Impacts of Vertical Resolution and Diurnally Varying Background Error Covariance on FY-4A/AGRI Data Assimilation over Land

Abstract This study aims at improving the assimilation of Fengyun-4A ( FY-4A )/Advanced Geostationary Radiation Imager (AGRI) clear-sky surface-sensitive brightness temperature over land using the Weather Research and Forecasting and the Gridpoint Statistical Interpolation three-dimensional variational data assimilation for summer convective cases over eastern China. We investigated the influences of the planetary boundary layer on the assimilation of FY-4A /AGRI surface-sensitive channels, focusing on model low-level vertical resolution and diurnal variation of background error covariance. The vertical levels are increased from 48 to 91 by adding more near-surface levels. First, stronger low-level vertical background error correlations of humidity and temperature are found during daytime than during nighttime. A total of five numerical experiments are then conducted to examine the impacts of FY-4A /AGRI assimilation with two vertical resolutions. The experiments WV48 and WV91 assimilate only the water vapor channels, and ALL48, ALL91, and ALL91M assimilate the water vapor and surface-sensitive channels. The diurnally varying background error covariance, which is estimated at 3-h intervals, is incorporated into ALL91M. The 6-h cycling data assimilation at 1-h interval prior to convection initiation is conducted for each of the seven cases. Overall, a comparison between WV48/WV91 and ALL48/ALL91M revealed a consistent positive impact of AGRI brightness temperature assimilation over land on convective precipitation forecasts. The added values of assimilating AGRI surface-sensitive channels are more pronounced by using the finer vertical resolution due to low-level humidity enhancements that are related to the rise of the planetary boundary layer. With the diurnally varying background error covariance, ALL91M outperformed ALL91 because of improved low-level analyzed structures that are associated with proper low-level vertical error correlations of humidity and thermal variables.