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Inter-comparison of Prediction Skills of Multiple Linear Regression Methods Using Monthly Temperature Simulated by Multi-Regional Climate Models
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  • Journal title : Atmosphere
  • Volume 25, Issue 4,  2015, pp.669-683
  • Publisher : Korean Meteorological Society
  • DOI : 10.14191/Atmos.2015.25.4.669
 Title & Authors
Inter-comparison of Prediction Skills of Multiple Linear Regression Methods Using Monthly Temperature Simulated by Multi-Regional Climate Models
Seong, Min-Gyu; Kim, Chansoo; Suh, Myoung-Seok;
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 Abstract
In this study, we investigated the prediction skills of four multiple linear regression methods for monthly air temperature over South Korea. We used simulation results from four regional climate models (RegCM4, SNURCM, WRF, and YSURSM) driven by two boundary conditions (NCEP/DOE Reanalysis 2 and ERA-Interim). We selected 15 years (1989~2003) as the training period and the last 5 years (2004~2008) as validation period. The four regression methods used in this study are as follows: 1) Homogeneous Multiple linear Regression (HMR), 2) Homogeneous Multiple linear Regression constraining the regression coefficients to be nonnegative (HMR+), 3) non-homogeneous multiple linear regression (EMOS; Ensemble Model Output Statistics), 4) EMOS with positive coefficients (EMOS+). It is same method as the third method except for constraining the coefficients to be nonnegative. The four regression methods showed similar prediction skills for the monthly air temperature over South Korea. However, the prediction skills of regression methods which don`t constrain regression coefficients to be nonnegative are clearly impacted by the existence of outliers. Among the four multiple linear regression methods, HMR+ and EMOS+ methods showed the best skill during the validation period. HMR+ and EMOS+ methods showed a very similar performance in terms of the MAE and RMSE. Therefore, we recommend the HMR+ as the best method because of ease of development and applications.
 Keywords
Ensemble forecasting;multiple linear regression;regional climate model;
 Language
Korean
 Cited by
1.
Intercomparison of prediction skills of ensemble methods using monthly mean temperature simulated by CMIP5 models, Asia-Pacific Journal of Atmospheric Sciences, 2017, 53, 3, 339  crossref(new windwow)
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