Using NetCDF data

import earthkit.data as ekd
ekd.download_example_file("test.nc")

fs = ekd.from_source("file", "test.nc")

type(fs)

earthkit.data.readers.netcdf.NetCDFFieldListReader

len(fs)

2

for f in fs:
    print(f)

NetCDFField(t2m,)
NetCDFField(msl,)

fs.to_xarray()

<xarray.Dataset> Size: 2kB
Dimensions:     (longitude: 19, latitude: 11)
Coordinates:
  * longitude   (longitude) float64 152B -27.0 -23.0 -19.0 ... 37.0 41.0 45.0
  * latitude    (latitude) float64 88B 73.0 69.0 65.0 61.0 ... 41.0 37.0 33.0
    number      int32 4B ...
    time        datetime64[ns] 8B ...
    step        timedelta64[ns] 8B ...
    surface     int32 4B ...
    valid_time  datetime64[ns] 8B ...
Data variables:
    t2m         (latitude, longitude) float32 836B dask.array<chunksize=(11, 19), meta=np.ndarray>
    msl         (latitude, longitude) float32 836B dask.array<chunksize=(11, 19), meta=np.ndarray>
Attributes:
    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:                 GRIB to CDM+CF via cfgrib-0.9.5/ecCodes-2.17.0 w...

xarray.Dataset

• Dimensions:
  • longitude: 19
  • latitude: 11
• Coordinates: (7)
  • longitude
    (longitude)
    float64
    -27.0 -23.0 -19.0 ... 41.0 45.0

    units :

    degrees_east

    standard_name :

    longitude

    long_name :

    longitude

    array([-27., -23., -19., -15., -11.,  -7.,  -3.,   1.,   5.,   9.,  13.,  17.,
            21.,  25.,  29.,  33.,  37.,  41.,  45.])

  • latitude
    (latitude)
    float64
    73.0 69.0 65.0 ... 41.0 37.0 33.0

    units :

    degrees_north

    standard_name :

    latitude

    long_name :

    latitude

    stored_direction :

    decreasing

    array([73., 69., 65., 61., 57., 53., 49., 45., 41., 37., 33.])

  • number
    ()
    int32
    ...

    long_name :

    ensemble member numerical id

    units :

    1

    standard_name :

    realization

    [1 values with dtype=int32]

  • time
    ()
    datetime64[ns]
    ...

    long_name :

    initial time of forecast

    standard_name :

    forecast_reference_time

    [1 values with dtype=datetime64[ns]]

  • step
    ()
    timedelta64[ns]
    ...

    long_name :

    time since forecast_reference_time

    standard_name :

    forecast_period

    [1 values with dtype=timedelta64[ns]]

  • surface
    ()
    int32
    ...

    long_name :

    original GRIB coordinate for key: level(surface)

    units :

    1

    [1 values with dtype=int32]

  • valid_time
    ()
    datetime64[ns]
    ...

    standard_name :

    time

    long_name :

    time

    [1 values with dtype=datetime64[ns]]

• Data variables: (2)
  • t2m
    (latitude, longitude)
    float32
    dask.array<chunksize=(11, 19), meta=np.ndarray>

    GRIB_paramId :

    167

    GRIB_shortName :

    2t

    GRIB_units :

    K

    GRIB_name :

    2 metre temperature

    GRIB_cfVarName :

    t2m

    GRIB_dataType :

    an

    GRIB_missingValue :

    9999

    GRIB_numberOfPoints :

    209

    GRIB_totalNumber :

    0

    GRIB_typeOfLevel :

    surface

    GRIB_NV :

    0

    GRIB_stepUnits :

    1

    GRIB_stepType :

    instant

    GRIB_gridType :

    regular_ll

    GRIB_gridDefinitionDescription :

    Latitude/Longitude Grid

    GRIB_Nx :

    19

    GRIB_iDirectionIncrementInDegrees :

    4.0

    GRIB_iScansNegatively :

    0

    GRIB_longitudeOfFirstGridPointInDegrees :

    -27.0

    GRIB_longitudeOfLastGridPointInDegrees :

    45.0

    GRIB_Ny :

    11

    GRIB_jDirectionIncrementInDegrees :

    4.0

    GRIB_jPointsAreConsecutive :

    0

    GRIB_jScansPositively :

    0

    GRIB_latitudeOfFirstGridPointInDegrees :

    73.0

    GRIB_latitudeOfLastGridPointInDegrees :

    33.0

    long_name :

    2 metre temperature

    units :

    K

    Array Chunk Bytes 836 B 836 B Shape (11, 19) (11, 19) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • msl
    (latitude, longitude)
    float32
    dask.array<chunksize=(11, 19), meta=np.ndarray>

    GRIB_paramId :

    151

    GRIB_shortName :

    msl

    GRIB_units :

    Pa

    GRIB_name :

    Mean sea level pressure

    GRIB_cfName :

    air_pressure_at_mean_sea_level

    GRIB_cfVarName :

    msl

    GRIB_dataType :

    an

    GRIB_missingValue :

    9999

    GRIB_numberOfPoints :

    209

    GRIB_totalNumber :

    0

    GRIB_typeOfLevel :

    surface

    GRIB_NV :

    0

    GRIB_stepUnits :

    1

    GRIB_stepType :

    instant

    GRIB_gridType :

    regular_ll

    GRIB_gridDefinitionDescription :

    Latitude/Longitude Grid

    GRIB_Nx :

    19

    GRIB_iDirectionIncrementInDegrees :

    4.0

    GRIB_iScansNegatively :

    0

    GRIB_longitudeOfFirstGridPointInDegrees :

    -27.0

    GRIB_longitudeOfLastGridPointInDegrees :

    45.0

    GRIB_Ny :

    11

    GRIB_jDirectionIncrementInDegrees :

    4.0

    GRIB_jPointsAreConsecutive :

    0

    GRIB_jScansPositively :

    0

    GRIB_latitudeOfFirstGridPointInDegrees :

    73.0

    GRIB_latitudeOfLastGridPointInDegrees :

    33.0

    long_name :

    Mean sea level pressure

    units :

    Pa

    standard_name :

    air_pressure_at_mean_sea_level

    Array Chunk Bytes 836 B 836 B Shape (11, 19) (11, 19) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

• Indexes: (2)
  • longitude
    PandasIndex

    PandasIndex(Index([-27.0, -23.0, -19.0, -15.0, -11.0,  -7.0,  -3.0,   1.0,   5.0,   9.0,
            13.0,  17.0,  21.0,  25.0,  29.0,  33.0,  37.0,  41.0,  45.0],
          dtype='float64', name='longitude'))

  • latitude
    PandasIndex

    PandasIndex(Index([73.0, 69.0, 65.0, 61.0, 57.0, 53.0, 49.0, 45.0, 41.0, 37.0, 33.0], dtype='float64', name='latitude'))

• Attributes: (7)

  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 :

  GRIB to CDM+CF via cfgrib-0.9.5/ecCodes-2.17.0 with source='/Users/baudouin/git/climet/test.grib', filter_by_keys={}, encode_cf=('parameter', 'time', 'geography', 'vertical')

fs.bounding_box()

[BoundingBox(north=73,west=-27,south=33,east=45),
 BoundingBox(north=73,west=-27,south=33,east=45)]