--- license: apache-2.0 language: - en tags: - OneScience - Earth Science - Ensemble Forecasting - Spherical Fourier Neural Operator - Bred Vectors - Global Weather frameworks: PyTorch ---

SFNO-BVMC

# Model Introduction 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. Paper: Huge ensembles - Part 1: Design of ensemble weather forecasts using spherical Fourier neural operators https://doi.org/10.5194/gmd-18-5575-2025 # Model Description 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. # Usage Instructions ```bash python scripts/fake_data.py python scripts/train.py python scripts/inference.py python scripts/result.py ``` 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. # Citation and License This repository is an independent engineering reproduction of the public SFNO-BVMC specifications, with code licensed under the Apache License 2.0. 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.