Typhoon Hato's precipitation characteristics based on PERSIANN

Abstract

Heavy precipitation induced by typhoons is the main driver of catastrophic flooding, and studying precipitation patterns is important for flood forecasting and early warning. Studying the space-time characteristics of heavy precipitation induced by typhoons requires a large range of observation data that cannot be obtained by ground-based rain gauge networks. Satellite-based estimation provides large domains of precipitation with high space-time resolution, facilitating the analysis of heavy precipitation patterns induced by typhoons. In this study, Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks (PERSIANN) satellite data were used to study the temporal and spatial features of precipitation induced by Typhoon Hato, which was the strongest typhoon of 2017 to make landfall in China. The results show that rainfall on the land lasted for six days from the typhoon making landfall to disappearing, reaching the maximum when the typhoon made landfall. Hato produced extremely high accumulated rainfall in South China, almost 300 mm in Guangdong Province and Guangxi Zhuang Autonomous Region and 260 mm in Hainan Province. The rainfall process was separated into three stages and rainfall was the focus in the second stage (5 h before making landfall to 35 h after making landfall).