Precipitation estimation by infrared brightness temperature measurement of FengYun-4A imager

Abstract

In this paper, based on the infrared channel brightness temperature from the advanced geosynchronous radiation imager (AGRI) of FengYun-4A satellite, the research on quantitative estimation of precipitation is carried out. The algorithm of precipitation estimation can be divided into three steps. Firstly, the dictionary of brightness temperature of FY-4A/AGRI infrared channel brightness temperature and the integrated multi-satellite retrievals for GPM (IMERG) precipitation is constructed as the historical training sample library. Secondly, the precipitation FOVs are identified. As prior information, the IMERG and ice cloud products are coupled to classification models of the K-nearest neighbor (KNN) and random forest to determine whether there is precipitation at the FOV to be estimated. Finally, the precipitation estimation is performed. Inverse problem regularization method and random forest regression model are used for precipitation estimation, respectively. On this basis, the preliminary experiments for precipitation estimation of and “Ampil (2018)” are carried out. The results show that the precipitation estimation accuracy with ice cloud products as prior information through the inverse problem regularization is better than that with the IMERG products as priori information, while the conclusion is the opposite for the random forest method. The accuracy of precipitation estimation based on the random forest method is better than that of the inverse problem regularization, especially in the “extreme” precipitation center.