Statistical Analysis of Rainstorm in Henan Caused by Typhoon Landing in East China

Gu Xiujie; Wang Youhe; Wu Wenbo; Su Yanhua; Wang Di

doi:10.16765/j.cnki.1673-7148.2023.06.002

Abstract：By using the typhoon data in 1959-2021 provided by the Tropical Cyclone Data Center of China Meteorological Administration(CMA),the national sounding data and the precipitation data of Henan Province in 1980-2021 provided by the Henan Meteorological Information Center as well as the methods of mathematical statistics,synoptic analysis and other methods,we statistically analyze the ty-phoons that landed in East China and brought regional rainstorms to Henan over the past years.The re-sults show that,during 1959 to 2021 there were 2.5 typhoons on average landing in East China every year,with 6 typhoon-landing cases as the most or zero cases as the least in the years;91.1％ of the ty-phoons that landed in East China were concentrated in July-September,of which 67 typhoons were recor-ded in August,the most among the three months.The earliest landfall date of typhoon was May 27,and the latest date was October 23.From 1980 to 2021,105 typhoons landed in East China,of which 18 caused 30 days of regional rainstorm in Henan,and 10 of them had daily rainfall exceeding 200 mm at a single station,accounting for 33.3％of the total regional rainstorm days.The maximum single station daily rainfall at Shangqiu Station was 364 mm,breaking the historical extreme value since the establish-ment of the station.18 typhoons that affected Henan landed in Fujian,Zhejiang and Shanghai,with a maximum of 11 in Fujian.The moving direction of typhoons after landfall can be classified into two types:northwest-to-north and westward.The analysis of the upper-altitude wind field during the rainstorm shows that the typhoon mainly affected Henan through the two situations of inverted trough and low pressure.By diagnosing and analyzing the large-scale circulation field at the time of typhoon landfall and its overland movement,we summarize three types of synoptic conceptual models for future forecasting.