| --- |
| license: apache-2.0 |
| language: |
| - en |
| tags: |
| - OneScience |
| - Earth Science |
| - Data Assimilation |
| - Global Weather |
| - Satellite Observations |
| - Cascaded Forecasting |
| frameworks: PyTorch |
| --- |
| <p align="center"><strong><span style="font-size: 30px;">FuXi-Weather</span></strong></p> |
| # Model Introduction |
| FuXi-Weather maps raw satellite observations to global forecasts through FuXi-DA and cascaded FuXi forecast models. |
|
|
| Paper: A data-to-forecast machine learning system for global weather |
| https://doi.org/10.1038/s41467-025-62024-1 |
| # Model Description |
| The system was proposed by teams from the Shanghai Academy of Artificial Intelligence for Science, Fudan University, CMA, and collaborators. It was trained with ERA5, microwave radiances from three polar-orbiting satellites, and GNSS radio occultation. Masked latent assimilation and Short/Medium forecasting support six-hour cycling and global forecasts to ten days. |
| # Use Cases |
| | Use Case | Description | |
| |---|---| |
| | Satellite assimilation | Fuse sparse observations and forecast backgrounds. | |
| | Global forecasting | Cascade short- and medium-range models. | |
| | Cycling analysis | Update global analyses and forecasts every six hours. | |
| | ModelScope/OneCode execution | Validate data, training, inference, metrics, and visualization. | |
| | Multi-GPU training | Start multi-process training through `torchrun`. | |
| # Usage Instructions |
| Use a GPU or DCU when available; CPU supports the default smoke configuration. DCU users should install a compatible DTK release. |
| ```bash |
| hf download OneScience-Group/FuXi-Weather --local-dir ./FuXi-Weather |
| cd FuXi-Weather |
| python scripts/fake_data.py |
| ``` |
| For single-process training, use: |
| ```bash |
| python scripts/train.py |
| ``` |
| For multi-process training, use: |
| ```bash |
| torchrun --standalone --nproc_per_node=2 scripts/train.py |
| ``` |
| Run inference and evaluation with: |
| ```bash |
| python scripts/inference.py |
| python scripts/result.py |
| ``` |
| Training jointly optimizes analysis and forecast objectives. Inference produces finite `[2,12,20,16,16]` cascaded forecasts, while evaluation reports lead-time RMSE under `result/evaluation/`. |
| ## Trained Weights |
| No weights are bundled under `weight/`. The FuXi model is available at https://zenodo.org/records/10401602, and the FuXi Weather model used by the paper is available at https://zenodo.org/records/15762985. |
| # Citation and License |
| This repository is an independent engineering reproduction of the public FuXi-Weather specifications. |
|
|
| The original paper is licensed under CC BY-NC-ND 4.0; the original paper, official code, model weights, and related data remain subject to their respective licenses and terms. |
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|
