Reading files

import earthkit.data as ekd

First, we ensure the example files used for this notebook are available.

ekd.download_example_file(["test.grib", "test4.grib", "test6.grib", "test.nc"])

Files can be read by from_source as a “file” source. The resulting object provides some details about the data and the types it can be converted to for further work.

# grib data
ds_in = ekd.from_source("file", "test.grib")
ds_in

GRIB file

---

path	test.grib
size	720
types	fieldlist, pandas, xarray, numpy, array

ds = ds_in.to_fieldlist().ls()

# netcdf data
ds_in = ekd.from_source("file", "test.nc")
ds_in

NetCDF file

---

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

ds = ds_in.to_xarray()
ds

<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"]}

See the other file related notebooks for more details:

• Reading multiple files

• Reading tar or zip archive

• Reading file parts

Reading files using patterns

The file source can also be specified by using patterns. In the example below when pattern “id” is substituted it will match two files: test4.grib and test6.grib:

ds = ekd.from_source("file-pattern", "./test{id}.grib", {"id": [4, 6]})
ds.to_fieldlist().ls()

	parameter.variable	time.valid_datetime	time.base_datetime	time.step	vertical.level	vertical.level_type	ensemble.member	geography.grid_type
0	t	2007-01-01 12:00:00	2007-01-01 12:00:00	0 days	500	pressure	0	regular_ll
1	z	2007-01-01 12:00:00	2007-01-01 12:00:00	0 days	500	pressure	0	regular_ll
2	t	2007-01-01 12:00:00	2007-01-01 12:00:00	0 days	850	pressure	0	regular_ll
3	z	2007-01-01 12:00:00	2007-01-01 12:00:00	0 days	850	pressure	0	regular_ll
4	t	2018-08-01 12:00:00	2018-08-01 12:00:00	0 days	1000	pressure	0	regular_ll
5	u	2018-08-01 12:00:00	2018-08-01 12:00:00	0 days	1000	pressure	0	regular_ll
6	v	2018-08-01 12:00:00	2018-08-01 12:00:00	0 days	1000	pressure	0	regular_ll
7	t	2018-08-01 12:00:00	2018-08-01 12:00:00	0 days	850	pressure	0	regular_ll
8	u	2018-08-01 12:00:00	2018-08-01 12:00:00	0 days	850	pressure	0	regular_ll
9	v	2018-08-01 12:00:00	2018-08-01 12:00:00	0 days	850	pressure	0	regular_ll