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Abstract

Fengyun-4A (FY-4A) is the first satellite in the second generation of China’s geostationary meteorological satellite series. The Advanced Geostationary Radiation Imager (AGRI) onboard FY-4A satellite can measure water vapor data at infrared band. In this study, &nbsp;to investigate the benefits of assimilating FY-4A water vapor data to weather forecasting performance over the South China region, we have performed the data assimilation experiments in the Weather Research and Forecasting (WRF) model for two periods with different water vapor conditions, i.e., February 2020 (dry period) and July 2020 (wet period). Two WRF schemes, i.e., WRF no data assimilation scheme (denoted as “WRF_NoDA”) and WRF with assimilation of FY-4A Precipitable Water Vapor (PWV) scheme (denoted as “WRF+FY-4A”), have been implemented. The PWV and rainfall forecasting results of the two WRF schemes are validated by Global Navigation Satellite System (GNSS) PWV and the rainfall observations of the surface meteorological stations, respectively. The evaluation results show that, compared with WRF_NoDA scheme, WRF+FY-4A indicates a better performance in both PWV and rainfall forecasting. Specifically, with the assimilation of FY-4A PWV, the average root mean squares error of WRF PWV forecasting results for the first 12 h after data assimilation is reduced from 2.34 kg/m² to 2.25 kg/m² over the February period and from 4.39 kg/m² to 4.35 kg/m² over the July period. Additionally, after assimilating FY-4A PWV, the equitable threat score of accumulated rainfall for the first 12 h after data assimilation increases from 0.217 to 0.237 when using 10 mm/12 h as rainfall detection threshold.<br>