DOI: 10.1175/JCLI-D-11-00457.1
Scopus记录号: 2-s2.0-84867677753
论文题名: Development of new ensemble methods based on the performance skills of regional climate models over South Korea
作者: Suh M.-S. ; Oh S.-G. ; Lee D.-K. ; Cha D.-H. ; Choi S.-J. ; Jin C.-S. ; Hong S.-Y.
刊名: Journal of Climate
ISSN: 8948755
出版年: 2012
卷: 25, 期: 20 起始页码: 7067
结束页码: 7082
语种: 英语
Scopus关键词: Down-scaling
; East Asia
; Ensemble averaging
; Ensemble methods
; Ensembles
; Horizontal resolution
; Model evaluation
; Multivariate linear regressions
; Number of Grids
; Reanalysis
; Regional climate
; Regional climate models
; Root-mean square errors
; Simulation data
; Simulation domain
; South Korea
; Weighting coefficient
; Climate models
; Digital storage
; Forecasting
; air temperature
; climate modeling
; climate prediction
; ensemble forecasting
; precipitation (climatology)
; regional climate
; South Korea
; Pisum sativum
英文摘要: In this paper, the prediction skills of five ensemble methods for temperature and precipitation are discussed by considering 20 yr of simulation results (from 1989 to 2008) for four regional climate models (RCMs) driven by NCEP-Department of Energy and ECMWF Interim Re-Analysis (ERA-Interim) boundary conditions. The simulation domain is the Coordinated Regional Downscaling Experiment (CORDEX) for East Asia, and the number of grid points is 197 3 233 with a 50-km horizontal resolution. Three new performance-based ensemble averaging (PEA) methods are developed in this study using 1) bias, root-mean-square errors (RMSEs) and absolute correlation (PEA_BRC), RMSE and absolute correlation (PEA_RAC), and RMSE and original correlation (PEA_ROC). The other two ensemble methods are equal-weighted averaging (EWA) and multivariate linear regression (Mul_Reg). To derive the weighting coefficients and cross validate the prediction skills of the five ensemble methods, the authors considered 15-yr and 5-yr data, respectively, from the 20-yr simulation data. Among the five ensemble methods, the Mul_Reg (EWA) method shows the best (worst) skill during the training period. The PEA_RAC and PEA_ROC methods show skills that are similar to those of Mul_Reg during the training period. However, the skills and stabilities of Mul_Reg were drastically reduced when this method was applied to the prediction period. But, the skills and stabilities of PEA_RAC were only slightly reduced in this case. As a result, PEA_RAC shows the best skill, irrespective of the seasons and variables, during the prediction period. This result confirms that the new ensemble method developed in this study, PEA_RAC, can be used for the prediction of regional climate. © 2012 American Meteorological Society. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/52195
Appears in Collections: 气候变化事实与影响
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作者单位: Kongju National University, Gongju, South Korea; Atmospheric Sciences Program, School of Earth and Environmental Sciences, Seoul National University, Seoul, South Korea; Department of Atmospheric Sciences, College of Science, Yonsei University, Seoul, South Korea
Recommended Citation:
Suh M.-S.,Oh S.-G.,Lee D.-K.,et al. Development of new ensemble methods based on the performance skills of regional climate models over South Korea[J]. Journal of Climate,2012-01-01,25(20)