Extract CAMS data

Registration

Access to ADS data is restricted to registred users. See ADS’s website for more detailed informations.

Note

You need to accept the licence to extract and use CAMS products here.

Configure

Once your account create, you need to configure CDS API following https://ads.atmosphere.copernicus.eu/how-to-api instructions.

Tip

I recommend to create a file called .adsapirc and not .cdsapirc because you can have conflict with CDS API key if you also extract ERA5 data from CDS server.

First you need to create a file called .adsapirc in your HOME directory. Your .adsapirc file needs to contain lines that look likes:

url: https://ads.atmosphere.copernicus.eu/api
key: your_key

Note

To fill this file go to https://ads.atmosphere.copernicus.eu/how-to-api.

Warning

You need to suppress rules for group and other users with chmod 600 .adsapirc.

Installation

I suggest you to use a dedicated conda environment. For that purpose you have to create an environment.yml file containing following code :

name: env_extract_cdsapi
channels:
    - conda-forge
dependencies:
    - cdsapi>=0.7.2
    - eccodes==2.35.0
    - pip==24.0
    - pip:
        - yaml-config==0.1.5

Note

This is the last version of cdsapi and yaml-config tested.
yaml-config is used to read .adsapirc file.
eccodes is used to concatenate grib files.

Then you can create your conda environment with :

conda env create -f environment.yml

Then load new created python environment :

conda activate env_extract_cdsapi

Caution

It’s mandatory to have a version of CDSAPI higher than 0.7.2.

Example

To extract CAMS data from example script, you have to :

create a script called your_script.sh containing the following code
and adapt the path for file .adsapirc inside this script

#!/bin/bash

# AREA = 'Norht, West, South, East'
export AREA='20, -20, -60, 80'

export YEAR='2019'
export MONTH='03'

for DAY in  '09' #  '10' '11' '12' '13' '14' '15'
do
for HOUR in  '00' # '06' '12' '18'
do

echo '--> Extacting date : ' ${YEAR}${MONTH}${DAY}_${HOUR}

# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
#   Create request file
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
cat > request_CAMS_${YEAR}${MONTH}${DAY}_${HOUR}.py << EOF

import cdsapi
import yaml

with open('/path/to/your/.adsapirc', 'r') as f: credentials = yaml.safe_load(f)

c = cdsapi.Client(url=credentials['url'], key=credentials['key'])

c.retrieve(
    'cams-global-reanalysis-eac4',
    {
        'date'     : '${YEAR}-${MONTH}-${DAY}/${YEAR}-${MONTH}-${DAY}',
        'format'   : 'netcdf',
        'variable' : [
            'acetone', 'acetone_product', 'aldehydes',
            'amine', 'ammonia', 'ammonium',
            'carbon_monoxide', 'dimethyl_sulfide', 'dinitrogen_pentoxide',
            'dust_aerosol_0.03-0.55um_mixing_ratio', 'dust_aerosol_0.55-0.9um_mixing_ratio', 'dust_aerosol_0.9-20um_mixing_ratio',
            'ethane', 'ethanol', 'ethene',
            'formaldehyde', 'formic_acid', 'hydrogen_peroxide',
            'hydroperoxy_radical', 'hydrophilic_black_carbon_aerosol_mixing_ratio', 'hydrophilic_organic_matter_aerosol_mixing_ratio',
            'hydrophobic_black_carbon_aerosol_mixing_ratio', 'hydrophobic_organic_matter_aerosol_mixing_ratio', 'hydroxyl_radical',
            'isoprene', 'methacrolein_mvk', 'methacrylic_acid',
            'methane_sulfonic_acid', 'methanol', 'methyl_glyoxal',
            'methyl_peroxide', 'methylperoxy_radical', 'nitrate',
            'nitrate_radical', 'nitric_acid', 'nitrogen_dioxide',
            'nitrogen_monoxide', 'olefins', 'organic_ethers',
            'organic_nitrates', 'ozone', 'paraffins',
            'pernitric_acid', 'peroxides', 'peroxy_acetyl_radical',
            'peroxyacetyl_nitrate', 'propane', 'propene',
            'sea_salt_aerosol_0.03-0.5um_mixing_ratio', 'sea_salt_aerosol_0.5-5um_mixing_ratio', 'sea_salt_aerosol_5-20um_mixing_ratio',
            'specific_humidity', 'sulphate_aerosol_mixing_ratio', 'sulphur_dioxide',
            'surface_pressure', 'temperature', 'terpenes',
        ],
        'model_level' : [
            '1', '2', '3',
            '4', '5', '6',
            '7', '8', '9',
            '10', '11', '12',
            '13', '14', '15',
            '16', '17', '18',
            '19', '20', '21',
            '22', '23', '24',
            '25', '26', '27',
            '28', '29', '30',
            '31', '32', '33',
            '34', '35', '36',
            '37', '38', '39',
            '40', '41', '42',
            '43', '44', '45',
            '46', '47', '48',
            '49', '50', '51',
            '52', '53', '54',
            '55', '56', '57',
            '58', '59', '60',
        ],
        'time': '${HOUR}:00',
        'area': [${AREA},
        ],
    },
    'download.nc')
EOF

# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
#   Send request
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
python request_CAMS_${YEAR}${MONTH}${DAY}_${HOUR}.py

# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
#   Unzip, concatenate and remove tmp files
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
unzip download.nc
ncks -A data_sfc.nc data_mlev.nc
mv data_mlev.nc CAMS_${YEAR}${MONTH}${DAY}_${HOUR}.nc
rm -f data_sfc.nc download.nc

done
done

Note

You need to have ncks installed.

Then, you can launch the extraction with :

./your_script.sh

Note

At the end of the extraction you need to have files called CAMS_${YEAR}${MONTH}${DAY}_${HOUR}.nc !
Once the example script works, you can adapt the area and the date to your case.