---
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.