Add English model card

README.md

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+---
+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.