CASIA OpenIR
Mixture probabilistic model for precipitation ensemble forecasting
Wu, Yajing1,2; Yang, Xuebing1; Zhang, Wensheng1; Kuang, Qiuming3
Source PublicationQUARTERLY JOURNAL OF THE ROYAL METEOROLOGICAL SOCIETY
ISSN0035-9009
2019-09-13
Pages19
Corresponding AuthorYang, Xuebing(yangxuebing2013@ia.ac.cn) ; Zhang, Wensheng(zhangwenshengia@hotmail.com)
AbstractStatistical post-processing approaches are widely employed to construct improved probabilistic meteorological forecasts from numerical weather prediction. However, generating calibrated and sharp probabilistic forecasts is challenging. In this article, a post-processing approach, Mixture Probabilistic Model (MPM), is proposed to calibrate probabilistic ensemble forecasts subject to sharpness. In particular, the proposed MPM is applied to precipitation forecasting. First, the Censored and Shifted Gamma (CSG0) distribution is considered as the probability density function (PDF) for precipitation. Then, the predictive PDF of MPM is mixed by the individual PDFs which are fitted from raw ensemble members. Finally, to estimate optimal weight parameters for the mixture of individual PDFs, the Dirichlet distribution is utilized and the skills of the mixture model and individuals are both taken into consideration. The proposed MPM was tested using Innsbruck ensemble precipitation data and 6 h accumulated precipitation ensemble forecast data in east China from August to November 2017. Compared with raw forecasts and three state-of-the-art post-processing approaches, MPM showed improved performance for all verification scores. The quantitative and qualitative analyses of results in both cases indicate the potential and effectiveness of MPM for precipitation ensemble forecasting.
Keywordensemble forecast post-processing precipitation probabilistic forecast
DOI10.1002/qj.3637
WOS KeywordCALIBRATION ; PREDICTION ; WEATHER
Indexed BySCI
Language英语
Funding ProjectNational Natural Science Foundation of China[61532006] ; National Natural Science Foundation of China[61602482] ; National Natural Science Foundation of China[U1636220]
Funding OrganizationNational Natural Science Foundation of China
WOS Research AreaMeteorology & Atmospheric Sciences
WOS SubjectMeteorology & Atmospheric Sciences
WOS IDWOS:000486524400001
PublisherWILEY
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/27279
Collection中国科学院自动化研究所
Corresponding AuthorYang, Xuebing; Zhang, Wensheng
Affiliation1.Chinese Acad Sci, Inst Automat, Res Ctr Precis Sensing & Control, 95 Zhongguancun East Rd, Beijing 100190, Peoples R China
2.Univ Chinese Acad Sci, Beijing, Peoples R China
3.China Meteorol Adm, Publ Meteorol Serv Ctr, Beijing, Peoples R China
First Author AffilicationChinese Acad Sci, Inst Automat, Res Ctr Precis Sensing & Control, Beijing 100190, Peoples R China
Corresponding Author AffilicationChinese Acad Sci, Inst Automat, Res Ctr Precis Sensing & Control, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Wu, Yajing,Yang, Xuebing,Zhang, Wensheng,et al. Mixture probabilistic model for precipitation ensemble forecasting[J]. QUARTERLY JOURNAL OF THE ROYAL METEOROLOGICAL SOCIETY,2019:19.
APA Wu, Yajing,Yang, Xuebing,Zhang, Wensheng,&Kuang, Qiuming.(2019).Mixture probabilistic model for precipitation ensemble forecasting.QUARTERLY JOURNAL OF THE ROYAL METEOROLOGICAL SOCIETY,19.
MLA Wu, Yajing,et al."Mixture probabilistic model for precipitation ensemble forecasting".QUARTERLY JOURNAL OF THE ROYAL METEOROLOGICAL SOCIETY (2019):19.
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