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Copernicus Atmosphere using ECMWFSearch plugin

In this tutorial we will show you how to use eodag to download data from providers using ECMWFSearch eodag plugin. You can currently find three providers that uses it, cop_ads, cop_cds and cop_ewds. For this tutorial we will use cop_ads, but cop_cds and cop_ewds are used the same way.

# Switch to minimal mode for the exception handlers
%xmode Minimal

from eodag import EODataAccessGateway

dag = EODataAccessGateway()

Exception reporting mode: Minimal

Search (build download request)

Validation

Requests on these collections require some search-parameters to be set. If none is set, validation will prompt an error:

dag.search(provider="cop_ads", collection="CAMS_EAC4", raise_errors=True)

ValidationError: 6 validation errors for Queryables
ecmwf:data_format
  Field required [type=missing, input_value={'collection': 'CAMS_EAC4'}, input_type=dict]
    For further information visit https://errors.pydantic.dev/2.11/v/missing
ecmwf:date
  Field required [type=missing, input_value={'collection': 'CAMS_EAC4'}, input_type=dict]
    For further information visit https://errors.pydantic.dev/2.11/v/missing
ecmwf:model_level
  Field required [type=missing, input_value={'collection': 'CAMS_EAC4'}, input_type=dict]
    For further information visit https://errors.pydantic.dev/2.11/v/missing
ecmwf:pressure_level
  Field required [type=missing, input_value={'collection': 'CAMS_EAC4'}, input_type=dict]
    For further information visit https://errors.pydantic.dev/2.11/v/missing
ecmwf:time
  Field required [type=missing, input_value={'collection': 'CAMS_EAC4'}, input_type=dict]
    For further information visit https://errors.pydantic.dev/2.11/v/missing
ecmwf:variable
  Field required [type=missing, input_value={'collection': 'CAMS_EAC4'}, input_type=dict]
    For further information visit https://errors.pydantic.dev/2.11/v/missing


The above exception was the direct cause of the following exception:

ValidationError: 6 error(s). ecmwf:data_format: Field required; ecmwf:pressure_level: Field required; ecmwf:time: Field required; ecmwf:model_level: Field required; ecmwf:variable: Field required; ecmwf:date: Field required

Check available queryables and default values:

Available queryables parameters and associated values can be checked using list_queryables() method, or through cop_ads or cop_cds websites:

queryables = dag.list_queryables(provider="cop_ads", collection="CAMS_EAC4")
queryables

QueryablesDict (9) - additional_properties=False
'ecmwf_area':  typing.Annotated[tuple[Annotated[float, FieldInfo(annotation=NoneType, required=False, default=None, description='West border of the bounding box', metadata=[Ge(ge=-180), ...])], Annotated[float, FieldInfo(annotation=NoneType, required=False, default=None, description='South border of the bounding box', metadata=[Ge(ge=-90), ...])], Annotated[float, FieldInfo(annotation=NoneType, required=False, default=None, description='East border of the bounding box', metadata=[Ge(ge=-180), ...])], Annotated[float, FieldInfo(annotation=NoneType, required=False, default=None, description='North border of the bounding box', metadata=[Ge(ge=-90), ...])]],  FieldInfo( 'required': False, ... )] typing.Annotated[ tuple[typing.Annotated[float, FieldInfo(annotation=NoneType, required=False, default=None, description='West border of the bounding box', metadata=[Ge(ge=-180), Le(le=180)])], typing.Annotated[float, FieldInfo(annotation=NoneType, required=False, default=None, description='South border of the bounding box', metadata=[Ge(ge=-90), Le(le=90)])], typing.Annotated[float, FieldInfo(annotation=NoneType, required=False, default=None, description='East border of the bounding box', metadata=[Ge(ge=-180), Le(le=180)])], typing.Annotated[float, FieldInfo(annotation=NoneType, required=False, default=None, description='North border of the bounding box', metadata=[Ge(ge=-90), Le(le=90)])]], FieldInfo(annotation=NoneType, required=False, default=None, alias_priority=2, validation_alias=AliasChoices(choices=['ecmwf:area', 'area']), serialization_alias='ecmwf:area', title='Sub-region extraction', description='Select a sub-region of the available area by providing its limits on latitude and longitude') ]
'ecmwf_data_format':  typing.Annotated[Literal['grib', ...],  FieldInfo( 'required': True, ... )] typing.Annotated[ typing.Literal['grib', 'netcdf_zip'], FieldInfo(annotation=NoneType, required=True, alias_priority=2, validation_alias=AliasChoices(choices=['ecmwf:data_format', 'data_format']), serialization_alias='ecmwf:data_format', title='Data format', description='Please select a format for the data files, the native format of MARS dataset is GRIB.') ]
'ecmwf_date':  typing.Annotated[Literal['2003-01-01/2024-12-31'],  FieldInfo( 'required': True, ... )] typing.Annotated[ typing.Literal['2003-01-01/2024-12-31'], FieldInfo(annotation=NoneType, required=True, alias_priority=2, validation_alias=AliasChoices(choices=['ecmwf:date', 'date']), serialization_alias='ecmwf:date', title='Date', description='date formatted like yyyy-mm-dd/yyyy-mm-dd') ]
'ecmwf_model_level':  typing.Annotated[list[Literal['1', ...]],  FieldInfo( 'required': True, ... )] typing.Annotated[ list[typing.Literal['1', '10', '11', '12', '13', '14', '15', '16', '17', '18', '19', '2', '20', '21', '22', '23', '24', '25', '26', '27', '28', '29', '3', '30', '31', '32', '33', '34', '35', '36', '37', '38', '39', '4', '40', '41', '42', '43', '44', '45', '46', '47', '48', '49', '5', '50', '51', '52', '53', '54', '55', '56', '57', '58', '59', '6', '60', '7', '8', '9']], FieldInfo(annotation=NoneType, required=True, alias_priority=2, validation_alias=AliasChoices(choices=['ecmwf:model_level', 'model_level']), serialization_alias='ecmwf:model_level', title='Model level', description="Model level 1 is the top of the atmosphere. Model level 60 is the Earth's surface.") ]
'ecmwf_pressure_level':  typing.Annotated[list[Literal['1', ...]],  FieldInfo( 'required': True, ... )] typing.Annotated[ list[typing.Literal['1', '10', '100', '1000', '150', '2', '20', '200', '250', '3', '30', '300', '400', '5', '50', '500', '600', '7', '70', '700', '800', '850', '900', '925', '950']], FieldInfo(annotation=NoneType, required=True, alias_priority=2, validation_alias=AliasChoices(choices=['ecmwf:pressure_level', 'pressure_level']), serialization_alias='ecmwf:pressure_level', title='Pressure level') ]
'ecmwf_time':  typing.Annotated[list[Literal['00:00', ...]],  FieldInfo( 'required': True, ... )] typing.Annotated[ list[typing.Literal['00:00', '03:00', '06:00', '09:00', '12:00', '15:00', '18:00', '21:00']], FieldInfo(annotation=NoneType, required=True, alias_priority=2, validation_alias=AliasChoices(choices=['ecmwf:time', 'time']), serialization_alias='ecmwf:time', title='Time', description='Model base time as HH:MM (UTC)') ]
'ecmwf_variable':  typing.Annotated[list[Literal['10m_u_component_of_wind', ...]],  FieldInfo( 'required': True, ... )] typing.Annotated[ list[typing.Literal['10m_u_component_of_wind', '10m_v_component_of_wind', '2m_dewpoint_temperature', '2m_temperature', 'acetone', 'acetone_product', 'aldehydes', 'amine', 'ammonia', 'ammonium', 'black_carbon_aerosol_optical_depth_550nm', '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', 'dust_aerosol_optical_depth_550nm', 'ethane', 'ethanol', 'ethene', 'formaldehyde', 'formic_acid', 'fraction_of_cloud_cover', 'geopotential', 'high_cloud_cover', 'high_vegetation_cover', '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', 'lake_cover', 'land_sea_mask', 'lead', 'leaf_area_index_high_vegetation', 'leaf_area_index_low_vegetation', 'low_cloud_cover', 'low_vegetation_cover', 'mean_altitude_of_maximum_injection', 'mean_sea_level_pressure', 'medium_cloud_cover', 'methacrolein_mvk', 'methacrylic_acid', 'methane_chemistry', 'methane_sulfonic_acid', 'methanol', 'methyl_glyoxal', 'methyl_peroxide', 'methylperoxy_radical', 'near_ir_albedo_for_diffuse_radiation', 'near_ir_albedo_for_direct_radiation', 'nitrate', 'nitrate_radical', 'nitric_acid', 'nitrogen_dioxide', 'nitrogen_monoxide', 'olefins', 'organic_ethers', 'organic_matter_aerosol_optical_depth_550nm', 'organic_nitrates', 'ozone', 'paraffins', 'particulate_matter_10um', 'particulate_matter_1um', 'particulate_matter_2.5um', 'pernitric_acid', 'peroxides', 'peroxy_acetyl_radical', 'peroxyacetyl_nitrate', 'potential_vorticity', 'propane', 'propene', 'radon', 'relative_humidity', 'sea_ice_cover', 'sea_salt_aerosol_0.03-0.5um_mixing_ratio', 'sea_salt_aerosol_0.5-5um_mixing_ratio', 'sea_salt_aerosol_5-20um_mixing_ratio', 'sea_salt_aerosol_optical_depth_550nm', 'sea_surface_temperature', 'skin_reservoir_content', 'skin_temperature', 'snow_albedo', 'snow_depth', 'soil_clay_content', 'soil_type', 'specific_cloud_ice_water_content', 'specific_cloud_liquid_water_content', 'specific_humidity', 'specific_rain_water_content', 'specific_snow_water_content', 'stratospheric_ozone_tracer', 'sulphate_aerosol_mixing_ratio', 'sulphate_aerosol_optical_depth_550nm', 'sulphur_dioxide', 'surface_geopotential', 'surface_pressure', 'surface_roughness', 'temperature', 'terpenes', 'total_aerosol_optical_depth_1240nm', 'total_aerosol_optical_depth_469nm', 'total_aerosol_optical_depth_550nm', 'total_aerosol_optical_depth_670nm', 'total_aerosol_optical_depth_865nm', 'total_cloud_cover', 'total_column_acetone', 'total_column_aldehydes', 'total_column_carbon_monoxide', 'total_column_ethane', 'total_column_ethanol', 'total_column_ethene', 'total_column_formaldehyde', 'total_column_formic_acid', 'total_column_hydrogen_peroxide', 'total_column_hydroxyl_radical', 'total_column_isoprene', 'total_column_methane', 'total_column_methanol', 'total_column_methyl_peroxide', 'total_column_nitric_acid', 'total_column_nitrogen_dioxide', 'total_column_nitrogen_monoxide', 'total_column_olefins', 'total_column_organic_nitrates', 'total_column_ozone', 'total_column_paraffins', 'total_column_peroxyacetyl_nitrate', 'total_column_propane', 'total_column_sulphur_dioxide', 'total_column_water', 'total_column_water_vapour', 'type_of_high_vegetation', 'type_of_low_vegetation', 'u_component_of_wind', 'uv_visible_albedo_for_diffuse_radiation', 'uv_visible_albedo_for_direct_radiation', 'v_component_of_wind', 'vertical_velocity', 'vertically_integrated_mass_of_dust_aerosol_0.03-0.55um', 'vertically_integrated_mass_of_dust_aerosol_0.55-9um', 'vertically_integrated_mass_of_dust_aerosol_9-20um', 'vertically_integrated_mass_of_hydrophilic_black_carbon_aerosol', 'vertically_integrated_mass_of_hydrophilic_organic_matter_aerosol', 'vertically_integrated_mass_of_hydrophobic_black_carbon_aerosol', 'vertically_integrated_mass_of_hydrophobic_organic_matter_aerosol', 'vertically_integrated_mass_of_sea_salt_aerosol_0.03-0.5um', 'vertically_integrated_mass_of_sea_salt_aerosol_0.5-5um', 'vertically_integrated_mass_of_sea_salt_aerosol_5-20um', 'vertically_integrated_mass_of_sulphate_aerosol']], FieldInfo(annotation=NoneType, required=True, alias_priority=2, validation_alias=AliasChoices(choices=['ecmwf:variable', 'variable']), serialization_alias='ecmwf:variable', title='Variable', description='Please, consult the product user guide in the documentation section for more information on these variables.') ]
'end':  typing.Annotated[str,  FieldInfo( 'required': False, ... )] typing.Annotated[ str, FieldInfo(annotation=NoneType, required=False, default=None, alias='end_datetime', alias_priority=2, description="Date/time as string in ISO 8601 format (e.g. '2024-06-10T12:00:00Z')") ]
'start':  typing.Annotated[str,  FieldInfo( 'required': False, ... )] typing.Annotated[ str, FieldInfo(annotation=NoneType, required=False, default=None, alias='start_datetime', alias_priority=2, description="Date/time as string in ISO 8601 format (e.g. '2024-06-10T12:00:00Z')") ]

Queryables values are returned using typing.Annoteted format. It contains parameter expected type, allowed alias for query, constraints and other metadata:

queryables["ecmwf_data_format"]

typing.Annotated[typing.Literal['grib', 'netcdf_zip'], FieldInfo(annotation=NoneType, required=True, alias_priority=2, validation_alias=AliasChoices(choices=['ecmwf:data_format', 'data_format']), serialization_alias='ecmwf:data_format', title='Data format', description='Please select a format for the data files, the native format of MARS dataset is GRIB.'), 'json_schema_required']

from typing import get_args

queryable_type, *queryable_metadata= get_args(queryables["ecmwf_data_format"])

queryable_type

typing.Literal['grib', 'netcdf_zip']

queryable_metadata[0].validation_alias

AliasChoices(choices=['ecmwf:data_format', 'data_format'])

queryable_metadata[0].is_required()

True

Search from an existing collection:

products_from_collection = dag.search(
    provider="cop_ads",
    collection="CAMS_EAC4",
    variable=["2m_dewpoint_temperature"],
    data_format="grib",
    start="2021-01-01",
    end="2021-01-02",
    time=["00:00"],
)
products_from_collection

SearchResult (1)

0  EOProduct(id=CAMS_EAC4_ORDERABLE_5bf2d5f16176c40994a963e611f42c653cbcccfa, provider=cop_ads) EOProduct provider: 'cop_ads', collection: 'CAMS_EAC4', properties["id"]: 'CAMS_EAC4_ORDERABLE_5bf2d5f16176c40994a963e611f42c653cbcccfa', properties["start_datetime"]: '2021-01-01T00:00:00.000Z', properties["end_datetime"]: '2021-01-02T00:00:00.000Z', properties: (14) { datetime: '2021-01-01T00:00:00.000Z', end_datetime: '2021-01-02T00:00:00.000Z', id: 'CAMS_EAC4_ORDERABLE_5bf2d5f16176c40994a963e611f42c653cbcccfa', qs: { 'data_format': 'grib' , 'date': '2021-01-01/2021-01-02' , 'time': ['00:00' ] , 'variable': ['2m_dewpoint_temperature' ] }, start_datetime: '2021-01-01T00:00:00.000Z', title: 'CAMS_EAC4_ORDERABLE_5bf2d5f16176c40994a963e611f42c653cbcccfa', ecmwf:data_format: 'grib', ecmwf:dataset: 'cams-global-reanalysis-eac4', ecmwf:date: '2021-01-01/2021-01-02', ecmwf:time: ['00:00' ], ecmwf:variable: ['2m_dewpoint_temperature' ], eodag:default_geometry: 'POLYGON((180 -90, 180 90, -180 90, -180 -90, 180 -90))', eodag:order_link: 'https://ads.atmosphere.copernicus.eu/api/retrieve/v1/processes/cams-global-reanalysis-eac4/execution?{"inputs": {"data_format": "grib", "date": "2021-01-01/2021-01-02", "time": ["00:00"], "variable": ["2m_dewpoint_temperature"]}}', order:status: 'orderable', } assets: (0) geometry

Search using a custom request:

If you know the collection / dataset id from cop_ads provider, you can also directly use it as collection, you will get the same result:

products_from_ads_req = dag.search(
    provider="cop_ads",
    collection="cams-global-reanalysis-eac4",
    variable=["2m_dewpoint_temperature"],
    data_format="grib",
    start="2021-01-01",
    end="2021-01-02",
    time=["00:00"],
)

# "eodag:order_link" property must be the same with the two request methods,
# as they are built from the same ADS request arguments
if (
    products_from_ads_req[0].properties["eodag:order_link"]
    == products_from_collection[0].properties["eodag:order_link"]
):
    print(
        "Request using collection or directly ADS dataset result to the\n",
        "same 'eodag:order_link' %s"
        % (
            products_from_ads_req[0].properties["eodag:order_link"],
        )
    )

Request using collection or directly ADS dataset result to the
 same 'eodag:order_link' https://ads.atmosphere.copernicus.eu/api/retrieve/v1/processes/cams-global-reanalysis-eac4/execution?{"inputs": {"data_format": "grib", "date": "2021-01-01/2021-01-02", "time": ["00:00"], "variable": ["2m_dewpoint_temperature"]}}

Send product retrieval request, download when available and return an xarray.DataArray

• download performed using ADS credentials set in ~/.config/eodag/eodag.yml as for other EO providers:

cop_ads:
    priority:
    download:
        output_dir: /my/path/to/data/eodag_data
    auth:
        credentials:
            apikey: myapikey

• you can check your request status from https://ads.atmosphere.copernicus.eu/requests?tab=all

• See support for any problem related to the provider

# Get XarrayDict
xd = products_from_ads_req[0].to_xarray()
xd

[Retry #1] Waiting 11.999867s until next order status check (retry every 0.2' for 10')

XarrayDict (1)

'CAMS-GLOBAL-REANALYSIS-EAC4_.grib':  xarray.Dataset (time: 2, latitude: 241, longitude: 480)  Size: 931kB <xarray.Dataset> Size: 931kB Dimensions: (time: 2, latitude: 241, longitude: 480) Coordinates: number int64 8B ... * time (time) datetime64[ns] 16B 2021-01-01 2021-01-02 step timedelta64[ns] 8B ... surface float64 8B ... * latitude (latitude) float64 2kB 90.0 89.25 88.5 ... -88.5 -89.25 -90.0 * longitude (longitude) float64 4kB 0.0 0.75 1.5 2.25 ... 357.8 358.5 359.2 valid_time (time) datetime64[ns] 16B ... Data variables: d2m (time, latitude, longitude) float32 925kB ... Attributes: (12/31) GRIB_edition: 1 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 ... ... order:date: 2026-01-19T16:11:55.584412 created: 2026-01-19T16:11:55.584412 updated: 2026-01-19T16:12:06.736935 eodag:request_params: {'date': '2021-01-01/2021-01-02', 'grid': '0.75/... published: 2026-01-19T16:12:06.736935 eodag:download_link: https://object-store.os-api.cci2.ecmwf.int:443/c... xarray.Dataset Dimensions: time: 2 latitude: 241 longitude: 480 Coordinates: (7) number () int64 ... long_name : ensemble member numerical id units : 1 standard_name : realization [1 values with dtype=int64] time (time) datetime64[ns] 2021-01-01 2021-01-02 long_name : initial time of forecast standard_name : forecast_reference_time array(['2021-01-01T00:00:00.000000000', '2021-01-02T00:00:00.000000000'], dtype='datetime64[ns]') step () timedelta64[ns] ... long_name : time since forecast_reference_time standard_name : forecast_period dtype : timedelta64[ns] [1 values with dtype=timedelta64[ns]] surface () float64 ... long_name : original GRIB coordinate for key: level(surface) units : 1 [1 values with dtype=float64] latitude (latitude) float64 90.0 89.25 88.5 ... -89.25 -90.0 units : degrees_north standard_name : latitude long_name : latitude stored_direction : decreasing array([ 90. , 89.25, 88.5 , ..., -88.5 , -89.25, -90. ]) longitude (longitude) float64 0.0 0.75 1.5 ... 357.8 358.5 359.2 units : degrees_east standard_name : longitude long_name : longitude array([ 0. , 0.75, 1.5 , ..., 357.75, 358.5 , 359.25]) valid_time (time) datetime64[ns] ... standard_name : time long_name : time [2 values with dtype=datetime64[ns]] Data variables: (1) d2m (time, latitude, longitude) float32 ... GRIB_paramId : 168 GRIB_dataType : an GRIB_numberOfPoints : 115680 GRIB_typeOfLevel : surface GRIB_stepUnits : 1 GRIB_stepType : instant GRIB_gridType : regular_ll GRIB_uvRelativeToGrid : 0 GRIB_NV : 0 GRIB_Nx : 480 GRIB_Ny : 241 GRIB_cfName : unknown GRIB_cfVarName : d2m GRIB_gridDefinitionDescription : Latitude/Longitude Grid GRIB_iDirectionIncrementInDegrees : 0.75 GRIB_iScansNegatively : 0 GRIB_jDirectionIncrementInDegrees : 0.75 GRIB_jPointsAreConsecutive : 0 GRIB_jScansPositively : 0 GRIB_latitudeOfFirstGridPointInDegrees : 90.0 GRIB_latitudeOfLastGridPointInDegrees : -90.0 GRIB_longitudeOfFirstGridPointInDegrees : 0.0 GRIB_longitudeOfLastGridPointInDegrees : 359.25 GRIB_missingValue : 3.4028234663852886e+38 GRIB_name : 2 metre dewpoint temperature GRIB_shortName : 2d GRIB_totalNumber : 0 GRIB_units : K long_name : 2 metre dewpoint temperature units : K standard_name : unknown [231360 values with dtype=float32] Indexes: (3) time PandasIndex PandasIndex(DatetimeIndex(['2021-01-01', '2021-01-02'], dtype='datetime64[ns]', name='time', freq=None)) latitude PandasIndex PandasIndex(Index([ 90.0, 89.25, 88.5, 87.75, 87.0, 86.25, 85.5, 84.75, 84.0, 83.25, ... -83.25, -84.0, -84.75, -85.5, -86.25, -87.0, -87.75, -88.5, -89.25, -90.0], dtype='float64', name='latitude', length=241)) longitude PandasIndex PandasIndex(Index([ 0.0, 0.75, 1.5, 2.25, 3.0, 3.75, 4.5, 5.25, 6.0, 6.75, ... 352.5, 353.25, 354.0, 354.75, 355.5, 356.25, 357.0, 357.75, 358.5, 359.25], dtype='float64', name='longitude', length=480)) Attributes: (31) GRIB_edition : 1 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 : 2026-01-19T17:12 GRIB to CDM+CF via cfgrib-0.9.15.1/ecCodes-2.44.1 with {"source": "../../../../../../../tmp/CAMS-GLOBAL-REANALYSIS-EAC4_3c38c153-292f-452f-892c-cda67f85a136/CAMS-GLOBAL-REANALYSIS-EAC4_3c38c153-292f-452f-892c-cda67f85a136.grib", "filter_by_keys": {}, "encode_cf": ["parameter", "time", "geography", "vertical"]} datetime : 2021-01-01T00:00:00.000Z end_datetime : 2021-01-02T00:00:00.000Z id : 3c38c153-292f-452f-892c-cda67f85a136 qs : {'data_format': 'grib', 'date': '2021-01-01/2021-01-02', 'time': ['00:00'], 'variable': ['2m_dewpoint_temperature']} start_datetime : 2021-01-01T00:00:00.000Z title : CAMS-GLOBAL-REANALYSIS-EAC4_3c38c153-292f-452f-892c-cda67f85a136 ecmwf:data_format : grib ecmwf:dataset : cams-global-reanalysis-eac4 ecmwf:date : 2021-01-01/2021-01-02 ecmwf:time : ['00:00'] ecmwf:variable : ['2m_dewpoint_temperature'] eodag:default_geometry : POLYGON((180 -90, 180 90, -180 90, -180 -90, 180 -90)) eodag:order_link : https://ads.atmosphere.copernicus.eu/api/retrieve/v1/processes/cams-global-reanalysis-eac4/execution?{"inputs": {"data_format": "grib", "date": "2021-01-01/2021-01-02", "time": ["00:00"], "variable": ["2m_dewpoint_temperature"]}} order:status : succeeded eodag:order_id : 3c38c153-292f-452f-892c-cda67f85a136 eodag:status_link : https://ads.atmosphere.copernicus.eu/api/retrieve/v1/jobs/3c38c153-292f-452f-892c-cda67f85a136?request=true eodag:search_link : https://ads.atmosphere.copernicus.eu/api/retrieve/v1/jobs/3c38c153-292f-452f-892c-cda67f85a136/results eodag:order_status : successful order:date : 2026-01-19T16:11:55.584412 created : 2026-01-19T16:11:55.584412 updated : 2026-01-19T16:12:06.736935 eodag:request_params : {'date': '2021-01-01/2021-01-02', 'grid': '0.75/0.75', 'time': ['00:00'], 'variable': ['2m_dewpoint_temperature'], 'data_format': 'grib'} published : 2026-01-19T16:12:06.736935 eodag:download_link : https://object-store.os-api.cci2.ecmwf.int:443/cci2-prod-cache-3/2026-01-19/e0be1888a7f789a5d93a6ab0a89e23b4.grib

# DataArray from XarrayDict first value
da = next(iter(xd.values())).d2m
da

<xarray.DataArray 'd2m' (time: 2, latitude: 241, longitude: 480)> Size: 925kB
[231360 values with dtype=float32]
Coordinates:
    number      int64 8B ...
  * time        (time) datetime64[ns] 16B 2021-01-01 2021-01-02
    step        timedelta64[ns] 8B ...
    surface     float64 8B ...
  * latitude    (latitude) float64 2kB 90.0 89.25 88.5 ... -88.5 -89.25 -90.0
  * longitude   (longitude) float64 4kB 0.0 0.75 1.5 2.25 ... 357.8 358.5 359.2
    valid_time  (time) datetime64[ns] 16B ...
Attributes: (12/31)
    GRIB_paramId:                             168
    GRIB_dataType:                            an
    GRIB_numberOfPoints:                      115680
    GRIB_typeOfLevel:                         surface
    GRIB_stepUnits:                           1
    GRIB_stepType:                            instant
    ...                                       ...
    GRIB_shortName:                           2d
    GRIB_totalNumber:                         0
    GRIB_units:                               K
    long_name:                                2 metre dewpoint temperature
    units:                                    K
    standard_name:                            unknown

xarray.DataArray

'd2m'

• time: 2
• latitude: 241
• longitude: 480

• ...

  [231360 values with dtype=float32]

• Coordinates: (7)
  • number
    ()
    int64
    ...

    long_name :

    ensemble member numerical id

    units :

    1

    standard_name :

    realization

    [1 values with dtype=int64]

  • time
    (time)
    datetime64[ns]
    2021-01-01 2021-01-02

    long_name :

    initial time of forecast

    standard_name :

    forecast_reference_time

    array(['2021-01-01T00:00:00.000000000', '2021-01-02T00:00:00.000000000'],
          dtype='datetime64[ns]')

  • step
    ()
    timedelta64[ns]
    ...

    long_name :

    time since forecast_reference_time

    standard_name :

    forecast_period

    dtype :

    timedelta64[ns]

    [1 values with dtype=timedelta64[ns]]

  • surface
    ()
    float64
    ...

    long_name :

    original GRIB coordinate for key: level(surface)

    units :

    1

    [1 values with dtype=float64]

  • latitude
    (latitude)
    float64
    90.0 89.25 88.5 ... -89.25 -90.0

    units :

    degrees_north

    standard_name :

    latitude

    long_name :

    latitude

    stored_direction :

    decreasing

    array([ 90.  ,  89.25,  88.5 , ..., -88.5 , -89.25, -90.  ])

  • longitude
    (longitude)
    float64
    0.0 0.75 1.5 ... 357.8 358.5 359.2

    units :

    degrees_east

    standard_name :

    longitude

    long_name :

    longitude

    array([  0.  ,   0.75,   1.5 , ..., 357.75, 358.5 , 359.25])

  • valid_time
    (time)
    datetime64[ns]
    ...

    standard_name :

    time

    long_name :

    time

    [2 values with dtype=datetime64[ns]]

• Indexes: (3)
  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['2021-01-01', '2021-01-02'], dtype='datetime64[ns]', name='time', freq=None))

  • latitude
    PandasIndex

    PandasIndex(Index([  90.0,  89.25,   88.5,  87.75,   87.0,  86.25,   85.5,  84.75,   84.0,
            83.25,
           ...
           -83.25,  -84.0, -84.75,  -85.5, -86.25,  -87.0, -87.75,  -88.5, -89.25,
            -90.0],
          dtype='float64', name='latitude', length=241))

  • longitude
    PandasIndex

    PandasIndex(Index([   0.0,   0.75,    1.5,   2.25,    3.0,   3.75,    4.5,   5.25,    6.0,
             6.75,
           ...
            352.5, 353.25,  354.0, 354.75,  355.5, 356.25,  357.0, 357.75,  358.5,
           359.25],
          dtype='float64', name='longitude', length=480))

• Attributes: (31)

  GRIB_paramId :

  168

  GRIB_dataType :

  an

  GRIB_numberOfPoints :

  115680

  GRIB_typeOfLevel :

  surface

  GRIB_stepUnits :

  1

  GRIB_stepType :

  instant

  GRIB_gridType :

  regular_ll

  GRIB_uvRelativeToGrid :

  0

  GRIB_NV :

  0

  GRIB_Nx :

  480

  GRIB_Ny :

  241

  GRIB_cfName :

  unknown

  GRIB_cfVarName :

  d2m

  GRIB_gridDefinitionDescription :

  Latitude/Longitude Grid

  GRIB_iDirectionIncrementInDegrees :

  0.75

  GRIB_iScansNegatively :

  0

  GRIB_jDirectionIncrementInDegrees :

  0.75

  GRIB_jPointsAreConsecutive :

  0

  GRIB_jScansPositively :

  0

  GRIB_latitudeOfFirstGridPointInDegrees :

  90.0

  GRIB_latitudeOfLastGridPointInDegrees :

  -90.0

  GRIB_longitudeOfFirstGridPointInDegrees :

  0.0

  GRIB_longitudeOfLastGridPointInDegrees :

  359.25

  GRIB_missingValue :

  3.4028234663852886e+38

  GRIB_name :

  2 metre dewpoint temperature

  GRIB_shortName :

  2d

  GRIB_totalNumber :

  0

  GRIB_units :

  K

  long_name :

  2 metre dewpoint temperature

  units :

  K

  standard_name :

  unknown

Plot using cartopy

import matplotlib.pyplot as plt
import cartopy.crs as ccrs

ax = plt.axes(projection=ccrs.Orthographic(0, 20))
ax.coastlines()
ax.gridlines(draw_labels=True)
da[0].plot.contourf(ax=ax, transform=ccrs.PlateCarree())

<cartopy.mpl.contour.GeoContourSet at 0x70f3fa6c18e0>