干旱区生态环境知识资源中心(Arid): Intercomparison of multiple statistical methods in post-processing ensemble precipitation and temperature forecasts

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DOI	10.1002/met.1935
	Intercomparison of multiple statistical methods in post-processing ensemble precipitation and temperature forecasts
	Li, Xiangquan; Chen, Jie; Xu, Chong-Yu; Chen, Hua; Guo, Shenglian
通讯作者	Chen, J
来源期刊	METEOROLOGICAL APPLICATIONS
ISSN	1350-4827
EISSN	1469-8080
出版年	2020
卷号	27 期号:4
英文摘要	Ensemble weather forecasting generally suffers from bias and under-dispersion, which limit its predictive power. Several post-processing methods have been developed to overcome these limitations, and an intercomparison is needed to understand their performance. Four state-of-the-art methods are compared in post-processing precipitation and air temperature of the Global Ensemble Forecasting System (GEFS) reforecasts using a simple bias correction (BC) method as a reference. These methods include extended logistic regression (ExLR), generator-based post-processing (GPP), Bayesian model averaging (BMA) and affine kernel dressing (AKD). All these methods are tested over 659 national standard meteorological stations in China. The research concerns are the influence of region and forecast date and the role of BC on ensemble weather forecasting. It was found that: (1) the deterministic methods (GPP and ExLR) are more skilful than the probabilistic methods (BMA and AKD) in obtaining the well-calibrated and skilful ensemble forecasts; (2) the forecast skill of the post-processed ensemble weather forecasts is comparably high in the northern arid areas for precipitation, while the forecast skill for air temperature is only low in the Qinghai-Tibetan Plateau area; (3) the skill difference of the post-processed forecasts on different forecast date is only evident for air temperature, while not apparent for precipitation; and (4) only correcting bias for the ensemble weather forecasts can achieve about 0-70% (for precipitation) and 30-100% (for air temperature) forecast skill improvement for deterministic methods.
英文关键词	ensemble weather forecasting Global Ensemble Forecasting System (GEFS) post-processing method comparison Bayesian model averaging
类型	Article
语种	英语
开放获取类型	Other Gold
收录类别	SCI-E
WOS记录号	WOS:000570249500009
WOS关键词	PROBABILISTIC FORECASTS ; LOGISTIC-REGRESSION ; RANK HISTOGRAMS ; MOS METHODS ; MODEL ; RELIABILITY ; OUTPUT ; ECMWF ; PREDICTIONS ; REFORECASTS
WOS类目	Meteorology & Atmospheric Sciences
WOS研究方向	Meteorology & Atmospheric Sciences
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/326351
作者单位	[Li, Xiangquan; Chen, Jie; Chen, Hua; Guo, Shenglian] Wuhan Univ, State Key Lab Water Resources & Hydropower Engn S, Wuhan 430072, Peoples R China; [Chen, Jie] Wuhan Univ, Hubei Prov Key Lab Water Syst Sci Sponge City Con, Wuhan, Peoples R China; [Xu, Chong-Yu] Univ Oslo, Dept Geosci, Oslo, Norway
推荐引用方式 GB/T 7714	Li, Xiangquan,Chen, Jie,Xu, Chong-Yu,et al. Intercomparison of multiple statistical methods in post-processing ensemble precipitation and temperature forecasts[J],2020,27(4).
APA	Li, Xiangquan,Chen, Jie,Xu, Chong-Yu,Chen, Hua,&Guo, Shenglian.(2020).Intercomparison of multiple statistical methods in post-processing ensemble precipitation and temperature forecasts.METEOROLOGICAL APPLICATIONS,27(4).
MLA	Li, Xiangquan,et al."Intercomparison of multiple statistical methods in post-processing ensemble precipitation and temperature forecasts".METEOROLOGICAL APPLICATIONS 27.4(2020).