A tool that fetches ERA5 data and generates a full year AMY (Actual Meteorological Year) EnergyPlus Weather file (EPW).
The tool takes care of fetching the necessary data from the Copernicus Climate Data Store (CDS) and the Copernicus Atmosphere Data Store (CAMS), processing it, and formatting it into the EPW format. It's designed for fast and efficient data retrieval.
Make sure to register for an API key and validate licences at:
- https://cds.climate.copernicus.eu/ (ERA5 data)
- https://ads.atmosphere.copernicus.eu/ (Copernicus Atmosphere Data Store)
Before proceeding further, it is required to accept all the licenses in the section "Your profile" in the website of Copernicus.
This is the easiest method. Use:
import os
os.environ["CDSADS_API_KEY"] = "YOUR_KEY"Then create the file ~/.cdsapirc with the following content:
url: https://cds.climate.copernicus.eu/api/v2
key: <your_api_key>Note: the URL will be dynamically managed by the script depending on the data source. The API key doesn't vary, it's the same for both ERA5 and CAMS data.
If you want to use the Earth Data Hub (EDH) as a data source instead of CDS, you need to set up your EDH personal access token.
Note
Earth Data Hub has a monthly quota of 500,000 requests per user. Data is available until the last closed month.
import os
os.environ["EDH_TOKEN"] = "edh_pat_..."Or in the shell:
export EDH_TOKEN="edh_pat_..."Create the file ~/.edh_token with your personal access token (plain text, first line):
edh_pat_...
pip install era5epwClone the current repository and install the required dependencies using Poetry:
git clone https://github.com/airboxlab/era5epw.git
poetry installNote
When running in a Jupyter notebook, to make progress bars and interactive widgets work, make sure to install ipywidgets and to enable the widgets extension.
pip install ipywidgets
# optional, not needed with Jupyter Notebook 7+
jupyter nbextension enable --py widgetsnbextensionExample usage:
# using poetry, execute in local repository
poetry run era5epw_download --year 2024 --latitude 49.4 --longitude 0.1 --city-name "Le Havre" --elevation 0 --time-zone 1
# using installed binary, after pypi package installation
era5epw_download --year 2024 --latitude 49.4 --longitude 0.1 --city-name "Le Havre" --elevation 0 --time-zone 1
# using Earth Data Hub as data source (faster downloads, requires EDH token)
era5epw_download --year 2024 --latitude 49.4 --longitude 0.1 --city-name "Le Havre" --elevation 0 --time-zone 1 --era5-data-source edhBy default, the time-zone argument is used only to populate the LOCATION header and data time is UTC. Use --apply-time-zone-to-data to apply it to the date and time fields (this will shift the UTC time by the provided time zone offset).
Use --help to have a list of available options.
Example usage:
from era5epw.main import download_and_make_epw
download_and_make_epw(
year=2025,
latitude=48.8,
longitude=2.4,
city_name="Paris",
time_zone=1,
elevation=0,
output_file="/tmp/era5epw_paris_2025.epw",
apply_time_zone_to_data=True,
)
# Or using Earth Data Hub as data source (faster downloads):
download_and_make_epw(
year=2025,
latitude=48.8,
longitude=2.4,
city_name="Paris",
time_zone=1,
elevation=0,
output_file="/tmp/era5epw_paris_2025.epw",
apply_time_zone_to_data=True,
era5_data_source="edh",
)The package provides a reader to load EPW files into Pandas DataFrames for inspection, data analysis, and further processing.
from era5epw.reader import read_epw_file
# Load EPW file into a DataFrame
df = read_epw_file("path/to/file.epw")
# Inspect the data
print(df.head())
print(df.describe())
# Access specific weather series
print(df["Dry Bulb Temperature"].mean())
# Filter by date
january_data = df[df.index.month == 1]The package includes an interactive visualization tool for EPW files that supports three types of plots: 2D line charts, 3D surface plots, and radar (polar) plots.
# List available weather series in an EPW file
era5epw_visualize path/to/file.epw --list-series
# Create a 2D line plot (default)
era5epw_visualize path/to/file.epw --series "Dry Bulb Temperature" --type 2D
# Create a 3D surface plot
era5epw_visualize path/to/file.epw --series "Wind Speed" --type 3D
# Create a radar plot showing daily min/max values
era5epw_visualize path/to/file.epw --series "Global Horizontal Radiation" --type radar
# Save visualization to HTML file
era5epw_visualize path/to/file.epw --series "Dry Bulb Temperature" --output visualization.htmlUse in Jupyter notebooks or Python scripts:
from era5epw.visualize import visualize_epw
# Create interactive 2D plot
fig = visualize_epw(
epw_file_path="path/to/file.epw",
series_name="Dry Bulb Temperature",
plot_type="2D",
show=True # Display immediately in Jupyter
)
# Create 3D surface plot
fig = visualize_epw(
epw_file_path="path/to/file.epw",
series_name="Wind Speed",
plot_type="3D",
show=True
)
# Create radar plot
fig = visualize_epw(
epw_file_path="path/to/file.epw",
series_name="Global Horizontal Radiation",
plot_type="radar",
show=True
)
# Save to HTML file
fig.write_html("visualization.html")
Datasets home pages:
- ERA5 hourly time-series data on single levels from 1940 to present (Experimental)
- ERA5 Land hourly time-series data from 1950 to present (Experimental)
- ERA5 hourly data on single levels from 1940 to present
- CAMS solar radiation time-series
- Earth Data Hub - ERA5 reanalysis single levels (License: Copernicus License)
View your API requests and download responses at:
Check CDS API status at CDS Live, it provides information about congestion for each dataset.