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Junting Zhong

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  1. CWAI folds pollutants and weather into one learned state, beating WRF-Chem error at 10 days across six pollutants

    Regional air-quality forecasting has relied on online-coupled numerical chemistry-weather models whose skill is sensitive to meteorological initial conditions, emission inventories and lateral boundary conditions. CWAI treats meteorology, particles and reactive gases as one joint atmospheric state, driven by two consecutive states, emissions and static surface descriptors through a hierarchical Swin Transformer, and rolls out 40 six-hour steps to a 10-day forecast. Against WRF-Chem over 62 January and July 2020 starts, CWAI had lower initialization-weighted RMSE for all six surface pollutants, from 15.8% for PM2.5 to 25.8% for NO2.

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