Impacts of FY-4A GIIRS Water Vapor Channels Data Assimilation on the Forecast of “21·7” Extreme Rainstorm in Henan, China with CMA-MESO

Abstract

A record-breaking extreme rainstorm occurred in Henan Province of China on 20 July 2021. To investigate the impacts of the Geostationary Interferometric Infrared Sounder (GIIRS) data assimilation on model analysis and forecasts of this rainfall event, the high temporal resolution GIIRS water vapor (WV) channel data were assimilated in the high-resolution CMA-MESO (Mesoscale Weather Numerical Forecast System of China Meteorological Administration) in this study. The results showed that the GIIRS WV radiance assimilation could improve the model WV analysis, which in turn adjusted the distributions of hydrometeors (radar composite reflectivities) and wind field, and finally improved the precipitation forecast. Additionally, although barely any GIIRS observations were assimilated over the cloudy area, the precipitation forecast errors of “21·7” extreme rainstorm events could be reduced by improving the structure of atmospheric circulations through the assimilation of neighboring data around Henan, especially over the upstream region. With the GIIRS WV data assimilation, the location error of maximum 24-h accumulated precipitation forecasts decreased from 128.48 km to 28.97 km (improved by 77.45%) for the cold start at 0000 UTC (Universal Time Coordinated) on 19 July 2021, and it was also reduced by about 60.52% for the warm start experiment at 0600 UTC on 19 July 2021. In addition, the GIIRS assimilation experiment showed an extraordinarily heavy rainfall area (above 250 mm/24 h) around Zhengzhou station, which did not appear in the control experiment, and was closer to the observed extreme precipitation. This study demonstrates the potential value of geostationary hyperspectral infrared sounders data assimilation in extreme weather early warning and forecasting.