A Machine Learning-Based Parameterized Tropical Cyclone Precipitation Model  

作　　者：Yi Lu Jie Yin Peiyan Chen Hui Yu Sirong Huang 

机构地区：[1]Shanghai Typhoon Institute,China Meteorological Administration,Shanghai 200030,China [2]School of Geographic Sciences,East China Normal University,Shanghai 200241,China [3]Asia-Pacific Typhoon Collaborative Research Center,Shanghai 200030,China [4]Shanghai Engineering Research Center of Intelligent Education and Bigdata,Shanghai Normal University,Shanghai 200030,China

出　　处：《International Journal of Disaster Risk Science》2024年第6期972-985,共14页国际灾害风险科学学报(英文版)

基　　金：sponsored by the National Key Research and Development Program of China(Grant No.2021YFC3000804);the National Natural Science Foundation of China(Grant Nos.U2142206,42371076);Shanghai Science and Technology Commission Project(No.23DZ1204701);Shanghai Pilot Program for Basic Research(No.TQ20240209);the Basic Research Fund of Shanghai Typhoon Institute(Nos.2023JB05,2024JB04)。

摘　　要：Current simulation models considerably underestimate local-scale,short-duration extreme precipitation induced by tropical cyclones(TCs).This problem needs to be addressed to establish active response policies for TC-induced disasters.Taking Shanghai,a coastal megacity,as a study area and based on the observations from 192 meteorological stations in the city during 2005–2018,this study optimized the parameterized Tropical Cyclone Precipitation Model(TCPM)initially designed for TCs at the national scale(China)to the local or regional scales by using machine learning(ML)methods,including the random forest(RF),extreme gradient boosting(XGBoost),and ensemble learning(EL)algorithms.The TCPM-ML was applied for multiple temporal scale hazard assessment.The results show that:(1)The TCPM-ML not only improved TCPM performance for simulating hourly extreme precipitations,but also preserved the physical meaning of the results,contrary to ML methods;(2)Machine learning algorithms enhanced the TCPM ability to reproduce observations,although the hourly extreme precipitations remained slightly underestimated;(3)Best performance was obtained with the XGBoost or EL algorithms.Combining the strengths of both XGBoost and RF,the EL algorithm yielded the best overall performance.This study provides essential model support for TC disaster risk assessment and response at the local and regional scales in China.

关 键 词：Extreme precipitation Machine learning Parameterized model-Shanghai Tropical cyclone 

分 类 号：P444[天文地球—大气科学及气象学]