Impact of assimilating multi-source observations on meteorological and PM2.5 forecast over Central China

Abstract

Data assimilation (DA) is a promising approach to improve meteorological and PM2.5 forecasts, but to what extent and by what process the DA of meteorological fields helps improve PM2.5 forecast still call for more discussion. By utilizing WRFDA and WRF-Chem models, we have assimilated AMSU-A, MHS radiances and conventional observations, and studied the influences of the meteorological DA on meteorological and PM2.5 forecasts over Central China through a series of experiments. The results show that multi-source meteorological DA helps improve temperature and relative humidity forecasts in the lower atmosphere, and the improved meteorological fields further improve PM2.5 forecast with a reduction of bias and RMSE by 7.4% and 4.1% over the study area, especially during PM2.5 episode. This study also helps understand how DA improve the PM2.5 forecasts over Central China.