Relating anomaly correlation to lead time: Clustering analysis of CFSv2 forecasts of summer precipitation in China

doi:10.1002/2017JD027018

	Relating anomaly correlation to lead time: Clustering analysis of CFSv2 forecasts of summer precipitation in China
	Zhao, Tongtiegang 1,2; Liu, Pan 1; Zhang, Yongyong 3; Ruan, Chengqing 4
刊名	JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES
	2017-09-16
卷号	122 期号:17 页码:9094-9106
ISSN号	2169-897X
DOI	10.1002/2017JD027018
英文摘要	Global climate model (GCM) forecasts are an integral part of long-range hydroclimatic forecasting. We propose to use clustering to explore anomaly correlation, which indicates the performance of raw GCM forecasts, in the three-dimensional space of latitude, longitude, and initialization time. Focusing on a certain period of the year, correlations for forecasts initialized at different preceding periods form a vector. The vectors of anomaly correlation across different GCM grid cells are clustered to reveal how GCM forecasts perform as time progresses. Through the case study of Climate Forecast System Version 2 (CFSv2) forecasts of summer precipitation in China, we observe that the correlation at a certain cell oscillates with lead time and can become negative. The use of clustering reveals two meaningful patterns that characterize the relationship between anomaly correlation and lead time. For some grid cells in Central and Southwest China, CFSv2 forecasts exhibit positive correlations with observations and they tend to improve as time progresses. This result suggests that CFSv2 forecasts tend to capture the summer precipitation induced by the East Asian monsoon and the South Asian monsoon. It also indicates that CFSv2 forecasts can potentially be applied to improving hydrological forecasts in these regions. For some other cells, the correlations are generally close to zero at different lead times. This outcome implies that CFSv2 forecasts still have plenty of room for further improvement. The robustness of the patterns has been tested using both hierarchical clustering and k-means clustering and examined with the Silhouette score.
资助项目	State Key Laboratory of Water Resources and Hydro-power Engineering at Wuhan University[2016SWK01]
WOS关键词	SYSTEM VERSION 2 ; NUMERICAL WEATHER PREDICTION ; MODEL OUTPUT STATISTICS ; SEA THERMAL CONTRAST ; MONSOON ; SIMULATION ; GCM ; PERFORMANCE ; SKILL ; PREDICTABILITY
WOS研究方向	Meteorology & Atmospheric Sciences
语种	英语
出版者	AMER GEOPHYSICAL UNION
WOS记录号	WOS:000416387300009
内容类型	期刊论文
源URL	[http://ir.fio.com.cn:8080/handle/2SI8HI0U/25013]
专题	自然资源部第一海洋研究所
通讯作者	Liu, Pan
作者单位	1.Wuhan Univ, State Key Lab Water Resources & Hydropower Engn S, Wuhan, Hubei, Peoples R China 2.Univ Melbourne, Dept Infrastruct Engn, Melbourne, Vic, Australia 3.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Water Cycle & Related Land Surface Proc, Beijing, Peoples R China 4.State Ocean Adm China, North China Sea Marine Forecasting Ctr, Qingdao, Peoples R China
推荐引用方式 GB/T 7714	Zhao, Tongtiegang,Liu, Pan,Zhang, Yongyong,et al. Relating anomaly correlation to lead time: Clustering analysis of CFSv2 forecasts of summer precipitation in China[J]. JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES,2017,122(17):9094-9106.
APA	Zhao, Tongtiegang,Liu, Pan,Zhang, Yongyong,&Ruan, Chengqing.(2017).Relating anomaly correlation to lead time: Clustering analysis of CFSv2 forecasts of summer precipitation in China.JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES,122(17),9094-9106.
MLA	Zhao, Tongtiegang,et al."Relating anomaly correlation to lead time: Clustering analysis of CFSv2 forecasts of summer precipitation in China".JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES 122.17(2017):9094-9106.