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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.

from eodag import EODataAccessGateway, setup_logging

setup_logging(1)  # 0: nothing, 1: only progress bars, 2: INFO, 3: DEBUG
dag = EODataAccessGateway()
dag.set_preferred_provider("cop_ads")

Search (build download request)

There are two use cases, a search for a product already configured in EODAG, or a search for a dataset not already configured, where you will have a little more to do.

We can add a variable (Temperature, temperature) and one model_level to the request because CAMS_EAC4 is configured to request for some pre-configured values. Check the dataset available values to make your selection.

Note: specifying your own variables will completely overwrite default values configured for CAMS_EAC4 product type.

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", productType="CAMS_EAC4")
queryables

QueryablesDict (8) - additional_properties=False
'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, title='Variable', description='Please, consult the product user guide in the documentation section for more information on these variables.') ]
'ecmwf:pressure_level':  typing.Annotated[list[Literal['1', ...]],  FieldInfo( 'required': False, ... )] 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=False, default=None, title='Pressure level') ]
'ecmwf:model_level':  typing.Annotated[list[Literal['1', ...]],  FieldInfo( 'required': False, ... )] 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=False, default=None, title='Model level', description="Model level 1 is the top of the atmosphere. Model level 60 is the Earth's surface.") ]
'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, title='Time', description='Model base time as HH:MM (UTC)') ]
'ecmwf:data_format':  typing.Annotated[Literal['grib', ...],  FieldInfo( 'required': False, ... )] typing.Annotated[ typing.Literal['grib', 'netcdf_zip'], FieldInfo(annotation=NoneType, required=False, default=None, title='Data format', description='Please select a format for the data files, the native format of MARS dataset is GRIB.') ]
'start':  typing.Annotated[str,  FieldInfo('default': '2003-01-01T00:00:00Z',  'required': False, ... )] typing.Annotated[ str, FieldInfo(annotation=NoneType, required=False, default='2003-01-01T00:00:00Z', alias='startTimeFromAscendingNode', alias_priority=2, description="Date/time as string in ISO 8601 format (e.g. '2024-06-10T12:00:00Z')") ]
'end':  typing.Annotated[str,  FieldInfo('default': '2003-01-01T00:00:00Z',  'required': False, ... )] typing.Annotated[ str, FieldInfo(annotation=NoneType, required=False, default='2003-01-01T00:00:00Z', alias='completionTimeFromAscendingNode', alias_priority=2, description="Date/time as string in ISO 8601 format (e.g. '2024-06-10T12:00:00Z')") ]
'geom':  typing.Annotated[Union[str, dict[str, ...], ...],  FieldInfo( 'required': False, ... )] typing.Annotated[ typing.Union[str, dict[str, float], shapely.geometry.base.BaseGeometry], FieldInfo(annotation=NoneType, required=False, default=None, description='Read EODAG documentation for all supported geometry format.') ]

queryables['ecmwf:variable']

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, title='Variable', description='Please, consult the product user guide in the documentation section for more information on these variables.'), 'json_schema_required']

Here we can see the list of available values for variable and that it’s a required parameter.

Search from an existing product type:

# Request for all parameters
products_from_product_type = dag.search(
    start="2021-01-01",
    end="2021-01-02",
    productType="CAMS_EAC4",
    variable="temperature",
    time="00:00",
    count=True
)
print(
    "%s product built %s,\n having variable = %s\n"
    % (
        products_from_product_type.number_matched,
        products_from_product_type[0],
        products_from_product_type[0].properties.get("ecmwf:variable"),
    )
)
# Request for temperature on one model level
products_from_product_type = dag.search(
    start="2021-01-01",
    end="2021-01-02",
    productType="CAMS_EAC4",
    count=True,
    **{"ecmwf:variable": ["temperature"],
       "ecmwf:model_level": ["1"],
       "ecmwf:time": ["00:00"],
       "ecmwf:data_format": "grib"},

)
print(
    "%s product built %s,\n having variable = %s\n"
    % (
        products_from_product_type.number_matched,
        products_from_product_type[0],
        products_from_product_type[0].properties.get("ecmwf:variable"),
    )
)

1 product built EOProduct(id=CAMS_EAC4_ORDERABLE_f1f5bfef0b462aea81370ab20f0bf3b356fc93fb, provider=cop_ads),
 having variable = temperature

1 product built EOProduct(id=CAMS_EAC4_ORDERABLE_245d95e7d751fac902fe4c40ea65035dc6fcee8c, provider=cop_ads),
 having variable = ['temperature']

Search using a custom request:

Here we will use a set of custom parameters corresponding to CAMS_EAC4, which should result to the same request sent to ads.

ads_req_params = {
    "productType": "CAMS_EAC4",
    "ecmwf:variable": ["temperature"],
    "ecmwf:model_level": ["1"],
    "ecmwf:time": ["00:00"],
    "ecmwf:data_format": "grib",
}

products_from_ads_req = dag.search(
    provider="cop_ads",
    start="2021-01-01",
    end="2021-01-02",
    **ads_req_params,
)

# orderLink 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["orderLink"]
    == products_from_product_type[0].properties["orderLink"]
):
    print(
        "Request using productType or directly ADS parameters result to the\n",
        "same orderLink %s"
        % (
            products_from_ads_req[0].properties["orderLink"],
        )
    )

Request using productType or directly ADS parameters result to the
 same orderLink 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", "model_level": ["1"], "time": ["00:00"], "variable": ["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 #2] Waiting 11.609895s until next order status check (retry every 0.2' for 10')

XarrayDict (1)

'CAMS-GLOBAL-REANALYSIS-EAC4_20210101_20210102_4a4a8a1a556e215789bbedbe797bed4292fece99.grib':  xarray.Dataset (time: 2, latitude: 241, longitude: 480)  Size: 931kB <xarray.Dataset> Size: 931kB Dimensions: (time: 2, latitude: 241, longitude: 480) Coordinates: * time (time) datetime64[ns] 16B 2021-01-01 2021-01-02 step timedelta64[ns] 8B ... hybrid 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: t (time, latitude, longitude) float32 925kB ... Attributes: (12/31) GRIB_edition: 2 GRIB_centre: ecmf GRIB_centreDescription: European Centre for Medium-Range Weathe... GRIB_subCentre: 0 Conventions: CF-1.7 institution: European Centre for Medium-Range Weathe... ... ... _dc_qs: %7B%22data_format%22%3A+%22grib%22%2C+%... orderId: 060f44e9-1b03-4775-b24a-2316d8deb63e orderStatusLink: https://ads.atmosphere.copernicus.eu/ap... searchLink: https://ads.atmosphere.copernicus.eu/ap... orderStatus: successful downloadLink: https://object-store.os-api.cci2.ecmwf.... xarray.Dataset Dimensions: time: 2 latitude: 241 longitude: 480 Coordinates: (6) 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 [1 values with dtype=timedelta64[ns]] hybrid () float64 ... long_name : hybrid level units : 1 positive : down standard_name : atmosphere_hybrid_sigma_pressure_coordinate [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) t (time, latitude, longitude) float32 ... GRIB_paramId : 130 GRIB_dataType : an GRIB_numberOfPoints : 115680 GRIB_typeOfLevel : hybrid GRIB_stepUnits : 1 GRIB_stepType : instant GRIB_gridType : regular_ll GRIB_uvRelativeToGrid : 0 GRIB_NV : 122 GRIB_Nx : 480 GRIB_Ny : 241 GRIB_cfName : air_temperature GRIB_cfVarName : t GRIB_gridDefinitionDescription : Latitude/longitude 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 : Temperature GRIB_shortName : t GRIB_units : K long_name : Temperature units : K standard_name : air_temperature [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 : 2 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 : 2025-01-13T15:12 GRIB to CDM+CF via cfgrib-0.9.14.1/ecCodes-2.38.3 with {"source": "../../../../../../../tmp/CAMS-GLOBAL-REANALYSIS-EAC4_20210101_20210102_4a4a8a1a556e215789bbedbe797bed4292fece99/CAMS-GLOBAL-REANALYSIS-EAC4_20210101_20210102_4a4a8a1a556e215789bbedbe797bed4292fece99.grib", "filter_by_keys": {}, "encode_cf": ["parameter", "time", "geography", "vertical"]} keywords : None license : None title : CAMS-GLOBAL-REANALYSIS-EAC4_20210101_20210102_4a4a8a1a556e215789bbedbe797bed4292fece99 missionStartDate : None productType : cams-global-reanalysis-eac4 startTimeFromAscendingNode : 2021-01-01 completionTimeFromAscendingNode : 2021-01-02 id : CAMS-GLOBAL-REANALYSIS-EAC4_20210101_20210102_4a4a8a1a556e215789bbedbe797bed4292fece99 ecmwf:dataset : cams-global-reanalysis-eac4 ecmwf:time : 00:00 ecmwf:data_format : grib ecmwf:model_level : 1 ecmwf:variable : temperature defaultGeometry : POLYGON((180 -90, 180 90, -180 90, -180 -90, 180 -90)) storageStatus : ONLINE qs : {'data_format': 'grib', 'date': ['2021-01-01/2021-01-02'], 'model_level': '1', 'time': '00:00', 'variable': 'temperature'} _date : 2021-01-01/2021-01-02 orderLink : 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"], "model_level": "1", "time": "00:00", "variable": "temperature"}} _dc_qs : %7B%22data_format%22%3A+%22grib%22%2C+%22date%22%3A+%5B%222021-01-01%2F2021-01-02%22%5D%2C+%22model_level%22%3A+%221%22%2C+%22time%22%3A+%2200%3A00%22%2C+%22variable%22%3A+%22temperature%22%7D orderId : 060f44e9-1b03-4775-b24a-2316d8deb63e orderStatusLink : https://ads.atmosphere.copernicus.eu/api/retrieve/v1/jobs/060f44e9-1b03-4775-b24a-2316d8deb63e searchLink : https://ads.atmosphere.copernicus.eu/api/retrieve/v1/jobs/060f44e9-1b03-4775-b24a-2316d8deb63e/results orderStatus : successful downloadLink : https://object-store.os-api.cci2.ecmwf.int:443/cci2-prod-cache/2100dafd95875804619fb14489033889.grib

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

<xarray.DataArray 't' (time: 2, latitude: 241, longitude: 480)> Size: 925kB
[231360 values with dtype=float32]
Coordinates:
  * time        (time) datetime64[ns] 16B 2021-01-01 2021-01-02
    step        timedelta64[ns] 8B ...
    hybrid      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: (12/30)
    GRIB_paramId:                             130
    GRIB_dataType:                            an
    GRIB_numberOfPoints:                      115680
    GRIB_typeOfLevel:                         hybrid
    GRIB_stepUnits:                           1
    GRIB_stepType:                            instant
    ...                                       ...
    GRIB_name:                                Temperature
    GRIB_shortName:                           t
    GRIB_units:                               K
    long_name:                                Temperature
    units:                                    K
    standard_name:                            air_temperature

xarray.DataArray

't'

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

• ...

  [231360 values with dtype=float32]

• Coordinates: (6)
  • 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

    [1 values with dtype=timedelta64[ns]]

  • hybrid
    ()
    float64
    ...

    long_name :

    hybrid level

    units :

    1

    positive :

    down

    standard_name :

    atmosphere_hybrid_sigma_pressure_coordinate

    [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: (30)

  GRIB_paramId :

  130

  GRIB_dataType :

  an

  GRIB_numberOfPoints :

  115680

  GRIB_typeOfLevel :

  hybrid

  GRIB_stepUnits :

  1

  GRIB_stepType :

  instant

  GRIB_gridType :

  regular_ll

  GRIB_uvRelativeToGrid :

  0

  GRIB_NV :

  122

  GRIB_Nx :

  480

  GRIB_Ny :

  241

  GRIB_cfName :

  air_temperature

  GRIB_cfVarName :

  t

  GRIB_gridDefinitionDescription :

  Latitude/longitude

  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 :

  Temperature

  GRIB_shortName :

  t

  GRIB_units :

  K

  long_name :

  Temperature

  units :

  K

  standard_name :

  air_temperature

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 0x797dc41cf260>