>\n",
+       "  <text x=\"126.000852\" y=\"83.294544\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,126.000852,83.294544)\">721</text>\n",
+       "  <text x=\"35.294118\" y=\"80.706734\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,35.294118,80.706734)\">552264</text>\n",
+       "</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'>(lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(721, 1440), meta=np.ndarray&gt;</div><input id='attrs-9e3b4e6d-8f75-45aa-8edd-11a5dd385e5c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9e3b4e6d-8f75-45aa-8edd-11a5dd385e5c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ede24c24-9a40-40f8-a118-2ffca23dc986' class='xr-var-data-in' type='checkbox'><label for='data-ede24c24-9a40-40f8-a118-2ffca23dc986' title='Show/Hide data 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",
+       "                <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> 3.96 MiB </td>\n",
+       "                        <td> 3.96 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (721, 1440) </td>\n",
+       "                        <td> (721, 1440) </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",
+       "        <svg width=\"170\" height=\"110\" 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=\"60\" x2=\"120\" y2=\"60\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"60\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"60\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,60.083333333333336 0.0,60.083333333333336\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"80.083333\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1440</text>\n",
+       "  <text x=\"140.000000\" y=\"30.041667\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,30.041667)\">721</text>\n",
+       "</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'>(lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(721, 1440), meta=np.ndarray&gt;</div><input id='attrs-b1be3af0-d6f1-4a0a-82e7-0587979d86b1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b1be3af0-d6f1-4a0a-82e7-0587979d86b1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-35a0281f-3ec3-4a66-b365-d1c6c50fa5ee' class='xr-var-data-in' type='checkbox'><label for='data-35a0281f-3ec3-4a66-b365-d1c6c50fa5ee' title='Show/Hide data 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",
+       "                <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> 3.96 MiB </td>\n",
+       "                        <td> 3.96 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (721, 1440) </td>\n",
+       "                        <td> (721, 1440) </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",
+       "        <svg width=\"170\" height=\"110\" 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=\"60\" x2=\"120\" y2=\"60\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"60\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"60\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,60.083333333333336 0.0,60.083333333333336\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"80.083333\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1440</text>\n",
+       "  <text x=\"140.000000\" y=\"30.041667\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,30.041667)\">721</text>\n",
+       "</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, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(48, 37, 721, 1440), meta=np.ndarray&gt;</div><input id='attrs-2083fa05-f450-47b8-8c06-df373cf72642' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2083fa05-f450-47b8-8c06-df373cf72642' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3335e247-3c84-4d7b-bad1-7ea7379af68c' class='xr-var-data-in' type='checkbox'><label for='data-3335e247-3c84-4d7b-bad1-7ea7379af68c' title='Show/Hide data 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",
+       "                <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> 77.18 TiB </td>\n",
+       "                        <td> 6.87 GiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (552264, 37, 721, 1440) </td>\n",
+       "                        <td> (48, 37, 721, 1440) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 11506 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",
+       "        <svg width=\"470\" height=\"90\" 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=\"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=\"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=\"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=\"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=\"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=\"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=\"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=\"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",
+       "  <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:#8B4903A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >552264</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",
+       "\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"190\" y1=\"0\" x2=\"204\" y2=\"14\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"190\" y1=\"25\" x2=\"204\" y2=\"40\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"190\" y1=\"0\" x2=\"190\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"204\" y1=\"14\" x2=\"204\" y2=\"40\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"190.0,0.0 204.9485979497544,14.948597949754403 204.9485979497544,40.36121446433689 190.0,25.41261651458249\" 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",
+       "  <line x1=\"215\" y1=\"0\" x2=\"230\" y2=\"14\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"190.0,0.0 215.41261651458248,0.0 230.36121446433688,14.948597949754403 204.9485979497544,14.948597949754403\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\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",
+       "  <polygon points=\"204.9485979497544,14.948597949754403 230.36121446433688,14.948597949754403 230.36121446433688,40.36121446433689 204.9485979497544,40.36121446433689\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"217.654906\" y=\"60.361214\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1440</text>\n",
+       "  <text x=\"250.361214\" y=\"27.654906\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,250.361214,27.654906)\">721</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)\">37</text>\n",
+       "</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'>(lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(721, 1440), meta=np.ndarray&gt;</div><input id='attrs-1b9a52e4-299b-4973-a2ae-4dd9975ed541' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1b9a52e4-299b-4973-a2ae-4dd9975ed541' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-03daff26-6e9e-4db9-908f-158e39a547ce' class='xr-var-data-in' type='checkbox'><label for='data-03daff26-6e9e-4db9-908f-158e39a547ce' title='Show/Hide data 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",
+       "                <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> 3.96 MiB </td>\n",
+       "                        <td> 3.96 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (721, 1440) </td>\n",
+       "                        <td> (721, 1440) </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",
+       "        <svg width=\"170\" height=\"110\" 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=\"60\" x2=\"120\" y2=\"60\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"60\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"60\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,60.083333333333336 0.0,60.083333333333336\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"80.083333\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1440</text>\n",
+       "  <text x=\"140.000000\" y=\"30.041667\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,30.041667)\">721</text>\n",
+       "</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'>(lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(721, 1440), meta=np.ndarray&gt;</div><input id='attrs-b73d1c39-2238-4a09-b9df-53d6e72699a9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b73d1c39-2238-4a09-b9df-53d6e72699a9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8c2f81ab-98c9-4aec-9b5d-6ca29f7e90a2' class='xr-var-data-in' type='checkbox'><label for='data-8c2f81ab-98c9-4aec-9b5d-6ca29f7e90a2' title='Show/Hide data 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",
+       "                <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> 3.96 MiB </td>\n",
+       "                        <td> 3.96 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (721, 1440) </td>\n",
+       "                        <td> (721, 1440) </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",
+       "        <svg width=\"170\" height=\"110\" 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=\"60\" x2=\"120\" y2=\"60\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"60\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"60\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,60.083333333333336 0.0,60.083333333333336\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"80.083333\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1440</text>\n",
+       "  <text x=\"140.000000\" y=\"30.041667\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,30.041667)\">721</text>\n",
+       "</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, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(48, 721, 1440), meta=np.ndarray&gt;</div><input id='attrs-0ab47861-82df-4660-882c-e3de056e2108' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0ab47861-82df-4660-882c-e3de056e2108' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-46e5f1f6-6c89-476c-a9ea-48c115bde7dc' class='xr-var-data-in' type='checkbox'><label for='data-46e5f1f6-6c89-476c-a9ea-48c115bde7dc' title='Show/Hide data 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",
+       "                <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> 2.09 TiB </td>\n",
+       "                        <td> 190.11 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (552264, 721, 1440) </td>\n",
+       "                        <td> (48, 721, 1440) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 11506 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",
+       "        <svg width=\"156\" height=\"146\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"10\" y1=\"25\" x2=\"80\" y2=\"96\" 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=\"13\" y1=\"3\" x2=\"13\" y2=\"29\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"17\" y2=\"32\" />\n",
+       "  <line x1=\"21\" y1=\"11\" x2=\"21\" y2=\"36\" />\n",
+       "  <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=\"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=\"69\" />\n",
+       "  <line x1=\"58\" y1=\"48\" x2=\"58\" y2=\"73\" />\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",
+       "  <line x1=\"76\" y1=\"66\" x2=\"76\" y2=\"92\" />\n",
+       "  <line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"96\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"10.0,0.0 80.58823529411765,70.58823529411765 80.58823529411765,96.00085180870013 10.0,25.41261651458249\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"35\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"13\" y1=\"3\" x2=\"39\" y2=\"3\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"42\" y2=\"7\" />\n",
+       "  <line x1=\"21\" y1=\"11\" x2=\"46\" y2=\"11\" />\n",
+       "  <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=\"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=\"79\" y2=\"44\" />\n",
+       "  <line x1=\"58\" y1=\"48\" x2=\"83\" y2=\"48\" />\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",
+       "  <line x1=\"76\" y1=\"66\" x2=\"102\" y2=\"66\" />\n",
+       "  <line x1=\"80\" y1=\"70\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"35\" y1=\"0\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"10.0,0.0 35.41261651458249,0.0 106.00085180870013,70.58823529411765 80.58823529411765,70.58823529411765\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"80\" y1=\"70\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"80\" y1=\"96\" x2=\"106\" y2=\"96\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"96\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"106\" y1=\"70\" x2=\"106\" y2=\"96\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"80.58823529411765,70.58823529411765 106.00085180870013,70.58823529411765 106.00085180870013,96.00085180870013 80.58823529411765,96.00085180870013\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"93.294544\" y=\"116.000852\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1440</text>\n",
+       "  <text x=\"126.000852\" y=\"83.294544\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,126.000852,83.294544)\">721</text>\n",
+       "  <text x=\"35.294118\" y=\"80.706734\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,35.294118,80.706734)\">552264</text>\n",
+       "</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, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(48, 37, 721, 1440), meta=np.ndarray&gt;</div><input id='attrs-8d73632e-3244-41a4-baa1-717570b4557f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8d73632e-3244-41a4-baa1-717570b4557f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-310a9f27-faec-47d8-b1aa-21fb5ea72a08' class='xr-var-data-in' type='checkbox'><label for='data-310a9f27-faec-47d8-b1aa-21fb5ea72a08' title='Show/Hide data 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",
+       "                <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> 77.18 TiB </td>\n",
+       "                        <td> 6.87 GiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (552264, 37, 721, 1440) </td>\n",
+       "                        <td> (48, 37, 721, 1440) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 11506 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",
+       "        <svg width=\"470\" height=\"90\" 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=\"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=\"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=\"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=\"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=\"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=\"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=\"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=\"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",
+       "  <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:#8B4903A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >552264</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",
+       "\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"190\" y1=\"0\" x2=\"204\" y2=\"14\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"190\" y1=\"25\" x2=\"204\" y2=\"40\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"190\" y1=\"0\" x2=\"190\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"204\" y1=\"14\" x2=\"204\" y2=\"40\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"190.0,0.0 204.9485979497544,14.948597949754403 204.9485979497544,40.36121446433689 190.0,25.41261651458249\" 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",
+       "  <line x1=\"215\" y1=\"0\" x2=\"230\" y2=\"14\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"190.0,0.0 215.41261651458248,0.0 230.36121446433688,14.948597949754403 204.9485979497544,14.948597949754403\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\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",
+       "  <polygon points=\"204.9485979497544,14.948597949754403 230.36121446433688,14.948597949754403 230.36121446433688,40.36121446433689 204.9485979497544,40.36121446433689\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"217.654906\" y=\"60.361214\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1440</text>\n",
+       "  <text x=\"250.361214\" y=\"27.654906\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,250.361214,27.654906)\">721</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)\">37</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>toa_incident_solar_radiation</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(48, 721, 1440), meta=np.ndarray&gt;</div><input id='attrs-44de4aaf-6ff6-4286-b7f5-2c23d35be3b5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-44de4aaf-6ff6-4286-b7f5-2c23d35be3b5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-098d39c0-7068-4c5a-9b5b-eacb4a79eb43' class='xr-var-data-in' type='checkbox'><label for='data-098d39c0-7068-4c5a-9b5b-eacb4a79eb43' title='Show/Hide data 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>TOA incident solar radiation</dd><dt><span>short_name :</span></dt><dd>tisr</dd><dt><span>units :</span></dt><dd>J 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> 2.09 TiB </td>\n",
+       "                        <td> 190.11 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (552264, 721, 1440) </td>\n",
+       "                        <td> (48, 721, 1440) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 11506 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",
+       "        <svg width=\"156\" height=\"146\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"10\" y1=\"25\" x2=\"80\" y2=\"96\" 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=\"13\" y1=\"3\" x2=\"13\" y2=\"29\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"17\" y2=\"32\" />\n",
+       "  <line x1=\"21\" y1=\"11\" x2=\"21\" y2=\"36\" />\n",
+       "  <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=\"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=\"69\" />\n",
+       "  <line x1=\"58\" y1=\"48\" x2=\"58\" y2=\"73\" />\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",
+       "  <line x1=\"76\" y1=\"66\" x2=\"76\" y2=\"92\" />\n",
+       "  <line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"96\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"10.0,0.0 80.58823529411765,70.58823529411765 80.58823529411765,96.00085180870013 10.0,25.41261651458249\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"35\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"13\" y1=\"3\" x2=\"39\" y2=\"3\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"42\" y2=\"7\" />\n",
+       "  <line x1=\"21\" y1=\"11\" x2=\"46\" y2=\"11\" />\n",
+       "  <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=\"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=\"79\" y2=\"44\" />\n",
+       "  <line x1=\"58\" y1=\"48\" x2=\"83\" y2=\"48\" />\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",
+       "  <line x1=\"76\" y1=\"66\" x2=\"102\" y2=\"66\" />\n",
+       "  <line x1=\"80\" y1=\"70\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"35\" y1=\"0\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"10.0,0.0 35.41261651458249,0.0 106.00085180870013,70.58823529411765 80.58823529411765,70.58823529411765\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"80\" y1=\"70\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"80\" y1=\"96\" x2=\"106\" y2=\"96\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"96\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"106\" y1=\"70\" x2=\"106\" y2=\"96\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"80.58823529411765,70.58823529411765 106.00085180870013,70.58823529411765 106.00085180870013,96.00085180870013 80.58823529411765,96.00085180870013\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"93.294544\" y=\"116.000852\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1440</text>\n",
+       "  <text x=\"126.000852\" y=\"83.294544\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,126.000852,83.294544)\">721</text>\n",
+       "  <text x=\"35.294118\" y=\"80.706734\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,35.294118,80.706734)\">552264</text>\n",
+       "</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, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(48, 721, 1440), meta=np.ndarray&gt;</div><input id='attrs-9b93c9ac-c025-4b10-8623-e16e695da738' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9b93c9ac-c025-4b10-8623-e16e695da738' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-75cf45cb-d209-4ba8-b873-6e57614e357f' class='xr-var-data-in' type='checkbox'><label for='data-75cf45cb-d209-4ba8-b873-6e57614e357f' title='Show/Hide data 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> 2.09 TiB </td>\n",
+       "                        <td> 190.11 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (552264, 721, 1440) </td>\n",
+       "                        <td> (48, 721, 1440) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 11506 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",
+       "        <svg width=\"156\" height=\"146\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"10\" y1=\"25\" x2=\"80\" y2=\"96\" 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=\"13\" y1=\"3\" x2=\"13\" y2=\"29\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"17\" y2=\"32\" />\n",
+       "  <line x1=\"21\" y1=\"11\" x2=\"21\" y2=\"36\" />\n",
+       "  <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=\"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=\"69\" />\n",
+       "  <line x1=\"58\" y1=\"48\" x2=\"58\" y2=\"73\" />\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",
+       "  <line x1=\"76\" y1=\"66\" x2=\"76\" y2=\"92\" />\n",
+       "  <line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"96\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"10.0,0.0 80.58823529411765,70.58823529411765 80.58823529411765,96.00085180870013 10.0,25.41261651458249\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"35\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"13\" y1=\"3\" x2=\"39\" y2=\"3\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"42\" y2=\"7\" />\n",
+       "  <line x1=\"21\" y1=\"11\" x2=\"46\" y2=\"11\" />\n",
+       "  <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=\"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=\"79\" y2=\"44\" />\n",
+       "  <line x1=\"58\" y1=\"48\" x2=\"83\" y2=\"48\" />\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",
+       "  <line x1=\"76\" y1=\"66\" x2=\"102\" y2=\"66\" />\n",
+       "  <line x1=\"80\" y1=\"70\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"35\" y1=\"0\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"10.0,0.0 35.41261651458249,0.0 106.00085180870013,70.58823529411765 80.58823529411765,70.58823529411765\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"80\" y1=\"70\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"80\" y1=\"96\" x2=\"106\" y2=\"96\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"96\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"106\" y1=\"70\" x2=\"106\" y2=\"96\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"80.58823529411765,70.58823529411765 106.00085180870013,70.58823529411765 106.00085180870013,96.00085180870013 80.58823529411765,96.00085180870013\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"93.294544\" y=\"116.000852\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1440</text>\n",
+       "  <text x=\"126.000852\" y=\"83.294544\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,126.000852,83.294544)\">721</text>\n",
+       "  <text x=\"35.294118\" y=\"80.706734\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,35.294118,80.706734)\">552264</text>\n",
+       "</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, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(48, 721, 1440), meta=np.ndarray&gt;</div><input id='attrs-2cd47e9b-b584-429f-a1f5-57fbb834eb33' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2cd47e9b-b584-429f-a1f5-57fbb834eb33' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a95c5dbe-6ae4-4b36-a485-7af15bb77457' class='xr-var-data-in' type='checkbox'><label for='data-a95c5dbe-6ae4-4b36-a485-7af15bb77457' title='Show/Hide data 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",
+       "                <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> 2.09 TiB </td>\n",
+       "                        <td> 190.11 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (552264, 721, 1440) </td>\n",
+       "                        <td> (48, 721, 1440) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 11506 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",
+       "        <svg width=\"156\" height=\"146\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"10\" y1=\"25\" x2=\"80\" y2=\"96\" 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=\"13\" y1=\"3\" x2=\"13\" y2=\"29\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"17\" y2=\"32\" />\n",
+       "  <line x1=\"21\" y1=\"11\" x2=\"21\" y2=\"36\" />\n",
+       "  <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=\"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=\"69\" />\n",
+       "  <line x1=\"58\" y1=\"48\" x2=\"58\" y2=\"73\" />\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",
+       "  <line x1=\"76\" y1=\"66\" x2=\"76\" y2=\"92\" />\n",
+       "  <line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"96\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"10.0,0.0 80.58823529411765,70.58823529411765 80.58823529411765,96.00085180870013 10.0,25.41261651458249\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"35\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"13\" y1=\"3\" x2=\"39\" y2=\"3\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"42\" y2=\"7\" />\n",
+       "  <line x1=\"21\" y1=\"11\" x2=\"46\" y2=\"11\" />\n",
+       "  <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=\"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=\"79\" y2=\"44\" />\n",
+       "  <line x1=\"58\" y1=\"48\" x2=\"83\" y2=\"48\" />\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",
+       "  <line x1=\"76\" y1=\"66\" x2=\"102\" y2=\"66\" />\n",
+       "  <line x1=\"80\" y1=\"70\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"35\" y1=\"0\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"10.0,0.0 35.41261651458249,0.0 106.00085180870013,70.58823529411765 80.58823529411765,70.58823529411765\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"80\" y1=\"70\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"80\" y1=\"96\" x2=\"106\" y2=\"96\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"96\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"106\" y1=\"70\" x2=\"106\" y2=\"96\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"80.58823529411765,70.58823529411765 106.00085180870013,70.58823529411765 106.00085180870013,96.00085180870013 80.58823529411765,96.00085180870013\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"93.294544\" y=\"116.000852\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1440</text>\n",
+       "  <text x=\"126.000852\" y=\"83.294544\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,126.000852,83.294544)\">721</text>\n",
+       "  <text x=\"35.294118\" y=\"80.706734\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,35.294118,80.706734)\">552264</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>total_precipitation</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(48, 721, 1440), meta=np.ndarray&gt;</div><input id='attrs-49daf8f0-54a2-4215-b06a-fd790e16a331' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-49daf8f0-54a2-4215-b06a-fd790e16a331' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1122e2c2-668d-48a9-91c2-e2526306d335' class='xr-var-data-in' type='checkbox'><label for='data-1122e2c2-668d-48a9-91c2-e2526306d335' title='Show/Hide data 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> 2.09 TiB </td>\n",
+       "                        <td> 190.11 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (552264, 721, 1440) </td>\n",
+       "                        <td> (48, 721, 1440) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 11506 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",
+       "        <svg width=\"156\" height=\"146\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"10\" y1=\"25\" x2=\"80\" y2=\"96\" 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=\"13\" y1=\"3\" x2=\"13\" y2=\"29\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"17\" y2=\"32\" />\n",
+       "  <line x1=\"21\" y1=\"11\" x2=\"21\" y2=\"36\" />\n",
+       "  <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=\"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=\"69\" />\n",
+       "  <line x1=\"58\" y1=\"48\" x2=\"58\" y2=\"73\" />\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",
+       "  <line x1=\"76\" y1=\"66\" x2=\"76\" y2=\"92\" />\n",
+       "  <line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"96\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"10.0,0.0 80.58823529411765,70.58823529411765 80.58823529411765,96.00085180870013 10.0,25.41261651458249\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"35\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"13\" y1=\"3\" x2=\"39\" y2=\"3\" />\n",
+       "  <line x1=\"17\" y1=\"7\" x2=\"42\" y2=\"7\" />\n",
+       "  <line x1=\"21\" y1=\"11\" x2=\"46\" y2=\"11\" />\n",
+       "  <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=\"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=\"79\" y2=\"44\" />\n",
+       "  <line x1=\"58\" y1=\"48\" x2=\"83\" y2=\"48\" />\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",
+       "  <line x1=\"76\" y1=\"66\" x2=\"102\" y2=\"66\" />\n",
+       "  <line x1=\"80\" y1=\"70\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"35\" y1=\"0\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"10.0,0.0 35.41261651458249,0.0 106.00085180870013,70.58823529411765 80.58823529411765,70.58823529411765\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"80\" y1=\"70\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"80\" y1=\"96\" x2=\"106\" y2=\"96\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"96\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"106\" y1=\"70\" x2=\"106\" y2=\"96\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"80.58823529411765,70.58823529411765 106.00085180870013,70.58823529411765 106.00085180870013,96.00085180870013 80.58823529411765,96.00085180870013\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"93.294544\" y=\"116.000852\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1440</text>\n",
+       "  <text x=\"126.000852\" y=\"83.294544\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,126.000852,83.294544)\">721</text>\n",
+       "  <text x=\"35.294118\" y=\"80.706734\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,35.294118,80.706734)\">552264</text>\n",
+       "</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'>(lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(721, 1440), meta=np.ndarray&gt;</div><input id='attrs-0df330ca-9ede-47df-bce7-938c60177c9e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0df330ca-9ede-47df-bce7-938c60177c9e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6df694d0-c3e9-4ffb-b106-8183895bf92c' class='xr-var-data-in' type='checkbox'><label for='data-6df694d0-c3e9-4ffb-b106-8183895bf92c' title='Show/Hide data 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> 3.96 MiB </td>\n",
+       "                        <td> 3.96 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (721, 1440) </td>\n",
+       "                        <td> (721, 1440) </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",
+       "        <svg width=\"170\" height=\"110\" 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=\"60\" x2=\"120\" y2=\"60\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"60\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"60\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,60.083333333333336 0.0,60.083333333333336\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"80.083333\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1440</text>\n",
+       "  <text x=\"140.000000\" y=\"30.041667\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,30.041667)\">721</text>\n",
+       "</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'>(lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(721, 1440), meta=np.ndarray&gt;</div><input id='attrs-cd7c84af-a1e6-456d-91ac-ecbe8049f4c2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cd7c84af-a1e6-456d-91ac-ecbe8049f4c2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-25d610ea-8bf2-45f5-aba6-f56e981df73d' class='xr-var-data-in' type='checkbox'><label for='data-25d610ea-8bf2-45f5-aba6-f56e981df73d' title='Show/Hide data 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> 3.96 MiB </td>\n",
+       "                        <td> 3.96 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (721, 1440) </td>\n",
+       "                        <td> (721, 1440) </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",
+       "        <svg width=\"170\" height=\"110\" 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=\"60\" x2=\"120\" y2=\"60\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"60\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"60\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,60.083333333333336 0.0,60.083333333333336\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"80.083333\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1440</text>\n",
+       "  <text x=\"140.000000\" y=\"30.041667\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,30.041667)\">721</text>\n",
+       "</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, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(48, 37, 721, 1440), meta=np.ndarray&gt;</div><input id='attrs-0b76d951-f788-4121-9a6b-4f8778da37aa' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0b76d951-f788-4121-9a6b-4f8778da37aa' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b7ce6219-1b81-4f5b-859d-8093b8f21e4d' class='xr-var-data-in' type='checkbox'><label for='data-b7ce6219-1b81-4f5b-859d-8093b8f21e4d' title='Show/Hide data 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> 77.18 TiB </td>\n",
+       "                        <td> 6.87 GiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (552264, 37, 721, 1440) </td>\n",
+       "                        <td> (48, 37, 721, 1440) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 11506 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",
+       "        <svg width=\"470\" height=\"90\" 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=\"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=\"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=\"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=\"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=\"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=\"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=\"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=\"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",
+       "  <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:#8B4903A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >552264</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",
+       "\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"190\" y1=\"0\" x2=\"204\" y2=\"14\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"190\" y1=\"25\" x2=\"204\" y2=\"40\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"190\" y1=\"0\" x2=\"190\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"204\" y1=\"14\" x2=\"204\" y2=\"40\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"190.0,0.0 204.9485979497544,14.948597949754403 204.9485979497544,40.36121446433689 190.0,25.41261651458249\" 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",
+       "  <line x1=\"215\" y1=\"0\" x2=\"230\" y2=\"14\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"190.0,0.0 215.41261651458248,0.0 230.36121446433688,14.948597949754403 204.9485979497544,14.948597949754403\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\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",
+       "  <polygon points=\"204.9485979497544,14.948597949754403 230.36121446433688,14.948597949754403 230.36121446433688,40.36121446433689 204.9485979497544,40.36121446433689\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"217.654906\" y=\"60.361214\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1440</text>\n",
+       "  <text x=\"250.361214\" y=\"27.654906\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,250.361214,27.654906)\">721</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)\">37</text>\n",
+       "</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, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(48, 37, 721, 1440), meta=np.ndarray&gt;</div><input id='attrs-43233997-3aac-482e-85ca-e10542ec19a0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-43233997-3aac-482e-85ca-e10542ec19a0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3c6052c8-eeaf-45ca-8d78-a43109a56c71' class='xr-var-data-in' type='checkbox'><label for='data-3c6052c8-eeaf-45ca-8d78-a43109a56c71' title='Show/Hide data 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> 77.18 TiB </td>\n",
+       "                        <td> 6.87 GiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (552264, 37, 721, 1440) </td>\n",
+       "                        <td> (48, 37, 721, 1440) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 11506 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",
+       "        <svg width=\"470\" height=\"90\" 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=\"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=\"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=\"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=\"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=\"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=\"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=\"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=\"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",
+       "  <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:#8B4903A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >552264</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",
+       "\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"190\" y1=\"0\" x2=\"204\" y2=\"14\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"190\" y1=\"25\" x2=\"204\" y2=\"40\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"190\" y1=\"0\" x2=\"190\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"204\" y1=\"14\" x2=\"204\" y2=\"40\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"190.0,0.0 204.9485979497544,14.948597949754403 204.9485979497544,40.36121446433689 190.0,25.41261651458249\" 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",
+       "  <line x1=\"215\" y1=\"0\" x2=\"230\" y2=\"14\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"190.0,0.0 215.41261651458248,0.0 230.36121446433688,14.948597949754403 204.9485979497544,14.948597949754403\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\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",
+       "  <polygon points=\"204.9485979497544,14.948597949754403 230.36121446433688,14.948597949754403 230.36121446433688,40.36121446433689 204.9485979497544,40.36121446433689\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"217.654906\" y=\"60.361214\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1440</text>\n",
+       "  <text x=\"250.361214\" y=\"27.654906\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,250.361214,27.654906)\">721</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)\">37</text>\n",
+       "</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, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(48, 37, 721, 1440), meta=np.ndarray&gt;</div><input id='attrs-004b616a-b18d-4827-a262-1ff58a2ece46' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-004b616a-b18d-4827-a262-1ff58a2ece46' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7dcfa647-2d36-491b-9be0-db7416907320' class='xr-var-data-in' type='checkbox'><label for='data-7dcfa647-2d36-491b-9be0-db7416907320' title='Show/Hide data 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> 77.18 TiB </td>\n",
+       "                        <td> 6.87 GiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (552264, 37, 721, 1440) </td>\n",
+       "                        <td> (48, 37, 721, 1440) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 11506 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",
+       "        <svg width=\"470\" height=\"90\" 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=\"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=\"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=\"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=\"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=\"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=\"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=\"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=\"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",
+       "  <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:#8B4903A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >552264</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",
+       "\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"190\" y1=\"0\" x2=\"204\" y2=\"14\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"190\" y1=\"25\" x2=\"204\" y2=\"40\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"190\" y1=\"0\" x2=\"190\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"204\" y1=\"14\" x2=\"204\" y2=\"40\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"190.0,0.0 204.9485979497544,14.948597949754403 204.9485979497544,40.36121446433689 190.0,25.41261651458249\" 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",
+       "  <line x1=\"215\" y1=\"0\" x2=\"230\" y2=\"14\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"190.0,0.0 215.41261651458248,0.0 230.36121446433688,14.948597949754403 204.9485979497544,14.948597949754403\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\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",
+       "  <polygon points=\"204.9485979497544,14.948597949754403 230.36121446433688,14.948597949754403 230.36121446433688,40.36121446433689 204.9485979497544,40.36121446433689\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"217.654906\" y=\"60.361214\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1440</text>\n",
+       "  <text x=\"250.361214\" y=\"27.654906\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,250.361214,27.654906)\">721</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)\">37</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-2bb7f374-2f2b-46a1-beee-4efeacc5a8be' class='xr-section-summary-in' type='checkbox'  ><label for='section-2bb7f374-2f2b-46a1-beee-4efeacc5a8be' 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>lat</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-1824ca9e-1ba3-4bde-91ec-d74a5a231aad' class='xr-index-data-in' type='checkbox'/><label for='index-1824ca9e-1ba3-4bde-91ec-d74a5a231aad' 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([  90.0,  89.75,   89.5,  89.25,   89.0,  88.75,   88.5,  88.25,   88.0,\n",
+       "        87.75,\n",
+       "       ...\n",
+       "       -87.75,  -88.0, -88.25,  -88.5, -88.75,  -89.0, -89.25,  -89.5, -89.75,\n",
+       "        -90.0],\n",
+       "      dtype=&#x27;float32&#x27;, name=&#x27;lat&#x27;, length=721))</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-48a375c1-497c-4343-8995-c18082976da5' class='xr-index-data-in' type='checkbox'/><label for='index-48a375c1-497c-4343-8995-c18082976da5' 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([   1,    2,    3,    5,    7,   10,   20,   30,   50,   70,  100,  125,\n",
+       "        150,  175,  200,  225,  250,  300,  350,  400,  450,  500,  550,  600,\n",
+       "        650,  700,  750,  775,  800,  825,  850,  875,  900,  925,  950,  975,\n",
+       "       1000],\n",
+       "      dtype=&#x27;int64&#x27;, name=&#x27;level&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lon</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-49530b8f-88bd-4f44-9d3c-986f58f50bc1' class='xr-index-data-in' type='checkbox'/><label for='index-49530b8f-88bd-4f44-9d3c-986f58f50bc1' 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,   0.25,    0.5,   0.75,    1.0,   1.25,    1.5,   1.75,    2.0,\n",
+       "         2.25,\n",
+       "       ...\n",
+       "        357.5, 357.75,  358.0, 358.25,  358.5, 358.75,  359.0, 359.25,  359.5,\n",
+       "       359.75],\n",
+       "      dtype=&#x27;float32&#x27;, name=&#x27;lon&#x27;, length=1440))</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-39e34f90-164e-4bb9-b2a7-ae1321c8cf30' class='xr-index-data-in' type='checkbox'/><label for='index-39e34f90-164e-4bb9-b2a7-ae1321c8cf30' 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 01:00:00&#x27;,\n",
+       "               &#x27;1959-01-01 02:00:00&#x27;, &#x27;1959-01-01 03:00:00&#x27;,\n",
+       "               &#x27;1959-01-01 04:00:00&#x27;, &#x27;1959-01-01 05:00:00&#x27;,\n",
+       "               &#x27;1959-01-01 06:00:00&#x27;, &#x27;1959-01-01 07:00:00&#x27;,\n",
+       "               &#x27;1959-01-01 08:00:00&#x27;, &#x27;1959-01-01 09:00:00&#x27;,\n",
+       "               ...\n",
+       "               &#x27;2021-12-31 14:00:00&#x27;, &#x27;2021-12-31 15:00:00&#x27;,\n",
+       "               &#x27;2021-12-31 16:00:00&#x27;, &#x27;2021-12-31 17:00:00&#x27;,\n",
+       "               &#x27;2021-12-31 18:00:00&#x27;, &#x27;2021-12-31 19:00:00&#x27;,\n",
+       "               &#x27;2021-12-31 20:00:00&#x27;, &#x27;2021-12-31 21:00:00&#x27;,\n",
+       "               &#x27;2021-12-31 22:00:00&#x27;, &#x27;2021-12-31 23:00:00&#x27;],\n",
+       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=552264, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-cc6ec00b-daa9-421f-b2b1-f57b2003b98b' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-cc6ec00b-daa9-421f-b2b1-f57b2003b98b' 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>\n",
+       "Dimensions:                                           (time: 552264, lat: 721,\n",
+       "                                                       lon: 1440, level: 37)\n",
+       "Coordinates:\n",
+       "  * lat                                               (lat) float32 90.0 ... ...\n",
+       "  * level                                             (level) int64 1 2 ... 1000\n",
+       "  * lon                                               (lon) float32 0.0 ... 3...\n",
+       "  * time                                              (time) datetime64[ns] 1...\n",
+       "Data variables: (12/31)\n",
+       "    10m_u_component_of_wind                           (time, lat, lon) float32 dask.array<chunksize=(48, 721, 1440), meta=np.ndarray>\n",
+       "    10m_v_component_of_wind                           (time, lat, lon) float32 dask.array<chunksize=(48, 721, 1440), meta=np.ndarray>\n",
+       "    2m_temperature                                    (time, lat, lon) float32 dask.array<chunksize=(48, 721, 1440), meta=np.ndarray>\n",
+       "    angle_of_sub_gridscale_orography                  (lat, lon) float32 dask.array<chunksize=(721, 1440), meta=np.ndarray>\n",
+       "    anisotropy_of_sub_gridscale_orography             (lat, lon) float32 dask.array<chunksize=(721, 1440), meta=np.ndarray>\n",
+       "    geopotential                                      (time, level, lat, lon) float32 dask.array<chunksize=(48, 37, 721, 1440), meta=np.ndarray>\n",
+       "    ...                                                ...\n",
+       "    total_precipitation                               (time, lat, lon) float32 dask.array<chunksize=(48, 721, 1440), meta=np.ndarray>\n",
+       "    type_of_high_vegetation                           (lat, lon) float32 dask.array<chunksize=(721, 1440), meta=np.ndarray>\n",
+       "    type_of_low_vegetation                            (lat, lon) float32 dask.array<chunksize=(721, 1440), meta=np.ndarray>\n",
+       "    u_component_of_wind                               (time, level, lat, lon) float32 dask.array<chunksize=(48, 37, 721, 1440), meta=np.ndarray>\n",
+       "    v_component_of_wind                               (time, level, lat, lon) float32 dask.array<chunksize=(48, 37, 721, 1440), meta=np.ndarray>\n",
+       "    vertical_velocity                                 (time, level, lat, lon) float32 dask.array<chunksize=(48, 37, 721, 1440), meta=np.ndarray>"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "da_mask_era5 = create_mask(ds=ds_era5, bbox=bbox)\n",
+    "ds_era5"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def create_lateral_boundary_mask(ds, bbox, width):\n",
+    "    da_mask_inner = create_mask(ds=ds, bbox=bbox)\n",
+    "    da_mask_outer = np.logical_and(\n",
+    "        np.logical_and(ds.lon > bbox[0] - width, ds.lon < bbox[1] + width),\n",
+    "        np.logical_and(ds.lat > bbox[2] - width, ds.lat < bbox[3] + width),\n",
+    "    )\n",
+    "    da_mask_edge = da_mask_outer ^ da_mask_inner\n",
+    "    return da_mask_edge"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "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",
+       "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n",
+       "<path d=\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
+       "<path d=\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
+       "</symbol>\n",
+       "<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n",
+       "<path d=\"M28.681 7.159c-0.694-0.947-1.662-2.053-2.724-3.116s-2.169-2.030-3.116-2.724c-1.612-1.182-2.393-1.319-2.841-1.319h-15.5c-1.378 0-2.5 1.121-2.5 2.5v27c0 1.378 1.122 2.5 2.5 2.5h23c1.378 0 2.5-1.122 2.5-2.5v-19.5c0-0.448-0.137-1.23-1.319-2.841zM24.543 5.457c0.959 0.959 1.712 1.825 2.268 2.543h-4.811v-4.811c0.718 0.556 1.584 1.309 2.543 2.268zM28 29.5c0 0.271-0.229 0.5-0.5 0.5h-23c-0.271 0-0.5-0.229-0.5-0.5v-27c0-0.271 0.229-0.5 0.5-0.5 0 0 15.499-0 15.5 0v7c0 0.552 0.448 1 1 1h7v19.5z\"></path>\n",
+       "<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.DataArray &#x27;lon&#x27; (lon: 1440, lat: 721)&gt;\n",
+       "array([[False, False, False, ..., False, False, False],\n",
+       "       [False, False, False, ..., False, False, False],\n",
+       "       [False, False, False, ..., False, False, False],\n",
+       "       ...,\n",
+       "       [False, False, False, ..., False, False, False],\n",
+       "       [False, False, False, ..., False, False, False],\n",
+       "       [False, False, False, ..., False, False, False]])\n",
+       "Coordinates:\n",
+       "  * lon      (lon) float32 0.0 0.25 0.5 0.75 1.0 ... 359.0 359.2 359.5 359.8\n",
+       "  * lat      (lat) float32 90.0 89.75 89.5 89.25 ... -89.25 -89.5 -89.75 -90.0</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'lon'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>lon</span>: 1440</li><li><span class='xr-has-index'>lat</span>: 721</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-b25daca5-838b-4c81-9240-52df491344e6' class='xr-array-in' type='checkbox' checked><label for='section-b25daca5-838b-4c81-9240-52df491344e6' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>False False False False False False ... False False False False False</span></div><div class='xr-array-data'><pre>array([[False, False, False, ..., False, False, False],\n",
+       "       [False, False, False, ..., False, False, False],\n",
+       "       [False, False, False, ..., False, False, False],\n",
+       "       ...,\n",
+       "       [False, False, False, ..., False, False, False],\n",
+       "       [False, False, False, ..., False, False, False],\n",
+       "       [False, False, False, ..., False, False, False]])</pre></div></div></li><li class='xr-section-item'><input id='section-79e78c79-ec92-486f-9194-7695c6f535dd' class='xr-section-summary-in' type='checkbox'  checked><label for='section-79e78c79-ec92-486f-9194-7695c6f535dd' class='xr-section-summary' >Coordinates: <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-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0 0.25 0.5 ... 359.2 359.5 359.8</div><input id='attrs-077a0d41-2ec9-41e2-a33a-8293f5ee6b59' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-077a0d41-2ec9-41e2-a33a-8293f5ee6b59' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6b629bf9-b458-4126-a1e5-4a02ea44c4a6' class='xr-var-data-in' type='checkbox'><label for='data-6b629bf9-b458-4126-a1e5-4a02ea44c4a6' title='Show/Hide data 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>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>array([0.0000e+00, 2.5000e-01, 5.0000e-01, ..., 3.5925e+02, 3.5950e+02,\n",
+       "       3.5975e+02], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>90.0 89.75 89.5 ... -89.75 -90.0</div><input id='attrs-be86c99b-71d8-4b6a-9ba4-23e550844aed' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-be86c99b-71d8-4b6a-9ba4-23e550844aed' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cb2e4e2a-4473-4b94-b603-e552762cb7f6' class='xr-var-data-in' type='checkbox'><label for='data-cb2e4e2a-4473-4b94-b603-e552762cb7f6' title='Show/Hide data 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>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><pre>array([ 90.  ,  89.75,  89.5 , ..., -89.5 , -89.75, -90.  ], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-f35df2a7-17c6-4120-9d65-b3d297e0643b' class='xr-section-summary-in' type='checkbox'  ><label for='section-f35df2a7-17c6-4120-9d65-b3d297e0643b' 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>lon</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-ed701846-048d-4276-bd5e-1ceea6da0092' class='xr-index-data-in' type='checkbox'/><label for='index-ed701846-048d-4276-bd5e-1ceea6da0092' 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,   0.25,    0.5,   0.75,    1.0,   1.25,    1.5,   1.75,    2.0,\n",
+       "         2.25,\n",
+       "       ...\n",
+       "        357.5, 357.75,  358.0, 358.25,  358.5, 358.75,  359.0, 359.25,  359.5,\n",
+       "       359.75],\n",
+       "      dtype=&#x27;float32&#x27;, name=&#x27;lon&#x27;, length=1440))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lat</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-e85ce562-aa34-4753-9823-d57e9eb0a4db' class='xr-index-data-in' type='checkbox'/><label for='index-e85ce562-aa34-4753-9823-d57e9eb0a4db' 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([  90.0,  89.75,   89.5,  89.25,   89.0,  88.75,   88.5,  88.25,   88.0,\n",
+       "        87.75,\n",
+       "       ...\n",
+       "       -87.75,  -88.0, -88.25,  -88.5, -88.75,  -89.0, -89.25,  -89.5, -89.75,\n",
+       "        -90.0],\n",
+       "      dtype=&#x27;float32&#x27;, name=&#x27;lat&#x27;, length=721))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-022348c1-ce1a-4b52-996a-aee16528e736' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-022348c1-ce1a-4b52-996a-aee16528e736' 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.DataArray 'lon' (lon: 1440, lat: 721)>\n",
+       "array([[False, False, False, ..., False, False, False],\n",
+       "       [False, False, False, ..., False, False, False],\n",
+       "       [False, False, False, ..., False, False, False],\n",
+       "       ...,\n",
+       "       [False, False, False, ..., False, False, False],\n",
+       "       [False, False, False, ..., False, False, False],\n",
+       "       [False, False, False, ..., False, False, False]])\n",
+       "Coordinates:\n",
+       "  * lon      (lon) float32 0.0 0.25 0.5 0.75 1.0 ... 359.0 359.2 359.5 359.8\n",
+       "  * lat      (lat) float32 90.0 89.75 89.5 89.25 ... -89.25 -89.5 -89.75 -90.0"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "da_mask_era5 = create_lateral_boundary_mask(ds=ds_era5, bbox=bbox, width=1.0)\n",
+    "da_mask_era5"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<cartopy.mpl.feature_artist.FeatureArtist at 0x7fa0bc12bc70>"
+      ]
+     },
+     "execution_count": 13,
+     "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(figsize=(10, 4), subplot_kw=dict(projection=ccrs.PlateCarree()))\n",
+    "da_mask_era5.plot(ax=ax, x=\"lon\", y=\"lat\", transform=ccrs.PlateCarree())\n",
+    "ax.gridlines(draw_labels=[\"top\", \"left\"])\n",
+    "ax.coastlines()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "da_mask_danra = create_mask(ds=ds_danra_hl, bbox=bbox)\n",
+    "ds_era5_edge = ds_era5.where(da_mask_era5, drop=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "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",
+       "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n",
+       "<path d=\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
+       "<path d=\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
+       "</symbol>\n",
+       "<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n",
+       "<path d=\"M28.681 7.159c-0.694-0.947-1.662-2.053-2.724-3.116s-2.169-2.030-3.116-2.724c-1.612-1.182-2.393-1.319-2.841-1.319h-15.5c-1.378 0-2.5 1.121-2.5 2.5v27c0 1.378 1.122 2.5 2.5 2.5h23c1.378 0 2.5-1.122 2.5-2.5v-19.5c0-0.448-0.137-1.23-1.319-2.841zM24.543 5.457c0.959 0.959 1.712 1.825 2.268 2.543h-4.811v-4.811c0.718 0.556 1.584 1.309 2.543 2.268zM28 29.5c0 0.271-0.229 0.5-0.5 0.5h-23c-0.271 0-0.5-0.229-0.5-0.5v-27c0-0.271 0.229-0.5 0.5-0.5 0 0 15.499-0 15.5 0v7c0 0.552 0.448 1 1 1h7v19.5z\"></path>\n",
+       "<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",
@@ -5949,15 +9174,15 @@
        "  stroke: currentColor;\n",
        "  fill: currentColor;\n",
        "}\n",
-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;t&#x27; (altitude: 1, y: 211, x: 229)&gt;\n",
-       "dask.array&lt;where, shape=(1, 211, 229), dtype=float64, chunksize=(1, 116, 152), chunktype=numpy.ndarray&gt;\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;t&#x27; (altitude: 1, y: 404, x: 360)&gt;\n",
+       "dask.array&lt;where, shape=(1, 404, 360), dtype=float64, chunksize=(1, 211, 227), chunktype=numpy.ndarray&gt;\n",
        "Coordinates:\n",
        "  * altitude  (altitude) int64 100\n",
-       "    lat       (y, x) float64 53.16 53.17 53.17 53.18 ... 58.76 58.76 58.76 58.77\n",
-       "    lon       (y, x) float64 8.371 8.407 8.443 8.48 ... 15.66 15.71 15.75 15.79\n",
-       "    time      datetime64[ns] 1990-09-01\n",
-       "  * x         (x) float64 -1.099e+06 -1.097e+06 ... -5.317e+05 -5.292e+05\n",
-       "  * y         (y) float64 -2.595e+05 -2.57e+05 -2.545e+05 ... 2.63e+05 2.655e+05\n",
+       "    lat       (y, x) float64 50.68 50.68 50.69 50.69 ... 61.1 61.1 61.1 61.1\n",
+       "    lon       (y, x) float64 6.605 6.639 6.673 6.708 ... 17.62 17.67 17.71 17.76\n",
+       "    time      datetime64[ns] 1990-09-01T18:00:00\n",
+       "  * x         (x) float64 -1.287e+06 -1.284e+06 ... -3.917e+05 -3.892e+05\n",
+       "  * y         (y) float64 -4.97e+05 -4.945e+05 -4.92e+05 ... 5.08e+05 5.105e+05\n",
        "Attributes:\n",
        "    level_type:   heightAboveGround\n",
        "    long_name:    Temperature\n",
@@ -5965,7 +9190,7 @@
        "    stepType:     instant\n",
        "    stepUnits:    1\n",
        "    typeOfLevel:  heightAboveGround\n",
-       "    units:        K</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'t'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>altitude</span>: 1</li><li><span class='xr-has-index'>y</span>: 211</li><li><span class='xr-has-index'>x</span>: 229</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-285f036a-27fa-49b2-a665-ad7ec80214c1' class='xr-array-in' type='checkbox' checked><label for='section-285f036a-27fa-49b2-a665-ad7ec80214c1' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>dask.array&lt;chunksize=(1, 116, 152), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>\n",
+       "    units:        K</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'t'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>altitude</span>: 1</li><li><span class='xr-has-index'>y</span>: 404</li><li><span class='xr-has-index'>x</span>: 360</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-e1f6575a-0e10-43a9-9e67-e72b7407998c' class='xr-array-in' type='checkbox' checked><label for='section-e1f6575a-0e10-43a9-9e67-e72b7407998c' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>dask.array&lt;chunksize=(1, 211, 227), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -5980,14 +9205,14 @@
        "                    \n",
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
-       "                        <td> 377.49 kiB </td>\n",
-       "                        <td> 137.75 kiB </td>\n",
+       "                        <td> 1.11 MiB </td>\n",
+       "                        <td> 374.20 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
-       "                        <td> (1, 211, 229) </td>\n",
-       "                        <td> (1, 116, 152) </td>\n",
+       "                        <td> (1, 404, 360) </td>\n",
+       "                        <td> (1, 211, 227) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
@@ -6001,108 +9226,108 @@
        "            </table>\n",
        "        </td>\n",
        "        <td>\n",
-       "        <svg width=\"194\" height=\"175\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "        <svg width=\"181\" height=\"184\" 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=\"60\" x2=\"24\" y2=\"75\" />\n",
-       "  <line x1=\"10\" y1=\"110\" x2=\"24\" y2=\"125\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"10\" y1=\"62\" x2=\"24\" y2=\"77\" />\n",
+       "  <line x1=\"10\" y1=\"120\" x2=\"24\" y2=\"134\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Vertical lines -->\n",
-       "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"110\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"125\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"134\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Colored Rectangle -->\n",
-       "  <polygon points=\"10.0,0.0 24.9485979497544,14.948597949754403 24.9485979497544,125.51628353927404 10.0,110.56768558951964\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "  <polygon points=\"10.0,0.0 24.9485979497544,14.948597949754403 24.9485979497544,134.9485979497544 10.0,120.0\" 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",
+       "  <line x1=\"10\" y1=\"0\" x2=\"116\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"24\" y1=\"14\" x2=\"131\" 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=\"89\" y1=\"0\" x2=\"104\" y2=\"14\" />\n",
-       "  <line x1=\"130\" y1=\"0\" x2=\"144\" y2=\"14\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"77\" y1=\"0\" x2=\"92\" y2=\"14\" />\n",
+       "  <line x1=\"116\" y1=\"0\" x2=\"131\" 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",
+       "  <polygon points=\"10.0,0.0 116.93069306930694,0.0 131.87929101906136,14.948597949754403 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=\"75\" x2=\"144\" y2=\"75\" />\n",
-       "  <line x1=\"24\" y1=\"125\" x2=\"144\" y2=\"125\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"24\" y1=\"14\" x2=\"131\" y2=\"14\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"24\" y1=\"77\" x2=\"131\" y2=\"77\" />\n",
+       "  <line x1=\"24\" y1=\"134\" x2=\"131\" y2=\"134\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Vertical lines -->\n",
-       "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"125\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"104\" y1=\"14\" x2=\"104\" y2=\"125\" />\n",
-       "  <line x1=\"144\" y1=\"14\" x2=\"144\" y2=\"125\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"134\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"92\" y1=\"14\" x2=\"92\" y2=\"134\" />\n",
+       "  <line x1=\"131\" y1=\"14\" x2=\"131\" y2=\"134\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Colored Rectangle -->\n",
-       "  <polygon points=\"24.9485979497544,14.948597949754403 144.9485979497544,14.948597949754403 144.9485979497544,125.51628353927404 24.9485979497544,125.51628353927404\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "  <polygon points=\"24.9485979497544,14.948597949754403 131.87929101906133,14.948597949754403 131.87929101906133,134.9485979497544 24.9485979497544,134.9485979497544\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
        "\n",
        "  <!-- Text -->\n",
-       "  <text x=\"84.948598\" y=\"145.516284\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >229</text>\n",
-       "  <text x=\"164.948598\" y=\"70.232441\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,164.948598,70.232441)\">211</text>\n",
-       "  <text x=\"7.474299\" y=\"138.041985\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,7.474299,138.041985)\">1</text>\n",
+       "  <text x=\"78.413944\" y=\"154.948598\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >360</text>\n",
+       "  <text x=\"151.879291\" y=\"74.948598\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,151.879291,74.948598)\">404</text>\n",
+       "  <text x=\"7.474299\" y=\"147.474299\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,7.474299,147.474299)\">1</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></div></li><li class='xr-section-item'><input id='section-64b5a8f8-f52d-45ad-950d-d1332b5a56a7' class='xr-section-summary-in' type='checkbox'  checked><label for='section-64b5a8f8-f52d-45ad-950d-d1332b5a56a7' 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-e34f0ad5-8463-4432-9be8-03e441bc2436' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e34f0ad5-8463-4432-9be8-03e441bc2436' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-778102f6-2278-4d6e-a62c-381980bed34e' class='xr-var-data-in' type='checkbox'><label for='data-778102f6-2278-4d6e-a62c-381980bed34e' title='Show/Hide data 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'>53.16 53.17 53.17 ... 58.76 58.77</div><input id='attrs-0e9da8c5-8899-4c9a-ab09-6753f9c6d1ec' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0e9da8c5-8899-4c9a-ab09-6753f9c6d1ec' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bb22b4bd-0afc-4dc2-878c-aa311fe32dc1' class='xr-var-data-in' type='checkbox'><label for='data-bb22b4bd-0afc-4dc2-878c-aa311fe32dc1' title='Show/Hide data 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'><pre>array([[53.16115242, 53.16654032, 53.17191669, ..., 54.07745658,\n",
-       "        54.08013375, 54.08279855],\n",
-       "       [53.18294874, 53.18833963, 53.19371897, ..., 54.09976837,\n",
-       "        54.10244707, 54.10511339],\n",
-       "       [53.20474484, 53.21013871, 53.21552103, ..., 54.12208036,\n",
-       "        54.12476059, 54.12742844],\n",
+       "</table></div></div></li><li class='xr-section-item'><input id='section-3b0b80a4-a500-4543-b432-161046433668' class='xr-section-summary-in' type='checkbox'  checked><label for='section-3b0b80a4-a500-4543-b432-161046433668' 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-2d729a64-bc17-4c70-acfe-e7355574d83d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2d729a64-bc17-4c70-acfe-e7355574d83d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-24106b73-fa04-415d-9c62-249bb08b48d2' class='xr-var-data-in' type='checkbox'><label for='data-24106b73-fa04-415d-9c62-249bb08b48d2' title='Show/Hide data 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'>50.68 50.68 50.69 ... 61.1 61.1</div><input id='attrs-665f60df-08e1-48fa-b737-6c246512283a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-665f60df-08e1-48fa-b737-6c246512283a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-afd34a86-a353-4503-9da6-689ee77cd020' class='xr-var-data-in' type='checkbox'><label for='data-afd34a86-a353-4503-9da6-689ee77cd020' title='Show/Hide data 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'><pre>array([[50.67542033, 50.68134774, 50.6872645 , ..., 52.08321424,\n",
+       "        52.0850909 , 52.08695571],\n",
+       "       [50.69699672, 50.70292731, 50.70884722, ..., 52.10556062,\n",
+       "        52.10743833, 52.10930419],\n",
+       "       [50.71857311, 50.72450686, 50.73042993, ..., 52.12790758,\n",
+       "        52.12978634, 52.13165324],\n",
        "       ...,\n",
-       "       [57.68372932, 57.68979277, 57.69584341, ..., 58.71733907,\n",
-       "        58.72036629, 58.72337957],\n",
-       "       [57.7053864 , 57.7114534 , 57.71750758, ..., 58.73961438,\n",
-       "        58.74264346, 58.74565859],\n",
-       "       [57.72704232, 57.73311287, 57.73917059, ..., 58.76188913,\n",
-       "        58.76492006, 58.76793703]])</pre></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'>8.371 8.407 8.443 ... 15.75 15.79</div><input id='attrs-4be101de-a228-45af-8112-d2994d789df7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4be101de-a228-45af-8112-d2994d789df7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-943f10c7-2149-463f-81da-6832577b05cf' class='xr-var-data-in' type='checkbox'><label for='data-943f10c7-2149-463f-81da-6832577b05cf' title='Show/Hide data 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'><pre>array([[ 8.37056599,  8.40692442,  8.44329237, ..., 16.7949401 ,\n",
-       "        16.83297233, 16.87100962],\n",
-       "       [ 8.36156527,  8.39794181,  8.43432789, ..., 16.79036385,\n",
-       "        16.82841689, 16.86647499],\n",
-       "       [ 8.35255499,  8.38894966,  8.42535389, ..., 16.78578251,\n",
-       "        16.82385638, 16.86193532],\n",
+       "       [59.28001499, 59.28743086, 59.29483382, ..., 61.05426207,\n",
+       "        61.05664546, 61.05901385],\n",
+       "       [59.30122503, 59.30864526, 59.31605259, ..., 61.07656165,\n",
+       "        61.07894657, 61.08131648],\n",
+       "       [59.32243312, 59.32985772, 59.33726941, ..., 61.09886036,\n",
+       "        61.10124681, 61.10361824]])</pre></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'>6.605 6.639 6.673 ... 17.71 17.76</div><input id='attrs-3a1c649d-ade5-4261-a0bb-34b171fff77b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3a1c649d-ade5-4261-a0bb-34b171fff77b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-739587c1-583b-4414-89d6-43a1da409e9b' class='xr-var-data-in' type='checkbox'><label for='data-739587c1-583b-4414-89d6-43a1da409e9b' title='Show/Hide data 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'><pre>array([[ 6.60537369,  6.63942594,  6.67348749, ..., 19.23837481,\n",
+       "        19.27474013, 19.31110869],\n",
+       "       [ 6.59600715,  6.63007505,  6.66415225, ..., 19.23530966,\n",
+       "        19.27169415, 19.30808187],\n",
+       "       [ 6.5866313 ,  6.62071486,  6.65480773, ..., 19.23224126,\n",
+       "        19.26864493, 19.30505185],\n",
        "       ...,\n",
-       "       [ 6.26808054,  6.30859971,  6.34913228, ..., 15.71960165,\n",
-       "        15.76250401, 15.80541365],\n",
-       "       [ 6.25671963,  6.29726089,  6.33781557, ..., 15.71375468,\n",
-       "        15.75668341, 15.79961944],\n",
-       "       [ 6.24534529,  6.28590865,  6.32648546, ..., 15.70790038,\n",
-       "        15.75085552, 15.79381796]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1990-09-01</div><input id='attrs-fe9ae24b-2e7a-41de-9319-83e4d2bed6ee' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-fe9ae24b-2e7a-41de-9319-83e4d2bed6ee' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-db556844-2727-49b1-916c-cc23b394fbf2' class='xr-var-data-in' type='checkbox'><label for='data-db556844-2727-49b1-916c-cc23b394fbf2' title='Show/Hide data 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;, 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'>-1.099e+06 ... -5.292e+05</div><input id='attrs-709f9d2e-1182-49c3-a77b-ad8857ec54ff' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-709f9d2e-1182-49c3-a77b-ad8857ec54ff' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2291817f-abf4-4635-8c46-7da612ac60bc' class='xr-var-data-in' type='checkbox'><label for='data-2291817f-abf4-4635-8c46-7da612ac60bc' title='Show/Hide data 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([-1099248.193226, -1096748.193226, -1094248.193226, ...,  -534248.193226,\n",
-       "        -531748.193226,  -529248.193226])</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'>-2.595e+05 -2.57e+05 ... 2.655e+05</div><input id='attrs-36e81b4d-eaa9-488a-9e02-d5c727abfac8' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-36e81b4d-eaa9-488a-9e02-d5c727abfac8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a4b99a90-e3b7-47bf-989b-496d1e3d5e39' class='xr-var-data-in' type='checkbox'><label for='data-a4b99a90-e3b7-47bf-989b-496d1e3d5e39' title='Show/Hide data 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([-259541.209133, -257041.209133, -254541.209133, ...,  260458.790867,\n",
-       "        262958.790867,  265458.790867])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-2f5d55eb-f257-4e55-9f14-ddd0ad49b708' class='xr-section-summary-in' type='checkbox'  ><label for='section-2f5d55eb-f257-4e55-9f14-ddd0ad49b708' 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>altitude</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-63690676-b965-45f6-a3e2-ca3db6b47fac' class='xr-index-data-in' type='checkbox'/><label for='index-63690676-b965-45f6-a3e2-ca3db6b47fac' 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>x</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-694ac1a3-13ee-4fa8-b326-6bdb3c069fa1' class='xr-index-data-in' type='checkbox'/><label for='index-694ac1a3-13ee-4fa8-b326-6bdb3c069fa1' 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([-1099248.1932260487, -1096748.1932260487, -1094248.1932260487,\n",
-       "       -1091748.1932260487, -1089248.1932260487, -1086748.1932260487,\n",
-       "       -1084248.1932260487, -1081748.1932260487, -1079248.1932260487,\n",
-       "       -1076748.1932260487,\n",
+       "       [ 1.92490377,  1.96643416,  2.00798214, ..., 17.67825543,\n",
+       "        17.72433498, 17.77042112],\n",
+       "       [ 1.91036542,  1.95191791,  1.99348801, ..., 17.67331206,\n",
+       "        17.71942225, 17.76553905],\n",
+       "       [ 1.89580945,  1.93738405,  1.9789763 , ..., 17.66836204,\n",
+       "        17.71450291, 17.7606504 ]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1990-09-01T18:00:00</div><input id='attrs-677ae3c9-4c6f-45c0-9ac0-72eea41ee284' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-677ae3c9-4c6f-45c0-9ac0-72eea41ee284' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-42d478eb-9c2b-4773-b96e-6308470b227b' class='xr-var-data-in' type='checkbox'><label for='data-42d478eb-9c2b-4773-b96e-6308470b227b' title='Show/Hide data 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;1990-09-01T18:00:00.000000000&#x27;, 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'>-1.287e+06 ... -3.892e+05</div><input id='attrs-254f8015-ca94-4113-81cc-ec91e817d8b1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-254f8015-ca94-4113-81cc-ec91e817d8b1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2c6fd274-2d47-4a31-af5e-bc3cf107d830' class='xr-var-data-in' type='checkbox'><label for='data-2c6fd274-2d47-4a31-af5e-bc3cf107d830' title='Show/Hide data 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([-1286748.193226, -1284248.193226, -1281748.193226, ...,  -394248.193226,\n",
+       "        -391748.193226,  -389248.193226])</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'>-4.97e+05 -4.945e+05 ... 5.105e+05</div><input id='attrs-5bd1f7ba-1bc0-4d8f-a19f-b62b16b1df53' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5bd1f7ba-1bc0-4d8f-a19f-b62b16b1df53' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-17a473b6-3e71-4548-9ee7-2874fdd12e32' class='xr-var-data-in' type='checkbox'><label for='data-17a473b6-3e71-4548-9ee7-2874fdd12e32' title='Show/Hide data 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([-497041.209133, -494541.209133, -492041.209133, ...,  505458.790867,\n",
+       "        507958.790867,  510458.790867])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-42d57102-cb3f-4cd3-8951-3927dc07bb36' class='xr-section-summary-in' type='checkbox'  ><label for='section-42d57102-cb3f-4cd3-8951-3927dc07bb36' 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>altitude</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-783f2178-4a5b-4c76-b07e-ba1abdbcc460' class='xr-index-data-in' type='checkbox'/><label for='index-783f2178-4a5b-4c76-b07e-ba1abdbcc460' 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>x</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-b8a2c0a8-11ed-4011-a0b4-b5f4851b42d8' class='xr-index-data-in' type='checkbox'/><label for='index-b8a2c0a8-11ed-4011-a0b4-b5f4851b42d8' 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([-1286748.1932260487, -1284248.1932260487, -1281748.1932260487,\n",
+       "       -1279248.1932260487, -1276748.1932260487, -1274248.1932260487,\n",
+       "       -1271748.1932260487, -1269248.1932260487, -1266748.1932260487,\n",
+       "       -1264248.1932260487,\n",
        "       ...\n",
-       "        -551748.1932260487,  -549248.1932260487,  -546748.1932260487,\n",
-       "        -544248.1932260487,  -541748.1932260487,  -539248.1932260487,\n",
-       "        -536748.1932260487,  -534248.1932260487,  -531748.1932260487,\n",
-       "        -529248.1932260487],\n",
-       "      dtype=&#x27;float64&#x27;, name=&#x27;x&#x27;, length=229))</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-181549f1-fcd1-42ec-975a-808148475a8a' class='xr-index-data-in' type='checkbox'/><label for='index-181549f1-fcd1-42ec-975a-808148475a8a' 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([-259541.2091329489, -257041.2091329489, -254541.2091329489,\n",
-       "       -252041.2091329489, -249541.2091329489, -247041.2091329489,\n",
-       "       -244541.2091329489, -242041.2091329489, -239541.2091329489,\n",
-       "       -237041.2091329489,\n",
+       "        -411748.1932260487,  -409248.1932260487,  -406748.1932260487,\n",
+       "        -404248.1932260487,  -401748.1932260487,  -399248.1932260487,\n",
+       "        -396748.1932260487,  -394248.1932260487,  -391748.1932260487,\n",
+       "        -389248.1932260487],\n",
+       "      dtype=&#x27;float64&#x27;, name=&#x27;x&#x27;, length=360))</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-b9075db2-0057-4095-a6ec-821c5275890d' class='xr-index-data-in' type='checkbox'/><label for='index-b9075db2-0057-4095-a6ec-821c5275890d' 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([-497041.2091329489, -494541.2091329489, -492041.2091329489,\n",
+       "       -489541.2091329489, -487041.2091329489, -484541.2091329489,\n",
+       "       -482041.2091329489, -479541.2091329489, -477041.2091329489,\n",
+       "       -474541.2091329489,\n",
        "       ...\n",
-       "        242958.7908670511,  245458.7908670511,  247958.7908670511,\n",
-       "        250458.7908670511,  252958.7908670511,  255458.7908670511,\n",
-       "        257958.7908670511,  260458.7908670511,  262958.7908670511,\n",
-       "        265458.7908670511],\n",
-       "      dtype=&#x27;float64&#x27;, name=&#x27;y&#x27;, length=211))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-7eb20c7a-ae8b-48ba-b493-06d53778d023' class='xr-section-summary-in' type='checkbox'  checked><label for='section-7eb20c7a-ae8b-48ba-b493-06d53778d023' class='xr-section-summary' >Attributes: <span>(7)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><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></li></ul></div></div>"
+       "        487958.7908670511,  490458.7908670511,  492958.7908670511,\n",
+       "        495458.7908670511,  497958.7908670511,  500458.7908670511,\n",
+       "        502958.7908670511,  505458.7908670511,  507958.7908670511,\n",
+       "        510458.7908670511],\n",
+       "      dtype=&#x27;float64&#x27;, name=&#x27;y&#x27;, length=404))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-7c35ed8b-920b-4323-8ad4-02978fcd144c' class='xr-section-summary-in' type='checkbox'  checked><label for='section-7c35ed8b-920b-4323-8ad4-02978fcd144c' class='xr-section-summary' >Attributes: <span>(7)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><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></li></ul></div></div>"
       ],
       "text/plain": [
-       "<xarray.DataArray 't' (altitude: 1, y: 211, x: 229)>\n",
-       "dask.array<where, shape=(1, 211, 229), dtype=float64, chunksize=(1, 116, 152), chunktype=numpy.ndarray>\n",
+       "<xarray.DataArray 't' (altitude: 1, y: 404, x: 360)>\n",
+       "dask.array<where, shape=(1, 404, 360), dtype=float64, chunksize=(1, 211, 227), chunktype=numpy.ndarray>\n",
        "Coordinates:\n",
        "  * altitude  (altitude) int64 100\n",
-       "    lat       (y, x) float64 53.16 53.17 53.17 53.18 ... 58.76 58.76 58.76 58.77\n",
-       "    lon       (y, x) float64 8.371 8.407 8.443 8.48 ... 15.66 15.71 15.75 15.79\n",
-       "    time      datetime64[ns] 1990-09-01\n",
-       "  * x         (x) float64 -1.099e+06 -1.097e+06 ... -5.317e+05 -5.292e+05\n",
-       "  * y         (y) float64 -2.595e+05 -2.57e+05 -2.545e+05 ... 2.63e+05 2.655e+05\n",
+       "    lat       (y, x) float64 50.68 50.68 50.69 50.69 ... 61.1 61.1 61.1 61.1\n",
+       "    lon       (y, x) float64 6.605 6.639 6.673 6.708 ... 17.62 17.67 17.71 17.76\n",
+       "    time      datetime64[ns] 1990-09-01T18:00:00\n",
+       "  * x         (x) float64 -1.287e+06 -1.284e+06 ... -3.917e+05 -3.892e+05\n",
+       "  * y         (y) float64 -4.97e+05 -4.945e+05 -4.92e+05 ... 5.08e+05 5.105e+05\n",
        "Attributes:\n",
        "    level_type:   heightAboveGround\n",
        "    long_name:    Temperature\n",
@@ -6113,19 +9338,19 @@
        "    units:        K"
       ]
      },
-     "execution_count": 33,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "da_danra = ds_danra_hl.isel(time=0).where(da_mask.compute(), drop=True).t\n",
+    "da_danra = ds_danra_hl.isel(time=6).where(da_mask.compute(), drop=True).t\n",
     "da_danra"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
@@ -6494,16 +9719,16 @@
        "  stroke: currentColor;\n",
        "  fill: currentColor;\n",
        "}\n",
-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;2m_temperature&#x27; (lat: 23, lon: 39)&gt;\n",
-       "dask.array&lt;getitem, shape=(23, 39), dtype=float32, chunksize=(23, 39), chunktype=numpy.ndarray&gt;\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;2m_temperature&#x27; (lat: 39, lon: 55)&gt;\n",
+       "dask.array&lt;getitem, shape=(39, 55), dtype=float32, chunksize=(39, 55), chunktype=numpy.ndarray&gt;\n",
        "Coordinates:\n",
-       "  * lat      (lat) float32 58.75 58.5 58.25 58.0 57.75 ... 54.0 53.75 53.5 53.25\n",
-       "  * lon      (lon) float32 7.25 7.5 7.75 8.0 8.25 ... 16.0 16.25 16.5 16.75\n",
-       "    time     datetime64[ns] 1990-09-01\n",
+       "  * lat      (lat) float32 60.75 60.5 60.25 60.0 59.75 ... 52.0 51.75 51.5 51.25\n",
+       "  * lon      (lon) float32 5.25 5.5 5.75 6.0 6.25 ... 18.0 18.25 18.5 18.75\n",
+       "    time     datetime64[ns] 1990-09-01T18:00:00\n",
        "Attributes:\n",
        "    long_name:   2 metre temperature\n",
        "    short_name:  t2m\n",
-       "    units:       K</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'2m_temperature'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>lat</span>: 23</li><li><span class='xr-has-index'>lon</span>: 39</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-df971509-d9ce-486a-acd0-8e441ef30856' class='xr-array-in' type='checkbox' checked><label for='section-df971509-d9ce-486a-acd0-8e441ef30856' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>dask.array&lt;chunksize=(23, 39), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>\n",
+       "    units:       K</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'2m_temperature'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>lat</span>: 39</li><li><span class='xr-has-index'>lon</span>: 55</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-b30c8db0-0606-4751-9eb3-9fbf896d69b9' class='xr-array-in' type='checkbox' checked><label for='section-b30c8db0-0606-4751-9eb3-9fbf896d69b9' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>dask.array&lt;chunksize=(39, 55), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -6518,14 +9743,14 @@
        "                    \n",
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
-       "                        <td> 3.50 kiB </td>\n",
-       "                        <td> 3.50 kiB </td>\n",
+       "                        <td> 8.38 kiB </td>\n",
+       "                        <td> 8.38 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
-       "                        <td> (23, 39) </td>\n",
-       "                        <td> (23, 39) </td>\n",
+       "                        <td> (39, 55) </td>\n",
+       "                        <td> (39, 55) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
@@ -6539,54 +9764,60 @@
        "            </table>\n",
        "        </td>\n",
        "        <td>\n",
-       "        <svg width=\"170\" height=\"120\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "        <svg width=\"170\" height=\"135\" 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=\"70\" x2=\"120\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"85\" x2=\"120\" y2=\"85\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Vertical lines -->\n",
-       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"70\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"70\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"85\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"85\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Colored Rectangle -->\n",
-       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,70.76923076923077 0.0,70.76923076923077\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,85.09090909090908 0.0,85.09090909090908\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
        "\n",
        "  <!-- Text -->\n",
-       "  <text x=\"60.000000\" y=\"90.769231\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >39</text>\n",
-       "  <text x=\"140.000000\" y=\"35.384615\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,35.384615)\">23</text>\n",
+       "  <text x=\"60.000000\" y=\"105.090909\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >55</text>\n",
+       "  <text x=\"140.000000\" y=\"42.545455\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,42.545455)\">39</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></div></li><li class='xr-section-item'><input id='section-2b8e74e0-0de8-409b-bdb1-963f1e6a2c7d' class='xr-section-summary-in' type='checkbox'  checked><label for='section-2b8e74e0-0de8-409b-bdb1-963f1e6a2c7d' class='xr-section-summary' >Coordinates: <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-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>58.75 58.5 58.25 ... 53.5 53.25</div><input id='attrs-d2464dbf-0a0b-49ec-b3ea-ab6d6bc1b06e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d2464dbf-0a0b-49ec-b3ea-ab6d6bc1b06e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cb25b39c-64d2-490e-b81e-2ef1600affa2' class='xr-var-data-in' type='checkbox'><label for='data-cb25b39c-64d2-490e-b81e-2ef1600affa2' title='Show/Hide data 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>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><pre>array([58.75, 58.5 , 58.25, 58.  , 57.75, 57.5 , 57.25, 57.  , 56.75, 56.5 ,\n",
-       "       56.25, 56.  , 55.75, 55.5 , 55.25, 55.  , 54.75, 54.5 , 54.25, 54.  ,\n",
-       "       53.75, 53.5 , 53.25], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>7.25 7.5 7.75 ... 16.25 16.5 16.75</div><input id='attrs-f8c09cb8-5910-48b6-9879-823c5a99de3d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f8c09cb8-5910-48b6-9879-823c5a99de3d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9ed9e79e-4f7c-412e-b37c-3bcce4697433' class='xr-var-data-in' type='checkbox'><label for='data-9ed9e79e-4f7c-412e-b37c-3bcce4697433' title='Show/Hide data 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>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>array([ 7.25,  7.5 ,  7.75,  8.  ,  8.25,  8.5 ,  8.75,  9.  ,  9.25,  9.5 ,\n",
-       "        9.75, 10.  , 10.25, 10.5 , 10.75, 11.  , 11.25, 11.5 , 11.75, 12.  ,\n",
-       "       12.25, 12.5 , 12.75, 13.  , 13.25, 13.5 , 13.75, 14.  , 14.25, 14.5 ,\n",
-       "       14.75, 15.  , 15.25, 15.5 , 15.75, 16.  , 16.25, 16.5 , 16.75],\n",
-       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1990-09-01</div><input id='attrs-483bb870-5e8a-4562-b304-c7babd6fa741' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-483bb870-5e8a-4562-b304-c7babd6fa741' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5e883eb2-2cd3-4386-9c60-acc1c59d55b0' class='xr-var-data-in' type='checkbox'><label for='data-5e883eb2-2cd3-4386-9c60-acc1c59d55b0' title='Show/Hide data 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;, dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-aceb82f8-e260-4095-9b42-803c02771361' class='xr-section-summary-in' type='checkbox'  ><label for='section-aceb82f8-e260-4095-9b42-803c02771361' 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>lat</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-1f759d01-5ae3-4367-9ea8-f7ccc921afd4' class='xr-index-data-in' type='checkbox'/><label for='index-1f759d01-5ae3-4367-9ea8-f7ccc921afd4' 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([58.75,  58.5, 58.25,  58.0, 57.75,  57.5, 57.25,  57.0, 56.75,  56.5,\n",
-       "       56.25,  56.0, 55.75,  55.5, 55.25,  55.0, 54.75,  54.5, 54.25,  54.0,\n",
-       "       53.75,  53.5, 53.25],\n",
-       "      dtype=&#x27;float32&#x27;, name=&#x27;lat&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lon</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-c28282c8-e48f-42c2-9e87-634f3881c148' class='xr-index-data-in' type='checkbox'/><label for='index-c28282c8-e48f-42c2-9e87-634f3881c148' 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([ 7.25,   7.5,  7.75,   8.0,  8.25,   8.5,  8.75,   9.0,  9.25,   9.5,\n",
-       "        9.75,  10.0, 10.25,  10.5, 10.75,  11.0, 11.25,  11.5, 11.75,  12.0,\n",
-       "       12.25,  12.5, 12.75,  13.0, 13.25,  13.5, 13.75,  14.0, 14.25,  14.5,\n",
-       "       14.75,  15.0, 15.25,  15.5, 15.75,  16.0, 16.25,  16.5, 16.75],\n",
-       "      dtype=&#x27;float32&#x27;, name=&#x27;lon&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-b1909003-5d93-4385-bfed-a59810db9f6a' class='xr-section-summary-in' type='checkbox'  checked><label for='section-b1909003-5d93-4385-bfed-a59810db9f6a' class='xr-section-summary' >Attributes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><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></li></ul></div></div>"
+       "</table></div></div></li><li class='xr-section-item'><input id='section-bf060235-f2e5-43f3-b94a-3a339b40fd6b' class='xr-section-summary-in' type='checkbox'  checked><label for='section-bf060235-f2e5-43f3-b94a-3a339b40fd6b' class='xr-section-summary' >Coordinates: <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-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>60.75 60.5 60.25 ... 51.5 51.25</div><input id='attrs-fb27d0f4-a6ca-483e-b638-27f07611a9ed' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fb27d0f4-a6ca-483e-b638-27f07611a9ed' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d1729d11-a969-4108-bd74-2869afa37a20' class='xr-var-data-in' type='checkbox'><label for='data-d1729d11-a969-4108-bd74-2869afa37a20' title='Show/Hide data 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>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><pre>array([60.75, 60.5 , 60.25, 60.  , 59.75, 59.5 , 59.25, 59.  , 58.75, 58.5 ,\n",
+       "       58.25, 58.  , 57.75, 57.5 , 57.25, 57.  , 56.75, 56.5 , 56.25, 56.  ,\n",
+       "       55.75, 55.5 , 55.25, 55.  , 54.75, 54.5 , 54.25, 54.  , 53.75, 53.5 ,\n",
+       "       53.25, 53.  , 52.75, 52.5 , 52.25, 52.  , 51.75, 51.5 , 51.25],\n",
+       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>5.25 5.5 5.75 ... 18.25 18.5 18.75</div><input id='attrs-cc46dc04-8800-487f-878f-6fb590666be4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cc46dc04-8800-487f-878f-6fb590666be4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e3ce4c8c-1430-499a-bdb8-b5d34c884b89' class='xr-var-data-in' type='checkbox'><label for='data-e3ce4c8c-1430-499a-bdb8-b5d34c884b89' title='Show/Hide data 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>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>array([ 5.25,  5.5 ,  5.75,  6.  ,  6.25,  6.5 ,  6.75,  7.  ,  7.25,  7.5 ,\n",
+       "        7.75,  8.  ,  8.25,  8.5 ,  8.75,  9.  ,  9.25,  9.5 ,  9.75, 10.  ,\n",
+       "       10.25, 10.5 , 10.75, 11.  , 11.25, 11.5 , 11.75, 12.  , 12.25, 12.5 ,\n",
+       "       12.75, 13.  , 13.25, 13.5 , 13.75, 14.  , 14.25, 14.5 , 14.75, 15.  ,\n",
+       "       15.25, 15.5 , 15.75, 16.  , 16.25, 16.5 , 16.75, 17.  , 17.25, 17.5 ,\n",
+       "       17.75, 18.  , 18.25, 18.5 , 18.75], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1990-09-01T18:00:00</div><input id='attrs-44592f93-ea0a-4155-ba22-fe26275d88c2' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-44592f93-ea0a-4155-ba22-fe26275d88c2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-75fe4d56-17d2-4db2-b7d7-494426967320' class='xr-var-data-in' type='checkbox'><label for='data-75fe4d56-17d2-4db2-b7d7-494426967320' title='Show/Hide data 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;1990-09-01T18:00:00.000000000&#x27;, dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-5e98121f-4bad-4557-8358-044e31c8c233' class='xr-section-summary-in' type='checkbox'  ><label for='section-5e98121f-4bad-4557-8358-044e31c8c233' 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>lat</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-7c65f01a-e872-4c13-afac-5d2c74ea1521' class='xr-index-data-in' type='checkbox'/><label for='index-7c65f01a-e872-4c13-afac-5d2c74ea1521' 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([60.75,  60.5, 60.25,  60.0, 59.75,  59.5, 59.25,  59.0, 58.75,  58.5,\n",
+       "       58.25,  58.0, 57.75,  57.5, 57.25,  57.0, 56.75,  56.5, 56.25,  56.0,\n",
+       "       55.75,  55.5, 55.25,  55.0, 54.75,  54.5, 54.25,  54.0, 53.75,  53.5,\n",
+       "       53.25,  53.0, 52.75,  52.5, 52.25,  52.0, 51.75,  51.5, 51.25],\n",
+       "      dtype=&#x27;float32&#x27;, name=&#x27;lat&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lon</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-a1723f8f-6aed-4859-8b28-0a51ac4804f6' class='xr-index-data-in' type='checkbox'/><label for='index-a1723f8f-6aed-4859-8b28-0a51ac4804f6' 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([ 5.25,   5.5,  5.75,   6.0,  6.25,   6.5,  6.75,   7.0,  7.25,   7.5,\n",
+       "        7.75,   8.0,  8.25,   8.5,  8.75,   9.0,  9.25,   9.5,  9.75,  10.0,\n",
+       "       10.25,  10.5, 10.75,  11.0, 11.25,  11.5, 11.75,  12.0, 12.25,  12.5,\n",
+       "       12.75,  13.0, 13.25,  13.5, 13.75,  14.0, 14.25,  14.5, 14.75,  15.0,\n",
+       "       15.25,  15.5, 15.75,  16.0, 16.25,  16.5, 16.75,  17.0, 17.25,  17.5,\n",
+       "       17.75,  18.0, 18.25,  18.5, 18.75],\n",
+       "      dtype=&#x27;float32&#x27;, name=&#x27;lon&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-9b0c10b8-a9b6-4ff9-9fa8-52a88c608bda' class='xr-section-summary-in' type='checkbox'  checked><label for='section-9b0c10b8-a9b6-4ff9-9fa8-52a88c608bda' class='xr-section-summary' >Attributes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><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></li></ul></div></div>"
       ],
       "text/plain": [
-       "<xarray.DataArray '2m_temperature' (lat: 23, lon: 39)>\n",
-       "dask.array<getitem, shape=(23, 39), dtype=float32, chunksize=(23, 39), chunktype=numpy.ndarray>\n",
+       "<xarray.DataArray '2m_temperature' (lat: 39, lon: 55)>\n",
+       "dask.array<getitem, shape=(39, 55), dtype=float32, chunksize=(39, 55), chunktype=numpy.ndarray>\n",
        "Coordinates:\n",
-       "  * lat      (lat) float32 58.75 58.5 58.25 58.0 57.75 ... 54.0 53.75 53.5 53.25\n",
-       "  * lon      (lon) float32 7.25 7.5 7.75 8.0 8.25 ... 16.0 16.25 16.5 16.75\n",
-       "    time     datetime64[ns] 1990-09-01\n",
+       "  * lat      (lat) float32 60.75 60.5 60.25 60.0 59.75 ... 52.0 51.75 51.5 51.25\n",
+       "  * lon      (lon) float32 5.25 5.5 5.75 6.0 6.25 ... 18.0 18.25 18.5 18.75\n",
+       "    time     datetime64[ns] 1990-09-01T18:00:00\n",
        "Attributes:\n",
        "    long_name:   2 metre temperature\n",
        "    short_name:  t2m\n",
        "    units:       K"
       ]
      },
-     "execution_count": 34,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -6598,22 +9829,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<cartopy.mpl.feature_artist.FeatureArtist at 0x7f319826fc40>"
+       "<cartopy.mpl.feature_artist.FeatureArtist at 0x7fa052803670>"
       ]
      },
-     "execution_count": 35,
+     "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": "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",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 1000x400 with 3 Axes>"
       ]
@@ -6629,6 +9860,13 @@
     "ax.gridlines(draw_labels=[\"top\", \"left\"])\n",
     "ax.coastlines()"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {

From f9d389714e497c85513425c8d7c3e9354d9306d7 Mon Sep 17 00:00:00 2001
From: Leif Denby <lcd@dmi.dk>
Date: Tue, 12 Mar 2024 15:11:12 +0000
Subject: [PATCH 4/4] update example with bbox cropping

---
 example.danra.yaml | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/example.danra.yaml b/example.danra.yaml
index 49c54f1..0f5235c 100644
--- a/example.danra.yaml
+++ b/example.danra.yaml
@@ -37,7 +37,7 @@ inputs:
       type: latlon-bbox
       coordinates:
         # give WESN bounding box around Denmark in degrees
-        wesn: [10, 50, 20, 60]
+        wesn: [6, 18, 52, 60]
     target_architecture_variable: state
 
   era_height_levels:
@@ -57,10 +57,10 @@ inputs:
         method: stack_variables_by_var_name
         name_format: f"{var_name}"
     subsetting:
-      type: latlon-bbox-frame
+      method: latlon-bbox-frame
       coordinates:
         # give WESN bounding box around Denmark in degrees
-        wesn: [10, 50, 20, 60]
+        wesn: [6, 18, 52, 60]
         # width in degrees
-        latlonwidth: 0.25
+        latlonwidth: 1.0
     target_architecture_variable: state