| --- |
| license: apache-2.0 |
| language: |
| - en |
| tags: |
| - OneScience |
| - Earth Science |
| - Ensemble Forecasting |
| - Spherical Fourier Neural Operator |
| - Bred Vectors |
| - Global Weather |
| frameworks: PyTorch |
| --- |
| |
| <p align="center"><strong><span style="font-size: 30px;">SFNO-BVMC</span></strong></p> |
|
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| # Model Introduction |
|
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| SFNO-BVMC reproduces the ensemble design of *Huge ensembles - Part 1*, combining spherical Fourier neural operators, multiple training checkpoints, and centered bred vectors. The approach efficiently creates large global ensembles carrying initial-condition and model uncertainty. |
|
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| Paper: Huge ensembles - Part 1: Design of ensemble weather forecasts using spherical Fourier neural operators |
| https://doi.org/10.5194/gmd-18-5575-2025 |
|
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| # Model Description |
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| The model was proposed by researchers from Lawrence Berkeley National Laboratory, the University of California Berkeley, NVIDIA, Indiana University, and collaborating institutions. It was trained with global 0.25-degree ERA5 reanalysis data, using 1979–2015 for training, 2018 for validation, and 2020 for testing. The model is suitable for global medium-range ensemble weather forecasting and for studying initial-condition uncertainty, model uncertainty, and extreme-event probabilities. Its key feature is the combination of spherical Fourier neural operators, multiple training checkpoints, and paired centered bred vectors to construct large ensembles efficiently. |
|
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| # Usage Instructions |
|
|
| ```bash |
| python scripts/fake_data.py |
| python scripts/train.py |
| python scripts/inference.py |
| python scripts/result.py |
| ``` |
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| Synthetic data preserve the full channel and logical `[721,1440]` global-coordinate protocol through sampled original-coordinate tiles. Runtime outputs are written under `result/` and are removed from the release package. |
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| # Citation and License |
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| This repository is an independent engineering reproduction of the public SFNO-BVMC specifications, with code licensed under the Apache License 2.0. |
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| The original paper is licensed under CC BY 4.0; the paper, official weights, and data remain subject to the licenses and terms of their respective projects. |
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