AUTHOR=Shi Yinglong , Luo Zhixian , Chen Xiangguo , Zhang Qian , Liu Yin , Liu Chun 

TITLE=Effects of joint assimilation of FY-4A AGRI and ground-based microwave radiometer on heavy rainfall prediction

JOURNAL=Frontiers in Environmental Science

VOLUME=Volume 11 - 2023

YEAR=2023

URL=https://www.frontiersin.org/journals/environmental-science/articles/10.3389/fenvs.2023.1083517

DOI=10.3389/fenvs.2023.1083517

ISSN=2296-665X

ABSTRACT=As the latest generation of Chinese Geostationary Weather Satellites, Fengyun-4 carries the Advanced Geosynchronous Radiation Imager (AGRI), which has more spectral bands and higher temporal and spatial resolution than the Visible Infrared Spin-Scan Radiometer (VISSR) onboard geostationary satellite FY-2. Direct assimilation of the FY-4A AGRI datasets has rarely been documented in the previous literature. To explore the effect of joint assimilation of AGRI radiance and ground-based MWR data on short-duration heavy rainfall prediction, RTTOV is used as the observational operator for FY-4A AGRI data assimilation. The data assimilation interface is built in WRFDA 4.3 to achieve direct assimilation of FY4A AGRI radiance. The forecasting effectiveness of the joint assimilation for a typical heavy rainfall event over northern China is analyzed with four simulation experiments. The main conclusions are: (1) Assimilating MWR data can improve the initial humidity condition in the middle-lower layers, while AGRI radiance assimilation favors the initial humidity correction in the middle-upper layers. The joint assimilation of two datasets can remarkably improve the initial humidity condition in the entire column. (2) Data assimilation effectively improves the 6-h accumulated rainfall simulation. The joint assimilation of AGRI radiance and MWR data is superior to assimilating either of them. The joint assimilation significantly improves the rainfall forecast over the Beijing area, where the seven MWRs are distributed. (3) Assimilation experiments present similar effects on predicted and initial humidity conditions. The MWR_DA experiment markedly improves the humidity forecast in the middle-lower layers, while AGRI_DA is effective in the middle-upper layers. The joint assimilation of AGRI radiance and ground-based MWR data could skillfully correct the humidity distribution in the entire layers, allowing for more accurate heavy rainfall prediction. This paper provides a valuable basis for further improving the application of FY-4A AGRI radiance in numerical weather models.