Impact Of Assimilating Fy-3c Mwhs2 Data In The Rmaps-St Forecast System On Its Rainfall Forecasts

In order to evaluate the impact of assimilating FY-3C satellite Microwave Humidity Sounder (MWHS2) data on rainfall forecasts in the new-generation Rapid-refresh Multi-scale Analysis and Prediction System-Short Term (RMAPS-ST) operational system, which is developed by the Institute of Urban Meteorology of the China Meteorological Administration, four experiments were carried out in this study: (i) Coldstart (no observations assimilated); (H) CON (assimilation of conventional observations); (iii) FY3 (assimilation of FY-3C MWHS2 only); and (iv) FY3+CON (simultaneous assimilation of FY-3C MWHS2 and conventional observations). A precipitation process that took place in central-eastern China during 4-6 June 2019 was selected as a case study. When the authors assimilated the FY-3C MWHS2 data in the RMAPS-ST operational system, data quality control and bias correction were performed so that the O-B (observation minus background) values of the five humidity channels of MWHS2 became closer to a normal distribution, and the data basically satisfied the unbiased assumption. The results showed that, in this case, the predictions of both precipitation location and intensity were improved in the FY3+CON experiment compared with the other three experiments. Meanwhile, the prediction of atmospheric parameters for the mesoscale field was also improved, and the RMSE of the specific humidity forecast at the 850-400 hPa height was reduced. This study implies that FY-3C MWHS2 data can be successfully assimilated in a regional numerical model and has the potential to improve the forecasting of rainfall.