[ACG51-P05] Improvement for infrared rainfall estimation algorithm of GSMaP using machine learning
Keywords:machine learning, GSMaP, Himawari-8
Hirose et al. (2019) verified the accuracy of HRA in the mid-latitudes of the Northern Hemisphere using radar-AMeDAS rain rate and showed that HRA has higher estimation accuracy for heavy rainfall caused by clouds with relatively low cloud-top height than GSMaP with only single IR band. In particular, the three water vapor bands, which became available for the first time on Himawari-8, has greatly contributed to the improvement for the estimation accuracy of the heavy rain. In this presentation, we show the results of global accuracy verification using ground-based radar network in the tropics. The HRA showed the same estimation accuracy in the tropics as the GSMaP MWR observation. In addition, GSMaP tended to overestimate rainfall over sea where rainfall was complemented by using only a single IR band, but HRA did not show such overestimation with IR multiband. The effectiveness of IR multi-band observation was confirmed even in the tropical region. We will also present the results of an attempt to improve estimation accuracy of the GSMaP cloud movement vector with single IR band by using machine learning approach.