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A tool to convert ERA5/ECMWF data to EnergyPlus Weather (EPW) format

Project description

tests PyPI - Version PyPI - Downloads GitHub License

ERA5 to EPW Converter

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.

Installation

Prerequisites

Make sure to register for an API key and validate licences at:

Before proceeding further, it is required to accept all the licenses in the section "Your profile" in the website of Copernicus.

CDS/ADS credentials

Passing the key as environment variable

This is the easiest method. Use:

import os
os.environ["CDSADS_API_KEY"] = "YOUR_KEY"

Installing the API key in your filesytem

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.

Earth Data Hub credentials (optional)

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.

Passing the token as environment variable

import os
os.environ["EDH_TOKEN"] = "edh_pat_..."

Or in the shell:

export EDH_TOKEN="edh_pat_..."

Installing the token in your filesystem

Create the file ~/.edh_token with your personal access token (plain text, first line):

edh_pat_...

Install the package

From PyPI

pip install era5epw

From source

Clone the current repository and install the required dependencies using Poetry:

git clone https://github.com/airboxlab/era5epw.git
poetry install

Usage

[!NOTE] 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 widgetsnbextension

Generating EPW Files

Command line interface

Example 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 edh

By 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.

Python API

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",
)

Reading EPW Files into a DataFrame

The package provides a reader to load EPW files into Pandas DataFrames for inspection, data analysis, and further processing.

Python API

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]

Visualizing EPW Files

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.

Command line interface

# 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.html

Python API

Use 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")

EPW Visualization

Documentation

ERA5
CAMS
EPW format
Earthkit

Datasets home pages:

View your API requests and download responses at:

CDS Requests
ADS Requests

Check CDS API status at CDS Live, it provides information about congestion for each dataset.

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