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OneScience-Group/AI-GAMFS

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<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 CaseDescription
Global aerosolsForecast AOD, optical components, and surface concentrations.
Dust and smokeTrack regional pollution transport.
Coupled weatherJointly forecast aerosols and meteorology.
ModelScope/OneCode executionValidate data, training, inference, aerosol metrics, and visualization.
Multi-GPU trainingStart 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.