Satellite-based inverse modeling has the potential to drive aerosol precursor emissions, but its efficacy for improving chemistry transport models (CTMs) remains elusive because of its likely inherent dependence on the error characteristics of a specific CTM used for the inversion. This issue is quantitively assessed here by using three CTMs. We show that SO₂ emissions from global GEOS-Chem adjoint model and OMI SO₂ data, when combined with spatial variation of bottom-up emissions, can largely improve WRF-Chem and WRF-CMAQ forecast of SO₂ and aerosol optical depth (in reference to MODIS data) in China. This suggests that the efficacy of satellite-based inversion of SO₂ emission appears to be high for CTMs that use similar or identical emission inventories. With the advent of geostationary air quality monitoring satellites in next three years, this study argues that an era of using top-down approach to rapidly update emission is emerging for regional air quality forecast, especially over Asia having highly varying emissions.
 

排放,大气污染,数值模型,卫星遥感,地基观测