Retieving data from WEkEO

The wekeo and wekeocds sources provide access to WEkEO.

Using the WEkEO grammar

When we want access data using the WEkEO grammar (see the hda documentation) we need to use the wekeo source. The following example retrieves Normalized Difference Vegetation Index data derived from EO satellite imagery in NetCDF format:

import earthkit.data as ekd

ds = ekd.from_source(
        "wekeo",
        "EO:CLMS:DAT:CLMS_GLOBAL_BA_300M_V3_MONTHLY_NETCDF",
        request={
            "dataset_id": "EO:CLMS:DAT:CLMS_GLOBAL_BA_300M_V3_MONTHLY_NETCDF",
            "startdate": "2019-01-01T00:00:00.000Z",
            "enddate": "2019-01-01T23:59:59.999Z",
        }
    )

ds.to_xarray()

<xarray.Dataset> Size: 23GB
Dimensions:      (time: 1, lat: 47040, lon: 120960)
Coordinates:
  * lat          (lat) float64 376kB 80.0 80.0 79.99 ... -59.99 -59.99 -60.0
  * lon          (lon) float64 968kB -180.0 -180.0 -180.0 ... 180.0 180.0 180.0
  * time         (time) datetime64[ns] 8B 2019-01-01
Data variables:
    crs          |S1 1B ...
    day_of_burn  (time, lat, lon) float32 23GB dask.array<chunksize=(1, 888, 2283), meta=np.ndarray>
Attributes: (12/19)
    Conventions:          CF-1.6
    processing_level:     L3
    identifier:           urn:cgls:global:ba300_v3_333m:BA300-NTC_20190101000...
    institution:          VITO NV
    time_coverage_end:    2019-01-31T23:59:59Z
    source:               Derived from EO satellite imagery
    ...                   ...
    time_coverage_start:  2019-01-01T00:00:00Z
    platform:             Sentinel-3
    title:                monthly Burnt Area 333M: GLOBE 2019-01-01T00:00:00Z
    archive_facility:     VITO
    parent_identifier:    urn:cgls:global:ba300_v3_333m
    history:              2023-09-04: Processing line BA

xarray.Dataset

• Dimensions:
  • time: 1
  • lat: 47040
  • lon: 120960
• Coordinates: (3)
  • lat
    (lat)
    float64
    80.0 80.0 79.99 ... -59.99 -60.0

    _CoordinateAxisType :

    Lat

    axis :

    Y

    DIMENSION_LABELS :

    lat

    long_name :

    latitude

    standard_name :

    latitude

    units :

    degrees_north

    array([ 80.      ,  79.997024,  79.994048, ..., -59.991071, -59.994048,
           -59.997024])

  • lon
    (lon)
    float64
    -180.0 -180.0 ... 180.0 180.0

    _CoordinateAxisType :

    Lon

    axis :

    X

    DIMENSION_LABELS :

    lon

    long_name :

    longitude

    standard_name :

    longitude

    units :

    degrees_east

    array([-180.      , -179.997024, -179.994048, ...,  179.991071,  179.994048,
            179.997024])

  • time
    (time)
    datetime64[ns]
    2019-01-01

    axis :

    T

    long_name :

    Time

    array(['2019-01-01T00:00:00.000000000'], dtype='datetime64[ns]')

• Data variables: (2)
  • crs
    ()
    |S1
    ...

    long_name :

    coordinate reference system

    _CoordinateAxisTypes :

    GeoX GeoY

    _CoordinateTransformType :

    Projection

    grid_mapping_name :

    latitude_longitude

    inverse_flattening :

    298.257223563

    longitude_of_prime_meridian :

    0.0

    semi_major_axis :

    6378137.0

    spatial_ref :

    GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],TOWGS84[0,0,0,0,0,0,0],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0,AUTHORITY["EPSG","8901"]],UNIT["degree",0.0174532925199433,AUTHORITY["EPSG","9108"]],AUTHORITY["EPSG","4326"]]

    [1 values with dtype=|S1]

  • day_of_burn
    (time, lat, lon)
    float32
    dask.array<chunksize=(1, 888, 2283), meta=np.ndarray>

    grid_mapping :

    crs

    units :

    valid_range :

    [ 0 366]

    flag_values :

    [-1 -2]

    flag_meanings :

    Missing Water

    Array Chunk Bytes 21.20 GiB 7.73 MiB Shape (1, 47040, 120960) (1, 888, 2283) Dask graph 2809 chunks in 2 graph layers Data type float32 numpy.ndarray

• Indexes: (3)
  • lat
    PandasIndex

    PandasIndex(Index([               80.0,   79.99702380952381,   79.99404761904762,
             79.99107142857143,   79.98809523809524,   79.98511904761905,
             79.98214285714286,   79.97916666666667,   79.97619047619048,
             79.97321428571429,
           ...
            -59.97023809520627, -59.973214285682445,  -59.97619047615865,
            -59.97916666663485,  -59.98214285711103, -59.985119047587204,
            -59.98809523806341,  -59.99107142853961,  -59.99404761901579,
           -59.997023809491964],
          dtype='float64', name='lat', length=47040))

  • lon
    PandasIndex

    PandasIndex(Index([             -180.0,  -179.9970238095238,  -179.9940476190476,
            -179.9910714285714, -179.98809523809518, -179.98511904761898,
           -179.98214285714278, -179.97916666666657, -179.97619047619037,
           -179.97321428571416,
           ...
            179.97023809687505,  179.97321428735125,  179.97619047782746,
            179.97916666830366,  179.98214285877987,  179.98511904925607,
            179.98809523973227,  179.99107143020848,  179.99404762068468,
             179.9970238111609],
          dtype='float64', name='lon', length=120960))

  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['2019-01-01'], dtype='datetime64[ns]', name='time', freq=None))

• Attributes: (19)

  Conventions :

  CF-1.6

  processing_level :

  L3

  identifier :

  urn:cgls:global:ba300_v3_333m:BA300-NTC_201901010000_GLOBE_S3_V3.1.1

  institution :

  VITO NV

  time_coverage_end :

  2019-01-31T23:59:59Z

  source :

  Derived from EO satellite imagery

  processing_mode :

  Offline

  copyright :

  Copernicus Service information 2023

  references :

  https://land.copernicus.eu/global/products/ba

  sensor :

  OLCI,SLSTR

  long_name :

  Burnt Area

  product_version :

  V3.1.1

  orbit_type :

  LEO

  time_coverage_start :

  2019-01-01T00:00:00Z

  platform :

  Sentinel-3

  title :

  monthly Burnt Area 333M: GLOBE 2019-01-01T00:00:00Z

  archive_facility :

  VITO

  parent_identifier :

  urn:cgls:global:ba300_v3_333m

  history :

  2023-09-04: Processing line BA

Using the CDSAPI grammar

We can use the wekeocds source to access Copernicus Climate Data Store (CDS) datasets served on WEkEO using the cdsapi standard grammar.

The following example retrieves ERA5 surface data in GRIB format:

ds = ekd.from_source(
        "wekeocds",
        "EO:ECMWF:DAT:REANALYSIS_ERA5_SINGLE_LEVELS_MONTHLY_MEANS",
        variable=["2m_temperature", "mean_sea_level_pressure"],
        product_type=["monthly_averaged_reanalysis_by_hour_of_day"],
        year="2012",
        month="12",
        time="12:00",
        data_format="grib",
        download_format="zip"
    )

ds.ls()

	centre	shortName	typeOfLevel	level	dataDate	dataTime	stepRange	dataType	number	gridType
0	ecmf	2t	surface	0	20121201	1200	0	an	0	regular_ll
1	ecmf	msl	surface	0	20121201	1200	0	an	0	regular_ll