FourCastNet 3
Produces global weather forecasts at 0.25-degree resolution, generating an ensemble rather than a single trajectory.
- Field
- Weather & climate
- Method
- Deep learning
- Data it takes
- gridded-fields
- License
- Apache-2.0
FourCastNet 3 forecasts global atmospheric state on a 0.25-degree grid. It is probabilistic by construction: each run produces an ensemble, so forecast spread comes out of the model rather than being assembled from separate runs.
Machine-learning forecast models run orders of magnitude faster than numerical weather prediction at comparable skill for many variables, which makes large ensembles and rapid re-forecasting practical.
Code and weights are both Apache-2.0.
Catalog entry last checked 2026-08-19. All models →
Run FourCastNet 3 on your data
The lab runs this on its own compute, in a container, with the inputs and parameters recorded alongside the result. Initial scoping conversations are free.
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