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Aurora

GlobalMRF202448 GBMicrosoftPyTorch

Import path: earth2studio.models.px.Aurora

View source on GitHub View install commands

Documentation

Bases: Module, AutoModelMixin, PrognosticMixin

Aurora 0.25 degree global forecast model. This model consists of single auto-regressive model with a time-step size of 6 hours. This model operates on 0.25 degree lat-lon grid (720, 1440) equirectangular grid with 4 surface-level variables, 5 atmospheric variables with 13 pressure levels and 3 static variables.

Note

This model uses the checkpoints from the original publication. For additional information see the following resources:

Warning

We encourage users to familiarize themselves with the license restrictions of this model's checkpoints.

Parameters:

  • core_model (Module) –

    Core Aurora model

  • z (Tensor) –

    Geopotential

  • slt (Tensor) –

    Soil type

  • lsm (Tensor) –

    Land sea mask

__call__ 

__call__(
    x: Tensor, coords: CoordSystem
) -> tuple[Tensor, CoordSystem]

Runs prognostic model 1 step.

Parameters:

  • x (Tensor) –

    Input tensor

  • coords (CoordSystem) –

    Input coordinate system

Returns:

  • tuple[Tensor, CoordSystem] –

    Output tensor and coordinate system 6 hours in the future

create_iterator 

create_iterator(
    x: Tensor, coords: CoordSystem
) -> Iterator[tuple[Tensor, CoordSystem]]

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 (Tensor) –

    Input tensor

  • coords (CoordSystem) –

    Input coordinate system

Yields:

  • Iterator[tuple[Tensor, CoordSystem]] –

    Iterator that generates time-steps of the prognostic model container the output data tensor and coordinate system dictionary.

load_default_package classmethod 

load_default_package() -> Package

Load prognostic package

load_model classmethod 

load_model(package: Package) -> PrognosticModel

Load prognostic from package

Examples using earth2studio.models.px.Aurora

Preview of Creating a Local Data SourceCreating a Local Data SourceCreate and save an offline dataset to use in an inference pipeline.