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An online model correction method based on an inverse problem: Part II-systematic model error correction
Xue, HL; Shen, XS; Chou, JF; Xue, HL (reprint author), Chinese Acad Meteorol Sci, State Key Lab Severe Weather, Beijing 100081, Peoples R China.
2015-11
Source PublicationADVANCES IN ATMOSPHERIC SCIENCES
ISSN0256-1530
Volume32Issue:11Pages:1493-1503
AbstractAn online systematic error correction is presented and examined as a technique to improve the accuracy of real-time numerical weather prediction, based on the dataset of model errors (MEs) in past intervals. Given the analyses, the ME in each interval (6 h) between two analyses can be iteratively obtained by introducing an unknown tendency term into the prediction equation, shown in Part I of this two-paper series. In this part, after analyzing the 5-year (2001-2005) GRAPES-GFS (Global Forecast System of the Global and Regional Assimilation and Prediction System) error patterns and evolution, a systematic model error correction is given based on the least-squares approach by firstly using the past MEs. To test the correction, we applied the approach in GRAPES-GFS for July 2009 and January 2010. The datasets associated with the initial condition and SST used in this study were based on NCEP (National Centers for Environmental Prediction) FNL (final) data. The results indicated that the Northern Hemispheric systematically underestimated equator-to-pole geopotential gradient and westerly wind of GRAPES-GFS were largely enhanced, and the biases of temperature and wind in the tropics were strongly reduced. Therefore, the correction results in a more skillful forecast with lower mean bias and root-mean-square error and higher anomaly correlation coefficient.
Keywordmodel error past data inverse problem error estimation model correction GRAPES-GFS
Subject AreaMeteorology & Atmospheric Sciences
PublisherSCIENCE CHINA PRESS
DOI10.1007/s00376-015-4262-0
Publication PlaceBEIJING
Indexed BySCIE ; CSCD
Language英语
First Inst
Funding Project国家自然科学基金项目 ; 国家科技支撑计划
Host of Journal中国科学院大气物理研究所
Project NumberNational Natural Science Foundation Science Fund for Youth [41405095] ; Key Projects in the National Science and Technology Pillar Program during the Twelfth Five-year Plan Period [2012BAC22B02] ; National Natural Science Foundation Science Fund for Creative Research Groups [41221064]
WOS IDWOS:000360861400005
CSCD IDCSCD:5506959
Funding OrganizationNSFC ; MOST
SubtypeArticle
IRIDWOS:000360861400005
Department[Xue Haile;
Shen Xueshun] Chinese Acad Meteorol Sci, State Key Lab Severe Weather, Beijing 100081, Peoples R China;
[Shen Xueshun] China Meteorol Adm, Ctr Numer Predict, Beijing 100081, Peoples R China;
[Xue Haile;
Chou Jifan] Lanzhou Univ, Sch Atmospher Sci, Lanzhou 730000, Peoples R China
Citation statistics
Document Type期刊论文
Identifierhttp://ir.lzu.edu.cn/handle/262010/182634
Collection大气科学学院
Corresponding AuthorXue, HL (reprint author), Chinese Acad Meteorol Sci, State Key Lab Severe Weather, Beijing 100081, Peoples R China.
Recommended Citation
GB/T 7714
Xue, HL,Shen, XS,Chou, JF,et al. An online model correction method based on an inverse problem: Part II-systematic model error correction[J]. ADVANCES IN ATMOSPHERIC SCIENCES,2015,32(11):1493-1503.
APA Xue, HL,Shen, XS,Chou, JF,&Xue, HL .(2015).An online model correction method based on an inverse problem: Part II-systematic model error correction.ADVANCES IN ATMOSPHERIC SCIENCES,32(11),1493-1503.
MLA Xue, HL,et al."An online model correction method based on an inverse problem: Part II-systematic model error correction".ADVANCES IN ATMOSPHERIC SCIENCES 32.11(2015):1493-1503.
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