On the Improvement of the DSAEF_LTP Model to Heavy Precipitation Simulation of Landfalling Tropical Cyclones over China in 2018

被引:1
作者
Chen Yu-xu [1 ,2 ]
Jia Li [2 ]
Jia Zuo [2 ]
Ding Chen-chen [2 ]
Ren Fu-min [2 ]
Li Guo-ping [1 ]
机构
[1] Chengdu Univ Informat Technol, Coll Atmospher Sci, Chengdu 610225, Peoples R China
[2] Chinese Acad Meteorol Sci, State Key Lab Severe Weather, Beijing 100081, Peoples R China
基金
国家重点研发计划; 中国国家自然科学基金;
关键词
landfalling tropical cyclone; heavy precipitation; simulation; the DSAEF_LTP model; similarity; region scheme; RAINFALL; FORECAST;
D O I
10.46267/j.1006-8775.2021.021
中图分类号
P4 [大气科学(气象学)];
学科分类号
0706 ; 070601 ;
摘要
In this study, the Dynamical-Statistical-Analog Ensemble Forecast model (DSAEF_LTP model) for landfalling tropical cyclone (LTC) precipitation was employed to simulate the precipitation of 10 LTCs that occurred over China in 2018. With similarity region scheme (SRS) parameter values added and TC intensity introduced to the generalized initial value (GIV), four groups of precipitation simulation experiments were designed to verify the forecasting ability- of the improved model for more TC samples. Results show that the simulation ability of the DSAELLTP model can be optimized regardless of whether adding SRS values only, or introducing TC intensity into GIV, while the experiment with both the two improvements shows a more prominent advantage in simulating the heavier precipitation of LTCs. Compared with four NWP models (i.e., ECMWF, GFS, GRAPES and SMS-WARMS), the overall forecasting performance of the DSAEF_LTP model achieves a better result in simulating precipitation at the thresholds over 250 mm and performs slightly better than NWP models at the thresholds over 100 mm
引用
收藏
页码:232 / 245
页数:14
相关论文
共 27 条
[1]   The quiet revolution of numerical weather prediction [J].
Bauer, Peter ;
Thorpe, Alan ;
Brunet, Gilbert .
NATURE, 2015, 525 (7567) :47-55
[2]  
[程正泉 Cheng Zhengquan], 2005, [气象, Meteorological Monthly], V31, P3
[3]  
[丁晨晨 Ding Chenchen], 2019, [气象, Meteorological Monthly], V45, P29
[4]   Ensemble Tropical Rainfall Potential (eTRaP) Forecasts [J].
Ebert, Elizabeth E. ;
Turk, Michael ;
Kusselson, Sheldon J. ;
Yang, Jianbin ;
Seybold, Matthew ;
Keehn, Peter R. ;
Kuligowski, Robert J. .
WEATHER AND FORECASTING, 2011, 26 (02) :213-224
[5]   IntroducingTCintensity into the DSAEF_LTP model and simulating precipitation of super-typhoonLekima(2019) [J].
Jia, Li ;
Jia, Zuo ;
Ren, Fumin ;
Ding, Chenchen ;
Wang, Mingyang ;
Feng, Tian .
QUARTERLY JOURNAL OF THE ROYAL METEOROLOGICAL SOCIETY, 2020, 146 (733) :3965-3979
[6]   An application of the LTP_DSEF model to heavy precipitation forecasts of landfalling tropical cyclones over China in 2018 [J].
Jia, Zuo ;
Ren, Fumin ;
Zhang, Dalin ;
Ding, Chenchen ;
Yang, Mingjen ;
Feng, Tian ;
Chen, Boyu ;
Yang, Hui .
SCIENCE CHINA-EARTH SCIENCES, 2020, 63 (01) :27-36
[7]   Contribution of Tropical Cyclones to the Global Precipitation from Eight Seasons of TRMM Data: Regional, Seasonal, and Interannual Variations [J].
Jiang, Haiyan ;
Zipser, Edward J. .
JOURNAL OF CLIMATE, 2010, 23 (06) :1526-1543
[8]   The tropical rainfall potential (TRaP) technique. Part I: Description and examples [J].
Kidder, SQ ;
Kusselson, SJ ;
Knaff, JA ;
Ferraro, RR ;
Kuligowski, RJ ;
Turk, M .
WEATHER AND FORECASTING, 2005, 20 (04) :456-464
[9]   A climatology model for forecasting typhoon rainfall in Taiwan [J].
Lee, CS ;
Huang, LR ;
Shen, HS ;
Wang, ST .
NATURAL HAZARDS, 2006, 37 (1-2) :87-105
[10]  
[李博 Li Bo], 2009, [气象, Meteorological Monthly], V35, P3