Mixture probabilistic model for precipitation ensemble forecasting

doi:10.1002/qj.3637

	Mixture probabilistic model for precipitation ensemble forecasting
	Wu, Yajing 1,2; Yang, Xuebing1 ; Zhang, Wensheng1 ; Kuang, Qiuming 3
刊名	QUARTERLY JOURNAL OF THE ROYAL METEOROLOGICAL SOCIETY
	2019-09-13
页码	19
关键词	ensemble forecast post-processing precipitation probabilistic forecast
ISSN号	0035-9009
DOI	10.1002/qj.3637
通讯作者	Yang, Xuebing(yangxuebing2013@ia.ac.cn) ; Zhang, Wensheng(zhangwenshengia@hotmail.com)
英文摘要	Statistical 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.
资助项目	National Natural Science Foundation of China[61532006] ; National Natural Science Foundation of China[61602482] ; National Natural Science Foundation of China[U1636220]
WOS关键词	CALIBRATION ; PREDICTION ; WEATHER
WOS研究方向	Meteorology & Atmospheric Sciences
语种	英语
出版者	WILEY
WOS记录号	WOS:000486524400001
资助机构	National Natural Science Foundation of China
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/27278]
专题	中国科学院自动化研究所
通讯作者	Yang, Xuebing; Zhang, Wensheng
作者单位	1.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
推荐引用方式 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.