Heavy Rainfall Forecast of Landfalling Tropical Cyclone Over China With an Upgraded DSAEF_LTP Model

Author:

Wang Mingyang12,Ding Chenchen3,Ren Fumin2ORCID,Zhang Da‐Lin24ORCID,Jia Li12ORCID

Affiliation:

1. Key Laboratory of Meteorological Disaster Ministry of Education (KLME) Nanjing University of Information Science and Technology Nanjing China

2. State Key Laboratory of Severe Weather Chinese Academy of Meteorological Sciences Beijing China

3. Public Meteorological Service Center of China Meteorological Administration Beijing China

4. Department of Atmospheric and Oceanic Science University of Maryland College Park MD USA

Abstract

AbstractSince the release of the Dynamical‐Statistical‐Analog Ensemble Forecast for Landfalling Typhoon Precipitation (DSAEF_LTP) model, a series of upgrades has been made to improve its heavy rainfall forecast performance for different cases and regions of China. However, little effort has been given to systematic evaluation of its multi‐upgraded version with a large sample size, especially for all the coastal regions of China. This study evaluates the performance of Version 1.0 (V1.0) and its improved Version 1.1 (V1.1) of the DSAEF_LTP model in simulating heavy rainfall amounts associated with 76 landfalling tropical cyclones (LTCs) over China during 2004–2016. The optimized schemes from these simulations are then applied to the heavy rainfall forecasts of 37 LTCs during 2017–2020. To further improve the forecast performance of V1.1, a subregional re‐ensemble scheme, referred to as V1.1R, is introduced after examining its performances over three coastal subregions of China. A comparison of the forecast performances of V1.0, V1.1, V1.1R, and four operational numerical weather prediction (NWP) models for the 37 LTCs shows that the performance of V1.1 is much better than that of V1.0, with a 65% higher summed threat score (TS) for over 250‐mm (TS250) and 100‐mm (TS100) rainfall; and that V1.1R's TS100 and TS250 values are further improved by 8% and 20%, respectively, as compared to V1.1. These values are superior to those of the four operational NWP models. Additionally, case studies reveal that the track distributions and intensities of LTCs may significantly impact the forecast performance of heavy rainfall.

Publisher

American Geophysical Union (AGU)

Subject

Space and Planetary Science,Earth and Planetary Sciences (miscellaneous),Atmospheric Science,Geophysics

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3