AirMPA: A Meteorology-to-Pollution Adapter for Global Air Quality Forecasting
Accurate real-time forecasting of atmospheric pollutants is essential for reducing exposure risks and improving public health. Traditional numerical forecasting systems suffer from incomplete parameterizations, uncertain inputs, and high computational cost. Existing deep learning approaches improve efficiency, but often entangle meteorological and pollutant variables within unified forecasting frameworks, limiting flexibility and pollutant-specific representation learning. To address these challenges, we propose AirMPA, a decoupled autoregressive framework for meteorology-to-pollution forecasting. AirMPA predicts 13 atmospheric pollutants at 0.4° spatial resolution and 12-h temporal resol...
| Authors | Yiheng Wang, Kai Zheng, Yuetan Lin et al. |
|---|---|
| Primary category | physics.ao-ph |
| Published | 19 October 2026 |
| Last updated at source | 19 October 2026 |
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