Add English model card

README.md

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+---
+license: apache-2.0
+language:
+- en
+tags:
+- OneScience
+- Earth Science
+- Aerosol Forecasting
+- Air Quality
+- Vision Transformer
+- Relay Forecasting
+frameworks: PyTorch
+---
+
+<p align="center"><strong><span style="font-size: 30px;">AI-GAMFS</span></strong></p>
+
+# Model Introduction
+
+AI-GAMFS is a machine-learning global aerosol-meteorology forecasting system producing five-day forecasts at three-hour intervals.
+
+Paper: Advancing operational global aerosol forecasting with machine learning  
+https://doi.org/10.1038/s41586-026-10234-y
+
+# Model Description
+
+The model was proposed by teams from the Chinese Academy of Meteorological Sciences, National Meteorological Center, NASA, and collaborators. It was trained with 54 MERRA-2 aerosol and meteorological variables from 1980–2021. Vision Transformer, U-Net, and 3/6/9/12-hour relay models support global AOD, aerosol-component, and air-quality forecasts.
+
+# Use Cases
+
+| Use Case | Description |
+| :---: | :--- |
+| Global aerosols | Forecast AOD, optical components, and surface concentrations. |
+| Dust and smoke | Track regional pollution transport. |
+| Coupled weather | Jointly forecast aerosols and meteorology. |
+| ModelScope/OneCode execution | Validate data, training, inference, aerosol 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.
+
+```bash
+hf download OneScience-Group/AI-GAMFS --local-dir ./AI-GAMFS
+cd AI-GAMFS
+```
+
+### Environment Dependencies
+
+**Hardware Requirements**
+
+- A GPU or DCU is recommended.
+- A CPU can be used for connectivity validation with the default small-sample configuration.
+- DCU users should install DTK 25.04.2 or a compatible OneScience-recommended version first.
+
+**DCU Environment**
+
+```bash
+# Activate DTK and Conda first
+conda create -n onescience311 python=3.11 -y
+conda activate onescience311
+pip install onescience[earth-dcu] -i http://mirrors.onescience.ai:3141/pypi/simple/ --trusted-host mirrors.onescience.ai
+```
+
+**GPU Environment**
+
+```bash
+# Activate Conda first
+conda create -n onescience311 python=3.11 -y libstdcxx-ng=12 libgcc-ng=12 gcc_linux-64=12 gxx_linux-64=12
+conda activate onescience311
+pip install onescience[earth-gpu] -i http://mirrors.onescience.ai:3141/pypi/simple/ --trusted-host mirrors.onescience.ai
+```
+
+```bash
+python scripts/fake_data.py
+python scripts/train.py
+torchrun --standalone --nproc_per_node=2 scripts/train.py
+python scripts/inference.py
+python scripts/result.py
+```
+Training optimizes four relay models. Inference produces 40 three-hourly forecasts and evaluation reports finite AOD RMSE and correlation.
+## Trained Weights
+No weights are bundled under `weight/`. The paper does not provide a directly loadable official pretrained-weight link.
+# Citation and License
+This repository is an independent engineering reproduction of the public AI-GAMFS specifications.
+
+The original paper is licensed under CC BY-NC-ND 4.0; official code, model weights, and related data retain their respective terms.