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 1440x721 (= 0.25 degrees) and 240x121 files contain the poles, i.e. -90 and 90 degree latitude, denoted with with_poles. 64x32 files do not contain the poles to ensure equal spacing.
Ground-truth datasets
ERA5
Our ERA5 datasets were downloaded from the Copernicus Climate Data Store and have a time range from 1959 to 2023 (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/era5/1959-2023_01_10-full_37-1h-0p25deg-chunk-1.zarr
Location: gs://weatherbench2/datasets/era5/
Files:
1959-2023_01_10-full_37-1h-0p25deg-chunk-1.zarr1959-2023_01_10-wb13-6h-1440x721_with_derived_variables.zarr1959-2023_01_10-6h-240x121_equiangular_with_poles_conservative.zarr1959-2023_01_10-6h-64x32_equiangular_conservative.zarr
Note: Older version of the ERA5 files exist in the bucket to ensure continuity.
See output below for a list of variables. The file also contains several derived variables which were computed using these methods.
xr.open_zarr('gs://weatherbench2/datasets/era5/1959-2023_01_10-wb13-6h-1440x721_with_derived_variables.zarr')
<xarray.Dataset>
Dimensions: (time: 93544,
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: (12/62)
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_dewpoint_temperature (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>
above_ground (time, level, latitude, longitude) float32 dask.array<chunksize=(1, 13, 721, 1440), meta=np.ndarray>
... ...
volumetric_soil_water_layer_1 (time, latitude, longitude) float32 dask.array<chunksize=(1, 721, 1440), meta=np.ndarray>
volumetric_soil_water_layer_2 (time, latitude, longitude) float32 dask.array<chunksize=(1, 721, 1440), meta=np.ndarray>
volumetric_soil_water_layer_3 (time, latitude, longitude) float32 dask.array<chunksize=(1, 721, 1440), meta=np.ndarray>
volumetric_soil_water_layer_4 (time, latitude, longitude) float32 dask.array<chunksize=(1, 721, 1440), meta=np.ndarray>
vorticity (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
- time: 93544
- latitude: 721
- longitude: 1440
- level: 13
- latitude(latitude)float3290.0 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)int6450 100 150 200 ... 700 850 925 1000
array([ 50, 100, 150, 200, 250, 300, 400, 500, 600, 700, 850, 925, 1000]) - longitude(longitude)float320.0 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 ... 2023-01-10T18:00:00
array(['1959-01-01T00:00:00.000000000', '1959-01-01T06:00:00.000000000', '1959-01-01T12:00:00.000000000', ..., '2023-01-10T06:00:00.000000000', '2023-01-10T12:00:00.000000000', '2023-01-10T18:00:00.000000000'], dtype='datetime64[ns]')
- 10m_u_component_of_wind(time, latitude, longitude)float32dask.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 361.80 GiB 3.96 MiB Shape (93544, 721, 1440) (1, 721, 1440) Dask graph 93544 chunks in 2 graph layers Data type float32 numpy.ndarray - 10m_v_component_of_wind(time, latitude, longitude)float32dask.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 361.80 GiB 3.96 MiB Shape (93544, 721, 1440) (1, 721, 1440) Dask graph 93544 chunks in 2 graph layers Data type float32 numpy.ndarray - 10m_wind_speed(time, latitude, longitude)float32dask.array<chunksize=(1, 721, 1440), meta=np.ndarray>
Array Chunk Bytes 361.80 GiB 3.96 MiB Shape (93544, 721, 1440) (1, 721, 1440) Dask graph 93544 chunks in 2 graph layers Data type float32 numpy.ndarray - 2m_dewpoint_temperature(time, latitude, longitude)float32dask.array<chunksize=(1, 721, 1440), meta=np.ndarray>
- long_name :
- 2 metre dewpoint temperature
- short_name :
- d2m
- units :
- K
Array Chunk Bytes 361.80 GiB 3.96 MiB Shape (93544, 721, 1440) (1, 721, 1440) Dask graph 93544 chunks in 2 graph layers Data type float32 numpy.ndarray - 2m_temperature(time, latitude, longitude)float32dask.array<chunksize=(1, 721, 1440), meta=np.ndarray>
- long_name :
- 2 metre temperature
- short_name :
- t2m
- units :
- K
Array Chunk Bytes 361.80 GiB 3.96 MiB Shape (93544, 721, 1440) (1, 721, 1440) Dask graph 93544 chunks in 2 graph layers Data type float32 numpy.ndarray - above_ground(time, level, latitude, longitude)float32dask.array<chunksize=(1, 13, 721, 1440), meta=np.ndarray>
Array Chunk Bytes 4.59 TiB 51.49 MiB Shape (93544, 13, 721, 1440) (1, 13, 721, 1440) Dask graph 93544 chunks in 2 graph layers Data type float32 numpy.ndarray - ageostrophic_wind_speed(time, level, latitude, longitude)float32dask.array<chunksize=(1, 13, 721, 1440), meta=np.ndarray>
Array Chunk Bytes 4.59 TiB 51.49 MiB Shape (93544, 13, 721, 1440) (1, 13, 721, 1440) Dask graph 93544 chunks in 2 graph layers Data type float32 numpy.ndarray - angle_of_sub_gridscale_orography(latitude, longitude)float32dask.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)float32dask.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 - boundary_layer_height(time, latitude, longitude)float32dask.array<chunksize=(1, 721, 1440), meta=np.ndarray>
- long_name :
- Boundary layer height
- short_name :
- blh
- units :
- m
Array Chunk Bytes 361.80 GiB 3.96 MiB Shape (93544, 721, 1440) (1, 721, 1440) Dask graph 93544 chunks in 2 graph layers Data type float32 numpy.ndarray - divergence(time, level, latitude, longitude)float32dask.array<chunksize=(1, 13, 721, 1440), meta=np.ndarray>
Array Chunk Bytes 4.59 TiB 51.49 MiB Shape (93544, 13, 721, 1440) (1, 13, 721, 1440) Dask graph 93544 chunks in 2 graph layers Data type float32 numpy.ndarray - eddy_kinetic_energy(time, latitude, longitude)float32dask.array<chunksize=(1, 721, 1440), meta=np.ndarray>
Array Chunk Bytes 361.80 GiB 3.96 MiB Shape (93544, 721, 1440) (1, 721, 1440) Dask graph 93544 chunks in 2 graph layers Data type float32 numpy.ndarray - geopotential(time, level, latitude, longitude)float32dask.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 4.59 TiB 51.49 MiB Shape (93544, 13, 721, 1440) (1, 13, 721, 1440) Dask graph 93544 chunks in 2 graph layers Data type float32 numpy.ndarray - geopotential_at_surface(latitude, longitude)float32dask.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 - geostrophic_wind_speed(time, level, latitude, longitude)float32dask.array<chunksize=(1, 13, 721, 1440), meta=np.ndarray>
Array Chunk Bytes 4.59 TiB 51.49 MiB Shape (93544, 13, 721, 1440) (1, 13, 721, 1440) Dask graph 93544 chunks in 2 graph layers Data type float32 numpy.ndarray - high_vegetation_cover(latitude, longitude)float32dask.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 - integrated_vapor_transport(time, latitude, longitude)float32dask.array<chunksize=(1, 721, 1440), meta=np.ndarray>
Array Chunk Bytes 361.80 GiB 3.96 MiB Shape (93544, 721, 1440) (1, 721, 1440) Dask graph 93544 chunks in 2 graph layers Data type float32 numpy.ndarray - lake_cover(latitude, longitude)float32dask.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 - land_sea_mask(latitude, longitude)float32dask.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 - lapse_rate(time, level, latitude, longitude)float32dask.array<chunksize=(1, 13, 721, 1440), meta=np.ndarray>
Array Chunk Bytes 4.59 TiB 51.49 MiB Shape (93544, 13, 721, 1440) (1, 13, 721, 1440) Dask graph 93544 chunks in 2 graph layers Data type float32 numpy.ndarray - leaf_area_index_high_vegetation(time, latitude, longitude)float32dask.array<chunksize=(1, 721, 1440), meta=np.ndarray>
- long_name :
- Leaf area index, high vegetation
- short_name :
- lai_hv
- units :
- m**2 m**-2
Array Chunk Bytes 361.80 GiB 3.96 MiB Shape (93544, 721, 1440) (1, 721, 1440) Dask graph 93544 chunks in 2 graph layers Data type float32 numpy.ndarray - leaf_area_index_low_vegetation(time, latitude, longitude)float32dask.array<chunksize=(1, 721, 1440), meta=np.ndarray>
- long_name :
- Leaf area index, low vegetation
- short_name :
- lai_lv
- units :
- m**2 m**-2
Array Chunk Bytes 361.80 GiB 3.96 MiB Shape (93544, 721, 1440) (1, 721, 1440) Dask graph 93544 chunks in 2 graph layers Data type float32 numpy.ndarray - low_vegetation_cover(latitude, longitude)float32dask.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)float32dask.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 361.80 GiB 3.96 MiB Shape (93544, 721, 1440) (1, 721, 1440) Dask graph 93544 chunks in 2 graph layers Data type float32 numpy.ndarray - mean_surface_latent_heat_flux(time, latitude, longitude)float32dask.array<chunksize=(1, 721, 1440), meta=np.ndarray>
- long_name :
- Mean surface latent heat flux
- short_name :
- mslhf
- units :
- W m**-2
Array Chunk Bytes 361.80 GiB 3.96 MiB Shape (93544, 721, 1440) (1, 721, 1440) Dask graph 93544 chunks in 2 graph layers Data type float32 numpy.ndarray - mean_surface_net_long_wave_radiation_flux(time, latitude, longitude)float32dask.array<chunksize=(1, 721, 1440), meta=np.ndarray>
- long_name :
- Mean surface net long-wave radiation flux
- short_name :
- msnlwrf
- units :
- W m**-2
Array Chunk Bytes 361.80 GiB 3.96 MiB Shape (93544, 721, 1440) (1, 721, 1440) Dask graph 93544 chunks in 2 graph layers Data type float32 numpy.ndarray - mean_surface_net_short_wave_radiation_flux(time, latitude, longitude)float32dask.array<chunksize=(1, 721, 1440), meta=np.ndarray>
- long_name :
- Mean surface net short-wave radiation flux
- short_name :
- msnswrf
- units :
- W m**-2
Array Chunk Bytes 361.80 GiB 3.96 MiB Shape (93544, 721, 1440) (1, 721, 1440) Dask graph 93544 chunks in 2 graph layers Data type float32 numpy.ndarray - mean_surface_sensible_heat_flux(time, latitude, longitude)float32dask.array<chunksize=(1, 721, 1440), meta=np.ndarray>
- long_name :
- Mean surface sensible heat flux
- short_name :
- msshf
- units :
- W m**-2
Array Chunk Bytes 361.80 GiB 3.96 MiB Shape (93544, 721, 1440) (1, 721, 1440) Dask graph 93544 chunks in 2 graph layers Data type float32 numpy.ndarray - mean_top_downward_short_wave_radiation_flux(time, latitude, longitude)float32dask.array<chunksize=(1, 721, 1440), meta=np.ndarray>
- long_name :
- Mean top downward short-wave radiation flux
- short_name :
- mtdwswrf
- units :
- W m**-2
Array Chunk Bytes 361.80 GiB 3.96 MiB Shape (93544, 721, 1440) (1, 721, 1440) Dask graph 93544 chunks in 2 graph layers Data type float32 numpy.ndarray - mean_top_net_long_wave_radiation_flux(time, latitude, longitude)float32dask.array<chunksize=(1, 721, 1440), meta=np.ndarray>
- long_name :
- Mean top net long-wave radiation flux
- short_name :
- mtnlwrf
- units :
- W m**-2
Array Chunk Bytes 361.80 GiB 3.96 MiB Shape (93544, 721, 1440) (1, 721, 1440) Dask graph 93544 chunks in 2 graph layers Data type float32 numpy.ndarray - mean_top_net_short_wave_radiation_flux(time, latitude, longitude)float32dask.array<chunksize=(1, 721, 1440), meta=np.ndarray>
- long_name :
- Mean top net short-wave radiation flux
- short_name :
- mtnswrf
- units :
- W m**-2
Array Chunk Bytes 361.80 GiB 3.96 MiB Shape (93544, 721, 1440) (1, 721, 1440) Dask graph 93544 chunks in 2 graph layers Data type float32 numpy.ndarray - mean_vertically_integrated_moisture_divergence(time, latitude, longitude)float32dask.array<chunksize=(1, 721, 1440), meta=np.ndarray>
- long_name :
- Mean vertically integrated moisture divergence
- short_name :
- mvimd
- units :
- kg m**-2 s**-1
Array Chunk Bytes 361.80 GiB 3.96 MiB Shape (93544, 721, 1440) (1, 721, 1440) Dask graph 93544 chunks in 2 graph layers Data type float32 numpy.ndarray - potential_vorticity(time, level, latitude, longitude)float32dask.array<chunksize=(1, 13, 721, 1440), meta=np.ndarray>
- long_name :
- Potential vorticity
- short_name :
- pv
- units :
- K m**2 kg**-1 s**-1
Array Chunk Bytes 4.59 TiB 51.49 MiB Shape (93544, 13, 721, 1440) (1, 13, 721, 1440) Dask graph 93544 chunks in 2 graph layers Data type float32 numpy.ndarray - relative_humidity(time, level, latitude, longitude)float32dask.array<chunksize=(1, 13, 721, 1440), meta=np.ndarray>
Array Chunk Bytes 4.59 TiB 51.49 MiB Shape (93544, 13, 721, 1440) (1, 13, 721, 1440) Dask graph 93544 chunks in 2 graph layers Data type float32 numpy.ndarray - sea_ice_cover(time, latitude, longitude)float32dask.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 361.80 GiB 3.96 MiB Shape (93544, 721, 1440) (1, 721, 1440) Dask graph 93544 chunks in 2 graph layers Data type float32 numpy.ndarray - sea_surface_temperature(time, latitude, longitude)float32dask.array<chunksize=(1, 721, 1440), meta=np.ndarray>
- long_name :
- Sea surface temperature
- short_name :
- sst
- units :
- K
Array Chunk Bytes 361.80 GiB 3.96 MiB Shape (93544, 721, 1440) (1, 721, 1440) Dask graph 93544 chunks in 2 graph layers Data type float32 numpy.ndarray - slope_of_sub_gridscale_orography(latitude, longitude)float32dask.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 - snow_depth(time, latitude, longitude)float32dask.array<chunksize=(1, 721, 1440), meta=np.ndarray>
- long_name :
- Snow depth
- short_name :
- sd
- standard_name :
- lwe_thickness_of_surface_snow_amount
- units :
- m of water equivalent
Array Chunk Bytes 361.80 GiB 3.96 MiB Shape (93544, 721, 1440) (1, 721, 1440) Dask graph 93544 chunks in 2 graph layers Data type float32 numpy.ndarray - soil_type(latitude, longitude)float32dask.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)float32dask.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 4.59 TiB 51.49 MiB Shape (93544, 13, 721, 1440) (1, 13, 721, 1440) Dask graph 93544 chunks in 2 graph layers Data type float32 numpy.ndarray - standard_deviation_of_filtered_subgrid_orography(latitude, longitude)float32dask.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)float32dask.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)float32dask.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 361.80 GiB 3.96 MiB Shape (93544, 721, 1440) (1, 721, 1440) Dask graph 93544 chunks in 2 graph layers Data type float32 numpy.ndarray - temperature(time, level, latitude, longitude)float32dask.array<chunksize=(1, 13, 721, 1440), meta=np.ndarray>
- long_name :
- Temperature
- short_name :
- t
- standard_name :
- air_temperature
- units :
- K
Array Chunk Bytes 4.59 TiB 51.49 MiB Shape (93544, 13, 721, 1440) (1, 13, 721, 1440) Dask graph 93544 chunks in 2 graph layers Data type float32 numpy.ndarray - total_cloud_cover(time, latitude, longitude)float32dask.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 361.80 GiB 3.96 MiB Shape (93544, 721, 1440) (1, 721, 1440) Dask graph 93544 chunks in 2 graph layers Data type float32 numpy.ndarray - total_column_vapor(time, latitude, longitude)float32dask.array<chunksize=(1, 721, 1440), meta=np.ndarray>
Array Chunk Bytes 361.80 GiB 3.96 MiB Shape (93544, 721, 1440) (1, 721, 1440) Dask graph 93544 chunks in 2 graph layers Data type float32 numpy.ndarray - total_column_water(time, latitude, longitude)float32dask.array<chunksize=(1, 721, 1440), meta=np.ndarray>
- long_name :
- Total column water
- short_name :
- tcw
- units :
- kg m**-2
Array Chunk Bytes 361.80 GiB 3.96 MiB Shape (93544, 721, 1440) (1, 721, 1440) Dask graph 93544 chunks in 2 graph layers Data type float32 numpy.ndarray - total_column_water_vapour(time, latitude, longitude)float32dask.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 361.80 GiB 3.96 MiB Shape (93544, 721, 1440) (1, 721, 1440) Dask graph 93544 chunks in 2 graph layers Data type float32 numpy.ndarray - total_precipitation_12hr(time, latitude, longitude)float32dask.array<chunksize=(1, 721, 1440), meta=np.ndarray>
- long_name :
- Total precipitation
- short_name :
- tp
- units :
- m
Array Chunk Bytes 361.80 GiB 3.96 MiB Shape (93544, 721, 1440) (1, 721, 1440) Dask graph 93544 chunks in 2 graph layers Data type float32 numpy.ndarray - total_precipitation_24hr(time, latitude, longitude)float32dask.array<chunksize=(1, 721, 1440), meta=np.ndarray>
- long_name :
- Total precipitation
- short_name :
- tp
- units :
- m
Array Chunk Bytes 361.80 GiB 3.96 MiB Shape (93544, 721, 1440) (1, 721, 1440) Dask graph 93544 chunks in 2 graph layers Data type float32 numpy.ndarray - total_precipitation_6hr(time, latitude, longitude)float32dask.array<chunksize=(1, 721, 1440), meta=np.ndarray>
- long_name :
- Total precipitation
- short_name :
- tp
- units :
- m
Array Chunk Bytes 361.80 GiB 3.96 MiB Shape (93544, 721, 1440) (1, 721, 1440) Dask graph 93544 chunks in 2 graph layers Data type float32 numpy.ndarray - type_of_high_vegetation(latitude, longitude)float32dask.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)float32dask.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)float32dask.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 4.59 TiB 51.49 MiB Shape (93544, 13, 721, 1440) (1, 13, 721, 1440) Dask graph 93544 chunks in 2 graph layers Data type float32 numpy.ndarray - v_component_of_wind(time, level, latitude, longitude)float32dask.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 4.59 TiB 51.49 MiB Shape (93544, 13, 721, 1440) (1, 13, 721, 1440) Dask graph 93544 chunks in 2 graph layers Data type float32 numpy.ndarray - vertical_velocity(time, level, latitude, longitude)float32dask.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 4.59 TiB 51.49 MiB Shape (93544, 13, 721, 1440) (1, 13, 721, 1440) Dask graph 93544 chunks in 2 graph layers Data type float32 numpy.ndarray - volumetric_soil_water_layer_1(time, latitude, longitude)float32dask.array<chunksize=(1, 721, 1440), meta=np.ndarray>
- long_name :
- Volumetric soil water layer 1
- short_name :
- swvl1
- units :
- m**3 m**-3
Array Chunk Bytes 361.80 GiB 3.96 MiB Shape (93544, 721, 1440) (1, 721, 1440) Dask graph 93544 chunks in 2 graph layers Data type float32 numpy.ndarray - volumetric_soil_water_layer_2(time, latitude, longitude)float32dask.array<chunksize=(1, 721, 1440), meta=np.ndarray>
- long_name :
- Volumetric soil water layer 2
- short_name :
- swvl2
- units :
- m**3 m**-3
Array Chunk Bytes 361.80 GiB 3.96 MiB Shape (93544, 721, 1440) (1, 721, 1440) Dask graph 93544 chunks in 2 graph layers Data type float32 numpy.ndarray - volumetric_soil_water_layer_3(time, latitude, longitude)float32dask.array<chunksize=(1, 721, 1440), meta=np.ndarray>
- long_name :
- Volumetric soil water layer 3
- short_name :
- swvl3
- units :
- m**3 m**-3
Array Chunk Bytes 361.80 GiB 3.96 MiB Shape (93544, 721, 1440) (1, 721, 1440) Dask graph 93544 chunks in 2 graph layers Data type float32 numpy.ndarray - volumetric_soil_water_layer_4(time, latitude, longitude)float32dask.array<chunksize=(1, 721, 1440), meta=np.ndarray>
- long_name :
- Volumetric soil water layer 4
- short_name :
- swvl4
- units :
- m**3 m**-3
Array Chunk Bytes 361.80 GiB 3.96 MiB Shape (93544, 721, 1440) (1, 721, 1440) Dask graph 93544 chunks in 2 graph layers Data type float32 numpy.ndarray - vorticity(time, level, latitude, longitude)float32dask.array<chunksize=(1, 13, 721, 1440), meta=np.ndarray>
Array Chunk Bytes 4.59 TiB 51.49 MiB Shape (93544, 13, 721, 1440) (1, 13, 721, 1440) Dask graph 93544 chunks in 2 graph layers Data type float32 numpy.ndarray - wind_speed(time, level, latitude, longitude)float32dask.array<chunksize=(1, 13, 721, 1440), meta=np.ndarray>
Array Chunk Bytes 4.59 TiB 51.49 MiB Shape (93544, 13, 721, 1440) (1, 13, 721, 1440) Dask graph 93544 chunks in 2 graph layers Data type float32 numpy.ndarray
- latitudePandasIndex
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)) - levelPandasIndex
PandasIndex(Index([50, 100, 150, 200, 250, 300, 400, 500, 600, 700, 850, 925, 1000], dtype='int64', name='level'))
- longitudePandasIndex
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)) - timePandasIndex
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', ... '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=93544, freq=None))
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.zarr1990-2017_6h_512x256_equiangular_conservative.zarr1990-2017_6h_240x121_equiangular_with_poles_conservative.zarr1990-2017_6h_64x32_equiangular_conservative.zarr1990-2019_6h_1440x721.zarr1990-2019_6h_512x256_equiangular_conservative.zarr1990-2019_6h_240x121_equiangular_with_poles_conservative.zarr1990-2019_6h_64x32_equiangular_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 ... 366
* hour (hour) int64 0 6 12 18
* latitude (latitude) float32 90.0 ....
* level (level) int64 50 ... 1000
* longitude (longitude) float32 0.0 ....
Data variables: (12/52)
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_dewpoint_temperature (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>
ageostrophic_wind_speed (hour, dayofyear, level, latitude, longitude) float32 dask.array<chunksize=(3, 3, 1, 721, 1440), meta=np.ndarray>
... ...
volumetric_soil_water_layer_1 (hour, dayofyear, latitude, longitude) float32 dask.array<chunksize=(3, 3, 721, 1440), meta=np.ndarray>
volumetric_soil_water_layer_2 (hour, dayofyear, latitude, longitude) float32 dask.array<chunksize=(3, 3, 721, 1440), meta=np.ndarray>
volumetric_soil_water_layer_3 (hour, dayofyear, latitude, longitude) float32 dask.array<chunksize=(3, 3, 721, 1440), meta=np.ndarray>
volumetric_soil_water_layer_4 (hour, dayofyear, latitude, longitude) float32 dask.array<chunksize=(3, 3, 721, 1440), meta=np.ndarray>
vorticity (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
- hour: 4
- dayofyear: 366
- latitude: 721
- longitude: 1440
- level: 13
- dayofyear(dayofyear)int641 2 3 4 5 6 ... 362 363 364 365 366
array([ 1, 2, 3, ..., 364, 365, 366])
- hour(hour)int640 6 12 18
array([ 0, 6, 12, 18])
- latitude(latitude)float3290.0 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)int6450 100 150 200 ... 700 850 925 1000
array([ 50, 100, 150, 200, 250, 300, 400, 500, 600, 700, 850, 925, 1000]) - longitude(longitude)float320.0 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)
- 10m_u_component_of_wind(hour, dayofyear, latitude, longitude)float32dask.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)float32dask.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)float32dask.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_dewpoint_temperature(hour, dayofyear, latitude, longitude)float32dask.array<chunksize=(3, 3, 721, 1440), meta=np.ndarray>
- long_name :
- 2 metre dewpoint temperature
- short_name :
- d2m
- 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 - 2m_temperature(hour, dayofyear, latitude, longitude)float32dask.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 - ageostrophic_wind_speed(hour, dayofyear, level, latitude, longitude)float32dask.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 - boundary_layer_height(hour, dayofyear, latitude, longitude)float32dask.array<chunksize=(3, 3, 721, 1440), meta=np.ndarray>
- long_name :
- Boundary layer height
- short_name :
- blh
- 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 - divergence(hour, dayofyear, level, latitude, longitude)float32dask.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 - eddy_kinetic_energy(hour, dayofyear, latitude, longitude)float32dask.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 - geopotential(hour, dayofyear, level, latitude, longitude)float32dask.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 - geostrophic_wind_speed(hour, dayofyear, level, latitude, longitude)float32dask.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 - integrated_vapor_transport(hour, dayofyear, latitude, longitude)float32dask.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 - lapse_rate(hour, dayofyear, level, latitude, longitude)float32dask.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 - leaf_area_index_high_vegetation(hour, dayofyear, latitude, longitude)float32dask.array<chunksize=(3, 3, 721, 1440), meta=np.ndarray>
- long_name :
- Leaf area index, high vegetation
- short_name :
- lai_hv
- units :
- m**2 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 - leaf_area_index_low_vegetation(hour, dayofyear, latitude, longitude)float32dask.array<chunksize=(3, 3, 721, 1440), meta=np.ndarray>
- long_name :
- Leaf area index, low vegetation
- short_name :
- lai_lv
- units :
- m**2 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 - mean_sea_level_pressure(hour, dayofyear, latitude, longitude)float32dask.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 - mean_surface_latent_heat_flux(hour, dayofyear, latitude, longitude)float32dask.array<chunksize=(3, 3, 721, 1440), meta=np.ndarray>
- long_name :
- Mean surface latent heat flux
- short_name :
- mslhf
- units :
- W 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 - mean_surface_net_long_wave_radiation_flux(hour, dayofyear, latitude, longitude)float32dask.array<chunksize=(3, 3, 721, 1440), meta=np.ndarray>
- long_name :
- Mean surface net long-wave radiation flux
- short_name :
- msnlwrf
- units :
- W 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 - mean_surface_net_short_wave_radiation_flux(hour, dayofyear, latitude, longitude)float32dask.array<chunksize=(3, 3, 721, 1440), meta=np.ndarray>
- long_name :
- Mean surface net short-wave radiation flux
- short_name :
- msnswrf
- units :
- W 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 - mean_surface_sensible_heat_flux(hour, dayofyear, latitude, longitude)float32dask.array<chunksize=(3, 3, 721, 1440), meta=np.ndarray>
- long_name :
- Mean surface sensible heat flux
- short_name :
- msshf
- units :
- W 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 - mean_top_downward_short_wave_radiation_flux(hour, dayofyear, latitude, longitude)float32dask.array<chunksize=(3, 3, 721, 1440), meta=np.ndarray>
- long_name :
- Mean top downward short-wave radiation flux
- short_name :
- mtdwswrf
- units :
- W 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 - mean_top_net_long_wave_radiation_flux(hour, dayofyear, latitude, longitude)float32dask.array<chunksize=(3, 3, 721, 1440), meta=np.ndarray>
- long_name :
- Mean top net long-wave radiation flux
- short_name :
- mtnlwrf
- units :
- W 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 - mean_top_net_short_wave_radiation_flux(hour, dayofyear, latitude, longitude)float32dask.array<chunksize=(3, 3, 721, 1440), meta=np.ndarray>
- long_name :
- Mean top net short-wave radiation flux
- short_name :
- mtnswrf
- units :
- W 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 - mean_vertically_integrated_moisture_divergence(hour, dayofyear, latitude, longitude)float32dask.array<chunksize=(3, 3, 721, 1440), meta=np.ndarray>
- long_name :
- Mean vertically integrated moisture divergence
- short_name :
- mvimd
- units :
- kg m**-2 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 - potential_vorticity(hour, dayofyear, level, latitude, longitude)float32dask.array<chunksize=(3, 3, 1, 721, 1440), meta=np.ndarray>
- long_name :
- Potential vorticity
- short_name :
- pv
- units :
- K m**2 kg**-1 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 - relative_humidity(hour, dayofyear, level, latitude, longitude)float32dask.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 - sea_ice_cover(hour, dayofyear, latitude, longitude)float32dask.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)float32dask.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 - snow_depth(hour, dayofyear, latitude, longitude)float32dask.array<chunksize=(3, 3, 721, 1440), meta=np.ndarray>
- long_name :
- Snow depth
- short_name :
- sd
- standard_name :
- lwe_thickness_of_surface_snow_amount
- units :
- m of water equivalent
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)float32dask.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)float32dask.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)float32dask.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 - total_cloud_cover(hour, dayofyear, latitude, longitude)float32dask.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_vapor(hour, dayofyear, latitude, longitude)float32dask.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 - total_column_water(hour, dayofyear, latitude, longitude)float32dask.array<chunksize=(3, 3, 721, 1440), meta=np.ndarray>
- long_name :
- Total column water
- short_name :
- tcw
- 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_column_water_vapour(hour, dayofyear, latitude, longitude)float32dask.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)float32dask.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)float32dask.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)float32dask.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)float32dask.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)float32dask.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)float32dask.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)float32dask.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)float32dask.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)float32dask.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)float32dask.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 - volumetric_soil_water_layer_1(hour, dayofyear, latitude, longitude)float32dask.array<chunksize=(3, 3, 721, 1440), meta=np.ndarray>
- long_name :
- Volumetric soil water layer 1
- short_name :
- swvl1
- units :
- m**3 m**-3
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 - volumetric_soil_water_layer_2(hour, dayofyear, latitude, longitude)float32dask.array<chunksize=(3, 3, 721, 1440), meta=np.ndarray>
- long_name :
- Volumetric soil water layer 2
- short_name :
- swvl2
- units :
- m**3 m**-3
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 - volumetric_soil_water_layer_3(hour, dayofyear, latitude, longitude)float32dask.array<chunksize=(3, 3, 721, 1440), meta=np.ndarray>
- long_name :
- Volumetric soil water layer 3
- short_name :
- swvl3
- units :
- m**3 m**-3
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 - volumetric_soil_water_layer_4(hour, dayofyear, latitude, longitude)float32dask.array<chunksize=(3, 3, 721, 1440), meta=np.ndarray>
- long_name :
- Volumetric soil water layer 4
- short_name :
- swvl4
- units :
- m**3 m**-3
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 - vorticity(hour, dayofyear, level, latitude, longitude)float32dask.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 - wind_speed(hour, dayofyear, level, latitude, longitude)float32dask.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
- dayofyearPandasIndex
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)) - hourPandasIndex
PandasIndex(Index([0, 6, 12, 18], dtype='int64', name='hour'))
- latitudePandasIndex
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)) - levelPandasIndex
PandasIndex(Index([50, 100, 150, 200, 250, 300, 400, 500, 600, 700, 850, 925, 1000], dtype='int64', name='level'))
- longitudePandasIndex
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))
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.zarr2016-2022-6h-512x256_equiangular_conservative.zarr2016-2022-6h-240x121_equiangular_with_poles_conservative.zarr2016-2022-6h-64x32_equiangular_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
- time: 10268
- latitude: 721
- longitude: 1440
- level: 13
- 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)int3250 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)float320.0 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]')
- 10m_u_component_of_wind(time, latitude, longitude)float32dask.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)float32dask.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)float32dask.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)float32dask.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)float32dask.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)float32dask.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)float32dask.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)float32dask.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)float32dask.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)float32dask.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)float32dask.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)float32dask.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)float32dask.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)float32dask.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
- latitudePandasIndex
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)) - levelPandasIndex
PandasIndex(Index([50, 100, 150, 200, 250, 300, 400, 500, 600, 700, 850, 925, 1000], dtype='int32', name='level'))
- longitudePandasIndex
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)) - timePandasIndex
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))
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.zarr2016-2022-0012-512x256_equiangular_conservative.zarr2016-2022-0012-240x121_equiangular_with_poles_conservative.zarr2016-2022-0012-64x32_equiangular_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
- time: 5134
- prediction_timedelta: 41
- latitude: 721
- longitude: 1440
- level: 13
- 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)int3250 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)float320.0 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]')
- 10m_u_component_of_wind(time, prediction_timedelta, latitude, longitude)float32dask.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)float32dask.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)float32dask.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)float32dask.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)float32dask.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)float32dask.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)float32dask.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)float32dask.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)float32dask.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)float32dask.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)float32dask.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)float32dask.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)float32dask.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)float32dask.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)float32dask.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)float32dask.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
- latitudePandasIndex
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)) - levelPandasIndex
PandasIndex(Index([50, 100, 150, 200, 250, 300, 400, 500, 600, 700, 850, 925, 1000], dtype='int32', name='level'))
- longitudePandasIndex
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_timedeltaPandasIndex
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)) - timePandasIndex
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))
IFS ENS
Downloading the full ensemble takes a very long time. We downloaded ensemble data from the TIGGE archive for 2018 to 2022 (incl.).
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/ifs_ens/
Files:
2018-2022-1440x721.zarr2018-2022-240x121_equiangular_with_poles_conservative.zarr2018-2022-64x32_equiangular_conservative.zarr
xr.open_zarr('gs://weatherbench2/datasets/ifs_ens/2018-2022-1440x721.zarr')
<xarray.Dataset>
Dimensions: (time: 3652, 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] 2018-01-01 ... 2022-12-31...
Data variables: (12/15)
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
- time: 3652
- number: 50
- prediction_timedelta: 61
- latitude: 721
- longitude: 1440
- level: 3
- 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)int32500 700 850
array([500, 700, 850], dtype=int32)
- longitude(longitude)float320.0 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)int321 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]2018-01-01 ... 2022-12-31T12:00:00
- long_name :
- initial time of forecast
- standard_name :
- forecast_reference_time
array(['2018-01-01T00:00:00.000000000', '2018-01-01T12:00:00.000000000', '2018-01-02T00:00:00.000000000', ..., '2022-12-30T12:00:00.000000000', '2022-12-31T00:00:00.000000000', '2022-12-31T12:00:00.000000000'], dtype='datetime64[ns]')
- 10m_u_component_of_wind(time, number, prediction_timedelta, latitude, longitude)float32dask.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 42.07 TiB 198.03 MiB Shape (3652, 50, 61, 721, 1440) (1, 50, 1, 721, 1440) Dask graph 222772 chunks in 2 graph layers Data type float32 numpy.ndarray - 10m_v_component_of_wind(time, number, prediction_timedelta, latitude, longitude)float32dask.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 42.07 TiB 198.03 MiB Shape (3652, 50, 61, 721, 1440) (1, 50, 1, 721, 1440) Dask graph 222772 chunks in 2 graph layers Data type float32 numpy.ndarray - 10m_wind_speed(time, number, prediction_timedelta, latitude, longitude)float32dask.array<chunksize=(1, 50, 1, 721, 1440), meta=np.ndarray>
Array Chunk Bytes 42.07 TiB 198.03 MiB Shape (3652, 50, 61, 721, 1440) (1, 50, 1, 721, 1440) Dask graph 222772 chunks in 2 graph layers Data type float32 numpy.ndarray - 2m_temperature(time, number, prediction_timedelta, latitude, longitude)float32dask.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 42.07 TiB 198.03 MiB Shape (3652, 50, 61, 721, 1440) (1, 50, 1, 721, 1440) Dask graph 222772 chunks in 2 graph layers Data type float32 numpy.ndarray - geopotential(time, number, prediction_timedelta, level, latitude, longitude)float32dask.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 126.21 TiB 594.09 MiB Shape (3652, 50, 61, 3, 721, 1440) (1, 50, 1, 3, 721, 1440) Dask graph 222772 chunks in 2 graph layers Data type float32 numpy.ndarray - mean_sea_level_pressure(time, number, prediction_timedelta, latitude, longitude)float32dask.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 42.07 TiB 198.03 MiB Shape (3652, 50, 61, 721, 1440) (1, 50, 1, 721, 1440) Dask graph 222772 chunks in 2 graph layers Data type float32 numpy.ndarray - relative_humidity(time, number, prediction_timedelta, level, latitude, longitude)float32dask.array<chunksize=(1, 50, 1, 3, 721, 1440), meta=np.ndarray>
Array Chunk Bytes 126.21 TiB 594.09 MiB Shape (3652, 50, 61, 3, 721, 1440) (1, 50, 1, 3, 721, 1440) Dask graph 222772 chunks in 2 graph layers Data type float32 numpy.ndarray - specific_humidity(time, number, prediction_timedelta, level, latitude, longitude)float32dask.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 126.21 TiB 594.09 MiB Shape (3652, 50, 61, 3, 721, 1440) (1, 50, 1, 3, 721, 1440) Dask graph 222772 chunks in 2 graph layers Data type float32 numpy.ndarray - temperature(time, number, prediction_timedelta, level, latitude, longitude)float32dask.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 126.21 TiB 594.09 MiB Shape (3652, 50, 61, 3, 721, 1440) (1, 50, 1, 3, 721, 1440) Dask graph 222772 chunks in 2 graph layers Data type float32 numpy.ndarray - total_precipitation(time, number, prediction_timedelta, latitude, longitude)float32dask.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 42.07 TiB 198.03 MiB Shape (3652, 50, 61, 721, 1440) (1, 50, 1, 721, 1440) Dask graph 222772 chunks in 2 graph layers Data type float32 numpy.ndarray - total_precipitation_24hr(time, number, prediction_timedelta, latitude, longitude)float32dask.array<chunksize=(1, 50, 1, 721, 1440), meta=np.ndarray>
Array Chunk Bytes 42.07 TiB 198.03 MiB Shape (3652, 50, 61, 721, 1440) (1, 50, 1, 721, 1440) Dask graph 222772 chunks in 2 graph layers Data type float32 numpy.ndarray - total_precipitation_6hr(time, number, prediction_timedelta, latitude, longitude)float32dask.array<chunksize=(1, 50, 1, 721, 1440), meta=np.ndarray>
Array Chunk Bytes 42.07 TiB 198.03 MiB Shape (3652, 50, 61, 721, 1440) (1, 50, 1, 721, 1440) Dask graph 222772 chunks in 2 graph layers Data type float32 numpy.ndarray - u_component_of_wind(time, number, prediction_timedelta, level, latitude, longitude)float32dask.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 126.21 TiB 594.09 MiB Shape (3652, 50, 61, 3, 721, 1440) (1, 50, 1, 3, 721, 1440) Dask graph 222772 chunks in 2 graph layers Data type float32 numpy.ndarray - v_component_of_wind(time, number, prediction_timedelta, level, latitude, longitude)float32dask.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 126.21 TiB 594.09 MiB Shape (3652, 50, 61, 3, 721, 1440) (1, 50, 1, 3, 721, 1440) Dask graph 222772 chunks in 2 graph layers Data type float32 numpy.ndarray - wind_speed(time, number, prediction_timedelta, level, latitude, longitude)float32dask.array<chunksize=(1, 50, 1, 3, 721, 1440), meta=np.ndarray>
Array Chunk Bytes 126.21 TiB 594.09 MiB Shape (3652, 50, 61, 3, 721, 1440) (1, 50, 1, 3, 721, 1440) Dask graph 222772 chunks in 2 graph layers Data type float32 numpy.ndarray
- latitudePandasIndex
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)) - levelPandasIndex
PandasIndex(Index([500, 700, 850], dtype='int32', name='level'))
- longitudePandasIndex
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)) - numberPandasIndex
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_timedeltaPandasIndex
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)) - timePandasIndex
PandasIndex(DatetimeIndex(['2018-01-01 00:00:00', '2018-01-01 12:00:00', '2018-01-02 00:00:00', '2018-01-02 12:00:00', '2018-01-03 00:00:00', '2018-01-03 12:00:00', '2018-01-04 00:00:00', '2018-01-04 12:00:00', '2018-01-05 00:00:00', '2018-01-05 12:00:00', ... '2022-12-27 00:00:00', '2022-12-27 12:00:00', '2022-12-28 00:00:00', '2022-12-28 12:00:00', '2022-12-29 00:00:00', '2022-12-29 12:00:00', '2022-12-30 00:00:00', '2022-12-30 12:00:00', '2022-12-31 00:00:00', '2022-12-31 12:00:00'], dtype='datetime64[ns]', name='time', length=3652, freq=None))
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:
2018-2022-1440x721_mean.zarr2018-2022-240x121_equiangular_with_poles_conservative_mean.zarr2018-2022-64x32_equiangular_conservative_mean.zarr
xr.open_zarr('gs://weatherbench2/datasets/ifs_ens/2018-2022-1440x721_mean.zarr')
<xarray.Dataset>
Dimensions: (time: 3652, 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] 2018-01-01 ... 2022-12-31...
Data variables: (12/15)
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
- time: 3652
- prediction_timedelta: 61
- latitude: 721
- longitude: 1440
- level: 3
- 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)int32500 700 850
array([500, 700, 850], dtype=int32)
- longitude(longitude)float320.0 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]2018-01-01 ... 2022-12-31T12:00:00
- long_name :
- initial time of forecast
- standard_name :
- forecast_reference_time
array(['2018-01-01T00:00:00.000000000', '2018-01-01T12:00:00.000000000', '2018-01-02T00:00:00.000000000', ..., '2022-12-30T12:00:00.000000000', '2022-12-31T00:00:00.000000000', '2022-12-31T12:00:00.000000000'], dtype='datetime64[ns]')
- 10m_u_component_of_wind(time, prediction_timedelta, latitude, longitude)float32dask.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 861.63 GiB 3.96 MiB Shape (3652, 61, 721, 1440) (1, 1, 721, 1440) Dask graph 222772 chunks in 2 graph layers Data type float32 numpy.ndarray - 10m_v_component_of_wind(time, prediction_timedelta, latitude, longitude)float32dask.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 861.63 GiB 3.96 MiB Shape (3652, 61, 721, 1440) (1, 1, 721, 1440) Dask graph 222772 chunks in 2 graph layers Data type float32 numpy.ndarray - 10m_wind_speed(time, prediction_timedelta, latitude, longitude)float32dask.array<chunksize=(1, 1, 721, 1440), meta=np.ndarray>
Array Chunk Bytes 861.63 GiB 3.96 MiB Shape (3652, 61, 721, 1440) (1, 1, 721, 1440) Dask graph 222772 chunks in 2 graph layers Data type float32 numpy.ndarray - 2m_temperature(time, prediction_timedelta, latitude, longitude)float32dask.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 861.63 GiB 3.96 MiB Shape (3652, 61, 721, 1440) (1, 1, 721, 1440) Dask graph 222772 chunks in 2 graph layers Data type float32 numpy.ndarray - geopotential(time, prediction_timedelta, level, latitude, longitude)float32dask.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 2.52 TiB 11.88 MiB Shape (3652, 61, 3, 721, 1440) (1, 1, 3, 721, 1440) Dask graph 222772 chunks in 2 graph layers Data type float32 numpy.ndarray - mean_sea_level_pressure(time, prediction_timedelta, latitude, longitude)float32dask.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 861.63 GiB 3.96 MiB Shape (3652, 61, 721, 1440) (1, 1, 721, 1440) Dask graph 222772 chunks in 2 graph layers Data type float32 numpy.ndarray - relative_humidity(time, prediction_timedelta, level, latitude, longitude)float32dask.array<chunksize=(1, 1, 3, 721, 1440), meta=np.ndarray>
Array Chunk Bytes 2.52 TiB 11.88 MiB Shape (3652, 61, 3, 721, 1440) (1, 1, 3, 721, 1440) Dask graph 222772 chunks in 2 graph layers Data type float32 numpy.ndarray - specific_humidity(time, prediction_timedelta, level, latitude, longitude)float32dask.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 2.52 TiB 11.88 MiB Shape (3652, 61, 3, 721, 1440) (1, 1, 3, 721, 1440) Dask graph 222772 chunks in 2 graph layers Data type float32 numpy.ndarray - temperature(time, prediction_timedelta, level, latitude, longitude)float32dask.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 2.52 TiB 11.88 MiB Shape (3652, 61, 3, 721, 1440) (1, 1, 3, 721, 1440) Dask graph 222772 chunks in 2 graph layers Data type float32 numpy.ndarray - total_precipitation(time, prediction_timedelta, latitude, longitude)float32dask.array<chunksize=(1, 1, 721, 1440), meta=np.ndarray>
- long_name :
- Total precipitation
- short_name :
- tp
- standard_name :
- unknown
- units :
- m
Array Chunk Bytes 861.63 GiB 3.96 MiB Shape (3652, 61, 721, 1440) (1, 1, 721, 1440) Dask graph 222772 chunks in 2 graph layers Data type float32 numpy.ndarray - total_precipitation_24hr(time, prediction_timedelta, latitude, longitude)float32dask.array<chunksize=(1, 1, 721, 1440), meta=np.ndarray>
Array Chunk Bytes 861.63 GiB 3.96 MiB Shape (3652, 61, 721, 1440) (1, 1, 721, 1440) Dask graph 222772 chunks in 2 graph layers Data type float32 numpy.ndarray - total_precipitation_6hr(time, prediction_timedelta, latitude, longitude)float32dask.array<chunksize=(1, 1, 721, 1440), meta=np.ndarray>
Array Chunk Bytes 861.63 GiB 3.96 MiB Shape (3652, 61, 721, 1440) (1, 1, 721, 1440) Dask graph 222772 chunks in 2 graph layers Data type float32 numpy.ndarray - u_component_of_wind(time, prediction_timedelta, level, latitude, longitude)float32dask.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 2.52 TiB 11.88 MiB Shape (3652, 61, 3, 721, 1440) (1, 1, 3, 721, 1440) Dask graph 222772 chunks in 2 graph layers Data type float32 numpy.ndarray - v_component_of_wind(time, prediction_timedelta, level, latitude, longitude)float32dask.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 2.52 TiB 11.88 MiB Shape (3652, 61, 3, 721, 1440) (1, 1, 3, 721, 1440) Dask graph 222772 chunks in 2 graph layers Data type float32 numpy.ndarray - wind_speed(time, prediction_timedelta, level, latitude, longitude)float32dask.array<chunksize=(1, 1, 3, 721, 1440), meta=np.ndarray>
Array Chunk Bytes 2.52 TiB 11.88 MiB Shape (3652, 61, 3, 721, 1440) (1, 1, 3, 721, 1440) Dask graph 222772 chunks in 2 graph layers Data type float32 numpy.ndarray
- latitudePandasIndex
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)) - levelPandasIndex
PandasIndex(Index([500, 700, 850], dtype='int32', name='level'))
- longitudePandasIndex
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_timedeltaPandasIndex
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)) - timePandasIndex
PandasIndex(DatetimeIndex(['2018-01-01 00:00:00', '2018-01-01 12:00:00', '2018-01-02 00:00:00', '2018-01-02 12:00:00', '2018-01-03 00:00:00', '2018-01-03 12:00:00', '2018-01-04 00:00:00', '2018-01-04 12:00:00', '2018-01-05 00:00:00', '2018-01-05 12:00:00', ... '2022-12-27 00:00:00', '2022-12-27 12:00:00', '2022-12-28 00:00:00', '2022-12-28 12:00:00', '2022-12-29 00:00:00', '2022-12-29 12:00:00', '2022-12-30 00:00:00', '2022-12-30 12:00:00', '2022-12-31 00:00:00', '2022-12-31 12:00:00'], dtype='datetime64[ns]', name='time', length=3652, freq=None))
ERA5 forecast
As an apples-to-apples baseline for ML forecasts trained and evaluated with ERA5, we downloaded the ERA5 forecasts, a set of experimental forecasts run by ECMWF using the ERA5 IFS version, starting from ERA5 initial conditions. We downloaded data for 2018 and 2020.
Location: gs://weatherbench2/datasets/era5-forecasts/
Files:
2018-1440x721.zarr/2018-240x121_equiangular_with_poles_conservative.zarr2018-64x32_equiangular_conservative.zarr2020-1440x721.zarr/2020-240x121_equiangular_with_poles_conservative.zarr2020-64x32_equiangular_conservative.zarr
xr.open_zarr('gs://weatherbench2/datasets/era5-forecasts/2020-1440x721.zarr')
<xarray.Dataset>
Dimensions: (time: 732, prediction_timedelta: 31,
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 ... 2020-12-31T...
Data variables:
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>
specific_humidity (time, prediction_timedelta, level, latitude, longitude) float32 dask.array<chunksize=(1, 1, 3, 721, 1440), meta=np.ndarray>
temperature (time, prediction_timedelta, level, latitude, longitude) float32 dask.array<chunksize=(1, 1, 3, 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>
vertical_velocity (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
- time: 732
- prediction_timedelta: 31
- latitude: 721
- longitude: 1440
- level: 3
- 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)int32500 700 850
array([500, 700, 850], dtype=int32)
- longitude(longitude)float320.0 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, 475200000000000, 518400000000000, 561600000000000, 604800000000000, 648000000000000, 691200000000000, 734400000000000, 777600000000000, 820800000000000, 864000000000000], 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]')
- 10m_u_component_of_wind(time, prediction_timedelta, latitude, longitude)float32dask.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 87.77 GiB 3.96 MiB Shape (732, 31, 721, 1440) (1, 1, 721, 1440) Dask graph 22692 chunks in 2 graph layers Data type float32 numpy.ndarray - 10m_v_component_of_wind(time, prediction_timedelta, latitude, longitude)float32dask.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 87.77 GiB 3.96 MiB Shape (732, 31, 721, 1440) (1, 1, 721, 1440) Dask graph 22692 chunks in 2 graph layers Data type float32 numpy.ndarray - 10m_wind_speed(time, prediction_timedelta, latitude, longitude)float32dask.array<chunksize=(1, 1, 721, 1440), meta=np.ndarray>
Array Chunk Bytes 87.77 GiB 3.96 MiB Shape (732, 31, 721, 1440) (1, 1, 721, 1440) Dask graph 22692 chunks in 2 graph layers Data type float32 numpy.ndarray - 2m_temperature(time, prediction_timedelta, latitude, longitude)float32dask.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 87.77 GiB 3.96 MiB Shape (732, 31, 721, 1440) (1, 1, 721, 1440) Dask graph 22692 chunks in 2 graph layers Data type float32 numpy.ndarray - geopotential(time, prediction_timedelta, level, latitude, longitude)float32dask.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 263.30 GiB 11.88 MiB Shape (732, 31, 3, 721, 1440) (1, 1, 3, 721, 1440) Dask graph 22692 chunks in 2 graph layers Data type float32 numpy.ndarray - mean_sea_level_pressure(time, prediction_timedelta, latitude, longitude)float32dask.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 87.77 GiB 3.96 MiB Shape (732, 31, 721, 1440) (1, 1, 721, 1440) Dask graph 22692 chunks in 2 graph layers Data type float32 numpy.ndarray - specific_humidity(time, prediction_timedelta, level, latitude, longitude)float32dask.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 263.30 GiB 11.88 MiB Shape (732, 31, 3, 721, 1440) (1, 1, 3, 721, 1440) Dask graph 22692 chunks in 2 graph layers Data type float32 numpy.ndarray - temperature(time, prediction_timedelta, level, latitude, longitude)float32dask.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 263.30 GiB 11.88 MiB Shape (732, 31, 3, 721, 1440) (1, 1, 3, 721, 1440) Dask graph 22692 chunks in 2 graph layers Data type float32 numpy.ndarray - u_component_of_wind(time, prediction_timedelta, level, latitude, longitude)float32dask.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 263.30 GiB 11.88 MiB Shape (732, 31, 3, 721, 1440) (1, 1, 3, 721, 1440) Dask graph 22692 chunks in 2 graph layers Data type float32 numpy.ndarray - v_component_of_wind(time, prediction_timedelta, level, latitude, longitude)float32dask.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 263.30 GiB 11.88 MiB Shape (732, 31, 3, 721, 1440) (1, 1, 3, 721, 1440) Dask graph 22692 chunks in 2 graph layers Data type float32 numpy.ndarray - vertical_velocity(time, prediction_timedelta, level, latitude, longitude)float32dask.array<chunksize=(1, 1, 3, 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 263.30 GiB 11.88 MiB Shape (732, 31, 3, 721, 1440) (1, 1, 3, 721, 1440) Dask graph 22692 chunks in 2 graph layers Data type float32 numpy.ndarray - wind_speed(time, prediction_timedelta, level, latitude, longitude)float32dask.array<chunksize=(1, 1, 3, 721, 1440), meta=np.ndarray>
Array Chunk Bytes 263.30 GiB 11.88 MiB Shape (732, 31, 3, 721, 1440) (1, 1, 3, 721, 1440) Dask graph 22692 chunks in 2 graph layers Data type float32 numpy.ndarray
- latitudePandasIndex
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)) - levelPandasIndex
PandasIndex(Index([500, 700, 850], dtype='int32', name='level'))
- longitudePandasIndex
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_timedeltaPandasIndex
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 12:00:00', '6 days 00:00:00', '6 days 12:00:00', '7 days 00:00:00', '7 days 12:00:00', '8 days 00:00:00', '8 days 12:00:00', '9 days 00:00:00', '9 days 12:00:00', '10 days 00:00:00'], dtype='timedelta64[ns]', name='prediction_timedelta', freq=None)) - timePandasIndex
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))
Keisler (2022)
Ryan Keisler provided us with forecast using the Graph Neural Network from his 2022 paper.
Location: gs://weatherbench2/datasets/keisler/
Files:
2020-360x181.zarr2020-240x121_equiangular_with_poles_conservative.zarr2020-64x32_equiangular_conservative.zarr
xr.open_zarr('gs://weatherbench2/datasets/keisler/2020-360x181.zarr')
<xarray.Dataset>
Dimensions: (level: 3, time: 732, prediction_timedelta: 41,
latitude: 181, longitude: 360)
Coordinates:
* latitude (latitude) float64 90.0 89.0 88.0 ... -89.0 -90.0
* level (level) int64 500 700 850
* longitude (longitude) float64 0.0 1.0 2.0 ... 357.0 358.0 359.0
* prediction_timedelta (prediction_timedelta) timedelta64[ns] 00:00:00 ......
* time (time) datetime64[ns] 2020-01-01 ... 2020-12-31T12:...
Data variables:
geopotential (level, time, prediction_timedelta, latitude, longitude) float32 dask.array<chunksize=(1, 1, 41, 181, 360), meta=np.ndarray>
specific_humidity (level, time, prediction_timedelta, latitude, longitude) float32 dask.array<chunksize=(1, 1, 41, 181, 360), meta=np.ndarray>
temperature (level, time, prediction_timedelta, latitude, longitude) float32 dask.array<chunksize=(1, 1, 41, 181, 360), meta=np.ndarray>
u_component_of_wind (level, time, prediction_timedelta, latitude, longitude) float32 dask.array<chunksize=(1, 1, 41, 181, 360), meta=np.ndarray>
v_component_of_wind (level, time, prediction_timedelta, latitude, longitude) float32 dask.array<chunksize=(1, 1, 41, 181, 360), meta=np.ndarray>
wind_speed (level, time, prediction_timedelta, latitude, longitude) float32 dask.array<chunksize=(1, 1, 41, 181, 360), meta=np.ndarray>xarray.Dataset
- level: 3
- time: 732
- prediction_timedelta: 41
- latitude: 181
- longitude: 360
- latitude(latitude)float6490.0 89.0 88.0 ... -89.0 -90.0
array([ 90., 89., 88., 87., 86., 85., 84., 83., 82., 81., 80., 79., 78., 77., 76., 75., 74., 73., 72., 71., 70., 69., 68., 67., 66., 65., 64., 63., 62., 61., 60., 59., 58., 57., 56., 55., 54., 53., 52., 51., 50., 49., 48., 47., 46., 45., 44., 43., 42., 41., 40., 39., 38., 37., 36., 35., 34., 33., 32., 31., 30., 29., 28., 27., 26., 25., 24., 23., 22., 21., 20., 19., 18., 17., 16., 15., 14., 13., 12., 11., 10., 9., 8., 7., 6., 5., 4., 3., 2., 1., 0., -1., -2., -3., -4., -5., -6., -7., -8., -9., -10., -11., -12., -13., -14., -15., -16., -17., -18., -19., -20., -21., -22., -23., -24., -25., -26., -27., -28., -29., -30., -31., -32., -33., -34., -35., -36., -37., -38., -39., -40., -41., -42., -43., -44., -45., -46., -47., -48., -49., -50., -51., -52., -53., -54., -55., -56., -57., -58., -59., -60., -61., -62., -63., -64., -65., -66., -67., -68., -69., -70., -71., -72., -73., -74., -75., -76., -77., -78., -79., -80., -81., -82., -83., -84., -85., -86., -87., -88., -89., -90.]) - level(level)int64500 700 850
array([500, 700, 850])
- longitude(longitude)float640.0 1.0 2.0 ... 357.0 358.0 359.0
array([ 0., 1., 2., ..., 357., 358., 359.])
- prediction_timedelta(prediction_timedelta)timedelta64[ns]0 days 00:00:00 ... 10 days 00:0...
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]2020-01-01 ... 2020-12-31T12:00:00
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]')
- geopotential(level, time, prediction_timedelta, latitude, longitude)float32dask.array<chunksize=(1, 1, 41, 181, 360), meta=np.ndarray>
Array Chunk Bytes 21.86 GiB 10.19 MiB Shape (3, 732, 41, 181, 360) (1, 1, 41, 181, 360) Dask graph 2196 chunks in 2 graph layers Data type float32 numpy.ndarray - specific_humidity(level, time, prediction_timedelta, latitude, longitude)float32dask.array<chunksize=(1, 1, 41, 181, 360), meta=np.ndarray>
Array Chunk Bytes 21.86 GiB 10.19 MiB Shape (3, 732, 41, 181, 360) (1, 1, 41, 181, 360) Dask graph 2196 chunks in 2 graph layers Data type float32 numpy.ndarray - temperature(level, time, prediction_timedelta, latitude, longitude)float32dask.array<chunksize=(1, 1, 41, 181, 360), meta=np.ndarray>
Array Chunk Bytes 21.86 GiB 10.19 MiB Shape (3, 732, 41, 181, 360) (1, 1, 41, 181, 360) Dask graph 2196 chunks in 2 graph layers Data type float32 numpy.ndarray - u_component_of_wind(level, time, prediction_timedelta, latitude, longitude)float32dask.array<chunksize=(1, 1, 41, 181, 360), meta=np.ndarray>
Array Chunk Bytes 21.86 GiB 10.19 MiB Shape (3, 732, 41, 181, 360) (1, 1, 41, 181, 360) Dask graph 2196 chunks in 2 graph layers Data type float32 numpy.ndarray - v_component_of_wind(level, time, prediction_timedelta, latitude, longitude)float32dask.array<chunksize=(1, 1, 41, 181, 360), meta=np.ndarray>
Array Chunk Bytes 21.86 GiB 10.19 MiB Shape (3, 732, 41, 181, 360) (1, 1, 41, 181, 360) Dask graph 2196 chunks in 2 graph layers Data type float32 numpy.ndarray - wind_speed(level, time, prediction_timedelta, latitude, longitude)float32dask.array<chunksize=(1, 1, 41, 181, 360), meta=np.ndarray>
Array Chunk Bytes 21.86 GiB 10.19 MiB Shape (3, 732, 41, 181, 360) (1, 1, 41, 181, 360) Dask graph 2196 chunks in 2 graph layers Data type float32 numpy.ndarray
- latitudePandasIndex
PandasIndex(Index([ 90.0, 89.0, 88.0, 87.0, 86.0, 85.0, 84.0, 83.0, 82.0, 81.0, ... -81.0, -82.0, -83.0, -84.0, -85.0, -86.0, -87.0, -88.0, -89.0, -90.0], dtype='float64', name='latitude', length=181)) - levelPandasIndex
PandasIndex(Index([500, 700, 850], dtype='int64', name='level'))
- longitudePandasIndex
PandasIndex(Index([ 0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, ... 350.0, 351.0, 352.0, 353.0, 354.0, 355.0, 356.0, 357.0, 358.0, 359.0], dtype='float64', name='longitude', length=360)) - prediction_timedeltaPandasIndex
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)) - timePandasIndex
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))
Pangu-Weather
We ran the Pangu model using the code available on GitHub.
Location: gs://weatherbench2/datasets/pangu/
Files:
2018-2022_0012_0p25.zarr2018-2022_0012_240x121_equiangular_with_poles_conservative.zarr2018-2022_0012_64x32_equiangular_conservative.zarr
xr.open_zarr('gs://weatherbench2/datasets/pangu/2018-2022_0012_0p25.zarr')
<xarray.Dataset>
Dimensions: (time: 3652, prediction_timedelta: 40,
latitude: 721, longitude: 1440, level: 13)
Coordinates:
* latitude (latitude) float32 90.0 89.75 89.5 ... -89.75 -90.0
* level (level) int64 1000 925 850 700 ... 200 150 100 50
* longitude (longitude) float32 0.0 0.25 0.5 ... 359.5 359.8
* prediction_timedelta (prediction_timedelta) timedelta64[ns] 06:00:00 ...
* time (time) datetime64[ns] 2018-01-01 ... 2022-12-31T...
Data variables:
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>
specific_humidity (time, prediction_timedelta, level, latitude, longitude) float32 dask.array<chunksize=(1, 1, 13, 721, 1440), meta=np.ndarray>
temperature (time, prediction_timedelta, level, latitude, longitude) float32 dask.array<chunksize=(1, 1, 13, 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>
wind_speed (time, prediction_timedelta, level, latitude, longitude) float32 dask.array<chunksize=(1, 1, 13, 721, 1440), meta=np.ndarray>xarray.Dataset
- time: 3652
- prediction_timedelta: 40
- latitude: 721
- longitude: 1440
- level: 13
- latitude(latitude)float3290.0 89.75 89.5 ... -89.75 -90.0
array([ 90. , 89.75, 89.5 , ..., -89.5 , -89.75, -90. ], dtype=float32)
- level(level)int641000 925 850 700 ... 200 150 100 50
array([1000, 925, 850, 700, 600, 500, 400, 300, 250, 200, 150, 100, 50]) - longitude(longitude)float320.0 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 06:00:00 ... 10 days 00:0...
array([ 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]2018-01-01 ... 2022-12-31T12:00:00
array(['2018-01-01T00:00:00.000000000', '2018-01-01T12:00:00.000000000', '2018-01-02T00:00:00.000000000', ..., '2022-12-30T12:00:00.000000000', '2022-12-31T00:00:00.000000000', '2022-12-31T12:00:00.000000000'], dtype='datetime64[ns]')
- 10m_u_component_of_wind(time, prediction_timedelta, latitude, longitude)float32dask.array<chunksize=(1, 1, 721, 1440), meta=np.ndarray>
Array Chunk Bytes 565.00 GiB 3.96 MiB Shape (3652, 40, 721, 1440) (1, 1, 721, 1440) Dask graph 146080 chunks in 2 graph layers Data type float32 numpy.ndarray - 10m_v_component_of_wind(time, prediction_timedelta, latitude, longitude)float32dask.array<chunksize=(1, 1, 721, 1440), meta=np.ndarray>
Array Chunk Bytes 565.00 GiB 3.96 MiB Shape (3652, 40, 721, 1440) (1, 1, 721, 1440) Dask graph 146080 chunks in 2 graph layers Data type float32 numpy.ndarray - 10m_wind_speed(time, prediction_timedelta, latitude, longitude)float32dask.array<chunksize=(1, 1, 721, 1440), meta=np.ndarray>
Array Chunk Bytes 565.00 GiB 3.96 MiB Shape (3652, 40, 721, 1440) (1, 1, 721, 1440) Dask graph 146080 chunks in 2 graph layers Data type float32 numpy.ndarray - 2m_temperature(time, prediction_timedelta, latitude, longitude)float32dask.array<chunksize=(1, 1, 721, 1440), meta=np.ndarray>
Array Chunk Bytes 565.00 GiB 3.96 MiB Shape (3652, 40, 721, 1440) (1, 1, 721, 1440) Dask graph 146080 chunks in 2 graph layers Data type float32 numpy.ndarray - geopotential(time, prediction_timedelta, level, latitude, longitude)float32dask.array<chunksize=(1, 1, 13, 721, 1440), meta=np.ndarray>
Array Chunk Bytes 7.17 TiB 51.49 MiB Shape (3652, 40, 13, 721, 1440) (1, 1, 13, 721, 1440) Dask graph 146080 chunks in 2 graph layers Data type float32 numpy.ndarray - mean_sea_level_pressure(time, prediction_timedelta, latitude, longitude)float32dask.array<chunksize=(1, 1, 721, 1440), meta=np.ndarray>
Array Chunk Bytes 565.00 GiB 3.96 MiB Shape (3652, 40, 721, 1440) (1, 1, 721, 1440) Dask graph 146080 chunks in 2 graph layers Data type float32 numpy.ndarray - specific_humidity(time, prediction_timedelta, level, latitude, longitude)float32dask.array<chunksize=(1, 1, 13, 721, 1440), meta=np.ndarray>
Array Chunk Bytes 7.17 TiB 51.49 MiB Shape (3652, 40, 13, 721, 1440) (1, 1, 13, 721, 1440) Dask graph 146080 chunks in 2 graph layers Data type float32 numpy.ndarray - temperature(time, prediction_timedelta, level, latitude, longitude)float32dask.array<chunksize=(1, 1, 13, 721, 1440), meta=np.ndarray>
Array Chunk Bytes 7.17 TiB 51.49 MiB Shape (3652, 40, 13, 721, 1440) (1, 1, 13, 721, 1440) Dask graph 146080 chunks in 2 graph layers Data type float32 numpy.ndarray - u_component_of_wind(time, prediction_timedelta, level, latitude, longitude)float32dask.array<chunksize=(1, 1, 13, 721, 1440), meta=np.ndarray>
Array Chunk Bytes 7.17 TiB 51.49 MiB Shape (3652, 40, 13, 721, 1440) (1, 1, 13, 721, 1440) Dask graph 146080 chunks in 2 graph layers Data type float32 numpy.ndarray - v_component_of_wind(time, prediction_timedelta, level, latitude, longitude)float32dask.array<chunksize=(1, 1, 13, 721, 1440), meta=np.ndarray>
Array Chunk Bytes 7.17 TiB 51.49 MiB Shape (3652, 40, 13, 721, 1440) (1, 1, 13, 721, 1440) Dask graph 146080 chunks in 2 graph layers Data type float32 numpy.ndarray - wind_speed(time, prediction_timedelta, level, latitude, longitude)float32dask.array<chunksize=(1, 1, 13, 721, 1440), meta=np.ndarray>
Array Chunk Bytes 7.17 TiB 51.49 MiB Shape (3652, 40, 13, 721, 1440) (1, 1, 13, 721, 1440) Dask graph 146080 chunks in 2 graph layers Data type float32 numpy.ndarray
- latitudePandasIndex
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)) - levelPandasIndex
PandasIndex(Index([1000, 925, 850, 700, 600, 500, 400, 300, 250, 200, 150, 100, 50], dtype='int64', name='level'))
- longitudePandasIndex
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_timedeltaPandasIndex
PandasIndex(TimedeltaIndex([ '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)) - timePandasIndex
PandasIndex(DatetimeIndex(['2018-01-01 00:00:00', '2018-01-01 12:00:00', '2018-01-02 00:00:00', '2018-01-02 12:00:00', '2018-01-03 00:00:00', '2018-01-03 12:00:00', '2018-01-04 00:00:00', '2018-01-04 12:00:00', '2018-01-05 00:00:00', '2018-01-05 12:00:00', ... '2022-12-27 00:00:00', '2022-12-27 12:00:00', '2022-12-28 00:00:00', '2022-12-28 12:00:00', '2022-12-29 00:00:00', '2022-12-29 12:00:00', '2022-12-30 00:00:00', '2022-12-30 12:00:00', '2022-12-31 00:00:00', '2022-12-31 12:00:00'], dtype='datetime64[ns]', name='time', length=3652, freq=None))
Pangu-Weather (operational)
We also ran Pangu in a quasi-operational setup with IFS HRES initial conditions.
Location: gs://weatherbench2/datasets/pangu_hres_init/
Files:
2020_0012_0p25.zarr2020_0012_240x121_equiangular_with_poles_conservative.zarr2020_0012_64x32_equiangular_conservative.zarr2021_0012_0p25.zarr2021_0012_240x121_equiangular_with_poles_conservative.zarr2021_0012_64x32_equiangular_conservative.zarr2022_0012_0p25.zarr2022_0012_240x121_equiangular_with_poles_conservative.zarr2022_0012_64x32_equiangular_conservative.zarr
xr.open_zarr('gs://weatherbench2/datasets/pangu_hres_init/2020_0012_0p25.zarr')
<xarray.Dataset>
Dimensions: (time: 732, prediction_timedelta: 40,
latitude: 721, longitude: 1440, level: 13)
Coordinates:
* latitude (latitude) float32 90.0 89.75 89.5 ... -89.75 -90.0
* level (level) int64 1000 925 850 700 ... 200 150 100 50
* longitude (longitude) float32 0.0 0.25 0.5 ... 359.5 359.8
* prediction_timedelta (prediction_timedelta) timedelta64[ns] 06:00:00 ...
* time (time) datetime64[ns] 2020-01-01 ... 2020-12-31T...
Data variables:
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>
specific_humidity (time, prediction_timedelta, level, latitude, longitude) float32 dask.array<chunksize=(1, 1, 13, 721, 1440), meta=np.ndarray>
temperature (time, prediction_timedelta, level, latitude, longitude) float32 dask.array<chunksize=(1, 1, 13, 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>
wind_speed (time, prediction_timedelta, level, latitude, longitude) float32 dask.array<chunksize=(1, 1, 13, 721, 1440), meta=np.ndarray>xarray.Dataset
- time: 732
- prediction_timedelta: 40
- latitude: 721
- longitude: 1440
- level: 13
- latitude(latitude)float3290.0 89.75 89.5 ... -89.75 -90.0
array([ 90. , 89.75, 89.5 , ..., -89.5 , -89.75, -90. ], dtype=float32)
- level(level)int641000 925 850 700 ... 200 150 100 50
array([1000, 925, 850, 700, 600, 500, 400, 300, 250, 200, 150, 100, 50]) - longitude(longitude)float320.0 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 06:00:00 ... 10 days 00:0...
array([ 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]2020-01-01 ... 2020-12-31T12:00:00
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]')
- 10m_u_component_of_wind(time, prediction_timedelta, latitude, longitude)float32dask.array<chunksize=(1, 1, 721, 1440), meta=np.ndarray>
Array Chunk Bytes 113.25 GiB 3.96 MiB Shape (732, 40, 721, 1440) (1, 1, 721, 1440) Dask graph 29280 chunks in 2 graph layers Data type float32 numpy.ndarray - 10m_v_component_of_wind(time, prediction_timedelta, latitude, longitude)float32dask.array<chunksize=(1, 1, 721, 1440), meta=np.ndarray>
Array Chunk Bytes 113.25 GiB 3.96 MiB Shape (732, 40, 721, 1440) (1, 1, 721, 1440) Dask graph 29280 chunks in 2 graph layers Data type float32 numpy.ndarray - 10m_wind_speed(time, prediction_timedelta, latitude, longitude)float32dask.array<chunksize=(1, 1, 721, 1440), meta=np.ndarray>
Array Chunk Bytes 113.25 GiB 3.96 MiB Shape (732, 40, 721, 1440) (1, 1, 721, 1440) Dask graph 29280 chunks in 2 graph layers Data type float32 numpy.ndarray - 2m_temperature(time, prediction_timedelta, latitude, longitude)float32dask.array<chunksize=(1, 1, 721, 1440), meta=np.ndarray>
Array Chunk Bytes 113.25 GiB 3.96 MiB Shape (732, 40, 721, 1440) (1, 1, 721, 1440) Dask graph 29280 chunks in 2 graph layers Data type float32 numpy.ndarray - geopotential(time, prediction_timedelta, level, latitude, longitude)float32dask.array<chunksize=(1, 1, 13, 721, 1440), meta=np.ndarray>
Array Chunk Bytes 1.44 TiB 51.49 MiB Shape (732, 40, 13, 721, 1440) (1, 1, 13, 721, 1440) Dask graph 29280 chunks in 2 graph layers Data type float32 numpy.ndarray - mean_sea_level_pressure(time, prediction_timedelta, latitude, longitude)float32dask.array<chunksize=(1, 1, 721, 1440), meta=np.ndarray>
Array Chunk Bytes 113.25 GiB 3.96 MiB Shape (732, 40, 721, 1440) (1, 1, 721, 1440) Dask graph 29280 chunks in 2 graph layers Data type float32 numpy.ndarray - specific_humidity(time, prediction_timedelta, level, latitude, longitude)float32dask.array<chunksize=(1, 1, 13, 721, 1440), meta=np.ndarray>
Array Chunk Bytes 1.44 TiB 51.49 MiB Shape (732, 40, 13, 721, 1440) (1, 1, 13, 721, 1440) Dask graph 29280 chunks in 2 graph layers Data type float32 numpy.ndarray - temperature(time, prediction_timedelta, level, latitude, longitude)float32dask.array<chunksize=(1, 1, 13, 721, 1440), meta=np.ndarray>
Array Chunk Bytes 1.44 TiB 51.49 MiB Shape (732, 40, 13, 721, 1440) (1, 1, 13, 721, 1440) Dask graph 29280 chunks in 2 graph layers Data type float32 numpy.ndarray - u_component_of_wind(time, prediction_timedelta, level, latitude, longitude)float32dask.array<chunksize=(1, 1, 13, 721, 1440), meta=np.ndarray>
Array Chunk Bytes 1.44 TiB 51.49 MiB Shape (732, 40, 13, 721, 1440) (1, 1, 13, 721, 1440) Dask graph 29280 chunks in 2 graph layers Data type float32 numpy.ndarray - v_component_of_wind(time, prediction_timedelta, level, latitude, longitude)float32dask.array<chunksize=(1, 1, 13, 721, 1440), meta=np.ndarray>
Array Chunk Bytes 1.44 TiB 51.49 MiB Shape (732, 40, 13, 721, 1440) (1, 1, 13, 721, 1440) Dask graph 29280 chunks in 2 graph layers Data type float32 numpy.ndarray - wind_speed(time, prediction_timedelta, level, latitude, longitude)float32dask.array<chunksize=(1, 1, 13, 721, 1440), meta=np.ndarray>
Array Chunk Bytes 1.44 TiB 51.49 MiB Shape (732, 40, 13, 721, 1440) (1, 1, 13, 721, 1440) Dask graph 29280 chunks in 2 graph layers Data type float32 numpy.ndarray
- latitudePandasIndex
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)) - levelPandasIndex
PandasIndex(Index([1000, 925, 850, 700, 600, 500, 400, 300, 250, 200, 150, 100, 50], dtype='int64', name='level'))
- longitudePandasIndex
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_timedeltaPandasIndex
PandasIndex(TimedeltaIndex([ '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)) - timePandasIndex
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))
GraphCast
GraphCast forecasts are available for 2018 and 2020. As described in the paper, the 2018 forecast were created with a model trained with data up to and including 2017, while the 2020 forecasts were created with a model trained with data up to and including 2019. These forecasts are initialized using ERA5.
Location: gs://weatherbench2/datasets/graphcast/
Files (see directory above for exact file names for each year):
date_range_YYYY-11-16_XXXX-02-01_12_hours_derived.zarrdate_range_YYYY-11-16_XXXX-02-01_12_hours-240x121_equiangular_with_poles_conservative.zarrdate_range_YYYY-11-16_XXXX-02-01_12_hours-64x32_equiangular_conservative.zarr
xr.open_zarr('gs://weatherbench2/datasets/graphcast/2020/date_range_2019-11-16_2021-02-01_12_hours_derived.zarr')
<xarray.Dataset>
Dimensions: (time: 886, prediction_timedelta: 40, lat: 721,
lon: 1440, level: 37)
Coordinates:
* lat (lat) float32 -90.0 -89.75 -89.5 ... 89.75 90.0
* level (level) int64 1 2 3 5 7 ... 900 925 950 975 1000
* lon (lon) float32 0.0 0.25 0.5 ... 359.2 359.5 359.8
* prediction_timedelta (prediction_timedelta) timedelta64[ns] 06:00:00...
* time (time) datetime64[ns] 2019-11-16 ... 2021-01-31...
Data variables: (12/14)
10m_u_component_of_wind (time, prediction_timedelta, lat, lon) float32 dask.array<chunksize=(1, 1, 721, 1440), meta=np.ndarray>
10m_v_component_of_wind (time, prediction_timedelta, lat, lon) float32 dask.array<chunksize=(1, 1, 721, 1440), meta=np.ndarray>
10m_wind_speed (time, prediction_timedelta, lat, lon) float32 dask.array<chunksize=(1, 1, 721, 1440), meta=np.ndarray>
2m_temperature (time, prediction_timedelta, lat, lon) float32 dask.array<chunksize=(1, 1, 721, 1440), meta=np.ndarray>
geopotential (time, prediction_timedelta, level, lat, lon) float32 dask.array<chunksize=(1, 1, 37, 721, 1440), meta=np.ndarray>
mean_sea_level_pressure (time, prediction_timedelta, lat, lon) float32 dask.array<chunksize=(1, 1, 721, 1440), meta=np.ndarray>
... ...
total_precipitation_24hr (time, prediction_timedelta, lat, lon) float32 dask.array<chunksize=(1, 1, 721, 1440), meta=np.ndarray>
total_precipitation_6hr (time, prediction_timedelta, lat, lon) float32 dask.array<chunksize=(1, 1, 721, 1440), meta=np.ndarray>
u_component_of_wind (time, prediction_timedelta, level, lat, lon) float32 dask.array<chunksize=(1, 1, 37, 721, 1440), meta=np.ndarray>
v_component_of_wind (time, prediction_timedelta, level, lat, lon) float32 dask.array<chunksize=(1, 1, 37, 721, 1440), meta=np.ndarray>
vertical_velocity (time, prediction_timedelta, level, lat, lon) float32 dask.array<chunksize=(1, 1, 37, 721, 1440), meta=np.ndarray>
wind_speed (time, prediction_timedelta, level, lat, lon) float32 dask.array<chunksize=(1, 1, 37, 721, 1440), meta=np.ndarray>xarray.Dataset
- time: 886
- prediction_timedelta: 40
- lat: 721
- lon: 1440
- level: 37
- lat(lat)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)int641 2 3 5 7 ... 900 925 950 975 1000
array([ 1, 2, 3, 5, 7, 10, 20, 30, 50, 70, 100, 125, 150, 175, 200, 225, 250, 300, 350, 400, 450, 500, 550, 600, 650, 700, 750, 775, 800, 825, 850, 875, 900, 925, 950, 975, 1000]) - lon(lon)float320.0 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) - prediction_timedelta(prediction_timedelta)timedelta64[ns]0 days 06:00:00 ... 10 days 00:0...
array([ 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]2019-11-16 ... 2021-01-31T12:00:00
array(['2019-11-16T00:00:00.000000000', '2019-11-16T12:00:00.000000000', '2019-11-17T00:00:00.000000000', ..., '2021-01-30T12:00:00.000000000', '2021-01-31T00:00:00.000000000', '2021-01-31T12:00:00.000000000'], dtype='datetime64[ns]')
- 10m_u_component_of_wind(time, prediction_timedelta, lat, lon)float32dask.array<chunksize=(1, 1, 721, 1440), meta=np.ndarray>
Array Chunk Bytes 137.07 GiB 3.96 MiB Shape (886, 40, 721, 1440) (1, 1, 721, 1440) Dask graph 35440 chunks in 2 graph layers Data type float32 numpy.ndarray - 10m_v_component_of_wind(time, prediction_timedelta, lat, lon)float32dask.array<chunksize=(1, 1, 721, 1440), meta=np.ndarray>
Array Chunk Bytes 137.07 GiB 3.96 MiB Shape (886, 40, 721, 1440) (1, 1, 721, 1440) Dask graph 35440 chunks in 2 graph layers Data type float32 numpy.ndarray - 10m_wind_speed(time, prediction_timedelta, lat, lon)float32dask.array<chunksize=(1, 1, 721, 1440), meta=np.ndarray>
Array Chunk Bytes 137.07 GiB 3.96 MiB Shape (886, 40, 721, 1440) (1, 1, 721, 1440) Dask graph 35440 chunks in 2 graph layers Data type float32 numpy.ndarray - 2m_temperature(time, prediction_timedelta, lat, lon)float32dask.array<chunksize=(1, 1, 721, 1440), meta=np.ndarray>
Array Chunk Bytes 137.07 GiB 3.96 MiB Shape (886, 40, 721, 1440) (1, 1, 721, 1440) Dask graph 35440 chunks in 2 graph layers Data type float32 numpy.ndarray - geopotential(time, prediction_timedelta, level, lat, lon)float32dask.array<chunksize=(1, 1, 37, 721, 1440), meta=np.ndarray>
Array Chunk Bytes 4.95 TiB 146.54 MiB Shape (886, 40, 37, 721, 1440) (1, 1, 37, 721, 1440) Dask graph 35440 chunks in 2 graph layers Data type float32 numpy.ndarray - mean_sea_level_pressure(time, prediction_timedelta, lat, lon)float32dask.array<chunksize=(1, 1, 721, 1440), meta=np.ndarray>
Array Chunk Bytes 137.07 GiB 3.96 MiB Shape (886, 40, 721, 1440) (1, 1, 721, 1440) Dask graph 35440 chunks in 2 graph layers Data type float32 numpy.ndarray - specific_humidity(time, prediction_timedelta, level, lat, lon)float32dask.array<chunksize=(1, 1, 37, 721, 1440), meta=np.ndarray>
Array Chunk Bytes 4.95 TiB 146.54 MiB Shape (886, 40, 37, 721, 1440) (1, 1, 37, 721, 1440) Dask graph 35440 chunks in 2 graph layers Data type float32 numpy.ndarray - temperature(time, prediction_timedelta, level, lat, lon)float32dask.array<chunksize=(1, 1, 37, 721, 1440), meta=np.ndarray>
Array Chunk Bytes 4.95 TiB 146.54 MiB Shape (886, 40, 37, 721, 1440) (1, 1, 37, 721, 1440) Dask graph 35440 chunks in 2 graph layers Data type float32 numpy.ndarray - total_precipitation_24hr(time, prediction_timedelta, lat, lon)float32dask.array<chunksize=(1, 1, 721, 1440), meta=np.ndarray>
Array Chunk Bytes 137.07 GiB 3.96 MiB Shape (886, 40, 721, 1440) (1, 1, 721, 1440) Dask graph 35440 chunks in 2 graph layers Data type float32 numpy.ndarray - total_precipitation_6hr(time, prediction_timedelta, lat, lon)float32dask.array<chunksize=(1, 1, 721, 1440), meta=np.ndarray>
Array Chunk Bytes 137.07 GiB 3.96 MiB Shape (886, 40, 721, 1440) (1, 1, 721, 1440) Dask graph 35440 chunks in 2 graph layers Data type float32 numpy.ndarray - u_component_of_wind(time, prediction_timedelta, level, lat, lon)float32dask.array<chunksize=(1, 1, 37, 721, 1440), meta=np.ndarray>
Array Chunk Bytes 4.95 TiB 146.54 MiB Shape (886, 40, 37, 721, 1440) (1, 1, 37, 721, 1440) Dask graph 35440 chunks in 2 graph layers Data type float32 numpy.ndarray - v_component_of_wind(time, prediction_timedelta, level, lat, lon)float32dask.array<chunksize=(1, 1, 37, 721, 1440), meta=np.ndarray>
Array Chunk Bytes 4.95 TiB 146.54 MiB Shape (886, 40, 37, 721, 1440) (1, 1, 37, 721, 1440) Dask graph 35440 chunks in 2 graph layers Data type float32 numpy.ndarray - vertical_velocity(time, prediction_timedelta, level, lat, lon)float32dask.array<chunksize=(1, 1, 37, 721, 1440), meta=np.ndarray>
Array Chunk Bytes 4.95 TiB 146.54 MiB Shape (886, 40, 37, 721, 1440) (1, 1, 37, 721, 1440) Dask graph 35440 chunks in 2 graph layers Data type float32 numpy.ndarray - wind_speed(time, prediction_timedelta, level, lat, lon)float32dask.array<chunksize=(1, 1, 37, 721, 1440), meta=np.ndarray>
Array Chunk Bytes 4.95 TiB 146.54 MiB Shape (886, 40, 37, 721, 1440) (1, 1, 37, 721, 1440) Dask graph 35440 chunks in 2 graph layers Data type float32 numpy.ndarray
- latPandasIndex
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='lat', length=721)) - levelPandasIndex
PandasIndex(Index([ 1, 2, 3, 5, 7, 10, 20, 30, 50, 70, 100, 125, 150, 175, 200, 225, 250, 300, 350, 400, 450, 500, 550, 600, 650, 700, 750, 775, 800, 825, 850, 875, 900, 925, 950, 975, 1000], dtype='int64', name='level')) - lonPandasIndex
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='lon', length=1440)) - prediction_timedeltaPandasIndex
PandasIndex(TimedeltaIndex([ '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)) - timePandasIndex
PandasIndex(DatetimeIndex(['2019-11-16 00:00:00', '2019-11-16 12:00:00', '2019-11-17 00:00:00', '2019-11-17 12:00:00', '2019-11-18 00:00:00', '2019-11-18 12:00:00', '2019-11-19 00:00:00', '2019-11-19 12:00:00', '2019-11-20 00:00:00', '2019-11-20 12:00:00', ... '2021-01-27 00:00:00', '2021-01-27 12:00:00', '2021-01-28 00:00:00', '2021-01-28 12:00:00', '2021-01-29 00:00:00', '2021-01-29 12:00:00', '2021-01-30 00:00:00', '2021-01-30 12:00:00', '2021-01-31 00:00:00', '2021-01-31 12:00:00'], dtype='datetime64[ns]', name='time', length=886, freq=None))
GraphCast (Operational)
These GraphCast forecasts are initialized using operational IFS HRES analyses. The model details differ from the ERA5 version above. See graphcast_operational on the GraphCast GitHub repository.
Location: gs://weatherbench2/datasets/graphcast_hres_init/2020/
Files (see directory above for exact file names for each year):
date_range_2019-11-16_2021-02-01_12_hours_derived.zarrdate_range_2019-11-16_2021-02-01_12_hours-240x121_equiangular_with_poles_conservative.zarrdate_range_2019-11-16_2021-02-01_12_hours-64x32_equiangular_conservative.zarr
xr.open_zarr('gs://weatherbench2/datasets/graphcast_hres_init/2020/date_range_2019-11-16_2021-02-01_12_hours_derived.zarr')
<xarray.Dataset>
Dimensions: (time: 732, prediction_timedelta: 40, lat: 721,
lon: 1440, level: 13)
Coordinates:
* lat (lat) float32 -90.0 -89.75 -89.5 ... 89.75 90.0
* level (level) int32 50 100 150 200 ... 700 850 925 1000
* lon (lon) float32 0.0 0.25 0.5 ... 359.2 359.5 359.8
* prediction_timedelta (prediction_timedelta) timedelta64[ns] 06:00:00...
* time (time) datetime64[ns] 2020-01-01 ... 2020-12-31...
Data variables: (12/14)
10m_u_component_of_wind (time, prediction_timedelta, lat, lon) float32 dask.array<chunksize=(1, 1, 721, 1440), meta=np.ndarray>
10m_v_component_of_wind (time, prediction_timedelta, lat, lon) float32 dask.array<chunksize=(1, 1, 721, 1440), meta=np.ndarray>
10m_wind_speed (time, prediction_timedelta, lat, lon) float32 dask.array<chunksize=(1, 1, 721, 1440), meta=np.ndarray>
2m_temperature (time, prediction_timedelta, lat, lon) float32 dask.array<chunksize=(1, 1, 721, 1440), meta=np.ndarray>
geopotential (time, prediction_timedelta, level, lat, lon) float32 dask.array<chunksize=(1, 1, 13, 721, 1440), meta=np.ndarray>
mean_sea_level_pressure (time, prediction_timedelta, lat, lon) float32 dask.array<chunksize=(1, 1, 721, 1440), meta=np.ndarray>
... ...
total_precipitation_24hr (time, prediction_timedelta, lat, lon) float32 dask.array<chunksize=(1, 1, 721, 1440), meta=np.ndarray>
total_precipitation_6hr (time, prediction_timedelta, lat, lon) float32 dask.array<chunksize=(1, 1, 721, 1440), meta=np.ndarray>
u_component_of_wind (time, prediction_timedelta, level, lat, lon) float32 dask.array<chunksize=(1, 1, 13, 721, 1440), meta=np.ndarray>
v_component_of_wind (time, prediction_timedelta, level, lat, lon) float32 dask.array<chunksize=(1, 1, 13, 721, 1440), meta=np.ndarray>
vertical_velocity (time, prediction_timedelta, level, lat, lon) float32 dask.array<chunksize=(1, 1, 13, 721, 1440), meta=np.ndarray>
wind_speed (time, prediction_timedelta, level, lat, lon) float32 dask.array<chunksize=(1, 1, 13, 721, 1440), meta=np.ndarray>xarray.Dataset
- time: 732
- prediction_timedelta: 40
- lat: 721
- lon: 1440
- level: 13
- lat(lat)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)int3250 100 150 200 ... 700 850 925 1000
array([ 50, 100, 150, 200, 250, 300, 400, 500, 600, 700, 850, 925, 1000], dtype=int32) - lon(lon)float320.0 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) - prediction_timedelta(prediction_timedelta)timedelta64[ns]0 days 06:00:00 ... 10 days 00:0...
array([ 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]2020-01-01 ... 2020-12-31T12:00:00
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]')
- 10m_u_component_of_wind(time, prediction_timedelta, lat, lon)float32dask.array<chunksize=(1, 1, 721, 1440), meta=np.ndarray>
Array Chunk Bytes 113.25 GiB 3.96 MiB Shape (732, 40, 721, 1440) (1, 1, 721, 1440) Dask graph 29280 chunks in 2 graph layers Data type float32 numpy.ndarray - 10m_v_component_of_wind(time, prediction_timedelta, lat, lon)float32dask.array<chunksize=(1, 1, 721, 1440), meta=np.ndarray>
Array Chunk Bytes 113.25 GiB 3.96 MiB Shape (732, 40, 721, 1440) (1, 1, 721, 1440) Dask graph 29280 chunks in 2 graph layers Data type float32 numpy.ndarray - 10m_wind_speed(time, prediction_timedelta, lat, lon)float32dask.array<chunksize=(1, 1, 721, 1440), meta=np.ndarray>
Array Chunk Bytes 113.25 GiB 3.96 MiB Shape (732, 40, 721, 1440) (1, 1, 721, 1440) Dask graph 29280 chunks in 2 graph layers Data type float32 numpy.ndarray - 2m_temperature(time, prediction_timedelta, lat, lon)float32dask.array<chunksize=(1, 1, 721, 1440), meta=np.ndarray>
Array Chunk Bytes 113.25 GiB 3.96 MiB Shape (732, 40, 721, 1440) (1, 1, 721, 1440) Dask graph 29280 chunks in 2 graph layers Data type float32 numpy.ndarray - geopotential(time, prediction_timedelta, level, lat, lon)float32dask.array<chunksize=(1, 1, 13, 721, 1440), meta=np.ndarray>
Array Chunk Bytes 1.44 TiB 51.49 MiB Shape (732, 40, 13, 721, 1440) (1, 1, 13, 721, 1440) Dask graph 29280 chunks in 2 graph layers Data type float32 numpy.ndarray - mean_sea_level_pressure(time, prediction_timedelta, lat, lon)float32dask.array<chunksize=(1, 1, 721, 1440), meta=np.ndarray>
Array Chunk Bytes 113.25 GiB 3.96 MiB Shape (732, 40, 721, 1440) (1, 1, 721, 1440) Dask graph 29280 chunks in 2 graph layers Data type float32 numpy.ndarray - specific_humidity(time, prediction_timedelta, level, lat, lon)float32dask.array<chunksize=(1, 1, 13, 721, 1440), meta=np.ndarray>
Array Chunk Bytes 1.44 TiB 51.49 MiB Shape (732, 40, 13, 721, 1440) (1, 1, 13, 721, 1440) Dask graph 29280 chunks in 2 graph layers Data type float32 numpy.ndarray - temperature(time, prediction_timedelta, level, lat, lon)float32dask.array<chunksize=(1, 1, 13, 721, 1440), meta=np.ndarray>
Array Chunk Bytes 1.44 TiB 51.49 MiB Shape (732, 40, 13, 721, 1440) (1, 1, 13, 721, 1440) Dask graph 29280 chunks in 2 graph layers Data type float32 numpy.ndarray - total_precipitation_24hr(time, prediction_timedelta, lat, lon)float32dask.array<chunksize=(1, 1, 721, 1440), meta=np.ndarray>
Array Chunk Bytes 113.25 GiB 3.96 MiB Shape (732, 40, 721, 1440) (1, 1, 721, 1440) Dask graph 29280 chunks in 2 graph layers Data type float32 numpy.ndarray - total_precipitation_6hr(time, prediction_timedelta, lat, lon)float32dask.array<chunksize=(1, 1, 721, 1440), meta=np.ndarray>
Array Chunk Bytes 113.25 GiB 3.96 MiB Shape (732, 40, 721, 1440) (1, 1, 721, 1440) Dask graph 29280 chunks in 2 graph layers Data type float32 numpy.ndarray - u_component_of_wind(time, prediction_timedelta, level, lat, lon)float32dask.array<chunksize=(1, 1, 13, 721, 1440), meta=np.ndarray>
Array Chunk Bytes 1.44 TiB 51.49 MiB Shape (732, 40, 13, 721, 1440) (1, 1, 13, 721, 1440) Dask graph 29280 chunks in 2 graph layers Data type float32 numpy.ndarray - v_component_of_wind(time, prediction_timedelta, level, lat, lon)float32dask.array<chunksize=(1, 1, 13, 721, 1440), meta=np.ndarray>
Array Chunk Bytes 1.44 TiB 51.49 MiB Shape (732, 40, 13, 721, 1440) (1, 1, 13, 721, 1440) Dask graph 29280 chunks in 2 graph layers Data type float32 numpy.ndarray - vertical_velocity(time, prediction_timedelta, level, lat, lon)float32dask.array<chunksize=(1, 1, 13, 721, 1440), meta=np.ndarray>
Array Chunk Bytes 1.44 TiB 51.49 MiB Shape (732, 40, 13, 721, 1440) (1, 1, 13, 721, 1440) Dask graph 29280 chunks in 2 graph layers Data type float32 numpy.ndarray - wind_speed(time, prediction_timedelta, level, lat, lon)float32dask.array<chunksize=(1, 1, 13, 721, 1440), meta=np.ndarray>
Array Chunk Bytes 1.44 TiB 51.49 MiB Shape (732, 40, 13, 721, 1440) (1, 1, 13, 721, 1440) Dask graph 29280 chunks in 2 graph layers Data type float32 numpy.ndarray
- latPandasIndex
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='lat', length=721)) - levelPandasIndex
PandasIndex(Index([50, 100, 150, 200, 250, 300, 400, 500, 600, 700, 850, 925, 1000], dtype='int32', name='level'))
- lonPandasIndex
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='lon', length=1440)) - prediction_timedeltaPandasIndex
PandasIndex(TimedeltaIndex([ '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)) - timePandasIndex
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))
Spherical CNN
Forecasts using a Spherical CNN are available for 2020.
Location: gs://weatherbench2/datasets/sphericalcnn/
Files:
2020-240x121_equiangular_with_poles.zarr2020-64x32_equiangular_conservative.zarr
xr.open_zarr('gs://weatherbench2/datasets/sphericalcnn/2020-240x121_equiangular_with_poles.zarr')
<xarray.Dataset>
Dimensions: (time: 178, prediction_timedelta: 40, level: 13,
longitude: 240, latitude: 121)
Coordinates:
* latitude (latitude) float64 -90.0 -88.5 -87.0 ... 88.5 90.0
* level (level) int64 50 100 150 200 250 ... 700 850 925 1000
* longitude (longitude) float64 0.0 1.5 3.0 ... 355.5 357.0 358.5
* prediction_timedelta (prediction_timedelta) timedelta64[ns] 0 days 06:00...
* time (time) datetime64[ns] 2020-01-01 ... 2020-12-20
Data variables:
geopotential (time, prediction_timedelta, level, longitude, latitude) float32 dask.array<chunksize=(10, 40, 13, 240, 121), meta=np.ndarray>
specific_humidity (time, prediction_timedelta, level, longitude, latitude) float32 dask.array<chunksize=(10, 40, 13, 240, 121), meta=np.ndarray>
temperature (time, prediction_timedelta, level, longitude, latitude) float32 dask.array<chunksize=(10, 40, 13, 240, 121), meta=np.ndarray>
u_component_of_wind (time, prediction_timedelta, level, longitude, latitude) float32 dask.array<chunksize=(10, 40, 13, 240, 121), meta=np.ndarray>
v_component_of_wind (time, prediction_timedelta, level, longitude, latitude) float32 dask.array<chunksize=(10, 40, 13, 240, 121), meta=np.ndarray>
wind_speed (time, prediction_timedelta, level, longitude, latitude) float32 dask.array<chunksize=(10, 40, 13, 240, 121), meta=np.ndarray>xarray.Dataset
- time: 178
- prediction_timedelta: 40
- level: 13
- longitude: 240
- latitude: 121
- latitude(latitude)float64-90.0 -88.5 -87.0 ... 88.5 90.0
array([-90. , -88.5, -87. , -85.5, -84. , -82.5, -81. , -79.5, -78. , -76.5, -75. , -73.5, -72. , -70.5, -69. , -67.5, -66. , -64.5, -63. , -61.5, -60. , -58.5, -57. , -55.5, -54. , -52.5, -51. , -49.5, -48. , -46.5, -45. , -43.5, -42. , -40.5, -39. , -37.5, -36. , -34.5, -33. , -31.5, -30. , -28.5, -27. , -25.5, -24. , -22.5, -21. , -19.5, -18. , -16.5, -15. , -13.5, -12. , -10.5, -9. , -7.5, -6. , -4.5, -3. , -1.5, 0. , 1.5, 3. , 4.5, 6. , 7.5, 9. , 10.5, 12. , 13.5, 15. , 16.5, 18. , 19.5, 21. , 22.5, 24. , 25.5, 27. , 28.5, 30. , 31.5, 33. , 34.5, 36. , 37.5, 39. , 40.5, 42. , 43.5, 45. , 46.5, 48. , 49.5, 51. , 52.5, 54. , 55.5, 57. , 58.5, 60. , 61.5, 63. , 64.5, 66. , 67.5, 69. , 70.5, 72. , 73.5, 75. , 76.5, 78. , 79.5, 81. , 82.5, 84. , 85.5, 87. , 88.5, 90. ]) - level(level)int6450 100 150 200 ... 700 850 925 1000
array([ 50, 100, 150, 200, 250, 300, 400, 500, 600, 700, 850, 925, 1000]) - longitude(longitude)float640.0 1.5 3.0 ... 355.5 357.0 358.5
array([ 0. , 1.5, 3. , ..., 355.5, 357. , 358.5])
- prediction_timedelta(prediction_timedelta)timedelta64[ns]0 days 06:00:00 ... 10 days 00:0...
array([ 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]2020-01-01 ... 2020-12-20
array(['2020-01-01T00:00:00.000000000', '2020-01-03T00:00:00.000000000', '2020-01-05T00:00:00.000000000', '2020-01-07T00:00:00.000000000', '2020-01-09T00:00:00.000000000', '2020-01-11T00:00:00.000000000', '2020-01-13T00:00:00.000000000', '2020-01-15T00:00:00.000000000', '2020-01-17T00:00:00.000000000', '2020-01-19T00:00:00.000000000', '2020-01-21T00:00:00.000000000', '2020-01-23T00:00:00.000000000', '2020-01-25T00:00:00.000000000', '2020-01-27T00:00:00.000000000', '2020-01-29T00:00:00.000000000', '2020-01-31T00:00:00.000000000', '2020-02-02T00:00:00.000000000', '2020-02-04T00:00:00.000000000', '2020-02-06T00:00:00.000000000', '2020-02-08T00:00:00.000000000', '2020-02-10T00:00:00.000000000', '2020-02-12T00:00:00.000000000', '2020-02-14T00:00:00.000000000', '2020-02-16T00:00:00.000000000', '2020-02-18T00:00:00.000000000', '2020-02-20T00:00:00.000000000', '2020-02-22T00:00:00.000000000', '2020-02-24T00:00:00.000000000', '2020-02-26T00:00:00.000000000', '2020-02-28T00:00:00.000000000', '2020-03-01T00:00:00.000000000', '2020-03-03T00:00:00.000000000', '2020-03-05T00:00:00.000000000', '2020-03-07T00:00:00.000000000', '2020-03-09T00:00:00.000000000', '2020-03-11T00:00:00.000000000', '2020-03-13T00:00:00.000000000', '2020-03-15T00:00:00.000000000', '2020-03-17T00:00:00.000000000', '2020-03-19T00:00:00.000000000', '2020-03-21T00:00:00.000000000', '2020-03-23T00:00:00.000000000', '2020-03-25T00:00:00.000000000', '2020-03-27T00:00:00.000000000', '2020-03-29T00:00:00.000000000', '2020-03-31T00:00:00.000000000', '2020-04-02T00:00:00.000000000', '2020-04-04T00:00:00.000000000', '2020-04-06T00:00:00.000000000', '2020-04-08T00:00:00.000000000', '2020-04-10T00:00:00.000000000', '2020-04-12T00:00:00.000000000', '2020-04-14T00:00:00.000000000', '2020-04-16T00:00:00.000000000', '2020-04-18T00:00:00.000000000', '2020-04-20T00:00:00.000000000', '2020-04-22T00:00:00.000000000', '2020-04-24T00:00:00.000000000', '2020-04-26T00:00:00.000000000', '2020-04-28T00:00:00.000000000', '2020-04-30T00:00:00.000000000', '2020-05-02T00:00:00.000000000', '2020-05-04T00:00:00.000000000', '2020-05-06T00:00:00.000000000', '2020-05-08T00:00:00.000000000', '2020-05-10T00:00:00.000000000', '2020-05-12T00:00:00.000000000', '2020-05-14T00:00:00.000000000', '2020-05-16T00:00:00.000000000', '2020-05-18T00:00:00.000000000', '2020-05-20T00:00:00.000000000', '2020-05-22T00:00:00.000000000', '2020-05-24T00:00:00.000000000', '2020-05-26T00:00:00.000000000', '2020-05-28T00:00:00.000000000', '2020-05-30T00:00:00.000000000', '2020-06-01T00:00:00.000000000', '2020-06-03T00:00:00.000000000', '2020-06-05T00:00:00.000000000', '2020-06-07T00:00:00.000000000', '2020-06-09T00:00:00.000000000', '2020-06-11T00:00:00.000000000', '2020-06-13T00:00:00.000000000', '2020-06-15T00:00:00.000000000', '2020-06-17T00:00:00.000000000', '2020-06-19T00:00:00.000000000', '2020-06-21T00:00:00.000000000', '2020-06-23T00:00:00.000000000', '2020-06-25T00:00:00.000000000', '2020-06-27T00:00:00.000000000', '2020-06-29T00:00:00.000000000', '2020-07-01T00:00:00.000000000', '2020-07-03T00:00:00.000000000', '2020-07-05T00:00:00.000000000', '2020-07-07T00:00:00.000000000', '2020-07-09T00:00:00.000000000', '2020-07-11T00:00:00.000000000', '2020-07-13T00:00:00.000000000', '2020-07-15T00:00:00.000000000', '2020-07-17T00:00:00.000000000', '2020-07-19T00:00:00.000000000', '2020-07-21T00:00:00.000000000', '2020-07-23T00:00:00.000000000', '2020-07-25T00:00:00.000000000', '2020-07-27T00:00:00.000000000', '2020-07-29T00:00:00.000000000', '2020-07-31T00:00:00.000000000', '2020-08-02T00:00:00.000000000', '2020-08-04T00:00:00.000000000', '2020-08-06T00:00:00.000000000', '2020-08-08T00:00:00.000000000', '2020-08-10T00:00:00.000000000', '2020-08-12T00:00:00.000000000', '2020-08-14T00:00:00.000000000', '2020-08-16T00:00:00.000000000', '2020-08-18T00:00:00.000000000', '2020-08-20T00:00:00.000000000', '2020-08-22T00:00:00.000000000', '2020-08-24T00:00:00.000000000', '2020-08-26T00:00:00.000000000', '2020-08-28T00:00:00.000000000', '2020-08-30T00:00:00.000000000', '2020-09-01T00:00:00.000000000', '2020-09-03T00:00:00.000000000', '2020-09-05T00:00:00.000000000', '2020-09-07T00:00:00.000000000', '2020-09-09T00:00:00.000000000', '2020-09-11T00:00:00.000000000', '2020-09-13T00:00:00.000000000', '2020-09-15T00:00:00.000000000', '2020-09-17T00:00:00.000000000', '2020-09-19T00:00:00.000000000', '2020-09-21T00:00:00.000000000', '2020-09-23T00:00:00.000000000', '2020-09-25T00:00:00.000000000', '2020-09-27T00:00:00.000000000', '2020-09-29T00:00:00.000000000', '2020-10-01T00:00:00.000000000', '2020-10-03T00:00:00.000000000', '2020-10-05T00:00:00.000000000', '2020-10-07T00:00:00.000000000', '2020-10-09T00:00:00.000000000', '2020-10-11T00:00:00.000000000', '2020-10-13T00:00:00.000000000', '2020-10-15T00:00:00.000000000', '2020-10-17T00:00:00.000000000', '2020-10-19T00:00:00.000000000', '2020-10-21T00:00:00.000000000', '2020-10-23T00:00:00.000000000', '2020-10-25T00:00:00.000000000', '2020-10-27T00:00:00.000000000', '2020-10-29T00:00:00.000000000', '2020-10-31T00:00:00.000000000', '2020-11-02T00:00:00.000000000', '2020-11-04T00:00:00.000000000', '2020-11-06T00:00:00.000000000', '2020-11-08T00:00:00.000000000', '2020-11-10T00:00:00.000000000', '2020-11-12T00:00:00.000000000', '2020-11-14T00:00:00.000000000', '2020-11-16T00:00:00.000000000', '2020-11-18T00:00:00.000000000', '2020-11-20T00:00:00.000000000', '2020-11-22T00:00:00.000000000', '2020-11-24T00:00:00.000000000', '2020-11-26T00:00:00.000000000', '2020-11-28T00:00:00.000000000', '2020-11-30T00:00:00.000000000', '2020-12-02T00:00:00.000000000', '2020-12-04T00:00:00.000000000', '2020-12-06T00:00:00.000000000', '2020-12-08T00:00:00.000000000', '2020-12-10T00:00:00.000000000', '2020-12-12T00:00:00.000000000', '2020-12-14T00:00:00.000000000', '2020-12-16T00:00:00.000000000', '2020-12-18T00:00:00.000000000', '2020-12-20T00:00:00.000000000'], dtype='datetime64[ns]')
- geopotential(time, prediction_timedelta, level, longitude, latitude)float32dask.array<chunksize=(10, 40, 13, 240, 121), meta=np.ndarray>
Array Chunk Bytes 10.01 GiB 576.05 MiB Shape (178, 40, 13, 240, 121) (10, 40, 13, 240, 121) Dask graph 18 chunks in 2 graph layers Data type float32 numpy.ndarray - specific_humidity(time, prediction_timedelta, level, longitude, latitude)float32dask.array<chunksize=(10, 40, 13, 240, 121), meta=np.ndarray>
Array Chunk Bytes 10.01 GiB 576.05 MiB Shape (178, 40, 13, 240, 121) (10, 40, 13, 240, 121) Dask graph 18 chunks in 2 graph layers Data type float32 numpy.ndarray - temperature(time, prediction_timedelta, level, longitude, latitude)float32dask.array<chunksize=(10, 40, 13, 240, 121), meta=np.ndarray>
Array Chunk Bytes 10.01 GiB 576.05 MiB Shape (178, 40, 13, 240, 121) (10, 40, 13, 240, 121) Dask graph 18 chunks in 2 graph layers Data type float32 numpy.ndarray - u_component_of_wind(time, prediction_timedelta, level, longitude, latitude)float32dask.array<chunksize=(10, 40, 13, 240, 121), meta=np.ndarray>
Array Chunk Bytes 10.01 GiB 576.05 MiB Shape (178, 40, 13, 240, 121) (10, 40, 13, 240, 121) Dask graph 18 chunks in 2 graph layers Data type float32 numpy.ndarray - v_component_of_wind(time, prediction_timedelta, level, longitude, latitude)float32dask.array<chunksize=(10, 40, 13, 240, 121), meta=np.ndarray>
Array Chunk Bytes 10.01 GiB 576.05 MiB Shape (178, 40, 13, 240, 121) (10, 40, 13, 240, 121) Dask graph 18 chunks in 2 graph layers Data type float32 numpy.ndarray - wind_speed(time, prediction_timedelta, level, longitude, latitude)float32dask.array<chunksize=(10, 40, 13, 240, 121), meta=np.ndarray>
Array Chunk Bytes 10.01 GiB 576.05 MiB Shape (178, 40, 13, 240, 121) (10, 40, 13, 240, 121) Dask graph 18 chunks in 2 graph layers Data type float32 numpy.ndarray
- latitudePandasIndex
PandasIndex(Index([-90.0, -88.5, -87.0, -85.5, -84.0, -82.5, -81.0, -79.5, -78.0, -76.5, ... 76.5, 78.0, 79.5, 81.0, 82.5, 84.0, 85.5, 87.0, 88.5, 90.0], dtype='float64', name='latitude', length=121)) - levelPandasIndex
PandasIndex(Index([50, 100, 150, 200, 250, 300, 400, 500, 600, 700, 850, 925, 1000], dtype='int64', name='level'))
- longitudePandasIndex
PandasIndex(Index([ 0.0, 1.5, 3.0, 4.5, 6.0, 7.5, 9.0, 10.5, 12.0, 13.5, ... 345.0, 346.5, 348.0, 349.5, 351.0, 352.5, 354.0, 355.5, 357.0, 358.5], dtype='float64', name='longitude', length=240)) - prediction_timedeltaPandasIndex
PandasIndex(TimedeltaIndex([ '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)) - timePandasIndex
PandasIndex(DatetimeIndex(['2020-01-01', '2020-01-03', '2020-01-05', '2020-01-07', '2020-01-09', '2020-01-11', '2020-01-13', '2020-01-15', '2020-01-17', '2020-01-19', ... '2020-12-02', '2020-12-04', '2020-12-06', '2020-12-08', '2020-12-10', '2020-12-12', '2020-12-14', '2020-12-16', '2020-12-18', '2020-12-20'], dtype='datetime64[ns]', name='time', length=178, freq=None))
FuXi
Forecasts using FuXi model are available for 2020.
Location: gs://weatherbench2/datasets/fuxi/
Files:
2020-1440x721.zarr2020-240x121_equiangular_with_poles_conservative.zarr2020-64x32_equiangular_conservative.zarr
xr.open_zarr('gs://weatherbench2/datasets/fuxi/2020-1440x721.zarr')
<xarray.Dataset>
Dimensions: (time: 702, prediction_timedelta: 60,
latitude: 721, longitude: 1440, level: 2)
Coordinates:
* latitude (latitude) float64 -90.0 -89.75 ... 90.0
* level (level) int32 500 850
* longitude (longitude) float64 0.0 0.25 ... 359.8
* prediction_timedelta (prediction_timedelta) timedelta64[ns] ...
* time (time) datetime64[ns] 2020-01-01 ... 2...
Data variables:
10m_u_component_of_wind (time, prediction_timedelta, latitude, longitude) float32 dask.array<chunksize=(1, 60, 721, 1440), meta=np.ndarray>
10m_v_component_of_wind (time, prediction_timedelta, latitude, longitude) float32 dask.array<chunksize=(1, 60, 721, 1440), meta=np.ndarray>
10m_wind_speed (time, prediction_timedelta, latitude, longitude) float32 dask.array<chunksize=(1, 60, 721, 1440), meta=np.ndarray>
2m_temperature (time, prediction_timedelta, latitude, longitude) float32 dask.array<chunksize=(1, 60, 721, 1440), meta=np.ndarray>
geopotential (time, prediction_timedelta, level, latitude, longitude) float32 dask.array<chunksize=(1, 60, 2, 721, 1440), meta=np.ndarray>
mean_sea_level_pressure (time, prediction_timedelta, latitude, longitude) float32 dask.array<chunksize=(1, 60, 721, 1440), meta=np.ndarray>
temperature (time, prediction_timedelta, level, latitude, longitude) float32 dask.array<chunksize=(1, 60, 2, 721, 1440), meta=np.ndarray>
total_precipitation_24hr_from_6hr (time, prediction_timedelta, latitude, longitude) float32 dask.array<chunksize=(1, 60, 721, 1440), meta=np.ndarray>
total_precipitation_6hr (time, prediction_timedelta, latitude, longitude) float32 dask.array<chunksize=(1, 60, 721, 1440), meta=np.ndarray>
u_component_of_wind (time, prediction_timedelta, level, latitude, longitude) float32 dask.array<chunksize=(1, 60, 2, 721, 1440), meta=np.ndarray>
v_component_of_wind (time, prediction_timedelta, level, latitude, longitude) float32 dask.array<chunksize=(1, 60, 2, 721, 1440), meta=np.ndarray>
wind_speed (time, prediction_timedelta, level, latitude, longitude) float32 dask.array<chunksize=(1, 60, 2, 721, 1440), meta=np.ndarray>xarray.Dataset
- time: 702
- prediction_timedelta: 60
- latitude: 721
- longitude: 1440
- level: 2
- latitude(latitude)float64-90.0 -89.75 -89.5 ... 89.75 90.0
array([-90. , -89.75, -89.5 , ..., 89.5 , 89.75, 90. ])
- level(level)int32500 850
array([500, 850], dtype=int32)
- longitude(longitude)float640.0 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]) - prediction_timedelta(prediction_timedelta)timedelta64[ns]0 days 06:00:00 ... 15 days 00:0...
array([ 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 ... 2020-12-16T12:00:00
array(['2020-01-01T00:00:00.000000000', '2020-01-01T12:00:00.000000000', '2020-01-02T00:00:00.000000000', ..., '2020-12-15T12:00:00.000000000', '2020-12-16T00:00:00.000000000', '2020-12-16T12:00:00.000000000'], dtype='datetime64[ns]')
- 10m_u_component_of_wind(time, prediction_timedelta, latitude, longitude)float32dask.array<chunksize=(1, 60, 721, 1440), meta=np.ndarray>
Array Chunk Bytes 162.91 GiB 237.63 MiB Shape (702, 60, 721, 1440) (1, 60, 721, 1440) Dask graph 702 chunks in 2 graph layers Data type float32 numpy.ndarray - 10m_v_component_of_wind(time, prediction_timedelta, latitude, longitude)float32dask.array<chunksize=(1, 60, 721, 1440), meta=np.ndarray>
Array Chunk Bytes 162.91 GiB 237.63 MiB Shape (702, 60, 721, 1440) (1, 60, 721, 1440) Dask graph 702 chunks in 2 graph layers Data type float32 numpy.ndarray - 10m_wind_speed(time, prediction_timedelta, latitude, longitude)float32dask.array<chunksize=(1, 60, 721, 1440), meta=np.ndarray>
Array Chunk Bytes 162.91 GiB 237.63 MiB Shape (702, 60, 721, 1440) (1, 60, 721, 1440) Dask graph 702 chunks in 2 graph layers Data type float32 numpy.ndarray - 2m_temperature(time, prediction_timedelta, latitude, longitude)float32dask.array<chunksize=(1, 60, 721, 1440), meta=np.ndarray>
Array Chunk Bytes 162.91 GiB 237.63 MiB Shape (702, 60, 721, 1440) (1, 60, 721, 1440) Dask graph 702 chunks in 2 graph layers Data type float32 numpy.ndarray - geopotential(time, prediction_timedelta, level, latitude, longitude)float32dask.array<chunksize=(1, 60, 2, 721, 1440), meta=np.ndarray>
Array Chunk Bytes 325.82 GiB 475.27 MiB Shape (702, 60, 2, 721, 1440) (1, 60, 2, 721, 1440) Dask graph 702 chunks in 2 graph layers Data type float32 numpy.ndarray - mean_sea_level_pressure(time, prediction_timedelta, latitude, longitude)float32dask.array<chunksize=(1, 60, 721, 1440), meta=np.ndarray>
Array Chunk Bytes 162.91 GiB 237.63 MiB Shape (702, 60, 721, 1440) (1, 60, 721, 1440) Dask graph 702 chunks in 2 graph layers Data type float32 numpy.ndarray - temperature(time, prediction_timedelta, level, latitude, longitude)float32dask.array<chunksize=(1, 60, 2, 721, 1440), meta=np.ndarray>
Array Chunk Bytes 325.82 GiB 475.27 MiB Shape (702, 60, 2, 721, 1440) (1, 60, 2, 721, 1440) Dask graph 702 chunks in 2 graph layers Data type float32 numpy.ndarray - total_precipitation_24hr_from_6hr(time, prediction_timedelta, latitude, longitude)float32dask.array<chunksize=(1, 60, 721, 1440), meta=np.ndarray>
Array Chunk Bytes 162.91 GiB 237.63 MiB Shape (702, 60, 721, 1440) (1, 60, 721, 1440) Dask graph 702 chunks in 2 graph layers Data type float32 numpy.ndarray - total_precipitation_6hr(time, prediction_timedelta, latitude, longitude)float32dask.array<chunksize=(1, 60, 721, 1440), meta=np.ndarray>
Array Chunk Bytes 162.91 GiB 237.63 MiB Shape (702, 60, 721, 1440) (1, 60, 721, 1440) Dask graph 702 chunks in 2 graph layers Data type float32 numpy.ndarray - u_component_of_wind(time, prediction_timedelta, level, latitude, longitude)float32dask.array<chunksize=(1, 60, 2, 721, 1440), meta=np.ndarray>
Array Chunk Bytes 325.82 GiB 475.27 MiB Shape (702, 60, 2, 721, 1440) (1, 60, 2, 721, 1440) Dask graph 702 chunks in 2 graph layers Data type float32 numpy.ndarray - v_component_of_wind(time, prediction_timedelta, level, latitude, longitude)float32dask.array<chunksize=(1, 60, 2, 721, 1440), meta=np.ndarray>
Array Chunk Bytes 325.82 GiB 475.27 MiB Shape (702, 60, 2, 721, 1440) (1, 60, 2, 721, 1440) Dask graph 702 chunks in 2 graph layers Data type float32 numpy.ndarray - wind_speed(time, prediction_timedelta, level, latitude, longitude)float32dask.array<chunksize=(1, 60, 2, 721, 1440), meta=np.ndarray>
Array Chunk Bytes 325.82 GiB 475.27 MiB Shape (702, 60, 2, 721, 1440) (1, 60, 2, 721, 1440) Dask graph 702 chunks in 2 graph layers Data type float32 numpy.ndarray
- latitudePandasIndex
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='float64', name='latitude', length=721)) - levelPandasIndex
PandasIndex(Index([500, 850], dtype='int32', name='level'))
- longitudePandasIndex
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='float64', name='longitude', length=1440)) - prediction_timedeltaPandasIndex
PandasIndex(TimedeltaIndex([ '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)) - timePandasIndex
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', ... '2020-12-12 00:00:00', '2020-12-12 12:00:00', '2020-12-13 00:00:00', '2020-12-13 12:00:00', '2020-12-14 00:00:00', '2020-12-14 12:00:00', '2020-12-15 00:00:00', '2020-12-15 12:00:00', '2020-12-16 00:00:00', '2020-12-16 12:00:00'], dtype='datetime64[ns]', name='time', length=702, freq=None))
NeuralGCM
Forecasts made with the Neural General Circulation Model are available for 2020. The deterministic model has a raw resolution of 0.7 degrees
Location: gs://weatherbench2/datasets/neuralgcm_deterministic/
Files:
2020-512x256.zarr2020-240x121_equiangular_with_poles_conservative.zarr2020-64x32_equiangular_conservative.zarr
xr.open_zarr('gs://weatherbench2/datasets/neuralgcm_deterministic/2020-240x121_equiangular_with_poles_conservative.zarr')
<xarray.Dataset>
Dimensions: (time: 732, prediction_timedelta: 31,
longitude: 240, latitude: 121,
level: 37)
Coordinates:
* latitude (latitude) float64 -90.0 -88.5 ... 90.0
* level (level) int64 1 2 3 5 ... 950 975 1000
* longitude (longitude) float64 0.0 1.5 ... 358.5
* prediction_timedelta (prediction_timedelta) timedelta64[ns] ...
* time (time) datetime64[ns] 2020-01-01 ......
Data variables:
P_minus_E_cumulative (time, prediction_timedelta, longitude, latitude) float32 dask.array<chunksize=(1, 8, 240, 121), meta=np.ndarray>
geopotential (time, prediction_timedelta, level, longitude, latitude) float32 dask.array<chunksize=(1, 8, 37, 240, 121), meta=np.ndarray>
specific_cloud_ice_water_content (time, prediction_timedelta, level, longitude, latitude) float32 dask.array<chunksize=(1, 8, 37, 240, 121), meta=np.ndarray>
specific_cloud_liquid_water_content (time, prediction_timedelta, level, longitude, latitude) float32 dask.array<chunksize=(1, 8, 37, 240, 121), meta=np.ndarray>
specific_humidity (time, prediction_timedelta, level, longitude, latitude) float32 dask.array<chunksize=(1, 8, 37, 240, 121), meta=np.ndarray>
temperature (time, prediction_timedelta, level, longitude, latitude) float32 dask.array<chunksize=(1, 8, 37, 240, 121), meta=np.ndarray>
u_component_of_wind (time, prediction_timedelta, level, longitude, latitude) float32 dask.array<chunksize=(1, 8, 37, 240, 121), meta=np.ndarray>
v_component_of_wind (time, prediction_timedelta, level, longitude, latitude) float32 dask.array<chunksize=(1, 8, 37, 240, 121), meta=np.ndarray>
wind_speed (time, prediction_timedelta, level, longitude, latitude) float32 dask.array<chunksize=(1, 8, 37, 240, 121), meta=np.ndarray>
Attributes:
experiment_id: 67001173
worker_id: 1xarray.Dataset
- time: 732
- prediction_timedelta: 31
- longitude: 240
- latitude: 121
- level: 37
- latitude(latitude)float64-90.0 -88.5 -87.0 ... 88.5 90.0
array([-90. , -88.5, -87. , -85.5, -84. , -82.5, -81. , -79.5, -78. , -76.5, -75. , -73.5, -72. , -70.5, -69. , -67.5, -66. , -64.5, -63. , -61.5, -60. , -58.5, -57. , -55.5, -54. , -52.5, -51. , -49.5, -48. , -46.5, -45. , -43.5, -42. , -40.5, -39. , -37.5, -36. , -34.5, -33. , -31.5, -30. , -28.5, -27. , -25.5, -24. , -22.5, -21. , -19.5, -18. , -16.5, -15. , -13.5, -12. , -10.5, -9. , -7.5, -6. , -4.5, -3. , -1.5, 0. , 1.5, 3. , 4.5, 6. , 7.5, 9. , 10.5, 12. , 13.5, 15. , 16.5, 18. , 19.5, 21. , 22.5, 24. , 25.5, 27. , 28.5, 30. , 31.5, 33. , 34.5, 36. , 37.5, 39. , 40.5, 42. , 43.5, 45. , 46.5, 48. , 49.5, 51. , 52.5, 54. , 55.5, 57. , 58.5, 60. , 61.5, 63. , 64.5, 66. , 67.5, 69. , 70.5, 72. , 73.5, 75. , 76.5, 78. , 79.5, 81. , 82.5, 84. , 85.5, 87. , 88.5, 90. ]) - level(level)int641 2 3 5 7 ... 900 925 950 975 1000
array([ 1, 2, 3, 5, 7, 10, 20, 30, 50, 70, 100, 125, 150, 175, 200, 225, 250, 300, 350, 400, 450, 500, 550, 600, 650, 700, 750, 775, 800, 825, 850, 875, 900, 925, 950, 975, 1000]) - longitude(longitude)float640.0 1.5 3.0 ... 355.5 357.0 358.5
array([ 0. , 1.5, 3. , ..., 355.5, 357. , 358.5])
- prediction_timedelta(prediction_timedelta)timedelta64[ns]0 days 00:00:00 ... 15 days 00:0...
array([ 0, 43200000000000, 86400000000000, 129600000000000, 172800000000000, 216000000000000, 259200000000000, 302400000000000, 345600000000000, 388800000000000, 432000000000000, 475200000000000, 518400000000000, 561600000000000, 604800000000000, 648000000000000, 691200000000000, 734400000000000, 777600000000000, 820800000000000, 864000000000000, 907200000000000, 950400000000000, 993600000000000, 1036800000000000, 1080000000000000, 1123200000000000, 1166400000000000, 1209600000000000, 1252800000000000, 1296000000000000], dtype='timedelta64[ns]') - time(time)datetime64[ns]2020-01-01 ... 2020-12-31T12:00:00
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]')
- P_minus_E_cumulative(time, prediction_timedelta, longitude, latitude)float32dask.array<chunksize=(1, 8, 240, 121), meta=np.ndarray>
Array Chunk Bytes 2.45 GiB 907.50 kiB Shape (732, 31, 240, 121) (1, 8, 240, 121) Dask graph 2928 chunks in 2 graph layers Data type float32 numpy.ndarray - geopotential(time, prediction_timedelta, level, longitude, latitude)float32dask.array<chunksize=(1, 8, 37, 240, 121), meta=np.ndarray>
Array Chunk Bytes 90.83 GiB 32.79 MiB Shape (732, 31, 37, 240, 121) (1, 8, 37, 240, 121) Dask graph 2928 chunks in 2 graph layers Data type float32 numpy.ndarray - specific_cloud_ice_water_content(time, prediction_timedelta, level, longitude, latitude)float32dask.array<chunksize=(1, 8, 37, 240, 121), meta=np.ndarray>
Array Chunk Bytes 90.83 GiB 32.79 MiB Shape (732, 31, 37, 240, 121) (1, 8, 37, 240, 121) Dask graph 2928 chunks in 2 graph layers Data type float32 numpy.ndarray - specific_cloud_liquid_water_content(time, prediction_timedelta, level, longitude, latitude)float32dask.array<chunksize=(1, 8, 37, 240, 121), meta=np.ndarray>
Array Chunk Bytes 90.83 GiB 32.79 MiB Shape (732, 31, 37, 240, 121) (1, 8, 37, 240, 121) Dask graph 2928 chunks in 2 graph layers Data type float32 numpy.ndarray - specific_humidity(time, prediction_timedelta, level, longitude, latitude)float32dask.array<chunksize=(1, 8, 37, 240, 121), meta=np.ndarray>
Array Chunk Bytes 90.83 GiB 32.79 MiB Shape (732, 31, 37, 240, 121) (1, 8, 37, 240, 121) Dask graph 2928 chunks in 2 graph layers Data type float32 numpy.ndarray - temperature(time, prediction_timedelta, level, longitude, latitude)float32dask.array<chunksize=(1, 8, 37, 240, 121), meta=np.ndarray>
Array Chunk Bytes 90.83 GiB 32.79 MiB Shape (732, 31, 37, 240, 121) (1, 8, 37, 240, 121) Dask graph 2928 chunks in 2 graph layers Data type float32 numpy.ndarray - u_component_of_wind(time, prediction_timedelta, level, longitude, latitude)float32dask.array<chunksize=(1, 8, 37, 240, 121), meta=np.ndarray>
Array Chunk Bytes 90.83 GiB 32.79 MiB Shape (732, 31, 37, 240, 121) (1, 8, 37, 240, 121) Dask graph 2928 chunks in 2 graph layers Data type float32 numpy.ndarray - v_component_of_wind(time, prediction_timedelta, level, longitude, latitude)float32dask.array<chunksize=(1, 8, 37, 240, 121), meta=np.ndarray>
Array Chunk Bytes 90.83 GiB 32.79 MiB Shape (732, 31, 37, 240, 121) (1, 8, 37, 240, 121) Dask graph 2928 chunks in 2 graph layers Data type float32 numpy.ndarray - wind_speed(time, prediction_timedelta, level, longitude, latitude)float32dask.array<chunksize=(1, 8, 37, 240, 121), meta=np.ndarray>
Array Chunk Bytes 90.83 GiB 32.79 MiB Shape (732, 31, 37, 240, 121) (1, 8, 37, 240, 121) Dask graph 2928 chunks in 2 graph layers Data type float32 numpy.ndarray
- latitudePandasIndex
PandasIndex(Index([ -90.0, -88.49999999999996, -86.99999999999996, -85.5, -83.99999999999997, -82.5, -81.00000000000003, -79.50000000000001, -77.99999999999999, -76.49999999999999, ... 76.49999999999999, 77.99999999999999, 79.49999999999997, 81.00000000000003, 82.5, 83.99999999999997, 85.5, 86.99999999999996, 88.49999999999996, 90.0], dtype='float64', name='latitude', length=121)) - levelPandasIndex
PandasIndex(Index([ 1, 2, 3, 5, 7, 10, 20, 30, 50, 70, 100, 125, 150, 175, 200, 225, 250, 300, 350, 400, 450, 500, 550, 600, 650, 700, 750, 775, 800, 825, 850, 875, 900, 925, 950, 975, 1000], dtype='int64', name='level')) - longitudePandasIndex
PandasIndex(Index([ 0.0, 1.5, 3.0, 4.5, 6.0, 7.499999999999999, 9.0, 10.499999999999998, 12.0, 13.5, ... 344.99999999999994, 346.5, 348.0, 349.5, 351.0, 352.5, 353.99999999999994, 355.5, 357.0, 358.5], dtype='float64', name='longitude', length=240)) - prediction_timedeltaPandasIndex
PandasIndex(TimedeltaIndex([ '0 days 00:00:00', '0 days 12:00:00', '1 days 00:00:00', '1 days 12:00:00', '2 days 00:00:00', '2 days 12:00:00', '3 days 00:00:00', '3 days 12:00:00', '4 days 00:00:00', '4 days 12:00:00', '5 days 00:00:00', '5 days 12:00:00', '6 days 00:00:00', '6 days 12:00:00', '7 days 00:00:00', '7 days 12:00:00', '8 days 00:00:00', '8 days 12:00:00', '9 days 00:00:00', '9 days 12:00:00', '10 days 00:00:00', '10 days 12:00:00', '11 days 00:00:00', '11 days 12:00:00', '12 days 00:00:00', '12 days 12:00:00', '13 days 00:00:00', '13 days 12:00:00', '14 days 00:00:00', '14 days 12:00:00', '15 days 00:00:00'], dtype='timedelta64[ns]', name='prediction_timedelta', freq=None)) - timePandasIndex
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))
- experiment_id :
- 67001173
- worker_id :
- 1
The ensemble version has a resolution of 1.4 degrees and has been run to produce 50 members. We also computed ensemble means
Location: gs://weatherbench2/datasets/neuralgcm_ens/
Files:
2020-256x128.zarr2020-240x121_equiangular_with_poles_conservative.zarr2020-240x121_equiangular_with_poles_conservative_mean.zarr2020-64x32_equiangular_conservative.zarr2020-64x32_equiangular_conservative_mean.zarr
xr.open_zarr('gs://weatherbench2/datasets/neuralgcm_ens/2020-240x121_equiangular_with_poles_conservative.zarr')
<xarray.Dataset>
Dimensions: (realization: 50, time: 732,
prediction_timedelta: 32, level: 37,
longitude: 240, latitude: 121)
Coordinates:
* latitude (latitude) float64 -90.0 -88.5 ... 90.0
* level (level) int64 1 2 3 5 ... 950 975 1000
* longitude (longitude) float64 0.0 1.5 ... 358.5
* prediction_timedelta (prediction_timedelta) timedelta64[ns] ...
* realization (realization) int64 0 1 2 ... 47 48 49
* time (time) datetime64[ns] 2020-01-01 ......
Data variables:
geopotential (realization, time, prediction_timedelta, level, longitude, latitude) float32 dask.array<chunksize=(50, 1, 2, 1, 240, 121), meta=np.ndarray>
specific_cloud_ice_water_content (realization, time, prediction_timedelta, level, longitude, latitude) float32 dask.array<chunksize=(50, 1, 2, 1, 240, 121), meta=np.ndarray>
specific_cloud_liquid_water_content (realization, time, prediction_timedelta, level, longitude, latitude) float32 dask.array<chunksize=(50, 1, 2, 1, 240, 121), meta=np.ndarray>
specific_humidity (realization, time, prediction_timedelta, level, longitude, latitude) float32 dask.array<chunksize=(50, 1, 2, 1, 240, 121), meta=np.ndarray>
temperature (realization, time, prediction_timedelta, level, longitude, latitude) float32 dask.array<chunksize=(50, 1, 2, 1, 240, 121), meta=np.ndarray>
u_component_of_wind (realization, time, prediction_timedelta, level, longitude, latitude) float32 dask.array<chunksize=(50, 1, 2, 1, 240, 121), meta=np.ndarray>
v_component_of_wind (realization, time, prediction_timedelta, level, longitude, latitude) float32 dask.array<chunksize=(50, 1, 2, 1, 240, 121), meta=np.ndarray>
wind_speed (realization, time, prediction_timedelta, level, longitude, latitude) float32 dask.array<chunksize=(50, 1, 2, 1, 240, 121), meta=np.ndarray>
Attributes:
experiment_id: 73974210
worker_id: 3xarray.Dataset
- realization: 50
- time: 732
- prediction_timedelta: 32
- level: 37
- longitude: 240
- latitude: 121
- latitude(latitude)float64-90.0 -88.5 -87.0 ... 88.5 90.0
array([-90. , -88.5, -87. , -85.5, -84. , -82.5, -81. , -79.5, -78. , -76.5, -75. , -73.5, -72. , -70.5, -69. , -67.5, -66. , -64.5, -63. , -61.5, -60. , -58.5, -57. , -55.5, -54. , -52.5, -51. , -49.5, -48. , -46.5, -45. , -43.5, -42. , -40.5, -39. , -37.5, -36. , -34.5, -33. , -31.5, -30. , -28.5, -27. , -25.5, -24. , -22.5, -21. , -19.5, -18. , -16.5, -15. , -13.5, -12. , -10.5, -9. , -7.5, -6. , -4.5, -3. , -1.5, 0. , 1.5, 3. , 4.5, 6. , 7.5, 9. , 10.5, 12. , 13.5, 15. , 16.5, 18. , 19.5, 21. , 22.5, 24. , 25.5, 27. , 28.5, 30. , 31.5, 33. , 34.5, 36. , 37.5, 39. , 40.5, 42. , 43.5, 45. , 46.5, 48. , 49.5, 51. , 52.5, 54. , 55.5, 57. , 58.5, 60. , 61.5, 63. , 64.5, 66. , 67.5, 69. , 70.5, 72. , 73.5, 75. , 76.5, 78. , 79.5, 81. , 82.5, 84. , 85.5, 87. , 88.5, 90. ]) - level(level)int641 2 3 5 7 ... 900 925 950 975 1000
array([ 1, 2, 3, 5, 7, 10, 20, 30, 50, 70, 100, 125, 150, 175, 200, 225, 250, 300, 350, 400, 450, 500, 550, 600, 650, 700, 750, 775, 800, 825, 850, 875, 900, 925, 950, 975, 1000]) - longitude(longitude)float640.0 1.5 3.0 ... 355.5 357.0 358.5
array([ 0. , 1.5, 3. , ..., 355.5, 357. , 358.5])
- prediction_timedelta(prediction_timedelta)timedelta64[ns]0 days 00:00:00 ... 15 days 12:0...
array([ 0, 43200000000000, 86400000000000, 129600000000000, 172800000000000, 216000000000000, 259200000000000, 302400000000000, 345600000000000, 388800000000000, 432000000000000, 475200000000000, 518400000000000, 561600000000000, 604800000000000, 648000000000000, 691200000000000, 734400000000000, 777600000000000, 820800000000000, 864000000000000, 907200000000000, 950400000000000, 993600000000000, 1036800000000000, 1080000000000000, 1123200000000000, 1166400000000000, 1209600000000000, 1252800000000000, 1296000000000000, 1339200000000000], dtype='timedelta64[ns]') - realization(realization)int640 1 2 3 4 5 6 ... 44 45 46 47 48 49
array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49]) - time(time)datetime64[ns]2020-01-01 ... 2020-12-31T12:00:00
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]')
- geopotential(realization, time, prediction_timedelta, level, longitude, latitude)float32dask.array<chunksize=(50, 1, 2, 1, 240, 121), meta=np.ndarray>
Array Chunk Bytes 4.58 TiB 11.08 MiB Shape (50, 732, 32, 37, 240, 121) (50, 1, 2, 1, 240, 121) Dask graph 433344 chunks in 2 graph layers Data type float32 numpy.ndarray - specific_cloud_ice_water_content(realization, time, prediction_timedelta, level, longitude, latitude)float32dask.array<chunksize=(50, 1, 2, 1, 240, 121), meta=np.ndarray>
Array Chunk Bytes 4.58 TiB 11.08 MiB Shape (50, 732, 32, 37, 240, 121) (50, 1, 2, 1, 240, 121) Dask graph 433344 chunks in 2 graph layers Data type float32 numpy.ndarray - specific_cloud_liquid_water_content(realization, time, prediction_timedelta, level, longitude, latitude)float32dask.array<chunksize=(50, 1, 2, 1, 240, 121), meta=np.ndarray>
Array Chunk Bytes 4.58 TiB 11.08 MiB Shape (50, 732, 32, 37, 240, 121) (50, 1, 2, 1, 240, 121) Dask graph 433344 chunks in 2 graph layers Data type float32 numpy.ndarray - specific_humidity(realization, time, prediction_timedelta, level, longitude, latitude)float32dask.array<chunksize=(50, 1, 2, 1, 240, 121), meta=np.ndarray>
Array Chunk Bytes 4.58 TiB 11.08 MiB Shape (50, 732, 32, 37, 240, 121) (50, 1, 2, 1, 240, 121) Dask graph 433344 chunks in 2 graph layers Data type float32 numpy.ndarray - temperature(realization, time, prediction_timedelta, level, longitude, latitude)float32dask.array<chunksize=(50, 1, 2, 1, 240, 121), meta=np.ndarray>
Array Chunk Bytes 4.58 TiB 11.08 MiB Shape (50, 732, 32, 37, 240, 121) (50, 1, 2, 1, 240, 121) Dask graph 433344 chunks in 2 graph layers Data type float32 numpy.ndarray - u_component_of_wind(realization, time, prediction_timedelta, level, longitude, latitude)float32dask.array<chunksize=(50, 1, 2, 1, 240, 121), meta=np.ndarray>
Array Chunk Bytes 4.58 TiB 11.08 MiB Shape (50, 732, 32, 37, 240, 121) (50, 1, 2, 1, 240, 121) Dask graph 433344 chunks in 2 graph layers Data type float32 numpy.ndarray - v_component_of_wind(realization, time, prediction_timedelta, level, longitude, latitude)float32dask.array<chunksize=(50, 1, 2, 1, 240, 121), meta=np.ndarray>
Array Chunk Bytes 4.58 TiB 11.08 MiB Shape (50, 732, 32, 37, 240, 121) (50, 1, 2, 1, 240, 121) Dask graph 433344 chunks in 2 graph layers Data type float32 numpy.ndarray - wind_speed(realization, time, prediction_timedelta, level, longitude, latitude)float32dask.array<chunksize=(50, 1, 2, 1, 240, 121), meta=np.ndarray>
Array Chunk Bytes 4.58 TiB 11.08 MiB Shape (50, 732, 32, 37, 240, 121) (50, 1, 2, 1, 240, 121) Dask graph 433344 chunks in 2 graph layers Data type float32 numpy.ndarray
- latitudePandasIndex
PandasIndex(Index([ -90.0, -88.49999999999996, -86.99999999999996, -85.5, -83.99999999999997, -82.5, -81.00000000000003, -79.50000000000001, -77.99999999999999, -76.49999999999999, ... 76.49999999999999, 77.99999999999999, 79.49999999999997, 81.00000000000003, 82.5, 83.99999999999997, 85.5, 86.99999999999996, 88.49999999999996, 90.0], dtype='float64', name='latitude', length=121)) - levelPandasIndex
PandasIndex(Index([ 1, 2, 3, 5, 7, 10, 20, 30, 50, 70, 100, 125, 150, 175, 200, 225, 250, 300, 350, 400, 450, 500, 550, 600, 650, 700, 750, 775, 800, 825, 850, 875, 900, 925, 950, 975, 1000], dtype='int64', name='level')) - longitudePandasIndex
PandasIndex(Index([ 0.0, 1.5, 3.0, 4.5, 6.0, 7.499999999999999, 9.0, 10.499999999999998, 12.0, 13.5, ... 344.99999999999994, 346.5, 348.0, 349.5, 351.0, 352.5, 353.99999999999994, 355.5, 357.0, 358.5], dtype='float64', name='longitude', length=240)) - prediction_timedeltaPandasIndex
PandasIndex(TimedeltaIndex([ '0 days 00:00:00', '0 days 12:00:00', '1 days 00:00:00', '1 days 12:00:00', '2 days 00:00:00', '2 days 12:00:00', '3 days 00:00:00', '3 days 12:00:00', '4 days 00:00:00', '4 days 12:00:00', '5 days 00:00:00', '5 days 12:00:00', '6 days 00:00:00', '6 days 12:00:00', '7 days 00:00:00', '7 days 12:00:00', '8 days 00:00:00', '8 days 12:00:00', '9 days 00:00:00', '9 days 12:00:00', '10 days 00:00:00', '10 days 12:00:00', '11 days 00:00:00', '11 days 12:00:00', '12 days 00:00:00', '12 days 12:00:00', '13 days 00:00:00', '13 days 12:00:00', '14 days 00:00:00', '14 days 12:00:00', '15 days 00:00:00', '15 days 12:00:00'], dtype='timedelta64[ns]', name='prediction_timedelta', freq=None)) - realizationPandasIndex
PandasIndex(Index([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49], dtype='int64', name='realization')) - timePandasIndex
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))
- experiment_id :
- 73974210
- worker_id :
- 3