BROWSE

Related Researcher

Author's Photo

Cha, Dong-Hyun
High-impact Weather Prediction Lab (HWPL)
Research Interests
  • Typhoon Modeling, Regional Climate Modeling, High-impact Weather

ITEM VIEW & DOWNLOAD

Development of New Ensemble Methods Based on the Performance Skills of Regional Climate Models over South Korea

Cited 7 times inthomson ciCited 7 times inthomson ci
Title
Development of New Ensemble Methods Based on the Performance Skills of Regional Climate Models over South Korea
Author
Suh, M. -S.Oh, S. -G.Lee, D. -K.Cha, Dong-HyunChoi, S. -J.Jin, C. -S.Hong, S. -Y.
Keywords
Climate models; Ensembles; Model evaluation/performance
Issue Date
2012-10
Publisher
AMER METEOROLOGICAL SOC
Citation
JOURNAL OF CLIMATE, v.25, no.20, pp.7067 - 7082
Abstract
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 x 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.
URI
Go to Link
DOI
10.1175/JCLI-D-11-00457.1
ISSN
0894-8755
Appears in Collections:
UEE_Journal Papers
Files in This Item:
2-s2.0-84867677753.pdf Download

find_unist can give you direct access to the published full text of this article. (UNISTARs only)

Show full item record

qrcode

  • mendeley

    citeulike

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.

MENU