oceanbench.datasets package¶
Submodules¶
oceanbench.datasets.challenger module¶
This module exposes the challenger datasets evaluated in the benchmark.
- oceanbench.datasets.challenger.glo12() Dataset¶
Open the GLO12 challenger dataset.
Returns¶
- Dataset
The Dataset containing GLO12 forecasts.
>>> glo12() <xarray.Dataset> Size: 2TB Dimensions: (first_day_datetime: 52, lead_day_index: 10, depth: 21, latitude: 2041, longitude: 4320) Coordinates: * depth (depth) float32 84B 0.494 47.37 ... 4.833e+03 5.275e+03 * latitude (latitude) float32 8kB -80.0 -79.92 ... 89.92 90.0 * longitude (longitude) float32 17kB -180.0 -179.9 ... 179.8 179.9 * lead_day_index (lead_day_index) int64 80B 0 1 2 3 4 5 6 7 8 9 * first_day_datetime (first_day_datetime) datetime64[...] 416B 2024-01-03 .... Data variables: so (first_day_datetime, lead_day_index, depth, latitude, longitude) float32 385GB dask.array<chunksize=(1, 1, 1, 75, 4320), meta=np.ndarray> thetao (first_day_datetime, lead_day_index, depth, latitude, longitude) float32 385GB dask.array<chunksize=(1, 1, 1, 75, 4320), meta=np.ndarray> uo (first_day_datetime, lead_day_index, depth, latitude, longitude) float32 385GB dask.array<chunksize=(1, 1, 1, 75, 4320), meta=np.ndarray> vo (first_day_datetime, lead_day_index, depth, latitude, longitude) float32 385GB dask.array<chunksize=(1, 1, 1, 75, 4320), meta=np.ndarray> zos (first_day_datetime, lead_day_index, latitude, longitude) float32 18GB dask.array<chunksize=(1, 1, 75, 4320), meta=np.ndarray> Attributes: Conventions: CF-1.8 area: Global contact: https://marine.copernicus.eu/contact credit: E.U. Copernicus Marine Service Information (CMEMS) institution: Mercator Ocean International licence: http://marine.copernicus.eu/services-portfolio/service-comm... producer: CMEMS - Global Monitoring and Forecasting Centre references: http://marine.copernicus.eu source: MOI GLO12 title: daily mean fields from Global Ocean Physics Analysis and Fo...
- oceanbench.datasets.challenger.glo12_1_degree() Dataset¶
Open the GLO12 challenger dataset interpolated to the 1 degree resolution.
Returns¶
- Dataset
The Dataset containing GLO12 forecasts interpolated to 1 degree resolution.
>>> glo12_1_degree() <xarray.Dataset> Size: 11GB Dimensions: (first_day_datetime: 52, lead_day_index: 10, depth: 21, latitude: 170, longitude: 360) Coordinates: * depth (depth) float32 84B 0.494 ... 5.275e+03 * lead_day_index (lead_day_index) int64 80B 0 1 2 ... 7 8 9 * first_day_datetime (first_day_datetime) datetime64[...] 416B ... * latitude (latitude) float64 1kB -79.5 -78.5 ... 89.5 * longitude (longitude) float64 3kB -179.5 ... 179.5 Data variables: sea_water_salinity (first_day_datetime, lead_day_index, depth, latitude, longitude) float32 3GB dask.array<chunksize=(1, 1, 1, 170, 360), meta=np.ndarray> sea_water_potential_temperature (first_day_datetime, lead_day_index, depth, latitude, longitude) float32 3GB dask.array<chunksize=(1, 1, 1, 170, 360), meta=np.ndarray> eastward_sea_water_velocity (first_day_datetime, lead_day_index, depth, latitude, longitude) float32 3GB dask.array<chunksize=(1, 1, 1, 170, 360), meta=np.ndarray> northward_sea_water_velocity (first_day_datetime, lead_day_index, depth, latitude, longitude) float32 3GB dask.array<chunksize=(1, 1, 1, 170, 360), meta=np.ndarray> sea_surface_height_above_geoid (first_day_datetime, lead_day_index, latitude, longitude) float32 127MB dask.array<chunksize=(1, 1, 170, 360), meta=np.ndarray> Attributes: Conventions: CF-1.8 area: Global contact: https://marine.copernicus.eu/contact credit: E.U. Copernicus Marine Service Information (CMEMS) institution: Mercator Ocean International licence: http://marine.copernicus.eu/services-portfolio/service-comm... producer: CMEMS - Global Monitoring and Forecasting Centre references: http://marine.copernicus.eu source: MOI GLO12 title: daily mean fields from Global Ocean Physics Analysis and Fo...
- oceanbench.datasets.challenger.glo36v1() Dataset¶
Open the GLO36V1 challenger dataset.
Returns¶
- Dataset
The Dataset containing GLO36V1 forecasts.
>>> glo36v1() <xarray.Dataset> Size: 3TB Dimensions: (first_day_datetime: 52, lead_day_index: 7, depth: 50, latitude: 2041, longitude: 4320) Coordinates: * depth (depth) float32 200B 0.494 1.541 ... 5.275e+03 5.728e+03 * latitude (latitude) float32 8kB -80.0 -79.92 ... 89.92 90.0 * lead_day_index (lead_day_index) int64 56B 0 1 2 3 4 5 6 * longitude (longitude) float32 17kB -180.0 -179.9 ... 179.8 179.9 * first_day_datetime (first_day_datetime) datetime64[...] 416B 2023-01-04 .... Data variables: so (first_day_datetime, lead_day_index, depth, latitude, longitude) float32 642GB dask.array<chunksize=(1, 1, 7, 256, 540), meta=np.ndarray> thetao (first_day_datetime, lead_day_index, depth, latitude, longitude) float32 642GB dask.array<chunksize=(1, 1, 7, 256, 540), meta=np.ndarray> uo (first_day_datetime, lead_day_index, depth, latitude, longitude) float32 642GB dask.array<chunksize=(1, 1, 7, 256, 540), meta=np.ndarray> vo (first_day_datetime, lead_day_index, depth, latitude, longitude) float32 642GB dask.array<chunksize=(1, 1, 7, 256, 540), meta=np.ndarray> zos (first_day_datetime, lead_day_index, latitude, longitude) float32 13GB dask.array<chunksize=(1, 1, 256, 1080), meta=np.ndarray>
- oceanbench.datasets.challenger.glonet() Dataset¶
Open the GLONET challenger dataset.
Returns¶
- Dataset
The Dataset containing GLONET forecasts.
>>> glonet() <xarray.Dataset> Size: 342GB Dimensions: (first_day_datetime: 52, lead_day_index: 10, depth: 21, lat: 672, lon: 1440) Coordinates: * depth (depth) float32 84B 0.494 47.37 ... 4.833e+03 5.275e+03 * lat (lat) float64 5kB -78.0 -77.75 -77.5 ... 89.5 89.75 * lon (lon) float64 12kB -180.0 -179.8 -179.5 ... 179.5 179.8 * lead_day_index (lead_day_index) int64 80B 0 1 2 3 4 5 6 7 8 9 * first_day_datetime (first_day_datetime) datetime64[...] 416B 2024-01-03 .... Data variables: so (first_day_datetime, lead_day_index, depth, lat, lon) float64 85GB dask.array<chunksize=(1, 10, 1, 672, 1440), meta=np.ndarray> thetao (first_day_datetime, lead_day_index, depth, lat, lon) float64 85GB dask.array<chunksize=(1, 10, 1, 672, 1440), meta=np.ndarray> uo (first_day_datetime, lead_day_index, depth, lat, lon) float64 85GB dask.array<chunksize=(1, 10, 1, 672, 1440), meta=np.ndarray> vo (first_day_datetime, lead_day_index, depth, lat, lon) float64 85GB dask.array<chunksize=(1, 10, 1, 672, 1440), meta=np.ndarray> zos (first_day_datetime, lead_day_index, lat, lon) float64 4GB dask.array<chunksize=(1, 10, 672, 1440), meta=np.ndarray> Attributes: regrid_method: bilinear
- oceanbench.datasets.challenger.glonet_1_degree() Dataset¶
Open the GLONET challenger dataset interpolated to the 1 degree resolution.
Returns¶
- Dataset
The Dataset containing GLONET forecasts interpolated to 1 degree resolution.
>>> glonet_1_degree() <xarray.Dataset> Size: 21GB Dimensions: (first_day_datetime: 52, lead_day_index: 10, depth: 21, latitude: 168, longitude: 360) Coordinates: * depth (depth) float32 84B 0.494 ... 5.275e+03 * lead_day_index (lead_day_index) int64 80B 0 1 2 ... 7 8 9 * first_day_datetime (first_day_datetime) datetime64[...] 416B ... * latitude (latitude) float64 1kB -77.5 -76.5 ... 89.5 * longitude (longitude) float64 3kB -179.5 ... 179.5 Data variables: sea_water_salinity (first_day_datetime, lead_day_index, depth, latitude, longitude) float64 5GB dask.array<chunksize=(1, 10, 1, 168, 360), meta=np.ndarray> sea_water_potential_temperature (first_day_datetime, lead_day_index, depth, latitude, longitude) float64 5GB dask.array<chunksize=(1, 10, 1, 168, 360), meta=np.ndarray> eastward_sea_water_velocity (first_day_datetime, lead_day_index, depth, latitude, longitude) float64 5GB dask.array<chunksize=(1, 10, 1, 168, 360), meta=np.ndarray> northward_sea_water_velocity (first_day_datetime, lead_day_index, depth, latitude, longitude) float64 5GB dask.array<chunksize=(1, 10, 1, 168, 360), meta=np.ndarray> sea_surface_height_above_geoid (first_day_datetime, lead_day_index, latitude, longitude) float64 252MB dask.array<chunksize=(1, 10, 168, 360), meta=np.ndarray> Attributes: regrid_method: bilinear
- oceanbench.datasets.challenger.wenhai() Dataset¶
Open the WenHai challenger dataset.
Returns¶
- Dataset
The Dataset containing WenHai forecasts.
>>> wenhai() <xarray.Dataset> Size: 2TB Dimensions: (first_day_datetime: 52, lead_day_index: 10, depth: 23, latitude: 2041, longitude: 4320) Coordinates: * depth (depth) float32 92B 0.494 2.646 5.078 ... 541.1 643.6 * latitude (latitude) float32 8kB -80.0 -79.92 ... 89.92 90.0 * longitude (longitude) float32 17kB -180.0 -179.9 ... 179.8 179.9 * lead_day_index (lead_day_index) int64 80B 0 1 2 3 4 5 6 7 8 9 * first_day_datetime (first_day_datetime) datetime64[...] 416B 2024-01-03 .... Data variables: so (first_day_datetime, lead_day_index, depth, latitude, longitude) float32 422GB dask.array<chunksize=(1, 1, 1, 75, 4320), meta=np.ndarray> thetao (first_day_datetime, lead_day_index, depth, latitude, longitude) float32 422GB dask.array<chunksize=(1, 1, 1, 75, 4320), meta=np.ndarray> uo (first_day_datetime, lead_day_index, depth, latitude, longitude) float32 422GB dask.array<chunksize=(1, 1, 1, 75, 4320), meta=np.ndarray> vo (first_day_datetime, lead_day_index, depth, latitude, longitude) float32 422GB dask.array<chunksize=(1, 1, 1, 75, 4320), meta=np.ndarray> zos (first_day_datetime, lead_day_index, latitude, longitude) float32 18GB dask.array<chunksize=(1, 1, 75, 4320), meta=np.ndarray>
- oceanbench.datasets.challenger.wenhai_1_degree() Dataset¶
Open the WenHai challenger dataset interpolated to the 1 degree resolution.
Returns¶
- Dataset
The Dataset containing WenHai forecasts interpolated to 1 degree resolution.
>>> wenhai_1_degree() <xarray.Dataset> Size: 12GB Dimensions: (first_day_datetime: 52, lead_day_index: 10, depth: 23, latitude: 170, longitude: 360) Coordinates: * depth (depth) float32 92B 0.494 2.646 ... 643.6 * lead_day_index (lead_day_index) int64 80B 0 1 2 ... 7 8 9 * first_day_datetime (first_day_datetime) datetime64[...] 416B ... * latitude (latitude) float64 1kB -79.5 -78.5 ... 89.5 * longitude (longitude) float64 3kB -179.5 ... 179.5 Data variables: sea_water_salinity (first_day_datetime, lead_day_index, depth, latitude, longitude) float32 3GB dask.array<chunksize=(1, 1, 1, 170, 360), meta=np.ndarray> sea_water_potential_temperature (first_day_datetime, lead_day_index, depth, latitude, longitude) float32 3GB dask.array<chunksize=(1, 1, 1, 170, 360), meta=np.ndarray> eastward_sea_water_velocity (first_day_datetime, lead_day_index, depth, latitude, longitude) float32 3GB dask.array<chunksize=(1, 1, 1, 170, 360), meta=np.ndarray> northward_sea_water_velocity (first_day_datetime, lead_day_index, depth, latitude, longitude) float32 3GB dask.array<chunksize=(1, 1, 1, 170, 360), meta=np.ndarray> sea_surface_height_above_geoid (first_day_datetime, lead_day_index, latitude, longitude) float32 127MB dask.array<chunksize=(1, 1, 170, 360), meta=np.ndarray>
- oceanbench.datasets.challenger.xihe() Dataset¶
Open the XiHe challenger dataset.
Returns¶
- Dataset
The Dataset containing XiHe forecasts.
>>> xihe() <xarray.Dataset> Size: 2TB Dimensions: (first_day_datetime: 52, lead_day_index: 10, depth: 23, latitude: 2041, longitude: 4320) Coordinates: * depth (depth) float64 184B 0.494 2.646 5.078 ... 541.1 643.6 * latitude (latitude) float32 8kB -80.0 -79.92 ... 89.92 90.0 * longitude (longitude) float32 17kB -180.0 -179.9 ... 179.8 179.9 * lead_day_index (lead_day_index) int64 80B 0 1 2 3 4 5 6 7 8 9 * first_day_datetime (first_day_datetime) datetime64[...] 416B 2024-01-03 .... Data variables: so (first_day_datetime, lead_day_index, depth, latitude, longitude) float32 422GB dask.array<chunksize=(1, 1, 1, 75, 4320), meta=np.ndarray> thetao (first_day_datetime, lead_day_index, depth, latitude, longitude) float32 422GB dask.array<chunksize=(1, 1, 1, 75, 4320), meta=np.ndarray> uo (first_day_datetime, lead_day_index, depth, latitude, longitude) float32 422GB dask.array<chunksize=(1, 1, 1, 75, 4320), meta=np.ndarray> vo (first_day_datetime, lead_day_index, depth, latitude, longitude) float32 422GB dask.array<chunksize=(1, 1, 1, 75, 4320), meta=np.ndarray> zos (first_day_datetime, lead_day_index, latitude, longitude) float32 18GB dask.array<chunksize=(1, 1, 75, 4320), meta=np.ndarray>
- oceanbench.datasets.challenger.xihe_1_degree() Dataset¶
Open the XiHe challenger dataset interpolated to the 1 degree resolution.
Returns¶
- Dataset
The Dataset containing XiHe forecasts interpolated to 1 degree resolution.
>>> xihe_1_degree() <xarray.Dataset> Size: 12GB Dimensions: (first_day_datetime: 52, lead_day_index: 10, depth: 23, latitude: 170, longitude: 360) Coordinates: * depth (depth) float64 184B 0.494 2.646 ... 643.6 * lead_day_index (lead_day_index) int64 80B 0 1 2 ... 7 8 9 * first_day_datetime (first_day_datetime) datetime64[...] 416B ... * latitude (latitude) float64 1kB -79.5 -78.5 ... 89.5 * longitude (longitude) float64 3kB -179.5 ... 179.5 Data variables: sea_water_salinity (first_day_datetime, lead_day_index, depth, latitude, longitude) float32 3GB dask.array<chunksize=(1, 1, 1, 170, 360), meta=np.ndarray> sea_water_potential_temperature (first_day_datetime, lead_day_index, depth, latitude, longitude) float32 3GB dask.array<chunksize=(1, 1, 1, 170, 360), meta=np.ndarray> eastward_sea_water_velocity (first_day_datetime, lead_day_index, depth, latitude, longitude) float32 3GB dask.array<chunksize=(1, 1, 1, 170, 360), meta=np.ndarray> northward_sea_water_velocity (first_day_datetime, lead_day_index, depth, latitude, longitude) float32 3GB dask.array<chunksize=(1, 1, 1, 170, 360), meta=np.ndarray> sea_surface_height_above_geoid (first_day_datetime, lead_day_index, latitude, longitude) float32 127MB dask.array<chunksize=(1, 1, 170, 360), meta=np.ndarray>
oceanbench.datasets.input module¶
This module exposes the input datasets for OceanBench challengers to produce forecast datasets to evaluate in the benchmark.
- oceanbench.datasets.input.glo12_nowcasts() Dataset¶
Open weekly GLO12 nowcasts from 2023 to 2025.
Returns¶
- Dataset
The Dataset containing GLO12 nowcasts.
>>> glo12_nowcasts() <xarray.Dataset> Size: 8TB Dimensions: (time: 1099, latitude: 2041, longitude: 4320, depth: 50) Coordinates: * time (time) datetime64[ns] 9kB 2022-12-28T12:00:00 ... 2025-... * latitude (latitude) float32 8kB -80.0 -79.92 -79.83 ... 89.83 89.92 90.0 * longitude (longitude) float32 17kB -180.0 -179.9 -179.8 ... 179.8 179.9 * depth (depth) float32 200B 0.494 1.541 2.646 ... 5.275e+03 5.728e+03 Data variables: siconc (time, latitude, longitude) float32 39GB dask.array<chunksize=(1, 640, 1280), meta=np.ndarray> sithick (time, latitude, longitude) float32 39GB dask.array<chunksize=(1, 640, 1280), meta=np.ndarray> so (time, depth, latitude, longitude) float32 2TB dask.array<chunksize=(1, 1, 640, 1280), meta=np.ndarray> thetao (time, depth, latitude, longitude) float32 2TB dask.array<chunksize=(1, 1, 640, 1280), meta=np.ndarray> uo (time, depth, latitude, longitude) float32 2TB dask.array<chunksize=(1, 1, 640, 1280), meta=np.ndarray> usi (time, latitude, longitude) float32 39GB dask.array<chunksize=(1, 640, 1280), meta=np.ndarray> vo (time, depth, latitude, longitude) float32 2TB dask.array<chunksize=(1, 1, 640, 1280), meta=np.ndarray> vsi (time, latitude, longitude) float32 39GB dask.array<chunksize=(1, 640, 1280), meta=np.ndarray> zos (time, latitude, longitude) float32 39GB dask.array<chunksize=(1, 640, 1280), meta=np.ndarray>
- oceanbench.datasets.input.ifs_forcings() Dataset¶
Open weekly IFS forcings from 2023 to 2025.
Returns¶
- Dataset
The Dataset containing IFS forcings.
>>> ifs_forcings() <xarray.Dataset> Size: 1TB Dimensions: (first_day_datetime: 157, lead_day_index: 10, lat: 2560, lon: 5120) Coordinates: * first_day_datetime (first_day_datetime) datetime64[ns] 1kB 2023-01-03 ... 2025-12-30 * lead_day_index (lead_day_index) int64 80B 0 1 2 3 4 5 6 7 8 9 * lat (lat) float64 20kB 89.95 89.88 89.81 ... -89.88 -89.95 * lon (lon) float64 41kB 0.0 0.07031 0.1406 ... 359.9 359.9 Data variables: cp (first_day_datetime, lead_day_index, lat, lon) float32 82GB dask.array<chunksize=(1, 1, 640, 1280), meta=np.ndarray> ewss (first_day_datetime, lead_day_index, lat, lon) float32 82GB dask.array<chunksize=(1, 1, 640, 1280), meta=np.ndarray> nsss (first_day_datetime, lead_day_index, lat, lon) float32 82GB dask.array<chunksize=(1, 1, 640, 1280), meta=np.ndarray> skt (first_day_datetime, lead_day_index, lat, lon) float32 82GB dask.array<chunksize=(1, 1, 640, 1280), meta=np.ndarray> sohumspe (first_day_datetime, lead_day_index, lat, lon) float32 82GB dask.array<chunksize=(1, 1, 640, 1280), meta=np.ndarray> somslpre (first_day_datetime, lead_day_index, lat, lon) float32 82GB dask.array<chunksize=(1, 1, 640, 1280), meta=np.ndarray> sosnowfa (first_day_datetime, lead_day_index, lat, lon) float32 82GB dask.array<chunksize=(1, 1, 640, 1280), meta=np.ndarray> sosudolw (first_day_datetime, lead_day_index, lat, lon) float32 82GB dask.array<chunksize=(1, 1, 640, 1280), meta=np.ndarray> sosudosw (first_day_datetime, lead_day_index, lat, lon) float32 82GB dask.array<chunksize=(1, 1, 640, 1280), meta=np.ndarray> sotemair (first_day_datetime, lead_day_index, lat, lon) float32 82GB dask.array<chunksize=(1, 1, 640, 1280), meta=np.ndarray> sotemhum (first_day_datetime, lead_day_index, lat, lon) float32 82GB dask.array<chunksize=(1, 1, 640, 1280), meta=np.ndarray> sowaprec (first_day_datetime, lead_day_index, lat, lon) float32 82GB dask.array<chunksize=(1, 1, 640, 1280), meta=np.ndarray> sowinu10 (first_day_datetime, lead_day_index, lat, lon) float32 82GB dask.array<chunksize=(1, 1, 640, 1280), meta=np.ndarray> sowinv10 (first_day_datetime, lead_day_index, lat, lon) float32 82GB dask.array<chunksize=(1, 1, 640, 1280), meta=np.ndarray> sp (first_day_datetime, lead_day_index, lat, lon) float32 82GB dask.array<chunksize=(1, 1, 640, 1280), meta=np.ndarray>
oceanbench.datasets.reference module¶
This module exposes the reference datasets used in OceanBench for OceanBench challenger to explore.
- oceanbench.datasets.reference.glo12_analysis() Dataset¶
Open GLO12 analysis with as an xarray.Dataset.
Returns¶
- Dataset
The Dataset containing GLO12 analysis.
>>> glo12_analysis() <xarray.Dataset> Size: 3TB Dimensions: (depth: 50, latitude: 2041, longitude: 4320, time: 366) Coordinates: * depth (depth) float32 200B 0.494 1.541 2.646 ... 5.275e+03 5.728e+03 * latitude (latitude) float32 8kB -80.0 -79.92 -79.83 ... 89.83 89.92 90.0 * longitude (longitude) float32 17kB -180.0 -179.9 -179.8 ... 179.8 179.9 * time (time) datetime64[ns] 3kB 2024-01-01 2024-01-02 ... 2024-12-31 Data variables: thetao (time, depth, latitude, longitude) float32 645GB dask.array<chunksize=(21, 1, 512, 2048), meta=np.ndarray> so (time, depth, latitude, longitude) float32 645GB dask.array<chunksize=(21, 1, 512, 2048), meta=np.ndarray> uo (time, depth, latitude, longitude) float32 645GB dask.array<chunksize=(21, 1, 512, 2048), meta=np.ndarray> vo (time, depth, latitude, longitude) float32 645GB dask.array<chunksize=(21, 1, 512, 2048), meta=np.ndarray> zos (time, latitude, longitude) float32 13GB dask.array<chunksize=(21, 1024, 2048), meta=np.ndarray> Attributes: credit: E.U. Copernicus Marine Service Information (CMEMS) contact: https://marine.copernicus.eu/contact source: MOI GLO12 producer: CMEMS - Global Monitoring and Forecasting Centre references: http://marine.copernicus.eu Conventions: CF-1.8 title: daily mean fields from Global Ocean Physics Analysis and Fo... institution: Mercator Ocean International
- oceanbench.datasets.reference.glorys_reanalysis() Dataset¶
Open GLORYS reanalysis with as an xarray.Dataset.
Returns¶
- Dataset
The Dataset containing GLORYS reanalysis.
>>> glorys_reanalysis() <xarray.Dataset> Size: 5TB Dimensions: (depth: 50, latitude: 2041, longitude: 4320, time: 366) Coordinates: * depth (depth) float32 200B 0.494 1.541 2.646 ... 5.275e+03 5.728e+03 * latitude (latitude) float32 8kB -80.0 -79.92 -79.83 ... 89.83 89.92 90.0 * longitude (longitude) float32 17kB -180.0 -179.9 -179.8 ... 179.8 179.9 * time (time) datetime64[ns] 3kB 2024-01-01 2024-01-02 ... 2024-12-31 Data variables: thetao (time, depth, latitude, longitude) float64 1TB dask.array<chunksize=(28, 1, 512, 2048), meta=np.ndarray> so (time, depth, latitude, longitude) float64 1TB dask.array<chunksize=(28, 1, 512, 2048), meta=np.ndarray> uo (time, depth, latitude, longitude) float64 1TB dask.array<chunksize=(28, 1, 512, 2048), meta=np.ndarray> vo (time, depth, latitude, longitude) float64 1TB dask.array<chunksize=(28, 1, 512, 2048), meta=np.ndarray> zos (time, latitude, longitude) float64 26GB dask.array<chunksize=(28, 512, 2048), meta=np.ndarray> Attributes: source: MERCATOR GLORYS12V1 institution: MERCATOR OCEAN comment: CMEMS product title: daily mean fields from Global Ocean Physics Analysis and Fo... references: http://www.mercator-ocean.fr history: 2023/06/01 16:20:05 MERCATOR OCEAN Netcdf creation Conventions: CF-1.4
- oceanbench.datasets.reference.glorys_reanalysis_1_degree() Dataset¶
Open the historical 1 degree GLORYS reanalysis as an xarray.Dataset.
This dataset is intended to support training on the 1 degree OceanBench track. It exposes the historical GLORYS reanalysis published at 1 degree resolution in the OceanBench public EDITO bucket.
Returns¶
- Dataset
The Dataset containing the historical 1 degree GLORYS reanalysis.
>>> glorys_reanalysis_1_degree() <xarray.Dataset> Size: 970GB Dimensions: (time: 9861, depth: 50, latitude: 170, longitude: 360) Coordinates: * time (time) datetime64[ns] 79kB 1993-01-01 ..... * depth (depth) float32 200B 0.494 ... 5.728e+03 * latitude (latitude) float64 1kB -79.5 -78.5 ... 89.5 * longitude (longitude) float64 3kB -179.5 ... 179.5 Data variables: eastward_sea_water_velocity (time, depth, latitude, longitude) float64 241GB dask.array<chunksize=(1, 1, 170, 360), meta=np.ndarray> northward_sea_water_velocity (time, depth, latitude, longitude) float64 241GB dask.array<chunksize=(1, 1, 170, 360), meta=np.ndarray> sea_surface_height_above_geoid (time, latitude, longitude) float64 5GB dask.array<chunksize=(1, 170, 360), meta=np.ndarray> sea_water_potential_temperature (time, depth, latitude, longitude) float64 241GB dask.array<chunksize=(1, 1, 170, 360), meta=np.ndarray> sea_water_salinity (time, depth, latitude, longitude) float64 241GB dask.array<chunksize=(1, 1, 170, 360), meta=np.ndarray> Attributes: Conventions: CF-1.4 comment: CMEMS product history: 2023/06/01 16:20:05 MERCATOR OCEAN Netcdf creation institution: MERCATOR OCEAN references: http://www.mercator-ocean.fr source: MERCATOR GLORYS12V1 title: daily mean fields from Global Ocean Physics Analysis and Fo...
Module contents¶
This module exposes the package python API to evaluate a challenger.