Mask Gridded Data with Geometry Data

This notebook demonstrates how to mask ERA5 temperature data using a vector geometry file (e.g., a country or region boundary) using earthkit-data and earthkit-transforms. Masking allows you to focus analysis on a particular region of interest.

from earthkit import data as ekd
from earthkit import transforms as ekt
from earthkit.transforms._tools import earthkit_remote_test_data_file

remote_era5_file = earthkit_remote_test_data_file("era5-Europe-sfc-2m-temperature-3deg-2015-2017.grib")
era5_data = ekd.from_source("url", remote_era5_file)
ds = era5_data.to_xarray()

remote_geometry_url = earthkit_remote_test_data_file("NUTS_RG_60M_2021_4326_LEVL_0.geojson")
geometry_data = ekd.from_source("url", remote_geometry_url).to_geopandas()

# Mask ERA5 data with geometry
masked = ekt.spatial.mask(ds, geometry_data)

masked

<xarray.Dataset> Size: 715MB
Dimensions:                  (index: 37, forecast_reference_time: 4384,
                              latitude: 19, longitude: 29)
Coordinates:
  * index                    (index) int64 296B 0 1 2 3 4 5 ... 32 33 34 35 36
  * forecast_reference_time  (forecast_reference_time) datetime64[us] 35kB 20...
  * latitude                 (latitude) float64 152B 79.0 76.0 ... 28.0 25.0
  * longitude                (longitude) float64 232B -25.0 -22.0 ... 56.0 59.0
Data variables:
    2t                       (index, forecast_reference_time, latitude, longitude) float64 715MB dask.array<chunksize=(1, 4384, 19, 29), meta=np.ndarray>
Attributes:
    Conventions:  CF-1.8
    institution:  ECMWF

xarray.Dataset

• Dimensions:
  • index: 37
  • forecast_reference_time: 4384
  • latitude: 19
  • longitude: 29
• Coordinates: (4)
  • index
    (index)
    int64
    0 1 2 3 4 5 6 ... 31 32 33 34 35 36

    [37 values with dtype=int64]

  • forecast_reference_time
    (forecast_reference_time)
    datetime64[us]
    2015-01-01 ... 2017-12-31T18:00:00

    standard_name :

    forecast_reference_time

    long_name :

    initial time of forecast

    array(['2015-01-01T00:00:00.000000', '2015-01-01T06:00:00.000000',
           '2015-01-01T12:00:00.000000', ..., '2017-12-31T06:00:00.000000',
           '2017-12-31T12:00:00.000000', '2017-12-31T18:00:00.000000'],
          shape=(4384,), dtype='datetime64[us]')

  • latitude
    (latitude)
    float64
    79.0 76.0 73.0 ... 31.0 28.0 25.0

    units :

    degrees_north

    standard_name :

    latitude

    long_name :

    latitude

    array([79., 76., 73., 70., 67., 64., 61., 58., 55., 52., 49., 46., 43., 40.,
           37., 34., 31., 28., 25.])

  • longitude
    (longitude)
    float64
    -25.0 -22.0 -19.0 ... 56.0 59.0

    units :

    degrees_east

    standard_name :

    longitude

    long_name :

    longitude

    array([-25., -22., -19., -16., -13., -10.,  -7.,  -4.,  -1.,   2.,   5.,   8.,
            11.,  14.,  17.,  20.,  23.,  26.,  29.,  32.,  35.,  38.,  41.,  44.,
            47.,  50.,  53.,  56.,  59.])

• Data variables: (1)
  • 2t
    (index, forecast_reference_time, latitude, longitude)
    float64
    dask.array<chunksize=(1, 4384, 19, 29), meta=np.ndarray>

    standard_name :

    unknown

    long_name :

    2 metre temperature

    units :

    kelvin

    level_type :

    surface

    _earthkit :

    {"message": {"__bytes_b64__": "R1JJQgAAbAEAADSAYv//gKcBAAAPAQEAAAEAAAAAAAAVAAAAAAAAAAAAAAAAAAAAARcCBAEwMDAxAAAAAAAgAP8AAB0AEwE0mIBhqIAAYagA5ngLuAu4AAAAAAAAAAwAgAJEuX1uAAA3Nzc3"}, "bitsPerValue": 16, "grid_spec": {"area": [79, -25, 25, 59], "grid": [3, 3], "reference": [2, 1]}}

    Array Chunk Bytes 681.89 MiB 18.43 MiB Shape (37, 4384, 19, 29) (1, 4384, 19, 29) Dask graph 37 chunks in 53 graph layers Data type float64 numpy.ndarray

• Attributes: (2)

  Conventions :

  CF-1.8

  institution :

  ECMWF