Reducing data-cubes using geometry objects

# If first time running, uncomment the line below to install any additional dependencies
# !bash requirements-for-notebooks.sh

import matplotlib.pyplot as plt
import numpy as np

from earthkit.transforms import aggregate as ek_aggregate
from earthkit import data as ek_data

from earthkit.data.testing import earthkit_remote_test_data_file

Load some test data

All earthkit-transforms methods can be called with earthkit-data objects (Readers and Wrappers) or with the pre-loaded xarray or geopandas objects.

In this example we will use hourly ERA5 2m temperature data on a 0.5x0.5 spatial grid for the year 2015 as our physical data; and we will use the NUTS geometries which are stored in a geojson file.

First we lazily load the ERA5 data and NUTS geometries from our test-data repository.

Note the data is only downloaded when we use it, e.g. at the .to_xarray line, additionally, the download is cached so the next time you run this cell you will not need to re-download the file (unless it has been a very long time since you have run the code, please see tutorials in earthkit-data for more details in cache management).

# Get some demonstration ERA5 data, this could be any url or path to an ERA5 grib or netCDF file.
# remote_era5_file = earthkit_remote_test_data_file("test-data", "era5_temperature_europe_2015.grib") # Large file
remote_era5_file = earthkit_remote_test_data_file("test-data", "era5_temperature_europe_20150101.grib")
era5_data = ek_data.from_source("url", remote_era5_file)
era5_data.to_xarray()

<xarray.Dataset> Size: 5MB
Dimensions:     (number: 1, time: 24, step: 1, surface: 1, latitude: 201,
                 longitude: 281)
Coordinates:
  * number      (number) int64 8B 0
  * time        (time) datetime64[ns] 192B 2015-01-01 ... 2015-01-01T23:00:00
  * step        (step) timedelta64[ns] 8B 00:00:00
  * surface     (surface) float64 8B 0.0
  * latitude    (latitude) float64 2kB 80.0 79.75 79.5 79.25 ... 30.5 30.25 30.0
  * longitude   (longitude) float64 2kB -10.0 -9.75 -9.5 ... 59.5 59.75 60.0
    valid_time  (time, step) datetime64[ns] 192B ...
Data variables:
    t2m         (number, time, step, surface, latitude, longitude) float32 5MB ...
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:                 2024-10-15T14:00 GRIB to CDM+CF via cfgrib-0.9.1...

xarray.Dataset

• Dimensions:
  • number: 1
  • time: 24
  • step: 1
  • surface: 1
  • latitude: 201
  • longitude: 281
• Coordinates: (7)
  • number
    (number)
    int64
    0

    long_name :

    ensemble member numerical id

    units :

    1

    standard_name :

    realization

    array([0])

  • time
    (time)
    datetime64[ns]
    2015-01-01 ... 2015-01-01T23:00:00

    long_name :

    initial time of forecast

    standard_name :

    forecast_reference_time

    array(['2015-01-01T00:00:00.000000000', '2015-01-01T01:00:00.000000000',
           '2015-01-01T02:00:00.000000000', '2015-01-01T03:00:00.000000000',
           '2015-01-01T04:00:00.000000000', '2015-01-01T05:00:00.000000000',
           '2015-01-01T06:00:00.000000000', '2015-01-01T07:00:00.000000000',
           '2015-01-01T08:00:00.000000000', '2015-01-01T09:00:00.000000000',
           '2015-01-01T10:00:00.000000000', '2015-01-01T11:00:00.000000000',
           '2015-01-01T12:00:00.000000000', '2015-01-01T13:00:00.000000000',
           '2015-01-01T14:00:00.000000000', '2015-01-01T15:00:00.000000000',
           '2015-01-01T16:00:00.000000000', '2015-01-01T17:00:00.000000000',
           '2015-01-01T18:00:00.000000000', '2015-01-01T19:00:00.000000000',
           '2015-01-01T20:00:00.000000000', '2015-01-01T21:00:00.000000000',
           '2015-01-01T22:00:00.000000000', '2015-01-01T23:00:00.000000000'],
          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
    (latitude)
    float64
    80.0 79.75 79.5 ... 30.5 30.25 30.0

    units :

    degrees_north

    standard_name :

    latitude

    long_name :

    latitude

    stored_direction :

    decreasing

    array([80.  , 79.75, 79.5 , ..., 30.5 , 30.25, 30.  ])

  • longitude
    (longitude)
    float64
    -10.0 -9.75 -9.5 ... 59.75 60.0

    units :

    degrees_east

    standard_name :

    longitude

    long_name :

    longitude

    array([-10.  ,  -9.75,  -9.5 , ...,  59.5 ,  59.75,  60.  ])

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

    standard_name :

    time

    long_name :

    time

    [24 values with dtype=datetime64[ns]]

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

    GRIB_paramId :

    167

    GRIB_dataType :

    an

    GRIB_numberOfPoints :

    56481

    GRIB_typeOfLevel :

    surface

    GRIB_stepUnits :

    1

    GRIB_stepType :

    instant

    GRIB_gridType :

    regular_ll

    GRIB_uvRelativeToGrid :

    0

    GRIB_NV :

    0

    GRIB_Nx :

    281

    GRIB_Ny :

    201

    GRIB_cfName :

    unknown

    GRIB_cfVarName :

    t2m

    GRIB_gridDefinitionDescription :

    Latitude/Longitude Grid

    GRIB_iDirectionIncrementInDegrees :

    0.25

    GRIB_iScansNegatively :

    0

    GRIB_jDirectionIncrementInDegrees :

    0.25

    GRIB_jPointsAreConsecutive :

    0

    GRIB_jScansPositively :

    0

    GRIB_latitudeOfFirstGridPointInDegrees :

    80.0

    GRIB_latitudeOfLastGridPointInDegrees :

    30.0

    GRIB_longitudeOfFirstGridPointInDegrees :

    -10.0

    GRIB_longitudeOfLastGridPointInDegrees :

    60.0

    GRIB_missingValue :

    9999

    GRIB_name :

    2 metre temperature

    GRIB_shortName :

    2t

    GRIB_totalNumber :

    0

    GRIB_units :

    K

    long_name :

    2 metre temperature

    units :

    K

    standard_name :

    unknown

    [1355544 values with dtype=float32]

• Indexes: (6)
  • number
    PandasIndex

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

  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['2015-01-01 00:00:00', '2015-01-01 01:00:00',
                   '2015-01-01 02:00:00', '2015-01-01 03:00:00',
                   '2015-01-01 04:00:00', '2015-01-01 05:00:00',
                   '2015-01-01 06:00:00', '2015-01-01 07:00:00',
                   '2015-01-01 08:00:00', '2015-01-01 09:00:00',
                   '2015-01-01 10:00:00', '2015-01-01 11:00:00',
                   '2015-01-01 12:00:00', '2015-01-01 13:00:00',
                   '2015-01-01 14:00:00', '2015-01-01 15:00:00',
                   '2015-01-01 16:00:00', '2015-01-01 17:00:00',
                   '2015-01-01 18:00:00', '2015-01-01 19:00:00',
                   '2015-01-01 20:00:00', '2015-01-01 21:00:00',
                   '2015-01-01 22:00:00', '2015-01-01 23:00:00'],
                  dtype='datetime64[ns]', name='time', freq=None))

  • step
    PandasIndex

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

  • surface
    PandasIndex

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

  • latitude
    PandasIndex

    PandasIndex(Index([ 80.0, 79.75,  79.5, 79.25,  79.0, 78.75,  78.5, 78.25,  78.0, 77.75,
           ...
           32.25,  32.0, 31.75,  31.5, 31.25,  31.0, 30.75,  30.5, 30.25,  30.0],
          dtype='float64', name='latitude', length=201))

  • longitude
    PandasIndex

    PandasIndex(Index([-10.0, -9.75,  -9.5, -9.25,  -9.0, -8.75,  -8.5, -8.25,  -8.0, -7.75,
           ...
           57.75,  58.0, 58.25,  58.5, 58.75,  59.0, 59.25,  59.5, 59.75,  60.0],
          dtype='float64', name='longitude', length=281))

• 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 :

  2024-10-15T14:00 GRIB to CDM+CF via cfgrib-0.9.14.1/ecCodes-2.38.0 with {"source": "N/A", "filter_by_keys": {}, "encode_cf": ["parameter", "time", "geography", "vertical"]}

# Use some demonstration polygons stored, this could be any url or path to geojson file
remote_nuts_url = earthkit_remote_test_data_file("test-data", "NUTS_RG_60M_2021_4326_LEVL_0.geojson")
nuts_data = ek_data.from_source("url", remote_nuts_url)

nuts_data.to_pandas()[:5]

	id	NUTS_ID	LEVL_CODE	CNTR_CODE	NAME_LATN	NUTS_NAME	MOUNT_TYPE	URBN_TYPE	COAST_TYPE	FID	geometry
0	DK	DK	0	DK	Danmark	Danmark	0	0	0	DK	MULTIPOLYGON (((15.1629 55.0937, 15.094 54.996...
1	RS	RS	0	RS	Serbia	Srbija/Сpбија	0	0	0	RS	POLYGON ((21.4792 45.193, 21.3585 44.8216, 22....
2	EE	EE	0	EE	Eesti	Eesti	0	0	0	EE	MULTIPOLYGON (((27.357 58.7871, 27.6449 57.981...
3	EL	EL	0	EL	Elláda	Ελλάδα	0	0	0	EL	MULTIPOLYGON (((28.0777 36.1182, 27.8606 35.92...
4	ES	ES	0	ES	España	España	0	0	0	ES	MULTIPOLYGON (((4.391 39.8617, 4.1907 39.7981,...

Reduce data

Default behaviour

The default behaviour is to reduce the data along the spatial dimensions, only, and return the reduced data in the Xarray format it was provided, i.e. xr.DataArray or xr.Dataset.

The returned object has a new dimension FID (feature id) which has a coordinate variable with the values of the FID column in the input geodataframe.

The new variable name is made up of the original variable name and the method used to reduce, e.g. t2m_mean

reduced_data = ek_aggregate.spatial.reduce(era5_data, nuts_data)
reduced_data

<xarray.Dataset> Size: 4kB
Dimensions:     (time: 24, index: 37)
Coordinates:
    number      int64 8B 0
  * time        (time) datetime64[ns] 192B 2015-01-01 ... 2015-01-01T23:00:00
    step        timedelta64[ns] 8B 00:00:00
    surface     float64 8B 0.0
    valid_time  (time) datetime64[ns] 192B 2015-01-01 ... 2015-01-01T23:00:00
  * index       (index) int64 296B 0 1 2 3 4 5 6 7 8 ... 29 30 31 32 33 34 35 36
Data variables:
    t2m         (index, time) float32 4kB 278.7 278.9 279.1 ... 272.7 272.6
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:                 2024-10-15T14:01 GRIB to CDM+CF via cfgrib-0.9.1...

xarray.Dataset

• Dimensions:
  • time: 24
  • index: 37
• Coordinates: (6)
  • number
    ()
    int64
    0

    long_name :

    ensemble member numerical id

    units :

    1

    standard_name :

    realization

    array(0)

  • time
    (time)
    datetime64[ns]
    2015-01-01 ... 2015-01-01T23:00:00

    long_name :

    initial time of forecast

    standard_name :

    forecast_reference_time

    array(['2015-01-01T00:00:00.000000000', '2015-01-01T01:00:00.000000000',
           '2015-01-01T02:00:00.000000000', '2015-01-01T03:00:00.000000000',
           '2015-01-01T04:00:00.000000000', '2015-01-01T05:00:00.000000000',
           '2015-01-01T06:00:00.000000000', '2015-01-01T07:00:00.000000000',
           '2015-01-01T08:00:00.000000000', '2015-01-01T09:00:00.000000000',
           '2015-01-01T10:00:00.000000000', '2015-01-01T11:00:00.000000000',
           '2015-01-01T12:00:00.000000000', '2015-01-01T13:00:00.000000000',
           '2015-01-01T14:00:00.000000000', '2015-01-01T15:00:00.000000000',
           '2015-01-01T16:00:00.000000000', '2015-01-01T17:00:00.000000000',
           '2015-01-01T18:00:00.000000000', '2015-01-01T19:00:00.000000000',
           '2015-01-01T20:00:00.000000000', '2015-01-01T21:00:00.000000000',
           '2015-01-01T22:00:00.000000000', '2015-01-01T23:00:00.000000000'],
          dtype='datetime64[ns]')

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

    long_name :

    time since forecast_reference_time

    standard_name :

    forecast_period

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

  • surface
    ()
    float64
    0.0

    long_name :

    original GRIB coordinate for key: level(surface)

    units :

    1

    array(0.)

  • valid_time
    (time)
    datetime64[ns]
    2015-01-01 ... 2015-01-01T23:00:00

    standard_name :

    time

    long_name :

    time

    array(['2015-01-01T00:00:00.000000000', '2015-01-01T01:00:00.000000000',
           '2015-01-01T02:00:00.000000000', '2015-01-01T03:00:00.000000000',
           '2015-01-01T04:00:00.000000000', '2015-01-01T05:00:00.000000000',
           '2015-01-01T06:00:00.000000000', '2015-01-01T07:00:00.000000000',
           '2015-01-01T08:00:00.000000000', '2015-01-01T09:00:00.000000000',
           '2015-01-01T10:00:00.000000000', '2015-01-01T11:00:00.000000000',
           '2015-01-01T12:00:00.000000000', '2015-01-01T13:00:00.000000000',
           '2015-01-01T14:00:00.000000000', '2015-01-01T15:00:00.000000000',
           '2015-01-01T16:00:00.000000000', '2015-01-01T17:00:00.000000000',
           '2015-01-01T18:00:00.000000000', '2015-01-01T19:00:00.000000000',
           '2015-01-01T20:00:00.000000000', '2015-01-01T21:00:00.000000000',
           '2015-01-01T22:00:00.000000000', '2015-01-01T23:00:00.000000000'],
          dtype='datetime64[ns]')

  • index
    (index)
    int64
    0 1 2 3 4 5 6 ... 31 32 33 34 35 36

    array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,
           18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,
           36])

• Data variables: (1)
  • t2m
    (index, time)
    float32
    278.7 278.9 279.1 ... 272.7 272.6

    GRIB_paramId :

    167

    GRIB_dataType :

    an

    GRIB_numberOfPoints :

    56481

    GRIB_typeOfLevel :

    surface

    GRIB_stepUnits :

    1

    GRIB_stepType :

    instant

    GRIB_gridType :

    regular_ll

    GRIB_uvRelativeToGrid :

    0

    GRIB_NV :

    0

    GRIB_Nx :

    281

    GRIB_Ny :

    201

    GRIB_cfName :

    unknown

    GRIB_cfVarName :

    t2m

    GRIB_gridDefinitionDescription :

    Latitude/Longitude Grid

    GRIB_iDirectionIncrementInDegrees :

    0.25

    GRIB_iScansNegatively :

    0

    GRIB_jDirectionIncrementInDegrees :

    0.25

    GRIB_jPointsAreConsecutive :

    0

    GRIB_jScansPositively :

    0

    GRIB_latitudeOfFirstGridPointInDegrees :

    80.0

    GRIB_latitudeOfLastGridPointInDegrees :

    30.0

    GRIB_longitudeOfFirstGridPointInDegrees :

    -10.0

    GRIB_longitudeOfLastGridPointInDegrees :

    60.0

    GRIB_missingValue :

    9999

    GRIB_name :

    2 metre temperature

    GRIB_shortName :

    2t

    GRIB_totalNumber :

    0

    GRIB_units :

    K

    long_name :

    2 metre temperature

    units :

    K

    standard_name :

    unknown

    reduce_dims :

    ['latitude', 'longitude']

    array([[278.70923, 278.93887, 279.06848, 279.24127, 279.4186 , 279.6389 ,
            279.765  , 279.8012 , 279.78036, 279.84116, 279.82227, 279.83392,
            279.77222, 279.8017 , 279.76913, 279.66055, 279.64795, 279.54572,
            279.57568, 279.51956, 279.46082, 279.44302, 279.4824 , 279.5979 ],
           [257.4673 , 257.3978 , 257.04678, 257.04346, 256.9975 , 256.4913 ,
            256.88065, 257.24512, 259.6847 , 260.8157 , 264.0798 , 264.63754,
            264.83875, 264.9982 , 264.61298, 264.38565, 264.2132 , 263.17706,
            263.44513, 263.19537, 263.27155, 263.21796, 263.6912 , 263.65784],
           [275.7629 , 275.7535 , 275.6221 , 275.6109 , 275.5637 , 275.48453,
            275.43472, 275.47476, 275.61002, 275.60583, 275.75012, 275.82904,
            275.91312, 276.05695, 276.32614, 276.44327, 276.50043, 276.7878 ,
            276.8525 , 276.87375, 277.2613 , 277.27295, 277.51285, 277.44867],
           [274.514  , 274.3928 , 273.94873, 273.77258, 273.6532 , 273.38406,
            273.25146, 273.38452, 274.34018, 274.7383 , 275.92004, 276.09802,
            275.95157, 275.91742, 275.68356, 275.399  , 274.99365, 274.20877,
            273.73346, 273.66196, 273.3085 , 273.21243, 272.60428, 272.43143],
           [273.99857, 273.74615, 273.04504, 272.79443, 272.8339 , 272.60416,
            272.70166, 272.5127 , 273.0187 , 274.68988, 279.53036, 281.1695 ,
            282.6711 , 283.3928 , 284.74   , 285.00592, 284.12387, 280.50793,
            278.778  , 277.68054, 276.2094 , 275.4994 , 274.6026 , 274.43246],
    ...
            264.19904, 264.37473, 264.48346, 265.4978 , 266.86734, 268.90665,
            270.43704, 271.15204, 271.0366 , 270.56934, 269.41626, 268.44394,
            268.087  , 267.90765, 267.74747, 267.53735, 267.3114 , 267.419  ],
           [286.1225 , 286.5079 , 286.7677 , 286.61523, 286.53387, 286.6881 ,
            286.37833, 286.47577, 287.73874, 288.10385, 288.1107 , 288.44193,
            288.16492, 287.67386, 286.5869 , 285.85788, 285.74057, 285.1178 ,
            285.258  , 285.29486, 284.97128, 285.00406, 284.64194, 284.40002],
           [270.75067, 270.92667, 271.12952, 271.36743, 271.5414 , 271.7161 ,
            271.9417 , 272.24994, 272.6613 , 273.04272, 273.43033, 273.8799 ,
            274.1975 , 274.44388, 274.47308, 273.90158, 273.47815, 273.39557,
            273.34665, 273.40036, 273.2703 , 273.07062, 272.79657, 272.8578 ],
           [274.46927, 274.37482, 274.17358, 274.05634, 273.9052 , 273.74893,
            273.63748, 273.5813 , 273.6051 , 273.93213, 274.54102, 275.19733,
            275.65186, 275.86438, 275.8291 , 275.3235 , 274.84744, 274.6163 ,
            274.5727 , 274.47748, 274.43185, 274.4594 , 274.48376, 274.5445 ],
           [272.67392, 272.66177, 272.69485, 272.70676, 272.74435, 272.82187,
            272.93872, 272.96075, 273.1067 , 273.2698 , 273.40872, 273.57346,
            273.56546, 273.49524, 273.26852, 273.02643, 272.9171 , 272.7344 ,
            272.65433, 272.73178, 272.66324, 272.72165, 272.6883 , 272.6264 ]],
          dtype=float32)

• Indexes: (2)
  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['2015-01-01 00:00:00', '2015-01-01 01:00:00',
                   '2015-01-01 02:00:00', '2015-01-01 03:00:00',
                   '2015-01-01 04:00:00', '2015-01-01 05:00:00',
                   '2015-01-01 06:00:00', '2015-01-01 07:00:00',
                   '2015-01-01 08:00:00', '2015-01-01 09:00:00',
                   '2015-01-01 10:00:00', '2015-01-01 11:00:00',
                   '2015-01-01 12:00:00', '2015-01-01 13:00:00',
                   '2015-01-01 14:00:00', '2015-01-01 15:00:00',
                   '2015-01-01 16:00:00', '2015-01-01 17:00:00',
                   '2015-01-01 18:00:00', '2015-01-01 19:00:00',
                   '2015-01-01 20:00:00', '2015-01-01 21:00:00',
                   '2015-01-01 22:00:00', '2015-01-01 23:00:00'],
                  dtype='datetime64[ns]', name='time', freq=None))

  • index
    PandasIndex

    PandasIndex(Index([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,
           18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,
           36],
          dtype='int64', name='index'))

• 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 :

  2024-10-15T14:01 GRIB to CDM+CF via cfgrib-0.9.14.1/ecCodes-2.38.0 with {"source": "N/A", "filter_by_keys": {}, "encode_cf": ["parameter", "time", "geography", "vertical"]}

Reduce along additional dimension

For example, any time dimension, this is advisable as it ensures correct handling missing values and weights.

The extra_reduce_dims argument takes a single string or a list of strings corresponding to dimensions to include in the reduction.

It is also possible to select a column in the geodataframe to use to populate the dimension and coordinate variable created by the reduction using the mask_dim kwarg, here we choose the "FID" column.

reduced_data = ek_aggregate.spatial.reduce(
    era5_data, nuts_data,
    mask_dim="FID", extra_reduce_dims='time', all_touched=True
)
reduced_data

<xarray.Dataset> Size: 468B
Dimensions:  (FID: 37)
Coordinates:
    number   int64 8B 0
    step     timedelta64[ns] 8B 00:00:00
    surface  float64 8B 0.0
  * FID      (FID) object 296B 'DK' 'RS' 'EE' 'EL' 'ES' ... 'CY' 'CZ' 'DE' 'NO'
Data variables:
    t2m      (FID) float32 148B 279.5 261.7 276.3 275.9 ... 272.7 274.4 273.1
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:                 2024-10-15T14:01 GRIB to CDM+CF via cfgrib-0.9.1...

xarray.Dataset

• Dimensions:
  • FID: 37
• Coordinates: (4)
  • number
    ()
    int64
    0

    long_name :

    ensemble member numerical id

    units :

    1

    standard_name :

    realization

    array(0)

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

    long_name :

    time since forecast_reference_time

    standard_name :

    forecast_period

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

  • surface
    ()
    float64
    0.0

    long_name :

    original GRIB coordinate for key: level(surface)

    units :

    1

    array(0.)

  • FID
    (FID)
    object
    'DK' 'RS' 'EE' ... 'CZ' 'DE' 'NO'

    array(['DK', 'RS', 'EE', 'EL', 'ES', 'FI', 'FR', 'HR', 'HU', 'IE', 'IS', 'IT',
           'LI', 'LT', 'LU', 'LV', 'ME', 'MK', 'MT', 'SE', 'SI', 'SK', 'TR', 'UK',
           'NL', 'PL', 'PT', 'RO', 'AL', 'AT', 'BE', 'BG', 'CH', 'CY', 'CZ', 'DE',
           'NO'], dtype=object)

• Data variables: (1)
  • t2m
    (FID)
    float32
    279.5 261.7 276.3 ... 274.4 273.1

    GRIB_paramId :

    167

    GRIB_dataType :

    an

    GRIB_numberOfPoints :

    56481

    GRIB_typeOfLevel :

    surface

    GRIB_stepUnits :

    1

    GRIB_stepType :

    instant

    GRIB_gridType :

    regular_ll

    GRIB_uvRelativeToGrid :

    0

    GRIB_NV :

    0

    GRIB_Nx :

    281

    GRIB_Ny :

    201

    GRIB_cfName :

    unknown

    GRIB_cfVarName :

    t2m

    GRIB_gridDefinitionDescription :

    Latitude/Longitude Grid

    GRIB_iDirectionIncrementInDegrees :

    0.25

    GRIB_iScansNegatively :

    0

    GRIB_jDirectionIncrementInDegrees :

    0.25

    GRIB_jPointsAreConsecutive :

    0

    GRIB_jScansPositively :

    0

    GRIB_latitudeOfFirstGridPointInDegrees :

    80.0

    GRIB_latitudeOfLastGridPointInDegrees :

    30.0

    GRIB_longitudeOfFirstGridPointInDegrees :

    -10.0

    GRIB_longitudeOfLastGridPointInDegrees :

    60.0

    GRIB_missingValue :

    9999

    GRIB_name :

    2 metre temperature

    GRIB_shortName :

    2t

    GRIB_totalNumber :

    0

    GRIB_units :

    K

    long_name :

    2 metre temperature

    units :

    K

    standard_name :

    unknown

    reduce_dims :

    ['latitude', 'longitude', 'time']

    array([279.4813 , 261.6594 , 276.27985, 275.88528, 277.61075, 273.94318,
           273.37723, 268.95154, 268.22455, 283.85107,       nan, 275.00357,
           266.8165 , 275.71933, 271.3531 , 275.9637 , 265.10547, 263.6609 ,
           282.9774 , 275.9291 , 267.32425, 268.9974 , 276.52567, 282.69446,
           276.59317, 274.24402, 279.8911 , 263.0193 , 269.31268, 269.5346 ,
           274.13318, 263.74713, 267.26654, 287.5035 , 272.74658, 274.4433 ,
           273.13065], dtype=float32)

• Indexes: (1)
  • FID
    PandasIndex

    PandasIndex(Index(['DK', 'RS', 'EE', 'EL', 'ES', 'FI', 'FR', 'HR', 'HU', 'IE', 'IS', 'IT',
           'LI', 'LT', 'LU', 'LV', 'ME', 'MK', 'MT', 'SE', 'SI', 'SK', 'TR', 'UK',
           'NL', 'PL', 'PT', 'RO', 'AL', 'AT', 'BE', 'BG', 'CH', 'CY', 'CZ', 'DE',
           'NO'],
          dtype='object', name='FID'))

• 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 :

  2024-10-15T14:01 GRIB to CDM+CF via cfgrib-0.9.14.1/ecCodes-2.38.0 with {"source": "N/A", "filter_by_keys": {}, "encode_cf": ["parameter", "time", "geography", "vertical"]}

Weighted reduction

Provide numpy/xarray arrays of weights, or use predefined weights options, i.e. latitude:

reduced_data_xr = ek_aggregate.spatial.reduce(
    era5_data, nuts_data, weights='latitude', mask_dim="FID", extra_reduce_dims='time',
    all_touched=True
)
reduced_data_xr

<xarray.Dataset> Size: 616B
Dimensions:  (FID: 37)
Coordinates:
    number   int64 8B 0
    step     timedelta64[ns] 8B 00:00:00
    surface  float64 8B 0.0
  * FID      (FID) object 296B 'DK' 'RS' 'EE' 'EL' 'ES' ... 'CY' 'CZ' 'DE' 'NO'
Data variables:
    t2m      (FID) float64 296B 279.5 261.6 276.3 276.0 ... 272.7 274.3 274.3
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:                 2024-10-15T14:01 GRIB to CDM+CF via cfgrib-0.9.1...

xarray.Dataset

• Dimensions:
  • FID: 37
• Coordinates: (4)
  • number
    ()
    int64
    0

    array(0)

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

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

  • surface
    ()
    float64
    0.0

    array(0.)

  • FID
    (FID)
    object
    'DK' 'RS' 'EE' ... 'CZ' 'DE' 'NO'

    array(['DK', 'RS', 'EE', 'EL', 'ES', 'FI', 'FR', 'HR', 'HU', 'IE', 'IS', 'IT',
           'LI', 'LT', 'LU', 'LV', 'ME', 'MK', 'MT', 'SE', 'SI', 'SK', 'TR', 'UK',
           'NL', 'PL', 'PT', 'RO', 'AL', 'AT', 'BE', 'BG', 'CH', 'CY', 'CZ', 'DE',
           'NO'], dtype=object)

• Data variables: (1)
  • t2m
    (FID)
    float64
    279.5 261.6 276.3 ... 274.3 274.3

    reduce_dims :

    ['latitude', 'longitude', 'time']

    array([279.47002416, 261.64499568, 276.27772487, 275.98962049,
           277.64390016, 274.09982527, 273.36560731, 269.02108326,
           268.21211015, 283.86089467,          nan, 275.15103536,
           266.81397081, 275.7115588 , 271.35351136, 275.95780848,
           265.14368958, 263.66396534, 282.97730942, 276.07883762,
           267.32177398, 268.99854179, 276.56777904, 282.73257555,
           276.57731918, 274.19793442, 279.91570366, 262.99323711,
           269.34489761, 269.50471443, 274.1172522 , 263.77306949,
           267.26004997, 287.5016478 , 272.73660136, 274.34400452,
           274.31742887])

• Indexes: (1)
  • FID
    PandasIndex

    PandasIndex(Index(['DK', 'RS', 'EE', 'EL', 'ES', 'FI', 'FR', 'HR', 'HU', 'IE', 'IS', 'IT',
           'LI', 'LT', 'LU', 'LV', 'ME', 'MK', 'MT', 'SE', 'SI', 'SK', 'TR', 'UK',
           'NL', 'PL', 'PT', 'RO', 'AL', 'AT', 'BE', 'BG', 'CH', 'CY', 'CZ', 'DE',
           'NO'],
          dtype='object', name='FID'))

• 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 :

  2024-10-15T14:01 GRIB to CDM+CF via cfgrib-0.9.14.1/ecCodes-2.38.0 with {"source": "N/A", "filter_by_keys": {}, "encode_cf": ["parameter", "time", "geography", "vertical"]}

Return as a pandas dataframe

WARNING: Returning reduced data in pandas format is considered experimental and may change in futureversions of earthkit

It is possible to return the reduced data in a fully expanded geopandas dataframe which contains the geometry and aggregated data. Additional columns for the data values and rows and indexes added to fully describe the reduced data.

The returned object fully supports pandas indexing and in-built convenience methods (e.g. plotting), but it comes with memory usage cost, hence in this example we reduce along all dimensions.

reduced_data_pd = ek_aggregate.spatial.reduce(era5_data, nuts_data, return_as="pandas", mask_dim="FID", extra_reduce_dims="time")
reduced_data_pd[:10]

Returning reduced data in pandas format is considered experimental and may change in futureversions of earthkit

	id	NUTS_ID	LEVL_CODE	CNTR_CODE	NAME_LATN	NUTS_NAME	MOUNT_TYPE	URBN_TYPE	COAST_TYPE	FID	geometry	number	step	surface	t2m
FID
DK	DK	DK	0	DK	Danmark	Danmark	0	0	0	DK	MULTIPOLYGON (((15.1629 55.0937, 15.094 54.996...	0	0 days	0.0	279.547363
RS	RS	RS	0	RS	Serbia	Srbija/Сpбија	0	0	0	RS	POLYGON ((21.4792 45.193, 21.3585 44.8216, 22....	0	0 days	0.0	261.353851
EE	EE	EE	0	EE	Eesti	Eesti	0	0	0	EE	MULTIPOLYGON (((27.357 58.7871, 27.6449 57.981...	0	0 days	0.0	276.197968
EL	EL	EL	0	EL	Elláda	Ελλάδα	0	0	0	EL	MULTIPOLYGON (((28.0777 36.1182, 27.8606 35.92...	0	0 days	0.0	274.270966
ES	ES	ES	0	ES	España	España	0	0	0	ES	MULTIPOLYGON (((4.391 39.8617, 4.1907 39.7981,...	0	0 days	0.0	277.095337
FI	FI	FI	0	FI	Suomi/Finland	Suomi/Finland	0	0	0	FI	MULTIPOLYGON (((28.8967 69.0426, 28.4782 68.51...	0	0 days	0.0	273.923889
FR	FR	FR	0	FR	France	France	0	0	0	FR	MULTIPOLYGON (((55.8498 -21.1858, 55.7858 -21....	0	0 days	0.0	272.965454
HR	HR	HR	0	HR	Hrvatska	Hrvatska	0	0	0	HR	MULTIPOLYGON (((17.6515 45.8478, 17.9121 45.79...	0	0 days	0.0	267.700714
HU	HU	HU	0	HU	Magyarország	Magyarország	0	0	0	HU	POLYGON ((22.1211 48.3783, 22.1553 48.4034, 22...	0	0 days	0.0	268.251129
IE	IE	IE	0	IE	Éire/Ireland	Éire/Ireland	0	0	0	IE	POLYGON ((-7.1885 54.3377, -6.8642 54.3302, -6...	0	0 days	0.0	283.832977

reduced_data_pd.plot("t2m")
print('# Note that the NUTS regions include French foreign territories, hence the extent of the figure.')

# Note that the NUTS regions include French foreign territories, hence the extent of the figure.

Appendix

Unadvised: return_as = ‘pandas’ for time-series

This results in very heavy memory usage but may be useful

reduced_data_pd = ek_aggregate.spatial.reduce(era5_data, nuts_data, return_as="pandas", mask_dim="FID")
reduced_data_pd[:5]

Returning reduced data in pandas format is considered experimental and may change in futureversions of earthkit

		id	NUTS_ID	LEVL_CODE	CNTR_CODE	NAME_LATN	NUTS_NAME	MOUNT_TYPE	URBN_TYPE	COAST_TYPE	FID	geometry	number	step	surface	valid_time	t2m
FID	time
DK	2015-01-01 00:00:00	DK	DK	0	DK	Danmark	Danmark	0	0	0	DK	MULTIPOLYGON (((15.1629 55.0937, 15.094 54.996...	0	0 days	0.0	2015-01-01 00:00:00	278.709229
	2015-01-01 01:00:00	DK	DK	0	DK	Danmark	Danmark	0	0	0	DK	MULTIPOLYGON (((15.1629 55.0937, 15.094 54.996...	0	0 days	0.0	2015-01-01 01:00:00	278.938873
	2015-01-01 02:00:00	DK	DK	0	DK	Danmark	Danmark	0	0	0	DK	MULTIPOLYGON (((15.1629 55.0937, 15.094 54.996...	0	0 days	0.0	2015-01-01 02:00:00	279.068481
	2015-01-01 03:00:00	DK	DK	0	DK	Danmark	Danmark	0	0	0	DK	MULTIPOLYGON (((15.1629 55.0937, 15.094 54.996...	0	0 days	0.0	2015-01-01 03:00:00	279.241272
	2015-01-01 04:00:00	DK	DK	0	DK	Danmark	Danmark	0	0	0	DK	MULTIPOLYGON (((15.1629 55.0937, 15.094 54.996...	0	0 days	0.0	2015-01-01 04:00:00	279.418610

feature_index = "FID"
plot_var = "t2m"
# plot_x_vals = reduced_data.attrs[f"{plot_var}_dims"]["time"]
fig, ax = plt.subplots(1)
for feature in reduced_data_pd.index.get_level_values(feature_index).unique()[:5]:
    temp = reduced_data_pd.xs(feature, level=feature_index)
    temp[plot_var].plot(ax=ax, label=feature)
fig.legend()

<matplotlib.legend.Legend at 0x1545fadb0>

Providing a bespoke function for the reduction

When providing a our own function to the reduce method it must conform the the Xarray requirements defined in their documentation pages. Specifically:

“Function which can be called in the form f(x, axis=axis, **kwargs) to return the result of reducing an np.ndarray over an integer valued axis.”

Here we will calculate the ratio of the standard deviation to the mean for demonstration purposes only. We use nanmean and nanstd so that we ignore the nan values.

def std_mean_ratio(x, axis=0, **kwargs):
    return np.nanstd(x, axis=axis, **kwargs) / np.nanmean(x, axis=axis, **kwargs)

reduced_data = ek_aggregate.spatial.reduce(
    era5_data, nuts_data,
    how = std_mean_ratio
)
reduced_data

/opt/homebrew/Caskroom/miniforge/base/envs/EARTHKIT/lib/python3.12/site-packages/numpy/lib/_nanfunctions_impl.py:2053: RuntimeWarning: Degrees of freedom <= 0 for slice.
  var = nanvar(a, axis=axis, dtype=dtype, out=out, ddof=ddof,
/opt/homebrew/Caskroom/miniforge/base/envs/EARTHKIT/lib/python3.12/site-packages/numpy/lib/_nanfunctions_impl.py:2053: RuntimeWarning: Degrees of freedom <= 0 for slice.
  var = nanvar(a, axis=axis, dtype=dtype, out=out, ddof=ddof,

<xarray.Dataset> Size: 4kB
Dimensions:     (time: 24, index: 37)
Coordinates:
    number      int64 8B 0
  * time        (time) datetime64[ns] 192B 2015-01-01 ... 2015-01-01T23:00:00
    step        timedelta64[ns] 8B 00:00:00
    surface     float64 8B 0.0
    valid_time  (time) datetime64[ns] 192B 2015-01-01 ... 2015-01-01T23:00:00
  * index       (index) int64 296B 0 1 2 3 4 5 6 7 8 ... 29 30 31 32 33 34 35 36
Data variables:
    t2m         (index, time) float32 4kB 0.002451 0.002455 ... 0.01687 0.01744
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:                 2024-10-15T15:07 GRIB to CDM+CF via cfgrib-0.9.1...

xarray.Dataset

• Dimensions:
  • time: 24
  • index: 37
• Coordinates: (6)
  • number
    ()
    int64
    0

    long_name :

    ensemble member numerical id

    units :

    1

    standard_name :

    realization

    array(0)

  • time
    (time)
    datetime64[ns]
    2015-01-01 ... 2015-01-01T23:00:00

    long_name :

    initial time of forecast

    standard_name :

    forecast_reference_time

    array(['2015-01-01T00:00:00.000000000', '2015-01-01T01:00:00.000000000',
           '2015-01-01T02:00:00.000000000', '2015-01-01T03:00:00.000000000',
           '2015-01-01T04:00:00.000000000', '2015-01-01T05:00:00.000000000',
           '2015-01-01T06:00:00.000000000', '2015-01-01T07:00:00.000000000',
           '2015-01-01T08:00:00.000000000', '2015-01-01T09:00:00.000000000',
           '2015-01-01T10:00:00.000000000', '2015-01-01T11:00:00.000000000',
           '2015-01-01T12:00:00.000000000', '2015-01-01T13:00:00.000000000',
           '2015-01-01T14:00:00.000000000', '2015-01-01T15:00:00.000000000',
           '2015-01-01T16:00:00.000000000', '2015-01-01T17:00:00.000000000',
           '2015-01-01T18:00:00.000000000', '2015-01-01T19:00:00.000000000',
           '2015-01-01T20:00:00.000000000', '2015-01-01T21:00:00.000000000',
           '2015-01-01T22:00:00.000000000', '2015-01-01T23:00:00.000000000'],
          dtype='datetime64[ns]')

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

    long_name :

    time since forecast_reference_time

    standard_name :

    forecast_period

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

  • surface
    ()
    float64
    0.0

    long_name :

    original GRIB coordinate for key: level(surface)

    units :

    1

    array(0.)

  • valid_time
    (time)
    datetime64[ns]
    2015-01-01 ... 2015-01-01T23:00:00

    standard_name :

    time

    long_name :

    time

    array(['2015-01-01T00:00:00.000000000', '2015-01-01T01:00:00.000000000',
           '2015-01-01T02:00:00.000000000', '2015-01-01T03:00:00.000000000',
           '2015-01-01T04:00:00.000000000', '2015-01-01T05:00:00.000000000',
           '2015-01-01T06:00:00.000000000', '2015-01-01T07:00:00.000000000',
           '2015-01-01T08:00:00.000000000', '2015-01-01T09:00:00.000000000',
           '2015-01-01T10:00:00.000000000', '2015-01-01T11:00:00.000000000',
           '2015-01-01T12:00:00.000000000', '2015-01-01T13:00:00.000000000',
           '2015-01-01T14:00:00.000000000', '2015-01-01T15:00:00.000000000',
           '2015-01-01T16:00:00.000000000', '2015-01-01T17:00:00.000000000',
           '2015-01-01T18:00:00.000000000', '2015-01-01T19:00:00.000000000',
           '2015-01-01T20:00:00.000000000', '2015-01-01T21:00:00.000000000',
           '2015-01-01T22:00:00.000000000', '2015-01-01T23:00:00.000000000'],
          dtype='datetime64[ns]')

  • index
    (index)
    int64
    0 1 2 3 4 5 6 ... 31 32 33 34 35 36

    array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,
           18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,
           36])

• Data variables: (1)
  • t2m
    (index, time)
    float32
    0.002451 0.002455 ... 0.01744

    GRIB_paramId :

    167

    GRIB_dataType :

    an

    GRIB_numberOfPoints :

    56481

    GRIB_typeOfLevel :

    surface

    GRIB_stepUnits :

    1

    GRIB_stepType :

    instant

    GRIB_gridType :

    regular_ll

    GRIB_uvRelativeToGrid :

    0

    GRIB_NV :

    0

    GRIB_Nx :

    281

    GRIB_Ny :

    201

    GRIB_cfName :

    unknown

    GRIB_cfVarName :

    t2m

    GRIB_gridDefinitionDescription :

    Latitude/Longitude Grid

    GRIB_iDirectionIncrementInDegrees :

    0.25

    GRIB_iScansNegatively :

    0

    GRIB_jDirectionIncrementInDegrees :

    0.25

    GRIB_jPointsAreConsecutive :

    0

    GRIB_jScansPositively :

    0

    GRIB_latitudeOfFirstGridPointInDegrees :

    80.0

    GRIB_latitudeOfLastGridPointInDegrees :

    30.0

    GRIB_longitudeOfFirstGridPointInDegrees :

    -10.0

    GRIB_longitudeOfLastGridPointInDegrees :

    60.0

    GRIB_missingValue :

    9999

    GRIB_name :

    2 metre temperature

    GRIB_shortName :

    2t

    GRIB_totalNumber :

    0

    GRIB_units :

    K

    long_name :

    2 metre temperature

    units :

    K

    standard_name :

    unknown

    reduce_dims :

    ['latitude', 'longitude']

    array([[0.00245101, 0.00245506, 0.00231444, 0.00211   , 0.00200833,
            0.00191058, 0.00179072, 0.00175748, 0.00180229, 0.00200394,
            0.00245537, 0.00285177, 0.00315153, 0.0033616 , 0.0033082 ,
            0.00325618, 0.00340029, 0.00347502, 0.00375802, 0.00392799,
            0.00408729, 0.00408243, 0.00377008, 0.00394082],
           [0.00919158, 0.00954795, 0.01004026, 0.01055119, 0.01085689,
            0.01127649, 0.01113738, 0.01087572, 0.00901874, 0.00738468,
            0.00628365, 0.00554155, 0.00555266, 0.00549427, 0.00665772,
            0.00817707, 0.00905498, 0.01078389, 0.01050796, 0.01167995,
            0.01328773, 0.01420207, 0.01461104, 0.01528443],
           [0.00178591, 0.00193115, 0.0019596 , 0.00198165, 0.00205413,
            0.00202633, 0.0021548 , 0.00232613, 0.00235862, 0.00249959,
            0.00302258, 0.00301325, 0.0029284 , 0.00281717, 0.00290197,
            0.00309379, 0.00307285, 0.00330203, 0.00357564, 0.00359726,
            0.0033565 , 0.00317872, 0.00251393, 0.00235554],
           [0.01933273, 0.01970429, 0.02025454, 0.02048882, 0.02067771,
            0.02132798, 0.02131201, 0.0207294 , 0.01782493, 0.01678994,
            0.01552164, 0.01533636, 0.01504732, 0.01506653, 0.01533673,
            0.01555959, 0.01602874, 0.01752209, 0.01854725, 0.0184631 ,
            0.01941974, 0.01985176, 0.02145041, 0.02167288],
    ...
           [0.00913794, 0.00871312, 0.00808239, 0.00834793, 0.00838904,
            0.00734009, 0.0073664 , 0.00575254, 0.00456501, 0.00431815,
            0.00463668, 0.00495777, 0.00566302, 0.00599215, 0.0067219 ,
            0.006436  , 0.00660087, 0.00655622, 0.00606082, 0.00573124,
            0.00624567, 0.00653937, 0.00655287, 0.00633915],
           [0.00811143, 0.00802741, 0.00791693, 0.00758717, 0.007572  ,
            0.00781225, 0.00758627, 0.00710262, 0.0065451 , 0.0062552 ,
            0.00613794, 0.00600845, 0.00576073, 0.00557909, 0.00488756,
            0.00364313, 0.00339399, 0.00385497, 0.00372758, 0.00332028,
            0.00331179, 0.00386604, 0.00512936, 0.00475914],
           [0.00976093, 0.009824  , 0.01004379, 0.01000212, 0.00979313,
            0.0099679 , 0.00983084, 0.00983417, 0.00936912, 0.00893964,
            0.00795649, 0.00751804, 0.00738702, 0.00740674, 0.00749699,
            0.00751148, 0.00803644, 0.00853616, 0.00867027, 0.00898792,
            0.00961538, 0.00992301, 0.01040486, 0.01085013],
           [0.01723548, 0.01743536, 0.01730915, 0.01727025, 0.01701286,
            0.01695473, 0.01711667, 0.01709055, 0.0165706 , 0.01646689,
            0.01632085, 0.0165086 , 0.01679969, 0.01665267, 0.01659088,
            0.01623134, 0.01628007, 0.01626903, 0.01646953, 0.01639122,
            0.01645934, 0.01652853, 0.01686764, 0.01744206]], dtype=float32)

• Indexes: (2)
  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['2015-01-01 00:00:00', '2015-01-01 01:00:00',
                   '2015-01-01 02:00:00', '2015-01-01 03:00:00',
                   '2015-01-01 04:00:00', '2015-01-01 05:00:00',
                   '2015-01-01 06:00:00', '2015-01-01 07:00:00',
                   '2015-01-01 08:00:00', '2015-01-01 09:00:00',
                   '2015-01-01 10:00:00', '2015-01-01 11:00:00',
                   '2015-01-01 12:00:00', '2015-01-01 13:00:00',
                   '2015-01-01 14:00:00', '2015-01-01 15:00:00',
                   '2015-01-01 16:00:00', '2015-01-01 17:00:00',
                   '2015-01-01 18:00:00', '2015-01-01 19:00:00',
                   '2015-01-01 20:00:00', '2015-01-01 21:00:00',
                   '2015-01-01 22:00:00', '2015-01-01 23:00:00'],
                  dtype='datetime64[ns]', name='time', freq=None))

  • index
    PandasIndex

    PandasIndex(Index([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,
           18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,
           36],
          dtype='int64', name='index'))

• 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 :

  2024-10-15T15:07 GRIB to CDM+CF via cfgrib-0.9.14.1/ecCodes-2.38.0 with {"source": "N/A", "filter_by_keys": {}, "encode_cf": ["parameter", "time", "geography", "vertical"]}