Download ERA data with python

Since the ECMWF currently advice user to migrate from ERA-interim to ERA5 (due ERA Interim is being phased out) and also to migrate from ECMWF Web API to CDS API, in this tutorial I will teach you how to download ERA5 data with python using CDS API.

Instalation

The CDS API can be installed with Conda or Pip, and it is recommended to install it within a python environment.

Installation using conda:

conda config --add channels conda-forge
conda install cdsapi

Installation using pip:

pip install cdsapi

Creates an account

Creates a CDS account here.

Install the CDS API key

• You need to login to continue, login here.

• After login, click here.

• Scroll down in the pageweb until the API key section and copy the UID and API Key codes.

• Creates .cdsapirc file

  For Linux: creates the file $HOME/.cdsapirc

  For Windows: creates the file at %USERPROFILE%\.cdsapirc

               More information for windows here and here.

• Copy the next code within .cdsapirc

       url: https://cds.climate.copernicus.eu/api/v2
       key: {uid}:{api-key}

For more detail see here.

Accept data licence and generate a basic CDS API script using the CDS web interface

• Go to the C3S climate data store (CDS)
• On the top menu bar, click on Datasets.
• On the left-hand side menu, expand Product type and select product type of interest (e.g. Seasonal forecasts to get all seasonal forecasts data listed, Reanalysis for ERA5 datasets)
• Follow the dataset title link of interest to the full dataset record
• Accept dataset licence and generate basic CDS API script

In my case, I selected Reanalysis in Product type, and I selected ERA5 hourly data on pressure levels from 1979 to present as a dataset. Then, selected the variables that I want to download and I did click on Show API request to see the python script.

import cdsapi

c = cdsapi.Client()

c.retrieve(
    'reanalysis-era5-pressure-levels',
    {
        'product_type': 'reanalysis',
        'format': 'netcdf',
        'variable': [
            'geopotential', 'potential_vorticity', 'temperature',
            'u_component_of_wind', 'v_component_of_wind',
        ],
        'pressure_level': [
            '100', '125', '150',
            '175', '200', '225',
            '250', '300', '350',
            '400', '450', '500',
            '550', '600', '650',
            '700', '750', '775',
            '800', '825', '850',
            '875', '900', '925',
            '950', '975', '1000',
        ],
        'year': '1979',
        'month': '01',
        'day': [
            '01', '02', '03',
            '04', '05', '06',
            '07', '08', '09',
            '10', '11', '12',
            '13', '14', '15',
            '16', '17', '18',
            '19', '20', '21',
            '22', '23', '24',
            '25', '26', '27',
            '28', '29', '30',
            '31',
        ],
        'time': [
            '00:00', '01:00', '02:00',
            '03:00', '04:00', '05:00',
            '06:00', '07:00', '08:00',
            '09:00', '10:00', '11:00',
            '12:00', '13:00', '14:00',
            '15:00', '16:00', '17:00',
            '18:00', '19:00', '20:00',
            '21:00', '22:00', '23:00',
        ],
        'area': [
            10, -120, -60,
            -20,
        ],
    },
    'download.nc')

Finally, I did run the python code from python environment where was installed CDS API, and I downloaded the ERA5 data.