DynamicalGFS_FX#

class earth2studio.data.DynamicalGFS_FX(cache=True, verbose=True)[source]#
Global

NOAA GFS forecast from the dynamical.org catalog.

Deterministic NOAA Global Forecast System forecasts (dimensions [time, lead_time, lat, lon]) on a global 0.25 degree regular latitude/longitude grid.

Parameters:

  • cache (bool, optional) – Retained for API parity; Icechunk reads chunks lazily on demand rather than caching whole files locally, by default True

  • verbose (bool, optional) – Print download progress, by default True

Examples

>>> source = DynamicalGFS_FX()
>>> source.variables  # variables are determined dynamically

Warning

This is a remote data source and can potentially download a large amount of data to your local machine for large requests.

Note

The dynamical.org dataset documentation and STAC metadata are available at:

__call__(time, lead_time, variable)[source]#

Retrieve forecast data.

Parameters:

  • time (datetime | list[datetime] | TimeArray) – Forecast initialization timestamps to return data for (UTC).

  • lead_time (timedelta | list[timedelta] | LeadTimeArray) – Forecast lead times to return.

  • variable (str | list[str] | VariableArray) – Variable(s) to return, in the dynamical.org lexicon or native to the collection.

Returns:

Data array with dimensions [time, lead_time, variable, ...].

Return type:

xr.DataArray

async fetch(time, lead_time, variable)[source]#

Async function to retrieve forecast data.

Parameters:

  • time (datetime | list[datetime] | TimeArray) – Forecast initialization timestamps to return data for (UTC).

  • lead_time (timedelta | list[timedelta] | LeadTimeArray) – Forecast lead times to return.

  • variable (str | list[str] | VariableArray) – Variable(s) to return, in the dynamical.org lexicon or native to the collection.

Returns:

Data array with dimensions [time, lead_time, variable, ...].

Return type:

xr.DataArray

available(time)[source]#

Check if a given time is available in this dynamical.org collection.

Unlike most Earth2Studio sources, availability is collection-dependent (each collection advertises its own temporal extent in its STAC metadata), so this is an instance method rather than a classmethod: it opens the collection and checks the requested time against the extent of the collection’s temporal dimension (time for analysis, init_time for forecast collections).

Parameters:

time (datetime | np.datetime64) – Time to check. For forecast collections this is the forecast initialization time.

Returns:

Whether the time falls within the collection’s temporal extent.

Return type:

bool