Download ERA5 Data from Copernicus Data Store API

Download ERA5 Data from Copernicus Data Store API#

This notebook demonstrates how to download ERA5 reanalysis data from the Copernicus Data Store (CDS) using the cdsapi library. It provides a step-by-step guide to retrieve, process, and visualize meteo data.

Key Steps#

  1. Import Required Libraries
    The notebook begins by importing essential Python libraries such as cdsapi for data retrieval, xarray for handling NetCDF files, matplotlib for visualization, and cartopy for geospatial plotting.

  2. Load Environment Variables
    Environment variables for the CDS API URL and key are loaded using the dotenv library to ensure secure and flexible configuration.

  3. Initialize the CDS Client
    The cdsapi.Client is initialized using the credentials stored in the environment variables, enabling access to the Copernicus Data Store.

  4. Retrieve ERA5 Data
    The notebook retrieves ERA5 single-level reanalysis data for a specific time period and variable (e.g., 2m temperature). The data is saved in NetCDF format for further analysis.

  5. Visualize the Data
    The downloaded data is visualized using matplotlib and cartopy. A sample plot of 2m temperature is generated, showcasing the spatial distribution of the variable.

Import the required libraries#

import os
import cdsapi
import xarray as xr
from matplotlib import pyplot as plt
import cartopy.crs as ccrs
from dotenv import load_dotenv
load_dotenv("../.env")

True

Get environment variables#

CDS_URL = os.getenv("CDS_URL")
CDS_KEY = os.getenv("CDS_KEY")

Initialize CDS Client#

client = cdsapi.Client(url=CDS_URL,key=CDS_KEY)

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Retrieve data#

output_path = "../data/era5_sample.nc"
os.makedirs(os.path.dirname(output_path),exist_ok=True)
client.retrieve(
    "reanalysis-era5-single-levels",
    {
        "product_type": "reanalysis",
        "variable": [
            "2m_temperature"
        ],
        "year": "2023",
        "month": "01",
        "day": ["01", "02", "03"],
        "time": [
            "00:00", "06:00", "12:00", "18:00"
        ],
        "data_format": "netcdf",
        "download_format": "unarchived"
    },
    output_path,
)

2025-06-20 12:49:49,927 INFO Request ID is 2e94cb50-74f3-4da4-abe3-3d6fc79f8900
2025-06-20 12:49:50,009 INFO status has been updated to accepted
2025-06-20 12:49:58,540 INFO status has been updated to running
2025-06-20 12:50:22,892 INFO status has been updated to successful

'../data/era5_sample.nc'

Visualize a variable from the downloaded data#

ds = xr.open_dataset(output_path,engine="netcdf4")
print (ds)

<xarray.Dataset> Size: 50MB
Dimensions:     (valid_time: 12, latitude: 721, longitude: 1440)
Coordinates:
    number      int64 8B ...
  * valid_time  (valid_time) datetime64[ns] 96B 2023-01-01 ... 2023-01-03T18:...
  * latitude    (latitude) float64 6kB 90.0 89.75 89.5 ... -89.5 -89.75 -90.0
  * longitude   (longitude) float64 12kB 0.0 0.25 0.5 0.75 ... 359.2 359.5 359.8
    expver      (valid_time) <U4 192B ...
Data variables:
    t2m         (valid_time, latitude, longitude) float32 50MB ...
Attributes:
    GRIB_centre:             ecmf
    GRIB_centreDescription:  European Centre for Medium-Range Weather Forecasts
    GRIB_subCentre:          0
    Conventions:             CF-1.7
    institution:             European Centre for Medium-Range Weather Forecasts
    history:                 2025-06-20T10:50 GRIB to CDM+CF via cfgrib-0.9.1...

t2m = ds['t2m'].isel(valid_time=0)
fig = plt.figure(figsize=(12, 6))
ax = plt.axes(projection=ccrs.PlateCarree())
t2m.plot(ax=ax, transform=ccrs.PlateCarree(), cmap="coolwarm", cbar_kwargs={"label": "K"})
ax.coastlines()
ax.set_title("2m Temperature")
plt.show()
../_images/056c847644c522d0e700789538c7e462a71aff25f7907cc547a6d015abbd09ff.png
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