Typhoon Hato's precipitation characteristics based on PERSIANN

作     者：Jiayang Zhang Yangbo Chen Chuan Li 

作者机构：School of Geography and PlanningSun Yat-Sen UniversityGuangzhouChina 

出 版 物：《Tropical Cyclone Research and Review》 (热带气旋研究与评论（英文版）)

年 卷 期：2021年第10卷第2期

页      面：75-86页

学科分类：07[理学] 070601[理学-气象学] 0706[理学-大气科学] 

基　　金：supported by the National Key Research and Development Program of China China(funding no.2017YFC1502702) 

主　　题：Typhoon Satellite estimated precipitation Precipitation characteristics 

摘      要：Heavy precipitation induced by typhoons is the main driver of catastrophic flooding,and studying precipitation patterns is important for flood forecasting and early *** 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 ***-based estimation provides large domains of precipitation with high space-time resolution,facilitating the analysis of heavy precipitation patterns induced by *** 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 *** 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 *** produced extremely high accumulated rainfall in South China,almost 300 mm in Guangdong Province and Guangxi Zhuang Autonomous Region and 260 mm in Hainan *** 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).