earth2studio.models.px
.GraphCastOperational#
- class earth2studio.models.px.GraphCastOperational(ckpt, diffs_stddev_by_level, mean_by_level, stddev_by_level)[source]#
GraphCast operational model
A high-resolution model (0.25 degree resolution, 13 pressure levels) pre-trained on ERA5 data from 1979 to 2017 and fine-tuned on HRES data from 2016 to 2021. This model can be initialized from HRES data (does not require precipitation inputs).
The model operates on a 0.25-degree lat-lon grid (south-pole including) equirectangular grid with 85 variables including:
Surface variables (2m temperature, 10m winds, etc.)
Pressure level variables (temperature, winds, geopotential, etc.)
Static variables (land-sea mask, surface geopotential)
Note
This model and checkpoint are based on the GraphCast architecture from DeepMind. For more information see the following references:
Warning
We encourage users to familiarize themselves with the license restrictions of this model’s checkpoints.
- Parameters:
ckpt (graphcast.CheckPoint) – Model checkpoint containing weights and configuration
diffs_stddev_by_level (xr.Dataset) – Standard deviation of differences by level for normalization
mean_by_level (xr.Dataset) – Mean values by level for normalization
stddev_by_level (xr.Dataset) – Standard deviation by level for normalization
- __call__(x, coords)[source]#
Runs prognostic model 1 step.
- Parameters:
x (torch.Tensor) – Input tensor
coords (CoordSystem) – Input coordinate system
- Returns:
Output tensor and coordinate system 6 hours in the future
- Return type:
tuple[torch.Tensor, CoordSystem]
- create_iterator(x, coords)[source]#
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]]]