Challenger datasets
datasets.challenger
datasets.challenger
This module exposes the challenger datasets evaluated in the benchmark.
Functions
| Name | Description |
|---|---|
| glo12 | Open the GLO12 challenger dataset. |
| glo12_1_degree | Open the GLO12 challenger dataset interpolated to the 1 degree resolution. |
| glo12_persistence | Open the GLO12 persistence baseline challenger dataset. |
| glo12_persistence_1_degree | Open the GLO12 persistence baseline challenger dataset interpolated to 1 degree. |
| glo36v1 | Open the GLO36V1 challenger dataset. |
| glonet | Open the GLONET challenger dataset. |
| glonet_1_degree | Open the GLONET challenger dataset interpolated to the 1 degree resolution. |
| hclimrep | Open the HClimRep challenger dataset. |
| hclimrep_1_degree | Open the HClimRep challenger dataset interpolated to the 1 degree resolution. |
| langya | Open the LangYa challenger dataset. |
| langya_1_degree | Open the LangYa challenger dataset interpolated to the 1 degree resolution. |
| wenhai | Open the WenHai challenger dataset. |
| wenhai_1_degree | Open the WenHai challenger dataset interpolated to the 1 degree resolution. |
| xihe | Open the XiHe challenger dataset. |
| xihe_1_degree | Open the XiHe challenger dataset interpolated to the 1 degree resolution. |
glo12
datasets.challenger.glo12(first_day_datetimes=None)Open the GLO12 challenger dataset.
Parameters
| Name | Type | Description | Default |
|---|---|---|---|
| first_day_datetimes | list[datetime] | Forecast start dates to open, all benchmark start dates by default. | None |
Returns
| Name | Type | Description |
|---|---|---|
| Dataset | The Dataset containing GLO12 forecasts. |
Examples
>>> glo12()
<xarray.Dataset> Size: 4TB
Dimensions: (first_day_datetime: 52, lead_day_index: 10, 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
* 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 917GB dask.array<chunksize=(1, 1, 1, 640, 1280), meta=np.ndarray>
thetao (first_day_datetime, lead_day_index, depth, latitude, longitude) float32 917GB dask.array<chunksize=(1, 1, 1, 640, 1280), meta=np.ndarray>
uo (first_day_datetime, lead_day_index, depth, latitude, longitude) float32 917GB dask.array<chunksize=(1, 1, 1, 640, 1280), meta=np.ndarray>
vo (first_day_datetime, lead_day_index, depth, latitude, longitude) float32 917GB dask.array<chunksize=(1, 1, 1, 640, 1280), meta=np.ndarray>
zos (first_day_datetime, lead_day_index, latitude, longitude) float32 18GB dask.array<chunksize=(1, 1, 640, 1280), meta=np.ndarray>glo12_1_degree
datasets.challenger.glo12_1_degree(first_day_datetimes=None)Open the GLO12 challenger dataset interpolated to the 1 degree resolution.
Parameters
| Name | Type | Description | Default |
|---|---|---|---|
| first_day_datetimes | list[datetime] | Forecast start dates to open, all benchmark start dates by default. | None |
Returns
| Name | Type | Description |
|---|---|---|
| Dataset | The Dataset containing GLO12 forecasts interpolated to 1 degree resolution. |
Examples
>>> glo12_1_degree()
<xarray.Dataset> Size: 26GB
Dimensions: (first_day_datetime: 52,
lead_day_index: 10, depth: 50,
latitude: 170, longitude: 360)
Coordinates:
* depth (depth) float32 200B 0.494 ... 5.728e+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 6GB 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 6GB 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 6GB 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 6GB 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>glo12_persistence
datasets.challenger.glo12_persistence(first_day_datetimes=None)Open the GLO12 persistence baseline challenger dataset.
Repeats the GLO12 nowcast for every lead day. Any useful forecast should beat it.
Parameters
| Name | Type | Description | Default |
|---|---|---|---|
| first_day_datetimes | list[datetime] | Forecast start dates to open, all benchmark start dates by default. | None |
Returns
| Name | Type | Description |
|---|---|---|
| Dataset | The Dataset containing GLO12 persistence baseline forecasts. |
Examples
>>> glo12_persistence()
<xarray.Dataset> Size: 4TB
Dimensions: (first_day_datetime: 52, lead_day_index: 10, 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
* 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 917GB dask.array<chunksize=(1, 1, 1, 640, 1280), meta=np.ndarray>
thetao (first_day_datetime, lead_day_index, depth, latitude, longitude) float32 917GB dask.array<chunksize=(1, 1, 1, 640, 1280), meta=np.ndarray>
uo (first_day_datetime, lead_day_index, depth, latitude, longitude) float32 917GB dask.array<chunksize=(1, 1, 1, 640, 1280), meta=np.ndarray>
vo (first_day_datetime, lead_day_index, depth, latitude, longitude) float32 917GB dask.array<chunksize=(1, 1, 1, 640, 1280), meta=np.ndarray>
zos (first_day_datetime, lead_day_index, latitude, longitude) float32 18GB dask.array<chunksize=(1, 1, 640, 1280), meta=np.ndarray>
Attributes: (12/13)
Conventions: CF-1.8
NCO: netCDF Operators version 4.9.7 (Homepage = ht...
area: Global
contact: https://marine.copernicus.eu/contact
credit: E.U. Copernicus Marine Service Information (C...
history: Mon Mar 31 11:20:47 2025: ncks -A /data/rd_ex...
... ...
institution: Mercator Ocean International
licence: http://marine.copernicus.eu/services-portfoli...
producer: CMEMS - Global Monitoring and Forecasting Centre
references: http://marine.copernicus.eu
source: MOI GLO12
title: daily mean fields from Global Ocean Physics A...glo12_persistence_1_degree
datasets.challenger.glo12_persistence_1_degree(first_day_datetimes=None)Open the GLO12 persistence baseline challenger dataset interpolated to 1 degree.
Parameters
| Name | Type | Description | Default |
|---|---|---|---|
| first_day_datetimes | list[datetime] | Forecast start dates to open, all benchmark start dates by default. | None |
Returns
| Name | Type | Description |
|---|---|---|
| Dataset | The Dataset containing GLO12 persistence baseline forecasts interpolated to 1 degree resolution. |
Examples
>>> glo12_persistence_1_degree()
<xarray.Dataset> Size: 26GB
Dimensions: (first_day_datetime: 52,
lead_day_index: 10, depth: 50,
latitude: 170, longitude: 360)
Coordinates:
* depth (depth) float32 200B 0.494 ... 5.728e+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 6GB 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 6GB 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 6GB 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 6GB 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: (12/13)
Conventions: CF-1.8
NCO: netCDF Operators version 4.9.7 (Homepage = ht...
area: Global
contact: https://marine.copernicus.eu/contact
credit: E.U. Copernicus Marine Service Information (C...
history: Mon Mar 31 11:20:47 2025: ncks -A /data/rd_ex...
... ...
institution: Mercator Ocean International
licence: http://marine.copernicus.eu/services-portfoli...
producer: CMEMS - Global Monitoring and Forecasting Centre
references: http://marine.copernicus.eu
source: MOI GLO12
title: daily mean fields from Global Ocean Physics A...glo36v1
datasets.challenger.glo36v1(first_day_datetimes=None)Open the GLO36V1 challenger dataset.
Parameters
| Name | Type | Description | Default |
|---|---|---|---|
| first_day_datetimes | list[datetime] | Forecast start dates to open, all benchmark start dates by default. | None |
Returns
| Name | Type | Description |
|---|---|---|
| Dataset | The Dataset containing GLO36V1 forecasts. |
Examples
>>> 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>glonet
datasets.challenger.glonet(first_day_datetimes=None)Open the GLONET challenger dataset.
Parameters
| Name | Type | Description | Default |
|---|---|---|---|
| first_day_datetimes | list[datetime] | Forecast start dates to open, all benchmark start dates by default. | None |
Returns
| Name | Type | Description |
|---|---|---|
| Dataset | The Dataset containing GLONET forecasts. |
Examples
>>> glonet()
<xarray.Dataset> Size: 342GB
Dimensions: (first_day_datetime: 52, lead_day_index: 10, depth: 21,
latitude: 672, longitude: 1440)
Coordinates:
* depth (depth) float32 84B 0.494 47.37 ... 4.833e+03 5.275e+03
* latitude (latitude) float64 5kB -78.0 -77.75 -77.5 ... 89.5 89.75
* longitude (longitude) float64 12kB -180.0 -179.8 ... 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, latitude, longitude) float64 85GB dask.array<chunksize=(1, 2, 3, 168, 360), meta=np.ndarray>
thetao (first_day_datetime, lead_day_index, depth, latitude, longitude) float64 85GB dask.array<chunksize=(1, 2, 3, 168, 360), meta=np.ndarray>
uo (first_day_datetime, lead_day_index, depth, latitude, longitude) float64 85GB dask.array<chunksize=(1, 2, 3, 168, 360), meta=np.ndarray>
vo (first_day_datetime, lead_day_index, depth, latitude, longitude) float64 85GB dask.array<chunksize=(1, 2, 3, 168, 360), meta=np.ndarray>
zos (first_day_datetime, lead_day_index, latitude, longitude) float64 4GB dask.array<chunksize=(1, 2, 168, 360), meta=np.ndarray>
Attributes:
Conventions: CF-1.8
area: Global
challenger: glonet
contact: glonet@mercator-ocean.eu
forecast_reference_time: 2024-01-02
institution: Mercator Ocean International
references: www.edito.eu
source: MOI GLONET
title: Daily mean fields from GLONET 1/4 degree resolu...glonet_1_degree
datasets.challenger.glonet_1_degree(first_day_datetimes=None)Open the GLONET challenger dataset interpolated to the 1 degree resolution.
Parameters
| Name | Type | Description | Default |
|---|---|---|---|
| first_day_datetimes | list[datetime] | Forecast start dates to open, all benchmark start dates by default. | None |
Returns
| Name | Type | Description |
|---|---|---|
| Dataset | The Dataset containing GLONET forecasts interpolated to 1 degree resolution. |
Examples
>>> 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, 2, 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, 2, 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, 2, 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, 2, 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, 2, 168, 360), meta=np.ndarray>
Attributes:
Conventions: CF-1.8
area: Global
challenger: glonet
contact: glonet@mercator-ocean.eu
forecast_reference_time: 2024-01-02
institution: Mercator Ocean International
references: www.edito.eu
source: MOI GLONET
title: Daily mean fields from GLONET 1/4 degree resolu...hclimrep
datasets.challenger.hclimrep(first_day_datetimes=None)Open the HClimRep challenger dataset.
Parameters
| Name | Type | Description | Default |
|---|---|---|---|
| first_day_datetimes | list[datetime] | Forecast start dates to open, all benchmark start dates by default. | None |
Returns
| Name | Type | Description |
|---|---|---|
| Dataset | The Dataset containing HClimRep forecasts. |
Examples
>>> hclimrep()
<xarray.Dataset> Size: 74GB
Dimensions: (first_day_datetime: 52, lead_day_index: 10, depth: 9,
latitude: 672, longitude: 1440)
Coordinates:
* depth (depth) float64 72B 0.494 47.37 92.33 ... 453.9 541.1
* latitude (latitude) float64 5kB -78.0 -77.75 -77.5 ... 89.5 89.75
* longitude (longitude) float64 12kB -180.0 -179.8 ... 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, latitude, longitude) float32 18GB dask.array<chunksize=(1, 1, 1, 672, 1440), meta=np.ndarray>
thetao (first_day_datetime, lead_day_index, depth, latitude, longitude) float32 18GB dask.array<chunksize=(1, 1, 1, 672, 1440), meta=np.ndarray>
uo (first_day_datetime, lead_day_index, depth, latitude, longitude) float32 18GB dask.array<chunksize=(1, 1, 1, 672, 1440), meta=np.ndarray>
vo (first_day_datetime, lead_day_index, depth, latitude, longitude) float32 18GB dask.array<chunksize=(1, 1, 1, 672, 1440), meta=np.ndarray>
zos (first_day_datetime, lead_day_index, latitude, longitude) float32 2GB dask.array<chunksize=(1, 1, 672, 1440), meta=np.ndarray>
Attributes:
Conventions: CF-1.8
area: Global
challenger: hclimrep
...hclimrep_1_degree
datasets.challenger.hclimrep_1_degree(first_day_datetimes=None)Open the HClimRep challenger dataset interpolated to the 1 degree resolution.
Parameters
| Name | Type | Description | Default |
|---|---|---|---|
| first_day_datetimes | list[datetime] | Forecast start dates to open, all benchmark start dates by default. | None |
Returns
| Name | Type | Description |
|---|---|---|
| Dataset | The Dataset containing HClimRep forecasts interpolated to 1 degree resolution. |
Examples
>>> hclimrep_1_degree()
<xarray.Dataset> Size: 5GB
Dimensions: (first_day_datetime: 52,
lead_day_index: 10, depth: 9,
latitude: 168, longitude: 360)
Coordinates:
* depth (depth) float64 72B 0.494 47.37 ... 541.1
* 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) float32 1GB dask.array<chunksize=(1, 1, 1, 168, 360), meta=np.ndarray>
sea_water_potential_temperature (first_day_datetime, lead_day_index, depth, latitude, longitude) float32 1GB dask.array<chunksize=(1, 1, 1, 168, 360), meta=np.ndarray>
eastward_sea_water_velocity (first_day_datetime, lead_day_index, depth, latitude, longitude) float32 1GB dask.array<chunksize=(1, 1, 1, 168, 360), meta=np.ndarray>
northward_sea_water_velocity (first_day_datetime, lead_day_index, depth, latitude, longitude) float32 1GB dask.array<chunksize=(1, 1, 1, 168, 360), meta=np.ndarray>
sea_surface_height_above_geoid (first_day_datetime, lead_day_index, latitude, longitude) float32 126MB dask.array<chunksize=(1, 1, 168, 360), meta=np.ndarray>
Attributes:
Conventions: CF-1.8
area: Global
challenger: hclimrep
...langya
datasets.challenger.langya(first_day_datetimes=None)Open the LangYa challenger dataset.
Parameters
| Name | Type | Description | Default |
|---|---|---|---|
| first_day_datetimes | list[datetime] | Forecast start dates to open, all benchmark start dates by default. | None |
Returns
| Name | Type | Description |
|---|---|---|
| Dataset | The Dataset containing LangYa forecasts. |
Examples
>>> langya()
<xarray.Dataset> Size: 2TB
Dimensions: (first_day_datetime: 52, lead_day_index: 7, depth: 32,
latitude: 2041, longitude: 4320)
Coordinates:
* depth (depth) float32 128B 0.494 1.541 2.646 ... 453.9 541.1
* latitude (latitude) float64 16kB -80.0 -79.92 ... 89.92 90.0
* longitude (longitude) float64 35kB -180.0 -179.9 ... 179.8 179.9
* lead_day_index (lead_day_index) int64 56B 0 1 2 3 4 5 6
* first_day_datetime (first_day_datetime) datetime64[...] 416B 2024-01-03 ....
Data variables:
so (first_day_datetime, lead_day_index, depth, latitude, longitude) float32 411GB dask.array<chunksize=(1, 1, 1, 256, 512), meta=np.ndarray>
thetao (first_day_datetime, lead_day_index, depth, latitude, longitude) float32 411GB dask.array<chunksize=(1, 1, 1, 256, 512), meta=np.ndarray>
uo (first_day_datetime, lead_day_index, depth, latitude, longitude) float32 411GB dask.array<chunksize=(1, 1, 1, 256, 512), meta=np.ndarray>
vo (first_day_datetime, lead_day_index, depth, latitude, longitude) float32 411GB dask.array<chunksize=(1, 1, 1, 256, 512), meta=np.ndarray>
zos (first_day_datetime, lead_day_index, latitude, longitude) float32 13GB dask.array<chunksize=(1, 1, 256, 512), meta=np.ndarray>
Attributes:
Conventions: CF-1.8
challenger: langya
forecast_reference_time: 2024-01-02langya_1_degree
datasets.challenger.langya_1_degree(first_day_datetimes=None)Open the LangYa challenger dataset interpolated to the 1 degree resolution.
Parameters
| Name | Type | Description | Default |
|---|---|---|---|
| first_day_datetimes | list[datetime] | Forecast start dates to open, all benchmark start dates by default. | None |
Returns
| Name | Type | Description |
|---|---|---|
| Dataset | The Dataset containing LangYa forecasts interpolated to 1 degree resolution. |
Examples
>>> langya_1_degree()
<xarray.Dataset> Size: 11GB
Dimensions: (first_day_datetime: 52,
lead_day_index: 7, depth: 32,
latitude: 170, longitude: 360)
Coordinates:
* depth (depth) float32 128B 0.494 1.541 ... 541.1
* lead_day_index (lead_day_index) int64 56B 0 1 2 3 4 5 6
* 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 89MB dask.array<chunksize=(1, 1, 170, 360), meta=np.ndarray>
Attributes:
Conventions: CF-1.8
challenger: langya
forecast_reference_time: 2024-01-02wenhai
datasets.challenger.wenhai(first_day_datetimes=None)Open the WenHai challenger dataset.
Parameters
| Name | Type | Description | Default |
|---|---|---|---|
| first_day_datetimes | list[datetime] | Forecast start dates to open, all benchmark start dates by default. | None |
Returns
| Name | Type | Description |
|---|---|---|
| Dataset | The Dataset containing WenHai forecasts. |
Examples
>>> 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, 256, 512), meta=np.ndarray>
thetao (first_day_datetime, lead_day_index, depth, latitude, longitude) float32 422GB dask.array<chunksize=(1, 1, 1, 256, 512), meta=np.ndarray>
uo (first_day_datetime, lead_day_index, depth, latitude, longitude) float32 422GB dask.array<chunksize=(1, 1, 1, 256, 512), meta=np.ndarray>
vo (first_day_datetime, lead_day_index, depth, latitude, longitude) float32 422GB dask.array<chunksize=(1, 1, 1, 256, 512), meta=np.ndarray>
zos (first_day_datetime, lead_day_index, latitude, longitude) float32 18GB dask.array<chunksize=(1, 1, 256, 512), meta=np.ndarray>
Attributes:
Conventions: CF-1.8
challenger: wenhai
forecast_reference_time: 2024-01-02wenhai_1_degree
datasets.challenger.wenhai_1_degree(first_day_datetimes=None)Open the WenHai challenger dataset interpolated to the 1 degree resolution.
Parameters
| Name | Type | Description | Default |
|---|---|---|---|
| first_day_datetimes | list[datetime] | Forecast start dates to open, all benchmark start dates by default. | None |
Returns
| Name | Type | Description |
|---|---|---|
| Dataset | The Dataset containing WenHai forecasts interpolated to 1 degree resolution. |
Examples
>>> 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>
Attributes:
Conventions: CF-1.8
challenger: wenhai
forecast_reference_time: 2024-01-02xihe
datasets.challenger.xihe(first_day_datetimes=None)Open the XiHe challenger dataset.
Parameters
| Name | Type | Description | Default |
|---|---|---|---|
| first_day_datetimes | list[datetime] | Forecast start dates to open, all benchmark start dates by default. | None |
Returns
| Name | Type | Description |
|---|---|---|
| Dataset | The Dataset containing XiHe forecasts. |
Examples
>>> xihe()
<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, 256, 512), meta=np.ndarray>
thetao (first_day_datetime, lead_day_index, depth, latitude, longitude) float32 422GB dask.array<chunksize=(1, 1, 1, 256, 512), meta=np.ndarray>
uo (first_day_datetime, lead_day_index, depth, latitude, longitude) float32 422GB dask.array<chunksize=(1, 1, 1, 256, 512), meta=np.ndarray>
vo (first_day_datetime, lead_day_index, depth, latitude, longitude) float32 422GB dask.array<chunksize=(1, 1, 1, 256, 512), meta=np.ndarray>
zos (first_day_datetime, lead_day_index, latitude, longitude) float32 18GB dask.array<chunksize=(1, 1, 256, 512), meta=np.ndarray>
Attributes:
Conventions: CF-1.8
challenger: xihe
forecast_reference_time: 2024-01-02xihe_1_degree
datasets.challenger.xihe_1_degree(first_day_datetimes=None)Open the XiHe challenger dataset interpolated to the 1 degree resolution.
Parameters
| Name | Type | Description | Default |
|---|---|---|---|
| first_day_datetimes | list[datetime] | Forecast start dates to open, all benchmark start dates by default. | None |
Returns
| Name | Type | Description |
|---|---|---|
| Dataset | The Dataset containing XiHe forecasts interpolated to 1 degree resolution. |
Examples
>>> 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) 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>
Attributes:
Conventions: CF-1.8
challenger: xihe
forecast_reference_time: 2024-01-02