Impacts of FY-4A Atmospheric Motion Vectors on the Henan 7.20 Rainstorm Forecast in 2021

Abstract

Atmospheric motion vectors (AMVs) derived from images of the geostationary satellite, Fengyun-4A (FY-4A), can provide high-spatiotemporal-resolution wind observations in the atmospheric middle and upper levels. To explore the potential benefits of these data for the numerical forecasting of severe weather events, the characteristics of FY-4A AMVs in different channels were analyzed and three groups of assimilation experiments were conducted in this study. The impacts of FY-4A AMVs on the forecasts of the rainstorm that occurred in Henan province in China on 20 July 2021, were investigated based on the Weather Research and Forecasting (WRF) model. The results show that FY-4A AMVs with a higher quality indicator (QI) exhibited a lower error characteristic at the cost of a reduced sample size. The assimilation of FY-4A AMVs reduced the error of the upper-level wind fields in 24 h forecasts. A positive impact could also be obtained for 10 m wind in 24 h forecasts, with an improvement of up to 9.74% for the mean bias and 3.0% for the root-mean-square error due to the inclusion of FY-4A AMVs with a QI > 70. Assimilating the AMVs with a QI > 80, there was an overall positive impact on the CSI score skills of 6 h accumulated precipitation above 1.0 mm in the 24 h forecast. A significant improvement could be found in the forecasting of heavy rainfall above 25.0 mm after 6 h of the forecast. The spatial distribution of the 24 h accumulated heavy rainfall zone was closer to the observations with the assimilation of the FY-4A AMVs. The adjustment of the initial wind fields resulting from the FY-4A AMVs brought a clear benefit to the quantitative precipitation forecasting skills in the event of the Henan 7.20 rainstorm; however, the AMV data assimilation still had difficulty in capturing the hourly maximum rainfall and intensity well.

Keywords

• atmospheric motion vectors
• FY-4A
• data assimilation
• rainstorm forecast