import xarray as xr

WeatherBench 2 Data Guide

One core part of WeatherBench 2 are ready-to-use, cloud-based datasets. This page lists and describes all the available datasets.

The datasets are stored in this public Google Cloud bucket: gs://weatherbench2/datasets. Please also check the LICENSE files for each dataset in the respective GCS buckets. Some datasets allow commercial use. Others only permit research use.

A note on resolutions

We provide the datasets at different resolutions. All files will have the number of longitude X latitude grid points in their filename, e.g. 64x32. For the WeatherBench 2 paper, all evaluation was done at 240x121 = 1.5 degree resolution. All datasets were regridded using first-order conservative regridding, i.e., with weights proportional to the area of overlap between grid cells on the original and desired grids.

The ERA5 resolution files (1440x721 = 0.25 degrees) contain the poles, i.e. -90 and 90 degree latitude. Most regridded files also do, denoted with with_poles. The 512x256 files do not contain the pole grid points.

Ground-truth datasets

ERA5

Our ERA5 datasets were downloaded from the Copernicus Climate Data Store and have a time range from 1959 to 2022 (incl.). The data here have been downsampled to 6h and 13 levels, even though a raw hourly dataset with 37 levels is also available at gs://weatherbench2/datasets/era5/1959-2022-full_37-1h-0p25deg-chunk-1.zarr-v2

Location: gs://weatherbench2/datasets/era5/

Files:

• 1959-2022-6h-1440x721.zarr

• 1959-2022-6h-512x256_equiangular_conservative.zarr

• 1959-2022-6h-240x121_equiangular_with_poles_conservative.zarr

• 1959-2022-6h-128x64_equiangular_with_poles_conservative.zarr

• 1959-2022-6h-64x32_equiangular_with_poles_conservative.zarr

See output below for a list of variables. Wind speed was derived using this method.

xr.open_zarr('gs://weatherbench2/datasets/era5/1959-2022-6h-1440x721.zarr')

/opt/miniconda3/envs/weatherbench2/lib/python3.11/site-packages/google/auth/_default.py:79: UserWarning: Your application has authenticated using end user credentials from Google Cloud SDK without a quota project. You might receive a "quota exceeded" or "API not enabled" error. We recommend you rerun `gcloud auth application-default login` and make sure a quota project is added. Or you can use service accounts instead. For more information about service accounts, see https://cloud.google.com/docs/authentication/
  warnings.warn(_CLOUD_SDK_CREDENTIALS_WARNING)

<xarray.Dataset>
Dimensions:                                           (time: 92044,
                                                       latitude: 721,
                                                       longitude: 1440,
                                                       level: 13)
Coordinates:
  * latitude                                          (latitude) float32 90.0...
  * level                                             (level) int64 50 ... 1000
  * longitude                                         (longitude) float32 0.0...
  * time                                              (time) datetime64[ns] 1...
Data variables: (12/38)
    10m_u_component_of_wind                           (time, latitude, longitude) float32 dask.array<chunksize=(1, 721, 1440), meta=np.ndarray>
    10m_v_component_of_wind                           (time, latitude, longitude) float32 dask.array<chunksize=(1, 721, 1440), meta=np.ndarray>
    10m_wind_speed                                    (time, latitude, longitude) float32 dask.array<chunksize=(1, 721, 1440), meta=np.ndarray>
    2m_temperature                                    (time, latitude, longitude) float32 dask.array<chunksize=(1, 721, 1440), meta=np.ndarray>
    angle_of_sub_gridscale_orography                  (latitude, longitude) float32 dask.array<chunksize=(721, 1440), meta=np.ndarray>
    anisotropy_of_sub_gridscale_orography             (latitude, longitude) float32 dask.array<chunksize=(721, 1440), meta=np.ndarray>
    ...                                                ...
    type_of_high_vegetation                           (latitude, longitude) float32 dask.array<chunksize=(721, 1440), meta=np.ndarray>
    type_of_low_vegetation                            (latitude, longitude) float32 dask.array<chunksize=(721, 1440), meta=np.ndarray>
    u_component_of_wind                               (time, level, latitude, longitude) float32 dask.array<chunksize=(1, 13, 721, 1440), meta=np.ndarray>
    v_component_of_wind                               (time, level, latitude, longitude) float32 dask.array<chunksize=(1, 13, 721, 1440), meta=np.ndarray>
    vertical_velocity                                 (time, level, latitude, longitude) float32 dask.array<chunksize=(1, 13, 721, 1440), meta=np.ndarray>
    wind_speed                                        (time, level, latitude, longitude) float32 dask.array<chunksize=(1, 13, 721, 1440), meta=np.ndarray>

xarray.Dataset

• Dimensions:
  • time: 92044
  • latitude: 721
  • longitude: 1440
  • level: 13
• Coordinates: (4)
  • latitude
    (latitude)
    float32
    90.0 89.75 89.5 ... -89.75 -90.0

    long_name :

    latitude

    units :

    degrees_north

    array([ 90.  ,  89.75,  89.5 , ..., -89.5 , -89.75, -90.  ], dtype=float32)

  • level
    (level)
    int64
    50 100 150 200 ... 700 850 925 1000

    array([  50,  100,  150,  200,  250,  300,  400,  500,  600,  700,  850,  925,
           1000])

  • longitude
    (longitude)
    float32
    0.0 0.25 0.5 ... 359.2 359.5 359.8

    long_name :

    longitude

    units :

    degrees_east

    array([0.0000e+00, 2.5000e-01, 5.0000e-01, ..., 3.5925e+02, 3.5950e+02,
           3.5975e+02], dtype=float32)

  • time
    (time)
    datetime64[ns]
    1959-01-01 ... 2021-12-31T18:00:00

    array(['1959-01-01T00:00:00.000000000', '1959-01-01T06:00:00.000000000',
           '1959-01-01T12:00:00.000000000', ..., '2021-12-31T06:00:00.000000000',
           '2021-12-31T12:00:00.000000000', '2021-12-31T18:00:00.000000000'],
          dtype='datetime64[ns]')

• Data variables: (38)
  • 10m_u_component_of_wind
    (time, latitude, longitude)
    float32
    dask.array<chunksize=(1, 721, 1440), meta=np.ndarray>

    long_name :

    10 metre U wind component

    short_name :

    u10

    units :

    m s**-1

    Array Chunk Bytes 356.00 GiB 3.96 MiB Shape (92044, 721, 1440) (1, 721, 1440) Dask graph 92044 chunks in 2 graph layers Data type float32 numpy.ndarray

  • 10m_v_component_of_wind
    (time, latitude, longitude)
    float32
    dask.array<chunksize=(1, 721, 1440), meta=np.ndarray>

    long_name :

    10 metre V wind component

    short_name :

    v10

    units :

    m s**-1

    Array Chunk Bytes 356.00 GiB 3.96 MiB Shape (92044, 721, 1440) (1, 721, 1440) Dask graph 92044 chunks in 2 graph layers Data type float32 numpy.ndarray

  • 10m_wind_speed
    (time, latitude, longitude)
    float32
    dask.array<chunksize=(1, 721, 1440), meta=np.ndarray>

    Array Chunk Bytes 356.00 GiB 3.96 MiB Shape (92044, 721, 1440) (1, 721, 1440) Dask graph 92044 chunks in 2 graph layers Data type float32 numpy.ndarray

  • 2m_temperature
    (time, latitude, longitude)
    float32
    dask.array<chunksize=(1, 721, 1440), meta=np.ndarray>

    long_name :

    2 metre temperature

    short_name :

    t2m

    units :

    K

    Array Chunk Bytes 356.00 GiB 3.96 MiB Shape (92044, 721, 1440) (1, 721, 1440) Dask graph 92044 chunks in 2 graph layers Data type float32 numpy.ndarray

  • angle_of_sub_gridscale_orography
    (latitude, longitude)
    float32
    dask.array<chunksize=(721, 1440), meta=np.ndarray>

    long_name :

    Angle of sub-gridscale orography

    short_name :

    anor

    units :

    radians

    Array Chunk Bytes 3.96 MiB 3.96 MiB Shape (721, 1440) (721, 1440) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • anisotropy_of_sub_gridscale_orography
    (latitude, longitude)
    float32
    dask.array<chunksize=(721, 1440), meta=np.ndarray>

    long_name :

    Anisotropy of sub-gridscale orography

    short_name :

    isor

    units :

    ~

    Array Chunk Bytes 3.96 MiB 3.96 MiB Shape (721, 1440) (721, 1440) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • geopotential
    (time, level, latitude, longitude)
    float32
    dask.array<chunksize=(1, 13, 721, 1440), meta=np.ndarray>

    long_name :

    Geopotential

    short_name :

    z

    standard_name :

    geopotential

    units :

    m**2 s**-2

    Array Chunk Bytes 4.52 TiB 51.49 MiB Shape (92044, 13, 721, 1440) (1, 13, 721, 1440) Dask graph 92044 chunks in 2 graph layers Data type float32 numpy.ndarray

  • geopotential_at_surface
    (latitude, longitude)
    float32
    dask.array<chunksize=(721, 1440), meta=np.ndarray>

    long_name :

    Geopotential

    short_name :

    z

    standard_name :

    geopotential

    units :

    m**2 s**-2

    Array Chunk Bytes 3.96 MiB 3.96 MiB Shape (721, 1440) (721, 1440) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • high_vegetation_cover
    (latitude, longitude)
    float32
    dask.array<chunksize=(721, 1440), meta=np.ndarray>

    long_name :

    High vegetation cover

    short_name :

    cvh

    units :

    (0 - 1)

    Array Chunk Bytes 3.96 MiB 3.96 MiB Shape (721, 1440) (721, 1440) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • lake_cover
    (latitude, longitude)
    float32
    dask.array<chunksize=(721, 1440), meta=np.ndarray>

    long_name :

    Lake cover

    short_name :

    cl

    units :

    (0 - 1)

    Array Chunk Bytes 3.96 MiB 3.96 MiB Shape (721, 1440) (721, 1440) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • lake_depth
    (latitude, longitude)
    float32
    dask.array<chunksize=(721, 1440), meta=np.ndarray>

    long_name :

    Lake total depth

    short_name :

    dl

    units :

    m

    Array Chunk Bytes 3.96 MiB 3.96 MiB Shape (721, 1440) (721, 1440) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • land_sea_mask
    (latitude, longitude)
    float32
    dask.array<chunksize=(721, 1440), meta=np.ndarray>

    long_name :

    Land-sea mask

    short_name :

    lsm

    standard_name :

    land_binary_mask

    units :

    (0 - 1)

    Array Chunk Bytes 3.96 MiB 3.96 MiB Shape (721, 1440) (721, 1440) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • low_vegetation_cover
    (latitude, longitude)
    float32
    dask.array<chunksize=(721, 1440), meta=np.ndarray>

    long_name :

    Low vegetation cover

    short_name :

    cvl

    units :

    (0 - 1)

    Array Chunk Bytes 3.96 MiB 3.96 MiB Shape (721, 1440) (721, 1440) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • mean_sea_level_pressure
    (time, latitude, longitude)
    float32
    dask.array<chunksize=(1, 721, 1440), meta=np.ndarray>

    long_name :

    Mean sea level pressure

    short_name :

    msl

    standard_name :

    air_pressure_at_mean_sea_level

    units :

    Pa

    Array Chunk Bytes 356.00 GiB 3.96 MiB Shape (92044, 721, 1440) (1, 721, 1440) Dask graph 92044 chunks in 2 graph layers Data type float32 numpy.ndarray

  • sea_ice_cover
    (time, latitude, longitude)
    float32
    dask.array<chunksize=(1, 721, 1440), meta=np.ndarray>

    long_name :

    Sea ice area fraction

    short_name :

    siconc

    standard_name :

    sea_ice_area_fraction

    units :

    (0 - 1)

    Array Chunk Bytes 356.00 GiB 3.96 MiB Shape (92044, 721, 1440) (1, 721, 1440) Dask graph 92044 chunks in 2 graph layers Data type float32 numpy.ndarray

  • sea_surface_temperature
    (time, latitude, longitude)
    float32
    dask.array<chunksize=(1, 721, 1440), meta=np.ndarray>

    long_name :

    Sea surface temperature

    short_name :

    sst

    units :

    K

    Array Chunk Bytes 356.00 GiB 3.96 MiB Shape (92044, 721, 1440) (1, 721, 1440) Dask graph 92044 chunks in 2 graph layers Data type float32 numpy.ndarray

  • slope_of_sub_gridscale_orography
    (latitude, longitude)
    float32
    dask.array<chunksize=(721, 1440), meta=np.ndarray>

    long_name :

    Slope of sub-gridscale orography

    short_name :

    slor

    units :

    ~

    Array Chunk Bytes 3.96 MiB 3.96 MiB Shape (721, 1440) (721, 1440) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • soil_type
    (latitude, longitude)
    float32
    dask.array<chunksize=(721, 1440), meta=np.ndarray>

    long_name :

    Soil type

    short_name :

    slt

    units :

    ~

    Array Chunk Bytes 3.96 MiB 3.96 MiB Shape (721, 1440) (721, 1440) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • specific_humidity
    (time, level, latitude, longitude)
    float32
    dask.array<chunksize=(1, 13, 721, 1440), meta=np.ndarray>

    long_name :

    Specific humidity

    short_name :

    q

    standard_name :

    specific_humidity

    units :

    kg kg**-1

    Array Chunk Bytes 4.52 TiB 51.49 MiB Shape (92044, 13, 721, 1440) (1, 13, 721, 1440) Dask graph 92044 chunks in 2 graph layers Data type float32 numpy.ndarray

  • standard_deviation_of_filtered_subgrid_orography
    (latitude, longitude)
    float32
    dask.array<chunksize=(721, 1440), meta=np.ndarray>

    long_name :

    Standard deviation of filtered subgrid orography

    short_name :

    sdfor

    units :

    m

    Array Chunk Bytes 3.96 MiB 3.96 MiB Shape (721, 1440) (721, 1440) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • standard_deviation_of_orography
    (latitude, longitude)
    float32
    dask.array<chunksize=(721, 1440), meta=np.ndarray>

    long_name :

    Standard deviation of orography

    short_name :

    sdor

    units :

    m

    Array Chunk Bytes 3.96 MiB 3.96 MiB Shape (721, 1440) (721, 1440) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • surface_pressure
    (time, latitude, longitude)
    float32
    dask.array<chunksize=(1, 721, 1440), meta=np.ndarray>

    long_name :

    Surface pressure

    short_name :

    sp

    standard_name :

    surface_air_pressure

    units :

    Pa

    Array Chunk Bytes 356.00 GiB 3.96 MiB Shape (92044, 721, 1440) (1, 721, 1440) Dask graph 92044 chunks in 2 graph layers Data type float32 numpy.ndarray

  • temperature
    (time, level, latitude, longitude)
    float32
    dask.array<chunksize=(1, 13, 721, 1440), meta=np.ndarray>

    long_name :

    Temperature

    short_name :

    t

    standard_name :

    air_temperature

    units :

    K

    Array Chunk Bytes 4.52 TiB 51.49 MiB Shape (92044, 13, 721, 1440) (1, 13, 721, 1440) Dask graph 92044 chunks in 2 graph layers Data type float32 numpy.ndarray

  • toa_incident_solar_radiation
    (time, latitude, longitude)
    float32
    dask.array<chunksize=(1, 721, 1440), meta=np.ndarray>

    long_name :

    TOA incident solar radiation

    short_name :

    tisr

    units :

    J m**-2

    Array Chunk Bytes 356.00 GiB 3.96 MiB Shape (92044, 721, 1440) (1, 721, 1440) Dask graph 92044 chunks in 2 graph layers Data type float32 numpy.ndarray

  • toa_incident_solar_radiation_12hr
    (time, latitude, longitude)
    float32
    dask.array<chunksize=(1, 721, 1440), meta=np.ndarray>

    long_name :

    TOA incident solar radiation

    short_name :

    tisr

    units :

    J m**-2

    Array Chunk Bytes 356.00 GiB 3.96 MiB Shape (92044, 721, 1440) (1, 721, 1440) Dask graph 92044 chunks in 2 graph layers Data type float32 numpy.ndarray

  • toa_incident_solar_radiation_24hr
    (time, latitude, longitude)
    float32
    dask.array<chunksize=(1, 721, 1440), meta=np.ndarray>

    long_name :

    TOA incident solar radiation

    short_name :

    tisr

    units :

    J m**-2

    Array Chunk Bytes 356.00 GiB 3.96 MiB Shape (92044, 721, 1440) (1, 721, 1440) Dask graph 92044 chunks in 2 graph layers Data type float32 numpy.ndarray

  • toa_incident_solar_radiation_6hr
    (time, latitude, longitude)
    float32
    dask.array<chunksize=(1, 721, 1440), meta=np.ndarray>

    long_name :

    TOA incident solar radiation

    short_name :

    tisr

    units :

    J m**-2

    Array Chunk Bytes 356.00 GiB 3.96 MiB Shape (92044, 721, 1440) (1, 721, 1440) Dask graph 92044 chunks in 2 graph layers Data type float32 numpy.ndarray

  • total_cloud_cover
    (time, latitude, longitude)
    float32
    dask.array<chunksize=(1, 721, 1440), meta=np.ndarray>

    long_name :

    Total cloud cover

    short_name :

    tcc

    standard_name :

    cloud_area_fraction

    units :

    (0 - 1)

    Array Chunk Bytes 356.00 GiB 3.96 MiB Shape (92044, 721, 1440) (1, 721, 1440) Dask graph 92044 chunks in 2 graph layers Data type float32 numpy.ndarray

  • total_column_water_vapour
    (time, latitude, longitude)
    float32
    dask.array<chunksize=(1, 721, 1440), meta=np.ndarray>

    long_name :

    Total column vertically-integrated water vapour

    short_name :

    tcwv

    standard_name :

    lwe_thickness_of_atmosphere_mass_content_of_water_vapor

    units :

    kg m**-2

    Array Chunk Bytes 356.00 GiB 3.96 MiB Shape (92044, 721, 1440) (1, 721, 1440) Dask graph 92044 chunks in 2 graph layers Data type float32 numpy.ndarray

  • total_precipitation_12hr
    (time, latitude, longitude)
    float32
    dask.array<chunksize=(1, 721, 1440), meta=np.ndarray>

    long_name :

    Total precipitation

    short_name :

    tp

    units :

    m

    Array Chunk Bytes 356.00 GiB 3.96 MiB Shape (92044, 721, 1440) (1, 721, 1440) Dask graph 92044 chunks in 2 graph layers Data type float32 numpy.ndarray

  • total_precipitation_24hr
    (time, latitude, longitude)
    float32
    dask.array<chunksize=(1, 721, 1440), meta=np.ndarray>

    long_name :

    Total precipitation

    short_name :

    tp

    units :

    m

    Array Chunk Bytes 356.00 GiB 3.96 MiB Shape (92044, 721, 1440) (1, 721, 1440) Dask graph 92044 chunks in 2 graph layers Data type float32 numpy.ndarray

  • total_precipitation_6hr
    (time, latitude, longitude)
    float32
    dask.array<chunksize=(1, 721, 1440), meta=np.ndarray>

    long_name :

    Total precipitation

    short_name :

    tp

    units :

    m

    Array Chunk Bytes 356.00 GiB 3.96 MiB Shape (92044, 721, 1440) (1, 721, 1440) Dask graph 92044 chunks in 2 graph layers Data type float32 numpy.ndarray

  • type_of_high_vegetation
    (latitude, longitude)
    float32
    dask.array<chunksize=(721, 1440), meta=np.ndarray>

    long_name :

    Type of high vegetation

    short_name :

    tvh

    units :

    ~

    Array Chunk Bytes 3.96 MiB 3.96 MiB Shape (721, 1440) (721, 1440) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • type_of_low_vegetation
    (latitude, longitude)
    float32
    dask.array<chunksize=(721, 1440), meta=np.ndarray>

    long_name :

    Type of low vegetation

    short_name :

    tvl

    units :

    ~

    Array Chunk Bytes 3.96 MiB 3.96 MiB Shape (721, 1440) (721, 1440) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • u_component_of_wind
    (time, level, latitude, longitude)
    float32
    dask.array<chunksize=(1, 13, 721, 1440), meta=np.ndarray>

    long_name :

    U component of wind

    short_name :

    u

    standard_name :

    eastward_wind

    units :

    m s**-1

    Array Chunk Bytes 4.52 TiB 51.49 MiB Shape (92044, 13, 721, 1440) (1, 13, 721, 1440) Dask graph 92044 chunks in 2 graph layers Data type float32 numpy.ndarray

  • v_component_of_wind
    (time, level, latitude, longitude)
    float32
    dask.array<chunksize=(1, 13, 721, 1440), meta=np.ndarray>

    long_name :

    V component of wind

    short_name :

    v

    standard_name :

    northward_wind

    units :

    m s**-1

    Array Chunk Bytes 4.52 TiB 51.49 MiB Shape (92044, 13, 721, 1440) (1, 13, 721, 1440) Dask graph 92044 chunks in 2 graph layers Data type float32 numpy.ndarray

  • vertical_velocity
    (time, level, latitude, longitude)
    float32
    dask.array<chunksize=(1, 13, 721, 1440), meta=np.ndarray>

    long_name :

    Vertical velocity

    short_name :

    w

    standard_name :

    lagrangian_tendency_of_air_pressure

    units :

    Pa s**-1

    Array Chunk Bytes 4.52 TiB 51.49 MiB Shape (92044, 13, 721, 1440) (1, 13, 721, 1440) Dask graph 92044 chunks in 2 graph layers Data type float32 numpy.ndarray

  • wind_speed
    (time, level, latitude, longitude)
    float32
    dask.array<chunksize=(1, 13, 721, 1440), meta=np.ndarray>

    Array Chunk Bytes 4.52 TiB 51.49 MiB Shape (92044, 13, 721, 1440) (1, 13, 721, 1440) Dask graph 92044 chunks in 2 graph layers Data type float32 numpy.ndarray

• Indexes: (4)
  • latitude
    PandasIndex

    PandasIndex(Index([  90.0,  89.75,   89.5,  89.25,   89.0,  88.75,   88.5,  88.25,   88.0,
            87.75,
           ...
           -87.75,  -88.0, -88.25,  -88.5, -88.75,  -89.0, -89.25,  -89.5, -89.75,
            -90.0],
          dtype='float32', name='latitude', length=721))

  • level
    PandasIndex

    PandasIndex(Index([50, 100, 150, 200, 250, 300, 400, 500, 600, 700, 850, 925, 1000], dtype='int64', name='level'))

  • longitude
    PandasIndex

    PandasIndex(Index([   0.0,   0.25,    0.5,   0.75,    1.0,   1.25,    1.5,   1.75,    2.0,
             2.25,
           ...
            357.5, 357.75,  358.0, 358.25,  358.5, 358.75,  359.0, 359.25,  359.5,
           359.75],
          dtype='float32', name='longitude', length=1440))

  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['1959-01-01 00:00:00', '1959-01-01 06:00:00',
                   '1959-01-01 12:00:00', '1959-01-01 18:00:00',
                   '1959-01-02 00:00:00', '1959-01-02 06:00:00',
                   '1959-01-02 12:00:00', '1959-01-02 18:00:00',
                   '1959-01-03 00:00:00', '1959-01-03 06:00:00',
                   ...
                   '2021-12-29 12:00:00', '2021-12-29 18:00:00',
                   '2021-12-30 00:00:00', '2021-12-30 06:00:00',
                   '2021-12-30 12:00:00', '2021-12-30 18:00:00',
                   '2021-12-31 00:00:00', '2021-12-31 06:00:00',
                   '2021-12-31 12:00:00', '2021-12-31 18:00:00'],
                  dtype='datetime64[ns]', name='time', length=92044, freq=None))

• Attributes: (0)

ERA5 Climatology

A climatology is used for e.g. computing anomaly metrics such as the ACC. For WeatherBench 2, the climatology was computed using a running window for smoothing (see paper and script) for each day of year and sixth hour of day. We have computed climatologies for 1990-2017 and 1990-2019.

Location: gs://weatherbench2/datasets/era5-hourly-climatology/

Files:

• 1990-2017_6h_1440x721.zarr

• 1990-2017_6h_512x256_equiangular_conservative.zarr

• 1990-2017_6h_240x121_equiangular_with_poles_conservative.zarr

• 1990-2017_6h_128x64_equiangular_with_poles_conservative.zarr

• 1990-2017_6h_64x32_equiangular_with_poles_conservative.zarr

• 1990-2019_6h_1440x721.zarr

• 1990-2019_6h_512x256_equiangular_conservative.zarr

• 1990-2019_6h_240x121_equiangular_with_poles_conservative.zarr

• 1990-2019_6h_128x64_equiangular_with_poles_conservative.zarr

• 1990-2019_6h_64x32_equiangular_with_poles_conservative.zarr

xr.open_zarr('gs://weatherbench2/datasets/era5-hourly-climatology/1990-2019_6h_1440x721.zarr')

<xarray.Dataset>
Dimensions:                                      (hour: 4, dayofyear: 366,
                                                  latitude: 721,
                                                  longitude: 1440, level: 13)
Coordinates:
  * dayofyear                                    (dayofyear) int64 1 2 ... 366
  * hour                                         (hour) int64 0 6 12 18
  * latitude                                     (latitude) float32 90.0 ... ...
  * level                                        (level) int64 50 100 ... 1000
  * longitude                                    (longitude) float32 0.0 ... ...
Data variables: (12/28)
    10m_u_component_of_wind                      (hour, dayofyear, latitude, longitude) float32 dask.array<chunksize=(3, 3, 721, 1440), meta=np.ndarray>
    10m_v_component_of_wind                      (hour, dayofyear, latitude, longitude) float32 dask.array<chunksize=(3, 3, 721, 1440), meta=np.ndarray>
    10m_wind_speed                               (hour, dayofyear, latitude, longitude) float32 dask.array<chunksize=(3, 3, 721, 1440), meta=np.ndarray>
    2m_temperature                               (hour, dayofyear, latitude, longitude) float32 dask.array<chunksize=(3, 3, 721, 1440), meta=np.ndarray>
    geopotential                                 (hour, dayofyear, level, latitude, longitude) float32 dask.array<chunksize=(3, 3, 1, 721, 1440), meta=np.ndarray>
    mean_sea_level_pressure                      (hour, dayofyear, latitude, longitude) float32 dask.array<chunksize=(3, 3, 721, 1440), meta=np.ndarray>
    ...                                           ...
    total_precipitation_6hr_seeps_dry_fraction   (hour, dayofyear, latitude, longitude) float32 dask.array<chunksize=(3, 3, 721, 1440), meta=np.ndarray>
    total_precipitation_6hr_seeps_threshold      (hour, dayofyear, latitude, longitude) float32 dask.array<chunksize=(3, 3, 721, 1440), meta=np.ndarray>
    u_component_of_wind                          (hour, dayofyear, level, latitude, longitude) float32 dask.array<chunksize=(3, 3, 1, 721, 1440), meta=np.ndarray>
    v_component_of_wind                          (hour, dayofyear, level, latitude, longitude) float32 dask.array<chunksize=(3, 3, 1, 721, 1440), meta=np.ndarray>
    vertical_velocity                            (hour, dayofyear, level, latitude, longitude) float32 dask.array<chunksize=(3, 3, 1, 721, 1440), meta=np.ndarray>
    wind_speed                                   (hour, dayofyear, level, latitude, longitude) float32 dask.array<chunksize=(3, 3, 1, 721, 1440), meta=np.ndarray>

xarray.Dataset

• Dimensions:
  • hour: 4
  • dayofyear: 366
  • latitude: 721
  • longitude: 1440
  • level: 13
• Coordinates: (5)
  • dayofyear
    (dayofyear)
    int64
    1 2 3 4 5 6 ... 362 363 364 365 366

    array([  1,   2,   3, ..., 364, 365, 366])

  • hour
    (hour)
    int64
    0 6 12 18

    array([ 0,  6, 12, 18])

  • latitude
    (latitude)
    float32
    90.0 89.75 89.5 ... -89.75 -90.0

    long_name :

    latitude

    units :

    degrees_north

    array([ 90.  ,  89.75,  89.5 , ..., -89.5 , -89.75, -90.  ], dtype=float32)

  • level
    (level)
    int64
    50 100 150 200 ... 700 850 925 1000

    array([  50,  100,  150,  200,  250,  300,  400,  500,  600,  700,  850,  925,
           1000])

  • longitude
    (longitude)
    float32
    0.0 0.25 0.5 ... 359.2 359.5 359.8

    long_name :

    longitude

    units :

    degrees_east

    array([0.0000e+00, 2.5000e-01, 5.0000e-01, ..., 3.5925e+02, 3.5950e+02,
           3.5975e+02], dtype=float32)

• Data variables: (28)
  • 10m_u_component_of_wind
    (hour, dayofyear, latitude, longitude)
    float32
    dask.array<chunksize=(3, 3, 721, 1440), meta=np.ndarray>

    long_name :

    10 metre U wind component

    short_name :

    u10

    units :

    m s**-1

    Array Chunk Bytes 5.66 GiB 35.65 MiB Shape (4, 366, 721, 1440) (3, 3, 721, 1440) Dask graph 244 chunks in 2 graph layers Data type float32 numpy.ndarray

  • 10m_v_component_of_wind
    (hour, dayofyear, latitude, longitude)
    float32
    dask.array<chunksize=(3, 3, 721, 1440), meta=np.ndarray>

    long_name :

    10 metre V wind component

    short_name :

    v10

    units :

    m s**-1

    Array Chunk Bytes 5.66 GiB 35.65 MiB Shape (4, 366, 721, 1440) (3, 3, 721, 1440) Dask graph 244 chunks in 2 graph layers Data type float32 numpy.ndarray

  • 10m_wind_speed
    (hour, dayofyear, latitude, longitude)
    float32
    dask.array<chunksize=(3, 3, 721, 1440), meta=np.ndarray>

    Array Chunk Bytes 5.66 GiB 35.65 MiB Shape (4, 366, 721, 1440) (3, 3, 721, 1440) Dask graph 244 chunks in 2 graph layers Data type float32 numpy.ndarray

  • 2m_temperature
    (hour, dayofyear, latitude, longitude)
    float32
    dask.array<chunksize=(3, 3, 721, 1440), meta=np.ndarray>

    long_name :

    2 metre temperature

    short_name :

    t2m

    units :

    K

    Array Chunk Bytes 5.66 GiB 35.65 MiB Shape (4, 366, 721, 1440) (3, 3, 721, 1440) Dask graph 244 chunks in 2 graph layers Data type float32 numpy.ndarray

  • geopotential
    (hour, dayofyear, level, latitude, longitude)
    float32
    dask.array<chunksize=(3, 3, 1, 721, 1440), meta=np.ndarray>

    long_name :

    Geopotential

    short_name :

    z

    standard_name :

    geopotential

    units :

    m**2 s**-2

    Array Chunk Bytes 73.61 GiB 35.65 MiB Shape (4, 366, 13, 721, 1440) (3, 3, 1, 721, 1440) Dask graph 3172 chunks in 2 graph layers Data type float32 numpy.ndarray

  • mean_sea_level_pressure
    (hour, dayofyear, latitude, longitude)
    float32
    dask.array<chunksize=(3, 3, 721, 1440), meta=np.ndarray>

    long_name :

    Mean sea level pressure

    short_name :

    msl

    standard_name :

    air_pressure_at_mean_sea_level

    units :

    Pa

    Array Chunk Bytes 5.66 GiB 35.65 MiB Shape (4, 366, 721, 1440) (3, 3, 721, 1440) Dask graph 244 chunks in 2 graph layers Data type float32 numpy.ndarray

  • sea_ice_cover
    (hour, dayofyear, latitude, longitude)
    float32
    dask.array<chunksize=(3, 3, 721, 1440), meta=np.ndarray>

    long_name :

    Sea ice area fraction

    short_name :

    siconc

    standard_name :

    sea_ice_area_fraction

    units :

    (0 - 1)

    Array Chunk Bytes 5.66 GiB 35.65 MiB Shape (4, 366, 721, 1440) (3, 3, 721, 1440) Dask graph 244 chunks in 2 graph layers Data type float32 numpy.ndarray

  • sea_surface_temperature
    (hour, dayofyear, latitude, longitude)
    float32
    dask.array<chunksize=(3, 3, 721, 1440), meta=np.ndarray>

    long_name :

    Sea surface temperature

    short_name :

    sst

    units :

    K

    Array Chunk Bytes 5.66 GiB 35.65 MiB Shape (4, 366, 721, 1440) (3, 3, 721, 1440) Dask graph 244 chunks in 2 graph layers Data type float32 numpy.ndarray

  • specific_humidity
    (hour, dayofyear, level, latitude, longitude)
    float32
    dask.array<chunksize=(3, 3, 1, 721, 1440), meta=np.ndarray>

    long_name :

    Specific humidity

    short_name :

    q

    standard_name :

    specific_humidity

    units :

    kg kg**-1

    Array Chunk Bytes 73.61 GiB 35.65 MiB Shape (4, 366, 13, 721, 1440) (3, 3, 1, 721, 1440) Dask graph 3172 chunks in 2 graph layers Data type float32 numpy.ndarray

  • surface_pressure
    (hour, dayofyear, latitude, longitude)
    float32
    dask.array<chunksize=(3, 3, 721, 1440), meta=np.ndarray>

    long_name :

    Surface pressure

    short_name :

    sp

    standard_name :

    surface_air_pressure

    units :

    Pa

    Array Chunk Bytes 5.66 GiB 35.65 MiB Shape (4, 366, 721, 1440) (3, 3, 721, 1440) Dask graph 244 chunks in 2 graph layers Data type float32 numpy.ndarray

  • temperature
    (hour, dayofyear, level, latitude, longitude)
    float32
    dask.array<chunksize=(3, 3, 1, 721, 1440), meta=np.ndarray>

    long_name :

    Temperature

    short_name :

    t

    standard_name :

    air_temperature

    units :

    K

    Array Chunk Bytes 73.61 GiB 35.65 MiB Shape (4, 366, 13, 721, 1440) (3, 3, 1, 721, 1440) Dask graph 3172 chunks in 2 graph layers Data type float32 numpy.ndarray

  • toa_incident_solar_radiation
    (hour, dayofyear, latitude, longitude)
    float32
    dask.array<chunksize=(3, 3, 721, 1440), meta=np.ndarray>

    long_name :

    TOA incident solar radiation

    short_name :

    tisr

    units :

    J m**-2

    Array Chunk Bytes 5.66 GiB 35.65 MiB Shape (4, 366, 721, 1440) (3, 3, 721, 1440) Dask graph 244 chunks in 2 graph layers Data type float32 numpy.ndarray

  • toa_incident_solar_radiation_12hr
    (hour, dayofyear, latitude, longitude)
    float32
    dask.array<chunksize=(3, 3, 721, 1440), meta=np.ndarray>

    long_name :

    TOA incident solar radiation

    short_name :

    tisr

    units :

    J m**-2

    Array Chunk Bytes 5.66 GiB 35.65 MiB Shape (4, 366, 721, 1440) (3, 3, 721, 1440) Dask graph 244 chunks in 2 graph layers Data type float32 numpy.ndarray

  • toa_incident_solar_radiation_24hr
    (hour, dayofyear, latitude, longitude)
    float32
    dask.array<chunksize=(3, 3, 721, 1440), meta=np.ndarray>

    long_name :

    TOA incident solar radiation

    short_name :

    tisr

    units :

    J m**-2

    Array Chunk Bytes 5.66 GiB 35.65 MiB Shape (4, 366, 721, 1440) (3, 3, 721, 1440) Dask graph 244 chunks in 2 graph layers Data type float32 numpy.ndarray

  • toa_incident_solar_radiation_6hr
    (hour, dayofyear, latitude, longitude)
    float32
    dask.array<chunksize=(3, 3, 721, 1440), meta=np.ndarray>

    long_name :

    TOA incident solar radiation

    short_name :

    tisr

    units :

    J m**-2

    Array Chunk Bytes 5.66 GiB 35.65 MiB Shape (4, 366, 721, 1440) (3, 3, 721, 1440) Dask graph 244 chunks in 2 graph layers Data type float32 numpy.ndarray

  • total_cloud_cover
    (hour, dayofyear, latitude, longitude)
    float32
    dask.array<chunksize=(3, 3, 721, 1440), meta=np.ndarray>

    long_name :

    Total cloud cover

    short_name :

    tcc

    standard_name :

    cloud_area_fraction

    units :

    (0 - 1)

    Array Chunk Bytes 5.66 GiB 35.65 MiB Shape (4, 366, 721, 1440) (3, 3, 721, 1440) Dask graph 244 chunks in 2 graph layers Data type float32 numpy.ndarray

  • total_column_water_vapour
    (hour, dayofyear, latitude, longitude)
    float32
    dask.array<chunksize=(3, 3, 721, 1440), meta=np.ndarray>

    long_name :

    Total column vertically-integrated water vapour

    short_name :

    tcwv

    standard_name :

    lwe_thickness_of_atmosphere_mass_content_of_water_vapor

    units :

    kg m**-2

    Array Chunk Bytes 5.66 GiB 35.65 MiB Shape (4, 366, 721, 1440) (3, 3, 721, 1440) Dask graph 244 chunks in 2 graph layers Data type float32 numpy.ndarray

  • total_precipitation_12hr
    (hour, dayofyear, latitude, longitude)
    float32
    dask.array<chunksize=(3, 3, 721, 1440), meta=np.ndarray>

    long_name :

    Total precipitation

    short_name :

    tp

    units :

    m

    Array Chunk Bytes 5.66 GiB 35.65 MiB Shape (4, 366, 721, 1440) (3, 3, 721, 1440) Dask graph 244 chunks in 2 graph layers Data type float32 numpy.ndarray

  • total_precipitation_24hr
    (hour, dayofyear, latitude, longitude)
    float32
    dask.array<chunksize=(3, 3, 721, 1440), meta=np.ndarray>

    long_name :

    Total precipitation

    short_name :

    tp

    units :

    m

    Array Chunk Bytes 5.66 GiB 35.65 MiB Shape (4, 366, 721, 1440) (3, 3, 721, 1440) Dask graph 244 chunks in 2 graph layers Data type float32 numpy.ndarray

  • total_precipitation_24hr_seeps_dry_fraction
    (hour, dayofyear, latitude, longitude)
    float32
    dask.array<chunksize=(3, 3, 721, 1440), meta=np.ndarray>

    long_name :

    Total precipitation

    short_name :

    tp

    units :

    m

    Array Chunk Bytes 5.66 GiB 35.65 MiB Shape (4, 366, 721, 1440) (3, 3, 721, 1440) Dask graph 244 chunks in 2 graph layers Data type float32 numpy.ndarray

  • total_precipitation_24hr_seeps_threshold
    (hour, dayofyear, latitude, longitude)
    float32
    dask.array<chunksize=(3, 3, 721, 1440), meta=np.ndarray>

    long_name :

    Total precipitation

    short_name :

    tp

    units :

    m

    Array Chunk Bytes 5.66 GiB 35.65 MiB Shape (4, 366, 721, 1440) (3, 3, 721, 1440) Dask graph 244 chunks in 2 graph layers Data type float32 numpy.ndarray

  • total_precipitation_6hr
    (hour, dayofyear, latitude, longitude)
    float32
    dask.array<chunksize=(3, 3, 721, 1440), meta=np.ndarray>

    long_name :

    Total precipitation

    short_name :

    tp

    units :

    m

    Array Chunk Bytes 5.66 GiB 35.65 MiB Shape (4, 366, 721, 1440) (3, 3, 721, 1440) Dask graph 244 chunks in 2 graph layers Data type float32 numpy.ndarray

  • total_precipitation_6hr_seeps_dry_fraction
    (hour, dayofyear, latitude, longitude)
    float32
    dask.array<chunksize=(3, 3, 721, 1440), meta=np.ndarray>

    long_name :

    Total precipitation

    short_name :

    tp

    units :

    m

    Array Chunk Bytes 5.66 GiB 35.65 MiB Shape (4, 366, 721, 1440) (3, 3, 721, 1440) Dask graph 244 chunks in 2 graph layers Data type float32 numpy.ndarray

  • total_precipitation_6hr_seeps_threshold
    (hour, dayofyear, latitude, longitude)
    float32
    dask.array<chunksize=(3, 3, 721, 1440), meta=np.ndarray>

    long_name :

    Total precipitation

    short_name :

    tp

    units :

    m

    Array Chunk Bytes 5.66 GiB 35.65 MiB Shape (4, 366, 721, 1440) (3, 3, 721, 1440) Dask graph 244 chunks in 2 graph layers Data type float32 numpy.ndarray

  • u_component_of_wind
    (hour, dayofyear, level, latitude, longitude)
    float32
    dask.array<chunksize=(3, 3, 1, 721, 1440), meta=np.ndarray>

    long_name :

    U component of wind

    short_name :

    u

    standard_name :

    eastward_wind

    units :

    m s**-1

    Array Chunk Bytes 73.61 GiB 35.65 MiB Shape (4, 366, 13, 721, 1440) (3, 3, 1, 721, 1440) Dask graph 3172 chunks in 2 graph layers Data type float32 numpy.ndarray

  • v_component_of_wind
    (hour, dayofyear, level, latitude, longitude)
    float32
    dask.array<chunksize=(3, 3, 1, 721, 1440), meta=np.ndarray>

    long_name :

    V component of wind

    short_name :

    v

    standard_name :

    northward_wind

    units :

    m s**-1

    Array Chunk Bytes 73.61 GiB 35.65 MiB Shape (4, 366, 13, 721, 1440) (3, 3, 1, 721, 1440) Dask graph 3172 chunks in 2 graph layers Data type float32 numpy.ndarray

  • vertical_velocity
    (hour, dayofyear, level, latitude, longitude)
    float32
    dask.array<chunksize=(3, 3, 1, 721, 1440), meta=np.ndarray>

    long_name :

    Vertical velocity

    short_name :

    w

    standard_name :

    lagrangian_tendency_of_air_pressure

    units :

    Pa s**-1

    Array Chunk Bytes 73.61 GiB 35.65 MiB Shape (4, 366, 13, 721, 1440) (3, 3, 1, 721, 1440) Dask graph 3172 chunks in 2 graph layers Data type float32 numpy.ndarray

  • wind_speed
    (hour, dayofyear, level, latitude, longitude)
    float32
    dask.array<chunksize=(3, 3, 1, 721, 1440), meta=np.ndarray>

    Array Chunk Bytes 73.61 GiB 35.65 MiB Shape (4, 366, 13, 721, 1440) (3, 3, 1, 721, 1440) Dask graph 3172 chunks in 2 graph layers Data type float32 numpy.ndarray

• Indexes: (5)
  • dayofyear
    PandasIndex

    PandasIndex(Index([  1,   2,   3,   4,   5,   6,   7,   8,   9,  10,
           ...
           357, 358, 359, 360, 361, 362, 363, 364, 365, 366],
          dtype='int64', name='dayofyear', length=366))

  • hour
    PandasIndex

    PandasIndex(Index([0, 6, 12, 18], dtype='int64', name='hour'))

  • latitude
    PandasIndex

    PandasIndex(Index([  90.0,  89.75,   89.5,  89.25,   89.0,  88.75,   88.5,  88.25,   88.0,
            87.75,
           ...
           -87.75,  -88.0, -88.25,  -88.5, -88.75,  -89.0, -89.25,  -89.5, -89.75,
            -90.0],
          dtype='float32', name='latitude', length=721))

  • level
    PandasIndex

    PandasIndex(Index([50, 100, 150, 200, 250, 300, 400, 500, 600, 700, 850, 925, 1000], dtype='int64', name='level'))

  • longitude
    PandasIndex

    PandasIndex(Index([   0.0,   0.25,    0.5,   0.75,    1.0,   1.25,    1.5,   1.75,    2.0,
             2.25,
           ...
            357.5, 357.75,  358.0, 358.25,  358.5, 358.75,  359.0, 359.25,  359.5,
           359.75],
          dtype='float32', name='longitude', length=1440))

• Attributes: (0)

IFS HRES t=0 “Analysis”

To evaluate IFS forecasts, we use the IFS analysis as the ground truth. Note that here we use the initial conditions of the HRES forecasts, i.e. the forecasts at lead time zero as analysis. This is not exactly the same as the analysis dataset provided by ECMWF (see paper for details).

Location: gs://weatherbench2/datasets/hres_t0/

Files:

• 2016-2022-6h-1440x721.zarr

• 2016-2022-6h-512x256_equiangular_conservative.zarr

• 2016-2022-6h-240x121_equiangular_with_poles_conservative.zarr

• 2016-2022-6h-128x64_equiangular_with_poles_conservative.zarr

• 2016-2022-6h-64x32_equiangular_with_poles_conservative.zarr

xr.open_zarr('gs://weatherbench2/datasets/hres_t0/2016-2022-6h-1440x721.zarr')

<xarray.Dataset>
Dimensions:                  (time: 10268, latitude: 721, longitude: 1440,
                              level: 13)
Coordinates:
  * latitude                 (latitude) float32 -90.0 -89.75 ... 89.75 90.0
  * level                    (level) int32 50 100 150 200 ... 700 850 925 1000
  * longitude                (longitude) float32 0.0 0.25 0.5 ... 359.5 359.8
  * time                     (time) datetime64[ns] 2016-01-01 ... 2023-01-10T...
Data variables: (12/14)
    10m_u_component_of_wind  (time, latitude, longitude) float32 dask.array<chunksize=(1, 721, 1440), meta=np.ndarray>
    10m_v_component_of_wind  (time, latitude, longitude) float32 dask.array<chunksize=(1, 721, 1440), meta=np.ndarray>
    10m_wind_speed           (time, latitude, longitude) float32 dask.array<chunksize=(1, 721, 1440), meta=np.ndarray>
    2m_temperature           (time, latitude, longitude) float32 dask.array<chunksize=(1, 721, 1440), meta=np.ndarray>
    geopotential             (time, level, latitude, longitude) float32 dask.array<chunksize=(1, 13, 721, 1440), meta=np.ndarray>
    mean_sea_level_pressure  (time, latitude, longitude) float32 dask.array<chunksize=(1, 721, 1440), meta=np.ndarray>
    ...                       ...
    temperature              (time, level, latitude, longitude) float32 dask.array<chunksize=(1, 13, 721, 1440), meta=np.ndarray>
    total_precipitation_6hr  (time, latitude, longitude) float32 dask.array<chunksize=(1, 721, 1440), meta=np.ndarray>
    u_component_of_wind      (time, level, latitude, longitude) float32 dask.array<chunksize=(1, 13, 721, 1440), meta=np.ndarray>
    v_component_of_wind      (time, level, latitude, longitude) float32 dask.array<chunksize=(1, 13, 721, 1440), meta=np.ndarray>
    vertical_velocity        (time, level, latitude, longitude) float32 dask.array<chunksize=(1, 13, 721, 1440), meta=np.ndarray>
    wind_speed               (time, level, latitude, longitude) float32 dask.array<chunksize=(1, 13, 721, 1440), meta=np.ndarray>

xarray.Dataset

• Dimensions:
  • time: 10268
  • latitude: 721
  • longitude: 1440
  • level: 13
• Coordinates: (4)
  • latitude
    (latitude)
    float32
    -90.0 -89.75 -89.5 ... 89.75 90.0

    array([-90.  , -89.75, -89.5 , ...,  89.5 ,  89.75,  90.  ], dtype=float32)

  • level
    (level)
    int32
    50 100 150 200 ... 700 850 925 1000

    array([  50,  100,  150,  200,  250,  300,  400,  500,  600,  700,  850,  925,
           1000], dtype=int32)

  • longitude
    (longitude)
    float32
    0.0 0.25 0.5 ... 359.2 359.5 359.8

    array([0.0000e+00, 2.5000e-01, 5.0000e-01, ..., 3.5925e+02, 3.5950e+02,
           3.5975e+02], dtype=float32)

  • time
    (time)
    datetime64[ns]
    2016-01-01 ... 2023-01-10T18:00:00

    long_name :

    initial time of forecast

    standard_name :

    forecast_reference_time

    array(['2016-01-01T00:00:00.000000000', '2016-01-01T06:00:00.000000000',
           '2016-01-01T12:00:00.000000000', ..., '2023-01-10T06:00:00.000000000',
           '2023-01-10T12:00:00.000000000', '2023-01-10T18:00:00.000000000'],
          dtype='datetime64[ns]')

• Data variables: (14)
  • 10m_u_component_of_wind
    (time, latitude, longitude)
    float32
    dask.array<chunksize=(1, 721, 1440), meta=np.ndarray>

    long_name :

    10 metre U wind component

    short_name :

    u10

    standard_name :

    unknown

    units :

    m s**-1

    Array Chunk Bytes 39.71 GiB 3.96 MiB Shape (10268, 721, 1440) (1, 721, 1440) Dask graph 10268 chunks in 2 graph layers Data type float32 numpy.ndarray

  • 10m_v_component_of_wind
    (time, latitude, longitude)
    float32
    dask.array<chunksize=(1, 721, 1440), meta=np.ndarray>

    long_name :

    10 metre V wind component

    short_name :

    v10

    standard_name :

    unknown

    units :

    m s**-1

    Array Chunk Bytes 39.71 GiB 3.96 MiB Shape (10268, 721, 1440) (1, 721, 1440) Dask graph 10268 chunks in 2 graph layers Data type float32 numpy.ndarray

  • 10m_wind_speed
    (time, latitude, longitude)
    float32
    dask.array<chunksize=(1, 721, 1440), meta=np.ndarray>

    Array Chunk Bytes 39.71 GiB 3.96 MiB Shape (10268, 721, 1440) (1, 721, 1440) Dask graph 10268 chunks in 2 graph layers Data type float32 numpy.ndarray

  • 2m_temperature
    (time, latitude, longitude)
    float32
    dask.array<chunksize=(1, 721, 1440), meta=np.ndarray>

    long_name :

    2 metre temperature

    short_name :

    t2m

    standard_name :

    unknown

    units :

    K

    Array Chunk Bytes 39.71 GiB 3.96 MiB Shape (10268, 721, 1440) (1, 721, 1440) Dask graph 10268 chunks in 2 graph layers Data type float32 numpy.ndarray

  • geopotential
    (time, level, latitude, longitude)
    float32
    dask.array<chunksize=(1, 13, 721, 1440), meta=np.ndarray>

    long_name :

    Geopotential

    short_name :

    z

    standard_name :

    geopotential

    units :

    m**2 s**-2

    Array Chunk Bytes 516.28 GiB 51.49 MiB Shape (10268, 13, 721, 1440) (1, 13, 721, 1440) Dask graph 10268 chunks in 2 graph layers Data type float32 numpy.ndarray

  • mean_sea_level_pressure
    (time, latitude, longitude)
    float32
    dask.array<chunksize=(1, 721, 1440), meta=np.ndarray>

    long_name :

    Mean sea level pressure

    short_name :

    msl

    standard_name :

    air_pressure_at_mean_sea_level

    units :

    Pa

    Array Chunk Bytes 39.71 GiB 3.96 MiB Shape (10268, 721, 1440) (1, 721, 1440) Dask graph 10268 chunks in 2 graph layers Data type float32 numpy.ndarray

  • specific_humidity
    (time, level, latitude, longitude)
    float32
    dask.array<chunksize=(1, 13, 721, 1440), meta=np.ndarray>

    long_name :

    Specific humidity

    short_name :

    q

    standard_name :

    specific_humidity

    units :

    kg kg**-1

    Array Chunk Bytes 516.28 GiB 51.49 MiB Shape (10268, 13, 721, 1440) (1, 13, 721, 1440) Dask graph 10268 chunks in 2 graph layers Data type float32 numpy.ndarray

  • surface_pressure
    (time, latitude, longitude)
    float32
    dask.array<chunksize=(1, 721, 1440), meta=np.ndarray>

    long_name :

    Surface pressure

    short_name :

    sp

    standard_name :

    surface_air_pressure

    units :

    Pa

    Array Chunk Bytes 39.71 GiB 3.96 MiB Shape (10268, 721, 1440) (1, 721, 1440) Dask graph 10268 chunks in 2 graph layers Data type float32 numpy.ndarray

  • temperature
    (time, level, latitude, longitude)
    float32
    dask.array<chunksize=(1, 13, 721, 1440), meta=np.ndarray>

    long_name :

    Temperature

    short_name :

    t

    standard_name :

    air_temperature

    units :

    K

    Array Chunk Bytes 516.28 GiB 51.49 MiB Shape (10268, 13, 721, 1440) (1, 13, 721, 1440) Dask graph 10268 chunks in 2 graph layers Data type float32 numpy.ndarray

  • total_precipitation_6hr
    (time, latitude, longitude)
    float32
    dask.array<chunksize=(1, 721, 1440), meta=np.ndarray>

    long_name :

    Total precipitation

    short_name :

    tp

    standard_name :

    unknown

    units :

    m

    Array Chunk Bytes 39.71 GiB 3.96 MiB Shape (10268, 721, 1440) (1, 721, 1440) Dask graph 10268 chunks in 2 graph layers Data type float32 numpy.ndarray

  • u_component_of_wind
    (time, level, latitude, longitude)
    float32
    dask.array<chunksize=(1, 13, 721, 1440), meta=np.ndarray>

    long_name :

    U component of wind

    short_name :

    u

    standard_name :

    eastward_wind

    units :

    m s**-1

    Array Chunk Bytes 516.28 GiB 51.49 MiB Shape (10268, 13, 721, 1440) (1, 13, 721, 1440) Dask graph 10268 chunks in 2 graph layers Data type float32 numpy.ndarray

  • v_component_of_wind
    (time, level, latitude, longitude)
    float32
    dask.array<chunksize=(1, 13, 721, 1440), meta=np.ndarray>

    long_name :

    V component of wind

    short_name :

    v

    standard_name :

    northward_wind

    units :

    m s**-1

    Array Chunk Bytes 516.28 GiB 51.49 MiB Shape (10268, 13, 721, 1440) (1, 13, 721, 1440) Dask graph 10268 chunks in 2 graph layers Data type float32 numpy.ndarray

  • vertical_velocity
    (time, level, latitude, longitude)
    float32
    dask.array<chunksize=(1, 13, 721, 1440), meta=np.ndarray>

    long_name :

    Vertical velocity

    short_name :

    w

    standard_name :

    lagrangian_tendency_of_air_pressure

    units :

    Pa s**-1

    Array Chunk Bytes 516.28 GiB 51.49 MiB Shape (10268, 13, 721, 1440) (1, 13, 721, 1440) Dask graph 10268 chunks in 2 graph layers Data type float32 numpy.ndarray

  • wind_speed
    (time, level, latitude, longitude)
    float32
    dask.array<chunksize=(1, 13, 721, 1440), meta=np.ndarray>

    Array Chunk Bytes 516.28 GiB 51.49 MiB Shape (10268, 13, 721, 1440) (1, 13, 721, 1440) Dask graph 10268 chunks in 2 graph layers Data type float32 numpy.ndarray

• Indexes: (4)
  • latitude
    PandasIndex

    PandasIndex(Index([ -90.0, -89.75,  -89.5, -89.25,  -89.0, -88.75,  -88.5, -88.25,  -88.0,
           -87.75,
           ...
            87.75,   88.0,  88.25,   88.5,  88.75,   89.0,  89.25,   89.5,  89.75,
             90.0],
          dtype='float32', name='latitude', length=721))

  • level
    PandasIndex

    PandasIndex(Index([50, 100, 150, 200, 250, 300, 400, 500, 600, 700, 850, 925, 1000], dtype='int32', name='level'))

  • longitude
    PandasIndex

    PandasIndex(Index([   0.0,   0.25,    0.5,   0.75,    1.0,   1.25,    1.5,   1.75,    2.0,
             2.25,
           ...
            357.5, 357.75,  358.0, 358.25,  358.5, 358.75,  359.0, 359.25,  359.5,
           359.75],
          dtype='float32', name='longitude', length=1440))

  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['2016-01-01 00:00:00', '2016-01-01 06:00:00',
                   '2016-01-01 12:00:00', '2016-01-01 18:00:00',
                   '2016-01-02 00:00:00', '2016-01-02 06:00:00',
                   '2016-01-02 12:00:00', '2016-01-02 18:00:00',
                   '2016-01-03 00:00:00', '2016-01-03 06:00:00',
                   ...
                   '2023-01-08 12:00:00', '2023-01-08 18:00:00',
                   '2023-01-09 00:00:00', '2023-01-09 06:00:00',
                   '2023-01-09 12:00:00', '2023-01-09 18:00:00',
                   '2023-01-10 00:00:00', '2023-01-10 06:00:00',
                   '2023-01-10 12:00:00', '2023-01-10 18:00:00'],
                  dtype='datetime64[ns]', name='time', length=10268, freq=None))

• Attributes: (0)

Forecast datasets

IFS HRES

Here, we provide the 00 and 12 UTC initializations of HRES.

Location: gs://weatherbench2/datasets/hres/

Files:

• 2016-2022-0012-1440x721.zarr

• 2016-2022-0012-512x256_equiangular_conservative.zarr

• 2016-2022-0012-240x121_equiangular_with_poles_conservative.zarr

• 2016-2022-0012-128x64_equiangular_with_poles_conservative.zarr

• 2016-2022-0012-64x32_equiangular_with_poles_conservative.zarr

xr.open_zarr('gs://weatherbench2/datasets/hres/2016-2022-0012-1440x721.zarr')

<xarray.Dataset>
Dimensions:                   (time: 5134, prediction_timedelta: 41,
                               latitude: 721, longitude: 1440, level: 13)
Coordinates:
  * latitude                  (latitude) float32 -90.0 -89.75 ... 89.75 90.0
  * level                     (level) int32 50 100 150 200 ... 700 850 925 1000
  * longitude                 (longitude) float32 0.0 0.25 0.5 ... 359.5 359.8
  * prediction_timedelta      (prediction_timedelta) timedelta64[ns] 00:00:00...
  * time                      (time) datetime64[ns] 2016-01-01 ... 2023-01-10...
Data variables: (12/16)
    10m_u_component_of_wind   (time, prediction_timedelta, latitude, longitude) float32 dask.array<chunksize=(1, 1, 721, 1440), meta=np.ndarray>
    10m_v_component_of_wind   (time, prediction_timedelta, latitude, longitude) float32 dask.array<chunksize=(1, 1, 721, 1440), meta=np.ndarray>
    10m_wind_speed            (time, prediction_timedelta, latitude, longitude) float32 dask.array<chunksize=(1, 1, 721, 1440), meta=np.ndarray>
    2m_temperature            (time, prediction_timedelta, latitude, longitude) float32 dask.array<chunksize=(1, 1, 721, 1440), meta=np.ndarray>
    geopotential              (time, prediction_timedelta, level, latitude, longitude) float32 dask.array<chunksize=(1, 1, 13, 721, 1440), meta=np.ndarray>
    mean_sea_level_pressure   (time, prediction_timedelta, latitude, longitude) float32 dask.array<chunksize=(1, 1, 721, 1440), meta=np.ndarray>
    ...                        ...
    total_precipitation_24hr  (time, prediction_timedelta, latitude, longitude) float32 dask.array<chunksize=(1, 1, 721, 1440), meta=np.ndarray>
    total_precipitation_6hr   (time, prediction_timedelta, latitude, longitude) float32 dask.array<chunksize=(1, 1, 721, 1440), meta=np.ndarray>
    u_component_of_wind       (time, prediction_timedelta, level, latitude, longitude) float32 dask.array<chunksize=(1, 1, 13, 721, 1440), meta=np.ndarray>
    v_component_of_wind       (time, prediction_timedelta, level, latitude, longitude) float32 dask.array<chunksize=(1, 1, 13, 721, 1440), meta=np.ndarray>
    vertical_velocity         (time, prediction_timedelta, level, latitude, longitude) float32 dask.array<chunksize=(1, 1, 13, 721, 1440), meta=np.ndarray>
    wind_speed                (time, prediction_timedelta, level, latitude, longitude) float32 dask.array<chunksize=(1, 1, 13, 721, 1440), meta=np.ndarray>

xarray.Dataset

• Dimensions:
  • time: 5134
  • prediction_timedelta: 41
  • latitude: 721
  • longitude: 1440
  • level: 13
• Coordinates: (5)
  • latitude
    (latitude)
    float32
    -90.0 -89.75 -89.5 ... 89.75 90.0

    array([-90.  , -89.75, -89.5 , ...,  89.5 ,  89.75,  90.  ], dtype=float32)

  • level
    (level)
    int32
    50 100 150 200 ... 700 850 925 1000

    array([  50,  100,  150,  200,  250,  300,  400,  500,  600,  700,  850,  925,
           1000], dtype=int32)

  • longitude
    (longitude)
    float32
    0.0 0.25 0.5 ... 359.2 359.5 359.8

    array([0.0000e+00, 2.5000e-01, 5.0000e-01, ..., 3.5925e+02, 3.5950e+02,
           3.5975e+02], dtype=float32)

  • prediction_timedelta
    (prediction_timedelta)
    timedelta64[ns]
    0 days 00:00:00 ... 10 days 00:0...

    long_name :

    time since forecast_reference_time

    standard_name :

    forecast_period

    array([              0,  21600000000000,  43200000000000,  64800000000000,
            86400000000000, 108000000000000, 129600000000000, 151200000000000,
           172800000000000, 194400000000000, 216000000000000, 237600000000000,
           259200000000000, 280800000000000, 302400000000000, 324000000000000,
           345600000000000, 367200000000000, 388800000000000, 410400000000000,
           432000000000000, 453600000000000, 475200000000000, 496800000000000,
           518400000000000, 540000000000000, 561600000000000, 583200000000000,
           604800000000000, 626400000000000, 648000000000000, 669600000000000,
           691200000000000, 712800000000000, 734400000000000, 756000000000000,
           777600000000000, 799200000000000, 820800000000000, 842400000000000,
           864000000000000], dtype='timedelta64[ns]')

  • time
    (time)
    datetime64[ns]
    2016-01-01 ... 2023-01-10T12:00:00

    long_name :

    initial time of forecast

    standard_name :

    forecast_reference_time

    array(['2016-01-01T00:00:00.000000000', '2016-01-01T12:00:00.000000000',
           '2016-01-02T00:00:00.000000000', ..., '2023-01-09T12:00:00.000000000',
           '2023-01-10T00:00:00.000000000', '2023-01-10T12:00:00.000000000'],
          dtype='datetime64[ns]')

• Data variables: (16)
  • 10m_u_component_of_wind
    (time, prediction_timedelta, latitude, longitude)
    float32
    dask.array<chunksize=(1, 1, 721, 1440), meta=np.ndarray>

    long_name :

    10 metre U wind component

    short_name :

    u10

    standard_name :

    unknown

    units :

    m s**-1

    Array Chunk Bytes 814.14 GiB 3.96 MiB Shape (5134, 41, 721, 1440) (1, 1, 721, 1440) Dask graph 210494 chunks in 2 graph layers Data type float32 numpy.ndarray

  • 10m_v_component_of_wind
    (time, prediction_timedelta, latitude, longitude)
    float32
    dask.array<chunksize=(1, 1, 721, 1440), meta=np.ndarray>

    long_name :

    10 metre V wind component

    short_name :

    v10

    standard_name :

    unknown

    units :

    m s**-1

    Array Chunk Bytes 814.14 GiB 3.96 MiB Shape (5134, 41, 721, 1440) (1, 1, 721, 1440) Dask graph 210494 chunks in 2 graph layers Data type float32 numpy.ndarray

  • 10m_wind_speed
    (time, prediction_timedelta, latitude, longitude)
    float32
    dask.array<chunksize=(1, 1, 721, 1440), meta=np.ndarray>

    Array Chunk Bytes 814.14 GiB 3.96 MiB Shape (5134, 41, 721, 1440) (1, 1, 721, 1440) Dask graph 210494 chunks in 2 graph layers Data type float32 numpy.ndarray

  • 2m_temperature
    (time, prediction_timedelta, latitude, longitude)
    float32
    dask.array<chunksize=(1, 1, 721, 1440), meta=np.ndarray>

    long_name :

    2 metre temperature

    short_name :

    t2m

    standard_name :

    unknown

    units :

    K

    Array Chunk Bytes 814.14 GiB 3.96 MiB Shape (5134, 41, 721, 1440) (1, 1, 721, 1440) Dask graph 210494 chunks in 2 graph layers Data type float32 numpy.ndarray

  • geopotential
    (time, prediction_timedelta, level, latitude, longitude)
    float32
    dask.array<chunksize=(1, 1, 13, 721, 1440), meta=np.ndarray>

    long_name :

    Geopotential

    short_name :

    z

    standard_name :

    geopotential

    units :

    m**2 s**-2

    Array Chunk Bytes 10.34 TiB 51.49 MiB Shape (5134, 41, 13, 721, 1440) (1, 1, 13, 721, 1440) Dask graph 210494 chunks in 2 graph layers Data type float32 numpy.ndarray

  • mean_sea_level_pressure
    (time, prediction_timedelta, latitude, longitude)
    float32
    dask.array<chunksize=(1, 1, 721, 1440), meta=np.ndarray>

    long_name :

    Mean sea level pressure

    short_name :

    msl

    standard_name :

    air_pressure_at_mean_sea_level

    units :

    Pa

    Array Chunk Bytes 814.14 GiB 3.96 MiB Shape (5134, 41, 721, 1440) (1, 1, 721, 1440) Dask graph 210494 chunks in 2 graph layers Data type float32 numpy.ndarray

  • specific_humidity
    (time, prediction_timedelta, level, latitude, longitude)
    float32
    dask.array<chunksize=(1, 1, 13, 721, 1440), meta=np.ndarray>

    long_name :

    Specific humidity

    short_name :

    q

    standard_name :

    specific_humidity

    units :

    kg kg**-1

    Array Chunk Bytes 10.34 TiB 51.49 MiB Shape (5134, 41, 13, 721, 1440) (1, 1, 13, 721, 1440) Dask graph 210494 chunks in 2 graph layers Data type float32 numpy.ndarray

  • surface_pressure
    (time, prediction_timedelta, latitude, longitude)
    float32
    dask.array<chunksize=(1, 1, 721, 1440), meta=np.ndarray>

    long_name :

    Surface pressure

    short_name :

    sp

    standard_name :

    surface_air_pressure

    units :

    Pa

    Array Chunk Bytes 814.14 GiB 3.96 MiB Shape (5134, 41, 721, 1440) (1, 1, 721, 1440) Dask graph 210494 chunks in 2 graph layers Data type float32 numpy.ndarray

  • temperature
    (time, prediction_timedelta, level, latitude, longitude)
    float32
    dask.array<chunksize=(1, 1, 13, 721, 1440), meta=np.ndarray>

    long_name :

    Temperature

    short_name :

    t

    standard_name :

    air_temperature

    units :

    K

    Array Chunk Bytes 10.34 TiB 51.49 MiB Shape (5134, 41, 13, 721, 1440) (1, 1, 13, 721, 1440) Dask graph 210494 chunks in 2 graph layers Data type float32 numpy.ndarray

  • total_precipitation
    (time, prediction_timedelta, latitude, longitude)
    float32
    dask.array<chunksize=(1, 1, 721, 1440), meta=np.ndarray>

    long_name :

    Total precipitation

    short_name :

    tp

    standard_name :

    unknown

    units :

    m

    Array Chunk Bytes 814.14 GiB 3.96 MiB Shape (5134, 41, 721, 1440) (1, 1, 721, 1440) Dask graph 210494 chunks in 2 graph layers Data type float32 numpy.ndarray

  • total_precipitation_24hr
    (time, prediction_timedelta, latitude, longitude)
    float32
    dask.array<chunksize=(1, 1, 721, 1440), meta=np.ndarray>

    Array Chunk Bytes 814.14 GiB 3.96 MiB Shape (5134, 41, 721, 1440) (1, 1, 721, 1440) Dask graph 210494 chunks in 2 graph layers Data type float32 numpy.ndarray

  • total_precipitation_6hr
    (time, prediction_timedelta, latitude, longitude)
    float32
    dask.array<chunksize=(1, 1, 721, 1440), meta=np.ndarray>

    Array Chunk Bytes 814.14 GiB 3.96 MiB Shape (5134, 41, 721, 1440) (1, 1, 721, 1440) Dask graph 210494 chunks in 2 graph layers Data type float32 numpy.ndarray

  • u_component_of_wind
    (time, prediction_timedelta, level, latitude, longitude)
    float32
    dask.array<chunksize=(1, 1, 13, 721, 1440), meta=np.ndarray>

    long_name :

    U component of wind

    short_name :

    u

    standard_name :

    eastward_wind

    units :

    m s**-1

    Array Chunk Bytes 10.34 TiB 51.49 MiB Shape (5134, 41, 13, 721, 1440) (1, 1, 13, 721, 1440) Dask graph 210494 chunks in 2 graph layers Data type float32 numpy.ndarray

  • v_component_of_wind
    (time, prediction_timedelta, level, latitude, longitude)
    float32
    dask.array<chunksize=(1, 1, 13, 721, 1440), meta=np.ndarray>

    long_name :

    V component of wind

    short_name :

    v

    standard_name :

    northward_wind

    units :

    m s**-1

    Array Chunk Bytes 10.34 TiB 51.49 MiB Shape (5134, 41, 13, 721, 1440) (1, 1, 13, 721, 1440) Dask graph 210494 chunks in 2 graph layers Data type float32 numpy.ndarray

  • vertical_velocity
    (time, prediction_timedelta, level, latitude, longitude)
    float32
    dask.array<chunksize=(1, 1, 13, 721, 1440), meta=np.ndarray>

    long_name :

    Vertical velocity

    short_name :

    w

    standard_name :

    lagrangian_tendency_of_air_pressure

    units :

    Pa s**-1

    Array Chunk Bytes 10.34 TiB 51.49 MiB Shape (5134, 41, 13, 721, 1440) (1, 1, 13, 721, 1440) Dask graph 210494 chunks in 2 graph layers Data type float32 numpy.ndarray

  • wind_speed
    (time, prediction_timedelta, level, latitude, longitude)
    float32
    dask.array<chunksize=(1, 1, 13, 721, 1440), meta=np.ndarray>

    Array Chunk Bytes 10.34 TiB 51.49 MiB Shape (5134, 41, 13, 721, 1440) (1, 1, 13, 721, 1440) Dask graph 210494 chunks in 2 graph layers Data type float32 numpy.ndarray

• Indexes: (5)
  • latitude
    PandasIndex

    PandasIndex(Index([ -90.0, -89.75,  -89.5, -89.25,  -89.0, -88.75,  -88.5, -88.25,  -88.0,
           -87.75,
           ...
            87.75,   88.0,  88.25,   88.5,  88.75,   89.0,  89.25,   89.5,  89.75,
             90.0],
          dtype='float32', name='latitude', length=721))

  • level
    PandasIndex

    PandasIndex(Index([50, 100, 150, 200, 250, 300, 400, 500, 600, 700, 850, 925, 1000], dtype='int32', name='level'))

  • longitude
    PandasIndex

    PandasIndex(Index([   0.0,   0.25,    0.5,   0.75,    1.0,   1.25,    1.5,   1.75,    2.0,
             2.25,
           ...
            357.5, 357.75,  358.0, 358.25,  358.5, 358.75,  359.0, 359.25,  359.5,
           359.75],
          dtype='float32', name='longitude', length=1440))

  • prediction_timedelta
    PandasIndex

    PandasIndex(TimedeltaIndex([ '0 days 00:00:00',  '0 days 06:00:00',  '0 days 12:00:00',
                     '0 days 18:00:00',  '1 days 00:00:00',  '1 days 06:00:00',
                     '1 days 12:00:00',  '1 days 18:00:00',  '2 days 00:00:00',
                     '2 days 06:00:00',  '2 days 12:00:00',  '2 days 18:00:00',
                     '3 days 00:00:00',  '3 days 06:00:00',  '3 days 12:00:00',
                     '3 days 18:00:00',  '4 days 00:00:00',  '4 days 06:00:00',
                     '4 days 12:00:00',  '4 days 18:00:00',  '5 days 00:00:00',
                     '5 days 06:00:00',  '5 days 12:00:00',  '5 days 18:00:00',
                     '6 days 00:00:00',  '6 days 06:00:00',  '6 days 12:00:00',
                     '6 days 18:00:00',  '7 days 00:00:00',  '7 days 06:00:00',
                     '7 days 12:00:00',  '7 days 18:00:00',  '8 days 00:00:00',
                     '8 days 06:00:00',  '8 days 12:00:00',  '8 days 18:00:00',
                     '9 days 00:00:00',  '9 days 06:00:00',  '9 days 12:00:00',
                     '9 days 18:00:00', '10 days 00:00:00'],
                   dtype='timedelta64[ns]', name='prediction_timedelta', freq=None))

  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['2016-01-01 00:00:00', '2016-01-01 12:00:00',
                   '2016-01-02 00:00:00', '2016-01-02 12:00:00',
                   '2016-01-03 00:00:00', '2016-01-03 12:00:00',
                   '2016-01-04 00:00:00', '2016-01-04 12:00:00',
                   '2016-01-05 00:00:00', '2016-01-05 12:00:00',
                   ...
                   '2023-01-06 00:00:00', '2023-01-06 12:00:00',
                   '2023-01-07 00:00:00', '2023-01-07 12:00:00',
                   '2023-01-08 00:00:00', '2023-01-08 12:00:00',
                   '2023-01-09 00:00:00', '2023-01-09 12:00:00',
                   '2023-01-10 00:00:00', '2023-01-10 12:00:00'],
                  dtype='datetime64[ns]', name='time', length=5134, freq=None))

• Attributes: (0)

IFS ENS

Downloading the full ensemble takes a very long time. We downloaded ensemble data from the TIGGE archive for 2018 and 2020. More years are still being downloaded.

All data from the TIGGE archive can only be used for research purposes. Please check the license for more specific constraints.

Location: gs://weatherbench2/datasets/ens/

Files:

• 2018-1440x721.zarr

• 2018-512x256_equiangular_conservative.zarr

• 2018-240x121_equiangular_with_poles_conservative.zarr

• 2018-128x64_equiangular_with_poles_conservative.zarr

• 2018-64x32_equiangular_with_poles_conservative.zarr

• 2020-1440x721.zarr

• 2020-512x256_equiangular_conservative.zarr

• 2020-240x121_equiangular_with_poles_conservative.zarr

• 2020-128x64_equiangular_with_poles_conservative.zarr

• 2020-64x32_equiangular_with_poles_conservative.zarr

xr.open_zarr('gs://weatherbench2/datasets/ens/2020-1440x721.zarr')

<xarray.Dataset>
Dimensions:                   (time: 732, number: 50, prediction_timedelta: 61,
                               latitude: 721, longitude: 1440, level: 3)
Coordinates:
  * latitude                  (latitude) float32 -90.0 -89.75 ... 89.75 90.0
  * level                     (level) int32 500 700 850
  * longitude                 (longitude) float32 0.0 0.25 0.5 ... 359.5 359.8
  * number                    (number) int32 1 2 3 4 5 6 7 ... 45 46 47 48 49 50
  * prediction_timedelta      (prediction_timedelta) timedelta64[ns] 00:00:00...
  * time                      (time) datetime64[ns] 2020-01-01 ... 2020-12-31...
Data variables: (12/14)
    10m_u_component_of_wind   (time, number, prediction_timedelta, latitude, longitude) float32 dask.array<chunksize=(1, 50, 1, 721, 1440), meta=np.ndarray>
    10m_v_component_of_wind   (time, number, prediction_timedelta, latitude, longitude) float32 dask.array<chunksize=(1, 50, 1, 721, 1440), meta=np.ndarray>
    10m_wind_speed            (time, number, prediction_timedelta, latitude, longitude) float32 dask.array<chunksize=(1, 50, 1, 721, 1440), meta=np.ndarray>
    2m_temperature            (time, number, prediction_timedelta, latitude, longitude) float32 dask.array<chunksize=(1, 50, 1, 721, 1440), meta=np.ndarray>
    geopotential              (time, number, prediction_timedelta, level, latitude, longitude) float32 dask.array<chunksize=(1, 50, 1, 3, 721, 1440), meta=np.ndarray>
    mean_sea_level_pressure   (time, number, prediction_timedelta, latitude, longitude) float32 dask.array<chunksize=(1, 50, 1, 721, 1440), meta=np.ndarray>
    ...                        ...
    total_precipitation       (time, number, prediction_timedelta, latitude, longitude) float32 dask.array<chunksize=(1, 50, 1, 721, 1440), meta=np.ndarray>
    total_precipitation_24hr  (time, number, prediction_timedelta, latitude, longitude) float32 dask.array<chunksize=(1, 50, 1, 721, 1440), meta=np.ndarray>
    total_precipitation_6hr   (time, number, prediction_timedelta, latitude, longitude) float32 dask.array<chunksize=(1, 50, 1, 721, 1440), meta=np.ndarray>
    u_component_of_wind       (time, number, prediction_timedelta, level, latitude, longitude) float32 dask.array<chunksize=(1, 50, 1, 3, 721, 1440), meta=np.ndarray>
    v_component_of_wind       (time, number, prediction_timedelta, level, latitude, longitude) float32 dask.array<chunksize=(1, 50, 1, 3, 721, 1440), meta=np.ndarray>
    wind_speed                (time, number, prediction_timedelta, level, latitude, longitude) float32 dask.array<chunksize=(1, 50, 1, 3, 721, 1440), meta=np.ndarray>

xarray.Dataset

• Dimensions:
  • time: 732
  • number: 50
  • prediction_timedelta: 61
  • latitude: 721
  • longitude: 1440
  • level: 3
• Coordinates: (6)
  • latitude
    (latitude)
    float32
    -90.0 -89.75 -89.5 ... 89.75 90.0

    array([-90.  , -89.75, -89.5 , ...,  89.5 ,  89.75,  90.  ], dtype=float32)

  • level
    (level)
    int32
    500 700 850

    array([500, 700, 850], dtype=int32)

  • longitude
    (longitude)
    float32
    0.0 0.25 0.5 ... 359.2 359.5 359.8

    array([0.0000e+00, 2.5000e-01, 5.0000e-01, ..., 3.5925e+02, 3.5950e+02,
           3.5975e+02], dtype=float32)

  • number
    (number)
    int32
    1 2 3 4 5 6 7 ... 45 46 47 48 49 50

    array([ 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,
           37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50], dtype=int32)

  • prediction_timedelta
    (prediction_timedelta)
    timedelta64[ns]
    0 days 00:00:00 ... 15 days 00:0...

    long_name :

    time since forecast_reference_time

    standard_name :

    forecast_period

    array([               0,   21600000000000,   43200000000000,   64800000000000,
             86400000000000,  108000000000000,  129600000000000,  151200000000000,
            172800000000000,  194400000000000,  216000000000000,  237600000000000,
            259200000000000,  280800000000000,  302400000000000,  324000000000000,
            345600000000000,  367200000000000,  388800000000000,  410400000000000,
            432000000000000,  453600000000000,  475200000000000,  496800000000000,
            518400000000000,  540000000000000,  561600000000000,  583200000000000,
            604800000000000,  626400000000000,  648000000000000,  669600000000000,
            691200000000000,  712800000000000,  734400000000000,  756000000000000,
            777600000000000,  799200000000000,  820800000000000,  842400000000000,
            864000000000000,  885600000000000,  907200000000000,  928800000000000,
            950400000000000,  972000000000000,  993600000000000, 1015200000000000,
           1036800000000000, 1058400000000000, 1080000000000000, 1101600000000000,
           1123200000000000, 1144800000000000, 1166400000000000, 1188000000000000,
           1209600000000000, 1231200000000000, 1252800000000000, 1274400000000000,
           1296000000000000], dtype='timedelta64[ns]')

  • time
    (time)
    datetime64[ns]
    2020-01-01 ... 2020-12-31T12:00:00

    long_name :

    initial time of forecast

    standard_name :

    forecast_reference_time

    array(['2020-01-01T00:00:00.000000000', '2020-01-01T12:00:00.000000000',
           '2020-01-02T00:00:00.000000000', ..., '2020-12-30T12:00:00.000000000',
           '2020-12-31T00:00:00.000000000', '2020-12-31T12:00:00.000000000'],
          dtype='datetime64[ns]')

• Data variables: (14)
  • 10m_u_component_of_wind
    (time, number, prediction_timedelta, latitude, longitude)
    float32
    dask.array<chunksize=(1, 50, 1, 721, 1440), meta=np.ndarray>

    long_name :

    10 metre U wind component

    short_name :

    u10

    standard_name :

    unknown

    units :

    m s**-1

    Array Chunk Bytes 8.43 TiB 198.03 MiB Shape (732, 50, 61, 721, 1440) (1, 50, 1, 721, 1440) Dask graph 44652 chunks in 2 graph layers Data type float32 numpy.ndarray

  • 10m_v_component_of_wind
    (time, number, prediction_timedelta, latitude, longitude)
    float32
    dask.array<chunksize=(1, 50, 1, 721, 1440), meta=np.ndarray>

    long_name :

    10 metre V wind component

    short_name :

    v10

    standard_name :

    unknown

    units :

    m s**-1

    Array Chunk Bytes 8.43 TiB 198.03 MiB Shape (732, 50, 61, 721, 1440) (1, 50, 1, 721, 1440) Dask graph 44652 chunks in 2 graph layers Data type float32 numpy.ndarray

  • 10m_wind_speed
    (time, number, prediction_timedelta, latitude, longitude)
    float32
    dask.array<chunksize=(1, 50, 1, 721, 1440), meta=np.ndarray>

    Array Chunk Bytes 8.43 TiB 198.03 MiB Shape (732, 50, 61, 721, 1440) (1, 50, 1, 721, 1440) Dask graph 44652 chunks in 2 graph layers Data type float32 numpy.ndarray

  • 2m_temperature
    (time, number, prediction_timedelta, latitude, longitude)
    float32
    dask.array<chunksize=(1, 50, 1, 721, 1440), meta=np.ndarray>

    long_name :

    2 metre temperature

    short_name :

    t2m

    standard_name :

    unknown

    units :

    K

    Array Chunk Bytes 8.43 TiB 198.03 MiB Shape (732, 50, 61, 721, 1440) (1, 50, 1, 721, 1440) Dask graph 44652 chunks in 2 graph layers Data type float32 numpy.ndarray

  • geopotential
    (time, number, prediction_timedelta, level, latitude, longitude)
    float32
    dask.array<chunksize=(1, 50, 1, 3, 721, 1440), meta=np.ndarray>

    long_name :

    Geopotential

    short_name :

    z

    standard_name :

    geopotential

    units :

    m**2 s**-2

    Array Chunk Bytes 25.30 TiB 594.09 MiB Shape (732, 50, 61, 3, 721, 1440) (1, 50, 1, 3, 721, 1440) Dask graph 44652 chunks in 2 graph layers Data type float32 numpy.ndarray

  • mean_sea_level_pressure
    (time, number, prediction_timedelta, latitude, longitude)
    float32
    dask.array<chunksize=(1, 50, 1, 721, 1440), meta=np.ndarray>

    long_name :

    Mean sea level pressure

    short_name :

    msl

    standard_name :

    air_pressure_at_mean_sea_level

    units :

    Pa

    Array Chunk Bytes 8.43 TiB 198.03 MiB Shape (732, 50, 61, 721, 1440) (1, 50, 1, 721, 1440) Dask graph 44652 chunks in 2 graph layers Data type float32 numpy.ndarray

  • specific_humidity
    (time, number, prediction_timedelta, level, latitude, longitude)
    float32
    dask.array<chunksize=(1, 50, 1, 3, 721, 1440), meta=np.ndarray>

    long_name :

    Specific humidity

    short_name :

    q

    standard_name :

    specific_humidity

    units :

    kg kg**-1

    Array Chunk Bytes 25.30 TiB 594.09 MiB Shape (732, 50, 61, 3, 721, 1440) (1, 50, 1, 3, 721, 1440) Dask graph 44652 chunks in 2 graph layers Data type float32 numpy.ndarray

  • temperature
    (time, number, prediction_timedelta, level, latitude, longitude)
    float32
    dask.array<chunksize=(1, 50, 1, 3, 721, 1440), meta=np.ndarray>

    long_name :

    Temperature

    short_name :

    t

    standard_name :

    air_temperature

    units :

    K

    Array Chunk Bytes 25.30 TiB 594.09 MiB Shape (732, 50, 61, 3, 721, 1440) (1, 50, 1, 3, 721, 1440) Dask graph 44652 chunks in 2 graph layers Data type float32 numpy.ndarray

  • total_precipitation
    (time, number, prediction_timedelta, latitude, longitude)
    float32
    dask.array<chunksize=(1, 50, 1, 721, 1440), meta=np.ndarray>

    long_name :

    Total precipitation

    short_name :

    tp

    standard_name :

    unknown

    units :

    m

    Array Chunk Bytes 8.43 TiB 198.03 MiB Shape (732, 50, 61, 721, 1440) (1, 50, 1, 721, 1440) Dask graph 44652 chunks in 2 graph layers Data type float32 numpy.ndarray

  • total_precipitation_24hr
    (time, number, prediction_timedelta, latitude, longitude)
    float32
    dask.array<chunksize=(1, 50, 1, 721, 1440), meta=np.ndarray>

    Array Chunk Bytes 8.43 TiB 198.03 MiB Shape (732, 50, 61, 721, 1440) (1, 50, 1, 721, 1440) Dask graph 44652 chunks in 2 graph layers Data type float32 numpy.ndarray

  • total_precipitation_6hr
    (time, number, prediction_timedelta, latitude, longitude)
    float32
    dask.array<chunksize=(1, 50, 1, 721, 1440), meta=np.ndarray>

    Array Chunk Bytes 8.43 TiB 198.03 MiB Shape (732, 50, 61, 721, 1440) (1, 50, 1, 721, 1440) Dask graph 44652 chunks in 2 graph layers Data type float32 numpy.ndarray

  • u_component_of_wind
    (time, number, prediction_timedelta, level, latitude, longitude)
    float32
    dask.array<chunksize=(1, 50, 1, 3, 721, 1440), meta=np.ndarray>

    long_name :

    U component of wind

    short_name :

    u

    standard_name :

    eastward_wind

    units :

    m s**-1

    Array Chunk Bytes 25.30 TiB 594.09 MiB Shape (732, 50, 61, 3, 721, 1440) (1, 50, 1, 3, 721, 1440) Dask graph 44652 chunks in 2 graph layers Data type float32 numpy.ndarray

  • v_component_of_wind
    (time, number, prediction_timedelta, level, latitude, longitude)
    float32
    dask.array<chunksize=(1, 50, 1, 3, 721, 1440), meta=np.ndarray>

    long_name :

    V component of wind

    short_name :

    v

    standard_name :

    northward_wind

    units :

    m s**-1

    Array Chunk Bytes 25.30 TiB 594.09 MiB Shape (732, 50, 61, 3, 721, 1440) (1, 50, 1, 3, 721, 1440) Dask graph 44652 chunks in 2 graph layers Data type float32 numpy.ndarray

  • wind_speed
    (time, number, prediction_timedelta, level, latitude, longitude)
    float32
    dask.array<chunksize=(1, 50, 1, 3, 721, 1440), meta=np.ndarray>

    Array Chunk Bytes 25.30 TiB 594.09 MiB Shape (732, 50, 61, 3, 721, 1440) (1, 50, 1, 3, 721, 1440) Dask graph 44652 chunks in 2 graph layers Data type float32 numpy.ndarray

• Indexes: (6)
  • latitude
    PandasIndex

    PandasIndex(Index([ -90.0, -89.75,  -89.5, -89.25,  -89.0, -88.75,  -88.5, -88.25,  -88.0,
           -87.75,
           ...
            87.75,   88.0,  88.25,   88.5,  88.75,   89.0,  89.25,   89.5,  89.75,
             90.0],
          dtype='float32', name='latitude', length=721))

  • level
    PandasIndex

    PandasIndex(Index([500, 700, 850], dtype='int32', name='level'))

  • longitude
    PandasIndex

    PandasIndex(Index([   0.0,   0.25,    0.5,   0.75,    1.0,   1.25,    1.5,   1.75,    2.0,
             2.25,
           ...
            357.5, 357.75,  358.0, 358.25,  358.5, 358.75,  359.0, 359.25,  359.5,
           359.75],
          dtype='float32', name='longitude', length=1440))

  • number
    PandasIndex

    PandasIndex(Index([ 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,
           37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50],
          dtype='int32', name='number'))

  • prediction_timedelta
    PandasIndex

    PandasIndex(TimedeltaIndex([ '0 days 00:00:00',  '0 days 06:00:00',  '0 days 12:00:00',
                     '0 days 18:00:00',  '1 days 00:00:00',  '1 days 06:00:00',
                     '1 days 12:00:00',  '1 days 18:00:00',  '2 days 00:00:00',
                     '2 days 06:00:00',  '2 days 12:00:00',  '2 days 18:00:00',
                     '3 days 00:00:00',  '3 days 06:00:00',  '3 days 12:00:00',
                     '3 days 18:00:00',  '4 days 00:00:00',  '4 days 06:00:00',
                     '4 days 12:00:00',  '4 days 18:00:00',  '5 days 00:00:00',
                     '5 days 06:00:00',  '5 days 12:00:00',  '5 days 18:00:00',
                     '6 days 00:00:00',  '6 days 06:00:00',  '6 days 12:00:00',
                     '6 days 18:00:00',  '7 days 00:00:00',  '7 days 06:00:00',
                     '7 days 12:00:00',  '7 days 18:00:00',  '8 days 00:00:00',
                     '8 days 06:00:00',  '8 days 12:00:00',  '8 days 18:00:00',
                     '9 days 00:00:00',  '9 days 06:00:00',  '9 days 12:00:00',
                     '9 days 18:00:00', '10 days 00:00:00', '10 days 06:00:00',
                    '10 days 12:00:00', '10 days 18:00:00', '11 days 00:00:00',
                    '11 days 06:00:00', '11 days 12:00:00', '11 days 18:00:00',
                    '12 days 00:00:00', '12 days 06:00:00', '12 days 12:00:00',
                    '12 days 18:00:00', '13 days 00:00:00', '13 days 06:00:00',
                    '13 days 12:00:00', '13 days 18:00:00', '14 days 00:00:00',
                    '14 days 06:00:00', '14 days 12:00:00', '14 days 18:00:00',
                    '15 days 00:00:00'],
                   dtype='timedelta64[ns]', name='prediction_timedelta', freq=None))

  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['2020-01-01 00:00:00', '2020-01-01 12:00:00',
                   '2020-01-02 00:00:00', '2020-01-02 12:00:00',
                   '2020-01-03 00:00:00', '2020-01-03 12:00:00',
                   '2020-01-04 00:00:00', '2020-01-04 12:00:00',
                   '2020-01-05 00:00:00', '2020-01-05 12:00:00',
                   ...
                   '2020-12-27 00:00:00', '2020-12-27 12:00:00',
                   '2020-12-28 00:00:00', '2020-12-28 12:00:00',
                   '2020-12-29 00:00:00', '2020-12-29 12:00:00',
                   '2020-12-30 00:00:00', '2020-12-30 12:00:00',
                   '2020-12-31 00:00:00', '2020-12-31 12:00:00'],
                  dtype='datetime64[ns]', name='time', length=732, freq=None))

• Attributes: (0)

IFS ENS mean

We also compute the ensemble mean and save it as a separate file, e.g. to use it as a deterministic baseline.

Location: gs://weatherbench2/datasets/ens/

Files:

• 2018-1440x721_mean.zarr

• 2018-512x256_equiangular_conservative_mean.zarr

• 2018-240x121_equiangular_with_poles_conservative_mean.zarr

• 2018-128x64_equiangular_with_poles_conservative_mean.zarr

• 2018-64x32_equiangular_with_poles_conservative_mean.zarr

• 2020-1440x721_mean.zarr

• 2020-512x256_equiangular_conservative_mean.zarr

• 2020-240x121_equiangular_with_poles_conservative_mean.zarr

• 2020-128x64_equiangular_with_poles_conservative_mean.zarr

• 2020-64x32_equiangular_with_poles_conservative_mean.zarr

xr.open_zarr('gs://weatherbench2/datasets/ens/2020-1440x721_mean.zarr')

<xarray.Dataset>
Dimensions:                   (time: 732, prediction_timedelta: 61,
                               latitude: 721, longitude: 1440, level: 3)
Coordinates:
  * latitude                  (latitude) float32 -90.0 -89.75 ... 89.75 90.0
  * level                     (level) int32 500 700 850
  * longitude                 (longitude) float32 0.0 0.25 0.5 ... 359.5 359.8
  * prediction_timedelta      (prediction_timedelta) timedelta64[ns] 00:00:00...
  * time                      (time) datetime64[ns] 2020-01-01 ... 2020-12-31...
Data variables: (12/14)
    10m_u_component_of_wind   (time, prediction_timedelta, latitude, longitude) float32 dask.array<chunksize=(1, 1, 721, 1440), meta=np.ndarray>
    10m_v_component_of_wind   (time, prediction_timedelta, latitude, longitude) float32 dask.array<chunksize=(1, 1, 721, 1440), meta=np.ndarray>
    10m_wind_speed            (time, prediction_timedelta, latitude, longitude) float32 dask.array<chunksize=(1, 1, 721, 1440), meta=np.ndarray>
    2m_temperature            (time, prediction_timedelta, latitude, longitude) float32 dask.array<chunksize=(1, 1, 721, 1440), meta=np.ndarray>
    geopotential              (time, prediction_timedelta, level, latitude, longitude) float32 dask.array<chunksize=(1, 1, 3, 721, 1440), meta=np.ndarray>
    mean_sea_level_pressure   (time, prediction_timedelta, latitude, longitude) float32 dask.array<chunksize=(1, 1, 721, 1440), meta=np.ndarray>
    ...                        ...
    total_precipitation       (time, prediction_timedelta, latitude, longitude) float32 dask.array<chunksize=(1, 1, 721, 1440), meta=np.ndarray>
    total_precipitation_24hr  (time, prediction_timedelta, latitude, longitude) float32 dask.array<chunksize=(1, 1, 721, 1440), meta=np.ndarray>
    total_precipitation_6hr   (time, prediction_timedelta, latitude, longitude) float32 dask.array<chunksize=(1, 1, 721, 1440), meta=np.ndarray>
    u_component_of_wind       (time, prediction_timedelta, level, latitude, longitude) float32 dask.array<chunksize=(1, 1, 3, 721, 1440), meta=np.ndarray>
    v_component_of_wind       (time, prediction_timedelta, level, latitude, longitude) float32 dask.array<chunksize=(1, 1, 3, 721, 1440), meta=np.ndarray>
    wind_speed                (time, prediction_timedelta, level, latitude, longitude) float32 dask.array<chunksize=(1, 1, 3, 721, 1440), meta=np.ndarray>

xarray.Dataset

• Dimensions:
  • time: 732
  • prediction_timedelta: 61
  • latitude: 721
  • longitude: 1440
  • level: 3
• Coordinates: (5)
  • latitude
    (latitude)
    float32
    -90.0 -89.75 -89.5 ... 89.75 90.0

    array([-90.  , -89.75, -89.5 , ...,  89.5 ,  89.75,  90.  ], dtype=float32)

  • level
    (level)
    int32
    500 700 850

    array([500, 700, 850], dtype=int32)

  • longitude
    (longitude)
    float32
    0.0 0.25 0.5 ... 359.2 359.5 359.8

    array([0.0000e+00, 2.5000e-01, 5.0000e-01, ..., 3.5925e+02, 3.5950e+02,
           3.5975e+02], dtype=float32)

  • prediction_timedelta
    (prediction_timedelta)
    timedelta64[ns]
    0 days 00:00:00 ... 15 days 00:0...

    long_name :

    time since forecast_reference_time

    standard_name :

    forecast_period

    array([               0,   21600000000000,   43200000000000,   64800000000000,
             86400000000000,  108000000000000,  129600000000000,  151200000000000,
            172800000000000,  194400000000000,  216000000000000,  237600000000000,
            259200000000000,  280800000000000,  302400000000000,  324000000000000,
            345600000000000,  367200000000000,  388800000000000,  410400000000000,
            432000000000000,  453600000000000,  475200000000000,  496800000000000,
            518400000000000,  540000000000000,  561600000000000,  583200000000000,
            604800000000000,  626400000000000,  648000000000000,  669600000000000,
            691200000000000,  712800000000000,  734400000000000,  756000000000000,
            777600000000000,  799200000000000,  820800000000000,  842400000000000,
            864000000000000,  885600000000000,  907200000000000,  928800000000000,
            950400000000000,  972000000000000,  993600000000000, 1015200000000000,
           1036800000000000, 1058400000000000, 1080000000000000, 1101600000000000,
           1123200000000000, 1144800000000000, 1166400000000000, 1188000000000000,
           1209600000000000, 1231200000000000, 1252800000000000, 1274400000000000,
           1296000000000000], dtype='timedelta64[ns]')

  • time
    (time)
    datetime64[ns]
    2020-01-01 ... 2020-12-31T12:00:00

    long_name :

    initial time of forecast

    standard_name :

    forecast_reference_time

    array(['2020-01-01T00:00:00.000000000', '2020-01-01T12:00:00.000000000',
           '2020-01-02T00:00:00.000000000', ..., '2020-12-30T12:00:00.000000000',
           '2020-12-31T00:00:00.000000000', '2020-12-31T12:00:00.000000000'],
          dtype='datetime64[ns]')

• Data variables: (14)
  • 10m_u_component_of_wind
    (time, prediction_timedelta, latitude, longitude)
    float32
    dask.array<chunksize=(1, 1, 721, 1440), meta=np.ndarray>

    long_name :

    10 metre U wind component

    short_name :

    u10

    standard_name :

    unknown

    units :

    m s**-1

    Array Chunk Bytes 172.70 GiB 3.96 MiB Shape (732, 61, 721, 1440) (1, 1, 721, 1440) Dask graph 44652 chunks in 2 graph layers Data type float32 numpy.ndarray

  • 10m_v_component_of_wind
    (time, prediction_timedelta, latitude, longitude)
    float32
    dask.array<chunksize=(1, 1, 721, 1440), meta=np.ndarray>

    long_name :

    10 metre V wind component

    short_name :

    v10

    standard_name :

    unknown

    units :

    m s**-1

    Array Chunk Bytes 172.70 GiB 3.96 MiB Shape (732, 61, 721, 1440) (1, 1, 721, 1440) Dask graph 44652 chunks in 2 graph layers Data type float32 numpy.ndarray

  • 10m_wind_speed
    (time, prediction_timedelta, latitude, longitude)
    float32
    dask.array<chunksize=(1, 1, 721, 1440), meta=np.ndarray>

    Array Chunk Bytes 172.70 GiB 3.96 MiB Shape (732, 61, 721, 1440) (1, 1, 721, 1440) Dask graph 44652 chunks in 2 graph layers Data type float32 numpy.ndarray

  • 2m_temperature
    (time, prediction_timedelta, latitude, longitude)
    float32
    dask.array<chunksize=(1, 1, 721, 1440), meta=np.ndarray>

    long_name :

    2 metre temperature

    short_name :

    t2m

    standard_name :

    unknown

    units :

    K

    Array Chunk Bytes 172.70 GiB 3.96 MiB Shape (732, 61, 721, 1440) (1, 1, 721, 1440) Dask graph 44652 chunks in 2 graph layers Data type float32 numpy.ndarray

  • geopotential
    (time, prediction_timedelta, level, latitude, longitude)
    float32
    dask.array<chunksize=(1, 1, 3, 721, 1440), meta=np.ndarray>

    long_name :

    Geopotential

    short_name :

    z

    standard_name :

    geopotential

    units :

    m**2 s**-2

    Array Chunk Bytes 518.11 GiB 11.88 MiB Shape (732, 61, 3, 721, 1440) (1, 1, 3, 721, 1440) Dask graph 44652 chunks in 2 graph layers Data type float32 numpy.ndarray

  • mean_sea_level_pressure
    (time, prediction_timedelta, latitude, longitude)
    float32
    dask.array<chunksize=(1, 1, 721, 1440), meta=np.ndarray>

    long_name :

    Mean sea level pressure

    short_name :

    msl

    standard_name :

    air_pressure_at_mean_sea_level

    units :

    Pa

    Array Chunk Bytes 172.70 GiB 3.96 MiB Shape (732, 61, 721, 1440) (1, 1, 721, 1440) Dask graph 44652 chunks in 2 graph layers Data type float32 numpy.ndarray

  • specific_humidity
    (time, prediction_timedelta, level, latitude, longitude)
    float32
    dask.array<chunksize=(1, 1, 3, 721, 1440), meta=np.ndarray>

    long_name :

    Specific humidity

    short_name :

    q

    standard_name :

    specific_humidity

    units :

    kg kg**-1

    Array Chunk Bytes 518.11 GiB 11.88 MiB Shape (732, 61, 3, 721, 1440) (1, 1, 3, 721, 1440) Dask graph 44652 chunks in 2 graph layers Data type float32 numpy.ndarray

  • temperature
    (time, prediction_timedelta, level, latitude, longitude)
    float32
    dask.array<chunksize=(1, 1, 3, 721, 1440), meta=np.ndarray>

    long_name :

    Temperature

    short_name :

    t

    standard_name :

    air_temperature

    units :

    K

    Array Chunk Bytes 518.11 GiB 11.88 MiB Shape (732, 61, 3, 721, 1440) (1, 1, 3, 721, 1440) Dask graph 44652 chunks in 2 graph layers Data type float32 numpy.ndarray

  • total_precipitation
    (time, prediction_timedelta, latitude, longitude)
    float32
    dask.array<chunksize=(1, 1, 721, 1440), meta=np.ndarray>

    long_name :

    Total precipitation

    short_name :

    tp

    standard_name :

    unknown

    units :

    m

    Array Chunk Bytes 172.70 GiB 3.96 MiB Shape (732, 61, 721, 1440) (1, 1, 721, 1440) Dask graph 44652 chunks in 2 graph layers Data type float32 numpy.ndarray

  • total_precipitation_24hr
    (time, prediction_timedelta, latitude, longitude)
    float32
    dask.array<chunksize=(1, 1, 721, 1440), meta=np.ndarray>

    Array Chunk Bytes 172.70 GiB 3.96 MiB Shape (732, 61, 721, 1440) (1, 1, 721, 1440) Dask graph 44652 chunks in 2 graph layers Data type float32 numpy.ndarray

  • total_precipitation_6hr
    (time, prediction_timedelta, latitude, longitude)
    float32
    dask.array<chunksize=(1, 1, 721, 1440), meta=np.ndarray>

    Array Chunk Bytes 172.70 GiB 3.96 MiB Shape (732, 61, 721, 1440) (1, 1, 721, 1440) Dask graph 44652 chunks in 2 graph layers Data type float32 numpy.ndarray

  • u_component_of_wind
    (time, prediction_timedelta, level, latitude, longitude)
    float32
    dask.array<chunksize=(1, 1, 3, 721, 1440), meta=np.ndarray>

    long_name :

    U component of wind

    short_name :

    u

    standard_name :

    eastward_wind

    units :

    m s**-1

    Array Chunk Bytes 518.11 GiB 11.88 MiB Shape (732, 61, 3, 721, 1440) (1, 1, 3, 721, 1440) Dask graph 44652 chunks in 2 graph layers Data type float32 numpy.ndarray

  • v_component_of_wind
    (time, prediction_timedelta, level, latitude, longitude)
    float32
    dask.array<chunksize=(1, 1, 3, 721, 1440), meta=np.ndarray>

    long_name :

    V component of wind

    short_name :

    v

    standard_name :

    northward_wind

    units :

    m s**-1

    Array Chunk Bytes 518.11 GiB 11.88 MiB Shape (732, 61, 3, 721, 1440) (1, 1, 3, 721, 1440) Dask graph 44652 chunks in 2 graph layers Data type float32 numpy.ndarray

  • wind_speed
    (time, prediction_timedelta, level, latitude, longitude)
    float32
    dask.array<chunksize=(1, 1, 3, 721, 1440), meta=np.ndarray>

    Array Chunk Bytes 518.11 GiB 11.88 MiB Shape (732, 61, 3, 721, 1440) (1, 1, 3, 721, 1440) Dask graph 44652 chunks in 2 graph layers Data type float32 numpy.ndarray