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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>
# 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.
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