| --- |
| 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. |
|
|
