5. Input grid types

import earthkit.data
import earthkit.maps

Equirectangular latitude-longitude projection

earthkit.data.download_example_file("era5-2m-temperature-dec-1993.grib")
data = earthkit.data.from_source("file", "era5-2m-temperature-dec-1993.grib")

earthkit.maps.quickplot(data)

{'central_longitude': 0.0}
{'semimajor_axis': 6378137.0, 'ellipse': 'WGS84'}
{'central_longitude': 0.0}
{'semimajor_axis': 6378137.0, 'ellipse': 'WGS84'}
{'central_longitude': 0.0}
{'semimajor_axis': 6378137.0, 'ellipse': 'WGS84'}

Non-equirectangular data

earthkit.data.download_example_file("efas.nc")
data = earthkit.data.from_source("file", "efas.nc")
data.ls()

	variable	level	time	units
0	dis06	None	None	m**3 s**-1
1	land_binary_mask	None	None	1
2	upArea	None	None	M2

data.to_xarray()

<xarray.Dataset>
Dimensions:                       (y: 950, x: 1000)
Coordinates:
  * y                             (y) float64 5.498e+06 5.492e+06 ... 7.525e+05
  * x                             (x) float64 2.502e+06 2.508e+06 ... 7.498e+06
    time                          datetime64[ns] ...
    step                          timedelta64[ns] ...
    surface                       float64 ...
    latitude                      (y, x) float64 dask.array<chunksize=(950, 1000), meta=np.ndarray>
    longitude                     (y, x) float64 dask.array<chunksize=(950, 1000), meta=np.ndarray>
    valid_time                    datetime64[ns] ...
Data variables:
    dis06                         (y, x) float32 dask.array<chunksize=(950, 1000), meta=np.ndarray>
    lambert_azimuthal_equal_area  int32 ...
    land_binary_mask              (y, x) int8 dask.array<chunksize=(950, 1000), meta=np.ndarray>
    upArea                        (y, x) float32 dask.array<chunksize=(950, 1000), meta=np.ndarray>
Attributes:
    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:                 2022-11-10T10:57 GRIB to CDM+CF via cfgrib-0.9.9...

xarray.Dataset

• Dimensions:
  • y: 950
  • x: 1000
• Coordinates: (8)
  • y
    (y)
    float64
    5.498e+06 5.492e+06 ... 7.525e+05

    units :

    Meter

    long_name :

    Y coordinate of projection

    standard_name :

    projection_Y_coordinate

    array([5497500., 5492500., 5487500., ...,  762500.,  757500.,  752500.])

  • x
    (x)
    float64
    2.502e+06 2.508e+06 ... 7.498e+06

    units :

    Meter

    long_name :

    x coordinate of projection

    standard_name :

    projection_x_coordinate

    array([2502500., 2507500., 2512500., ..., 7487500., 7492500., 7497500.])

  • 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]

  • latitude
    (y, x)
    float64
    dask.array<chunksize=(950, 1000), meta=np.ndarray>

    units :

    degrees_north

    standard_name :

    latitude

    long_name :

    latitude

    Array Chunk Bytes 7.25 MiB 7.25 MiB Shape (950, 1000) (950, 1000) Count 2 Graph Layers 1 Chunks Type float64 numpy.ndarray

  • longitude
    (y, x)
    float64
    dask.array<chunksize=(950, 1000), meta=np.ndarray>

    units :

    degrees_east

    standard_name :

    longitude

    long_name :

    longitude

    Array Chunk Bytes 7.25 MiB 7.25 MiB Shape (950, 1000) (950, 1000) Count 2 Graph Layers 1 Chunks Type float64 numpy.ndarray

  • valid_time
    ()
    datetime64[ns]
    ...

    standard_name :

    time

    long_name :

    time

    [1 values with dtype=datetime64[ns]]

• Data variables: (4)
  • dis06
    (y, x)
    float32
    dask.array<chunksize=(950, 1000), meta=np.ndarray>

    GRIB_paramId :

    240023

    GRIB_dataType :

    sfo

    GRIB_numberOfPoints :

    950000

    GRIB_typeOfLevel :

    surface

    GRIB_stepUnits :

    1

    GRIB_stepType :

    avg

    GRIB_gridType :

    lambert_azimuthal_equal_area

    GRIB_NV :

    0

    GRIB_cfName :

    unknown

    GRIB_cfVarName :

    dis06

    GRIB_gridDefinitionDescription :

    Lambert azimuthal equal area projection

    GRIB_missingValue :

    9999

    GRIB_name :

    Mean discharge in the last 6 hours

    GRIB_shortName :

    dis06

    GRIB_units :

    m**3 s**-1

    long_name :

    Mean discharge in the last 6 hours

    units :

    m**3 s**-1

    standard_name :

    unknown

    grid_mapping :

    lambert_azimuthal_equal_area

    Array Chunk Bytes 3.62 MiB 3.62 MiB Shape (950, 1000) (950, 1000) Count 2 Graph Layers 1 Chunks Type float32 numpy.ndarray

  • lambert_azimuthal_equal_area
    ()
    int32
    ...

    semi_major_axis :

    6378137.0

    EPSG_code :

    EPSG:3035

    latitude_of_projection_origin :

    52.0

    inverse_flattening :

    298.257223563

    longitude_of_projection_origin :

    10.0

    proj4_params :

    +proj=laea +lat_0=52 +lon_0=10 +x_0=4321000 +y_0=3210000 +ellps=GRS80 +units=m +no_defs

    false_northing :

    3210000.0

    grid_mapping_name :

    lambert_azimuthal_equal_area

    false_easting :

    4321000.0

    [1 values with dtype=int32]

  • land_binary_mask
    (y, x)
    int8
    dask.array<chunksize=(950, 1000), meta=np.ndarray>

    standard_name :

    land_binary_mask

    long_name :

    masking_area

    units :

    1

    grid_mapping :

    lambert_azimuthal_equal_area

    esri_pe_string :

    PROJCS["ETRS_1989_LAEA",GEOGCS["GCS_ETRS_1989",DATUM["D_ETRS_1989",SPHEROID["GRS_1980",6378137.0,298.257222101]],PRIMEM["Greenwich",0.0],UNIT["Degree",0.0174532925199433]],PROJECTION["Lambert_Azimuthal_Equal_Area"],PARAMETER["false_easting",4321000.0],PARAMETER["false_northing",3210000.0],PARAMETER["central_meridian",10.0],PARAMETER["latitude_of_origin",52.0],UNIT["Meter",1.0]]

    Array Chunk Bytes 0.91 MiB 0.91 MiB Shape (950, 1000) (950, 1000) Count 2 Graph Layers 1 Chunks Type int8 numpy.ndarray

  • upArea
    (y, x)
    float32
    dask.array<chunksize=(950, 1000), meta=np.ndarray>

    grid_mapping :

    lambert_azimuthal_equal_area

    long_name :

    upstream_area

    standard_name :

    upstream

    units :

    M2

    esri_pe_string :

    PROJCS["ETRS_1989_LAEA",GEOGCS["GCS_ETRS_1989",DATUM["D_ETRS_1989",SPHEROID["GRS_1980",6378137.0,298.257222101]],PRIMEM["Greenwich",0.0],UNIT["Degree",0.0174532925199433]],PROJECTION["Lambert_Azimuthal_Equal_Area"],PARAMETER["false_easting",4321000.0],PARAMETER["false_northing",3210000.0],PARAMETER["central_meridian",10.0],PARAMETER["latitude_of_origin",52.0],UNIT["Meter",1.0]]

    cell_methods :

    time: mean

    Array Chunk Bytes 3.62 MiB 3.62 MiB Shape (950, 1000) (950, 1000) Count 2 Graph Layers 1 Chunks Type float32 numpy.ndarray

• Indexes: (2)
  • y
    PandasIndex

    PandasIndex(Float64Index([5497500.0, 5492500.0, 5487500.0, 5482500.0, 5477500.0, 5472500.0,
                  5467500.0, 5462500.0, 5457500.0, 5452500.0,
                  ...
                   797500.0,  792500.0,  787500.0,  782500.0,  777500.0,  772500.0,
                   767500.0,  762500.0,  757500.0,  752500.0],
                 dtype='float64', name='y', length=950))

  • x
    PandasIndex

    PandasIndex(Float64Index([2502500.0, 2507500.0, 2512500.0, 2517500.0, 2522500.0, 2527500.0,
                  2532500.0, 2537500.0, 2542500.0, 2547500.0,
                  ...
                  7452500.0, 7457500.0, 7462500.0, 7467500.0, 7472500.0, 7477500.0,
                  7482500.0, 7487500.0, 7492500.0, 7497500.0],
                 dtype='float64', name='x', length=1000))

• Attributes: (7)

  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 :

  2022-11-10T10:57 GRIB to CDM+CF via cfgrib-0.9.9.1/ecCodes-2.26.0 with {"source": "/cache/tmp/874481db-8e12-4a6e-852a-f25eee02c659-adaptor.mars_and_url.retrieve-1668077838.6116145-1675-22-tmp.grib", "filter_by_keys": {}, "encode_cf": ["parameter", "time", "geography", "vertical"]}

earthkit.maps.quickplot(data[0])

{'central_latitude': 52.0, 'central_longitude': 10.0, 'false_easting': 4321000.0, 'false_northing': 3210000.0}
{'ellipse': 'GRS80'}
{'central_latitude': 52.0, 'central_longitude': 10.0, 'false_easting': 4321000.0, 'false_northing': 3210000.0}
{'ellipse': 'GRS80'}
{'central_latitude': 52.0, 'central_longitude': 10.0, 'false_easting': 4321000.0, 'false_northing': 3210000.0}
{'ellipse': 'GRS80'}

Reduced gaussian grids

data = earthkit.data.from_source(
    "url",
    "https://get.ecmwf.int/repository/test-data/earthkit-regrid/test-data/global_0_360/O32.grib",
)
data.ls()

	centre	shortName	typeOfLevel	level	dataDate	dataTime	stepRange	dataType	number	gridType
0	ecmf	2t	surface	0	20231010	1200	0	an	0	reduced_gg

data.to_xarray()

<xarray.Dataset>
Dimensions:     (number: 1, time: 1, step: 1, surface: 1, values: 5248)
Coordinates:
  * number      (number) int64 0
  * time        (time) datetime64[ns] 2023-10-10T12:00:00
  * step        (step) timedelta64[ns] 00:00:00
  * surface     (surface) float64 0.0
    latitude    (values) float64 ...
    longitude   (values) float64 ...
    valid_time  (time, step) datetime64[ns] ...
Dimensions without coordinates: values
Data variables:
    t2m         (number, time, step, surface, values) float32 ...
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:                 2023-12-11T16:22 GRIB to CDM+CF via cfgrib-0.9.1...

xarray.Dataset

• Dimensions:
  • number: 1
  • time: 1
  • step: 1
  • surface: 1
  • values: 5248
• Coordinates: (7)
  • number
    (number)
    int64
    0

    long_name :

    ensemble member numerical id

    units :

    1

    standard_name :

    realization

    array([0])

  • time
    (time)
    datetime64[ns]
    2023-10-10T12:00:00

    long_name :

    initial time of forecast

    standard_name :

    forecast_reference_time

    array(['2023-10-10T12:00:00.000000000'], dtype='datetime64[ns]')

  • step
    (step)
    timedelta64[ns]
    00:00:00

    long_name :

    time since forecast_reference_time

    standard_name :

    forecast_period

    array([0], dtype='timedelta64[ns]')

  • surface
    (surface)
    float64
    0.0

    long_name :

    original GRIB coordinate for key: level(surface)

    units :

    1

    array([0.])

  • latitude
    (values)
    float64
    ...

    units :

    degrees_north

    standard_name :

    latitude

    long_name :

    latitude

    [5248 values with dtype=float64]

  • longitude
    (values)
    float64
    ...

    units :

    degrees_east

    standard_name :

    longitude

    long_name :

    longitude

    [5248 values with dtype=float64]

  • valid_time
    (time, step)
    datetime64[ns]
    ...

    standard_name :

    time

    long_name :

    time

    [1 values with dtype=datetime64[ns]]

• Data variables: (1)
  • t2m
    (number, time, step, surface, values)
    float32
    ...

    GRIB_paramId :

    167

    GRIB_dataType :

    an

    GRIB_numberOfPoints :

    5248

    GRIB_typeOfLevel :

    surface

    GRIB_stepUnits :

    1

    GRIB_stepType :

    instant

    GRIB_gridType :

    reduced_gg

    GRIB_N :

    32

    GRIB_NV :

    0

    GRIB_cfName :

    unknown

    GRIB_cfVarName :

    t2m

    GRIB_gridDefinitionDescription :

    Gaussian Latitude/Longitude Grid

    GRIB_missingValue :

    9999

    GRIB_name :

    2 metre temperature

    GRIB_pl :

    [ 20 24 28 32 36 40 44 48 52 56 60 64 68 72 76 80 84 88 92 96 100 104 108 112 116 120 124 128 132 136 140 144 144 140 136 132 128 124 120 116 112 108 104 100 96 92 88 84 80 76 72 68 64 60 56 52 48 44 40 36 32 28 24 20]

    GRIB_shortName :

    2t

    GRIB_totalNumber :

    0

    GRIB_units :

    K

    long_name :

    2 metre temperature

    units :

    K

    standard_name :

    unknown

    [5248 values with dtype=float32]

• Indexes: (4)
  • number
    PandasIndex

    PandasIndex(Int64Index([0], dtype='int64', name='number'))

  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['2023-10-10 12:00:00'], dtype='datetime64[ns]', name='time', freq=None))

  • step
    PandasIndex

    PandasIndex(TimedeltaIndex(['0 days'], dtype='timedelta64[ns]', name='step', freq=None))

  • surface
    PandasIndex

    PandasIndex(Float64Index([0.0], dtype='float64', name='surface'))

• 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 :

  2023-12-11T16:22 GRIB to CDM+CF via cfgrib-0.9.10.4/ecCodes-2.27.0 with {"source": "N/A", "filter_by_keys": {}, "encode_cf": ["parameter", "time", "geography", "vertical"]}

earthkit.maps.quickplot(data, units="celsius")

{'central_longitude': 0.0}
{'semimajor_axis': 6378137.0, 'ellipse': 'WGS84'}
{'central_longitude': 0.0}
{'semimajor_axis': 6378137.0, 'ellipse': 'WGS84'}