Ensemble data assimilation and prediction of typhoon and associated hazards using TEDAPS:evaluation for 2015-2018 seasons

作     者：Hong LI Jingyao LUO Mengting XU 

作者机构：Shanghai Typhoon InstituteChina Meteorological AdministrationShanghai 200030China Key Laboratory of Numerical Modeling for Tropical CyclonesChina Meteorological AdministrationShanghai 200030China 

出 版 物：《Frontiers of Earth Science》 (地球科学前沿（英文版）)

年 卷 期：2019年第13卷第4期

页      面：733-743页

核心收录：

学科分类：07[理学] 070601[理学-气象学] 0708[理学-地球物理学] 0706[理学-大气科学] 0704[理学-天文学] 

基　　金：The authors would like to thank Dr.Lina Bai in STI for providing the best-track data.This research was primarily supported by National Key R&D Program of China(Grant No.2018YFC1506404) the National Basic Research Program of China(Grant No.2015CB452806) National Natural Science Foundation of China(Grant No.41575107) in part by Shanghai Sailing Program(Grant No.19YF1458700) Scientific Research Program of Shanghai Science&Technology Commission(Grant No.19dz1200101) National Programme on Global Change and Air-Sea Interaction(Grant No.GASI-IPOVAI-04) Shanghai Typhoon Innovation Team grants to Shanghai Typhoon Institute 

主　　题：ensemble data assimilation ensemble forecasting tropical cyclones 

摘      要：The initial condition accuracy is a major concern for tropical cyclone(TC)numerical *** ensemble-based data assimilation techniques have shown great promise to initialize TC *** addition to initial condition uncertainty,representing model errors(*** deficiencies)is another important issue in an ensemble forecasting *** improve TC prediction from both deterministic and probabilistic standpoints,a Typhoon Ensemble Data Assimilation and Prediction System(TEDAPS)using an ensemble-based data assimilation scheme and a multi-physics approach based on Weather Research and Forecasting(WRF)model,has been developed in Shanghai Typhoon Institute and running realtime since *** of TED APS in the prediction of track,intensity and associated disaster has been evaluated for the Western North Pacific TCs in the years of 2015-2018,and compared against the NCEP *** APS produces markedly better intensity forecast by effectively reducing the weak biases and therefore the degree of underdispersion compared to *** errors of TED APS track forecasts are comparative with(slightly worse than)those of GEFS at longer(shorter)forecast *** ensemble-mean exhibits advantage over deterministic forecast in track forecasts at long lead times,whereas this superiority is limited to typhoon or weaker TCs in intensity forecasts due to systematical *** case-studies for three landfalling cyclones and one recurving cyclone demonstrate the capacities of TEDAPS in predicting some challenging TCs,as well as in capturing the forecast uncertainty and the potential threat from TC-associated hazards.