earth2studio.models.px.FCN3

class earth2studio.models.px.FCN3(core_model, variables=array(['u10m', 'v10m', 'u100m', 'v100m', 't2m', 'msl', 'tcwv', 'u50', 'u100', 'u150', 'u200', 'u250', 'u300', 'u400', 'u500', 'u600', 'u700', 'u850', 'u925', 'u1000', 'v50', 'v100', 'v150', 'v200', 'v250', 'v300', 'v400', 'v500', 'v600', 'v700', 'v850', 'v925', 'v1000', 'z50', 'z100', 'z150', 'z200', 'z250', 'z300', 'z400', 'z500', 'z600', 'z700', 'z850', 'z925', 'z1000', 't50', 't100', 't150', 't200', 't250', 't300', 't400', 't500', 't600', 't700', 't850', 't925', 't1000', 'q50', 'q100', 'q150', 'q200', 'q250', 'q300', 'q400', 'q500', 'q600', 'q700', 'q850', 'q925', 'q1000'], dtype='<U5'))

FourCastNet 3 advances global weather modeling by implementing a scalable, geometric machine learning (ML) approach to probabilistic ensemble forecasting. The approach is designed to respect spherical geometry and to accurately model the spatially correlated probabilistic nature of the problem, resulting in stable spectra and realistic dynamics across multiple scales.

FourCastNet 3 is a global probabilistic prognostic model. It operates on a 0.25 degree lat-lon grid (south-pole excluding) equirectangular grid with 72 variables.

Note

This model requires at least 60 GB of GPU memory to run.

References

• https://arxiv.org/abs/2507.12144v2

• https://arxiv.org/abs/2402.16845

• https://catalog.ngc.nvidia.com/orgs/nvidia/teams/earth-2/models/fourcastnet3

Parameters:

• core_model (torch.nn.Module) – Core PyTorch model with loaded weights

• variables (np.array, optional) – Variables associated with model, by default 72 variable model.

__call__(x, coords)

Runs prognostic model 1 step

Parameters:

• x (torch.Tensor) – Input tensor

• coords (CoordSystem) – Input coordinate system

Returns:

Output tensor and coordinate system

Return type:

tuple[torch.Tensor, CoordSystem]

create_iterator(x, coords)

Creates a iterator which can be used to perform time-integration of the prognostic model. Will return the initial condition first (0th step).

Parameters:

• x (torch.Tensor) – Input tensor

• coords (CoordSystem) – Input coordinate system

Yields:

Iterator[tuple[torch.Tensor, CoordSystem]] – Iterator that generates time-steps of the prognostic model container the output data tensor and coordinate system dictionary.

Return type:

Iterator[tuple[Tensor, OrderedDict[str, ndarray]]]

classmethod load_default_package()

Load prognostic package

Return type:

Package

classmethod load_model(package, variables=['u10m', 'v10m', 'u100m', 'v100m', 't2m', 'msl', 'tcwv', 'u50', 'u100', 'u150', 'u200', 'u250', 'u300', 'u400', 'u500', 'u600', 'u700', 'u850', 'u925', 'u1000', 'v50', 'v100', 'v150', 'v200', 'v250', 'v300', 'v400', 'v500', 'v600', 'v700', 'v850', 'v925', 'v1000', 'z50', 'z100', 'z150', 'z200', 'z250', 'z300', 'z400', 'z500', 'z600', 'z700', 'z850', 'z925', 'z1000', 't50', 't100', 't150', 't200', 't250', 't300', 't400', 't500', 't600', 't700', 't850', 't925', 't1000', 'q50', 'q100', 'q150', 'q200', 'q250', 'q300', 'q400', 'q500', 'q600', 'q700', 'q850', 'q925', 'q1000'])

Load prognostic from package

Parameters:

• package (Package) – Package to load model from

• variables (list, optional) – Model variable override, by default VARIABLES for FCN3 72 channel

Returns:

Prognostic model

Return type:

PrognosticModel