Using NetCDF data

import earthkit.data as ekd

ekd.download_example_file("test.nc")

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

NetCDF file

---

path	test.nc
size	28.1 KiB
types	xarray, pandas, fieldlist, numpy, array

d.to_xarray()

<xarray.Dataset> Size: 1kB
Dimensions:     (latitude: 8, longitude: 13)
Coordinates:
  * latitude    (latitude) float64 64B 70.0 65.0 60.0 55.0 50.0 45.0 40.0 35.0
  * longitude   (longitude) float64 104B -20.0 -15.0 -10.0 ... 30.0 35.0 40.0
    number      int64 8B ...
    time        datetime64[ns] 8B ...
    step        timedelta64[ns] 8B ...
    surface     float64 8B ...
    valid_time  datetime64[ns] 8B ...
Data variables:
    t2m         (latitude, longitude) float32 416B dask.array<chunksize=(8, 13), meta=np.ndarray>
    msl         (latitude, longitude) float32 416B dask.array<chunksize=(8, 13), 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:                 2026-03-23T18:21 GRIB to CDM+CF via cfgrib-0.9.1...

xarray.Dataset

• Dimensions:
  • latitude: 8
  • longitude: 13
• Coordinates: (7)
  • latitude
    (latitude)
    float64
    70.0 65.0 60.0 ... 45.0 40.0 35.0

    units :

    degrees_north

    standard_name :

    latitude

    long_name :

    latitude

    stored_direction :

    decreasing

    array([70., 65., 60., 55., 50., 45., 40., 35.])

  • longitude
    (longitude)
    float64
    -20.0 -15.0 -10.0 ... 35.0 40.0

    units :

    degrees_east

    standard_name :

    longitude

    long_name :

    longitude

    array([-20., -15., -10.,  -5.,   0.,   5.,  10.,  15.,  20.,  25.,  30.,  35.,
            40.])

  • number
    ()
    int64
    ...

    long_name :

    ensemble member numerical id

    units :

    1

    standard_name :

    realization

    [1 values with dtype=int64]

  • 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
    ()
    float64
    ...

    long_name :

    original GRIB coordinate for key: level(surface)

    units :

    1

    [1 values with dtype=float64]

  • 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=(8, 13), meta=np.ndarray>

    GRIB_paramId :

    167

    GRIB_dataType :

    an

    GRIB_numberOfPoints :

    104

    GRIB_typeOfLevel :

    surface

    GRIB_stepUnits :

    1

    GRIB_stepType :

    instant

    GRIB_gridType :

    regular_ll

    GRIB_uvRelativeToGrid :

    0

    GRIB_NV :

    0

    GRIB_Nx :

    13

    GRIB_Ny :

    8

    GRIB_cfName :

    unknown

    GRIB_cfVarName :

    t2m

    GRIB_gridDefinitionDescription :

    Latitude/Longitude Grid

    GRIB_iDirectionIncrementInDegrees :

    5.0

    GRIB_iScansNegatively :

    0

    GRIB_jDirectionIncrementInDegrees :

    5.0

    GRIB_jPointsAreConsecutive :

    0

    GRIB_jScansPositively :

    0

    GRIB_latitudeOfFirstGridPointInDegrees :

    70.0

    GRIB_latitudeOfLastGridPointInDegrees :

    35.0

    GRIB_longitudeOfFirstGridPointInDegrees :

    -20.0

    GRIB_longitudeOfLastGridPointInDegrees :

    40.0

    GRIB_missingValue :

    3.4028234663852886e+38

    GRIB_name :

    2 metre temperature

    GRIB_shortName :

    2t

    GRIB_totalNumber :

    0

    GRIB_units :

    K

    long_name :

    2 metre temperature

    units :

    K

    standard_name :

    unknown

    Array Chunk Bytes 416 B 416 B Shape (8, 13) (8, 13) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • msl
    (latitude, longitude)
    float32
    dask.array<chunksize=(8, 13), meta=np.ndarray>

    GRIB_paramId :

    151

    GRIB_dataType :

    an

    GRIB_numberOfPoints :

    104

    GRIB_typeOfLevel :

    surface

    GRIB_stepUnits :

    1

    GRIB_stepType :

    instant

    GRIB_gridType :

    regular_ll

    GRIB_uvRelativeToGrid :

    0

    GRIB_NV :

    0

    GRIB_Nx :

    13

    GRIB_Ny :

    8

    GRIB_cfName :

    air_pressure_at_mean_sea_level

    GRIB_cfVarName :

    msl

    GRIB_gridDefinitionDescription :

    Latitude/Longitude Grid

    GRIB_iDirectionIncrementInDegrees :

    5.0

    GRIB_iScansNegatively :

    0

    GRIB_jDirectionIncrementInDegrees :

    5.0

    GRIB_jPointsAreConsecutive :

    0

    GRIB_jScansPositively :

    0

    GRIB_latitudeOfFirstGridPointInDegrees :

    70.0

    GRIB_latitudeOfLastGridPointInDegrees :

    35.0

    GRIB_longitudeOfFirstGridPointInDegrees :

    -20.0

    GRIB_longitudeOfLastGridPointInDegrees :

    40.0

    GRIB_missingValue :

    3.4028234663852886e+38

    GRIB_name :

    Mean sea level pressure

    GRIB_shortName :

    msl

    GRIB_totalNumber :

    0

    GRIB_units :

    Pa

    long_name :

    Mean sea level pressure

    units :

    Pa

    standard_name :

    air_pressure_at_mean_sea_level

    Array Chunk Bytes 416 B 416 B Shape (8, 13) (8, 13) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

• 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 :

  2026-03-23T18:21 GRIB to CDM+CF via cfgrib-0.9.15.1/ecCodes-2.47.1 with {"source": "../../docs/examples/test.grib", "filter_by_keys": {}, "encode_cf": ["parameter", "time", "geography", "vertical"]}

fl = d.to_fieldlist()
fl.ls()

	parameter.variable	time.valid_datetime	time.base_datetime	time.step	vertical.level	vertical.level_type	ensemble.member	geography.grid_type
0	t2m	2020-05-13 12:00:00	2020-05-13 12:00:00	0 days	0.0	surface	0	None
1	msl	2020-05-13 12:00:00	2020-05-13 12:00:00	0 days	0.0	surface	0	None

for f in fl:
    print(f)

Field(t2m, 2020-05-13 12:00:00, 2020-05-13 12:00:00, 0:00:00, 0.0, surface, 0, None)
Field(msl, 2020-05-13 12:00:00, 2020-05-13 12:00:00, 0:00:00, 0.0, surface, 0, None)