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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.
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