Improving the regional model forecasting of persistent severe rainfall over the Yangtze River Valley using the spectral nudging and update cycle methods: a case study

被引：5

作者：

Zhao, Yanfeng ^{[1
,2
,3
]}

Wang, Donghai ^{[1
,4
]}

Xu, Jianjun ^{[5
]}

机构：

[1] Chinese Acad Meteorol Sci, State Key Lab Severe Weather, Beijing, Peoples R China

[2] Nanjing Univ Informat Sci & Technol, Coll Atmospher Sci, Nanjing, Jiangsu, Peoples R China

[3] George Mason Univ, Global Environm & Nat Resources Inst GENRI, Coll Sci, Fairfax, VA USA

[4] Sun Yat Sen Univ, Sch Atmospher Sci, Guangdong Prov Key Lab Climate Change & Nat Disas, Guangzhou, Guangdong, Peoples R China

[5] Guangdong Ocean Univ, Coll Ocean & Meteorol, Zhanjiang, Peoples R China

来源：

ATMOSPHERIC SCIENCE LETTERS | 2017年 / 18卷 / 02期

基金：

中国国家自然科学基金;

关键词：

persistent severe rainfall; precipitation forecasting; spectral nudging; update cycle; EXTREME PRECIPITATION; CHINA; WEATHER; PARAMETERIZATION; PREDICTABILITY; SIMULATION; RESOLUTION; EVENTS; SYSTEM; SCHEME;

D O I：

10.1002/asl.731

中图分类号：

P3 [地球物理学]; P59 [地球化学];

学科分类号：

0708 ; 070902 ;

摘要：

China's worst flooding since 1998 occurred over the Yangtze River Valley from 30 June to 6 July 2016. This study investigated the event using a new method-the spectral nudging and update cycle (SN+UIC)-in the regional Weather Research and Forecasting model, fuller use of small-scale features by using multi-scale blending. The SN+UIC was found to be successful in improving the prediction of this persistent severe rainfall event; and the larger the magnitude and longer the lead time, the more obvious the improvement. It was also found that the use of this new method decreased the root-mean-square error for related meteorological variables.

引用

页码：96 / 102

页数：7

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