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http://dx.doi.org/10.5467/JKESS.2007.28.2.214

A Correction of East Asian Summer Precipitation Simulated by PNU/CME CGCM Using Multiple Linear Regression  

Hwang, Yoon-Jeong (Department of Atmosphere Science, Pusan National University)
Ahn, Joong-Bae (Department of Atmosphere Science, Pusan National University)
Publication Information
Journal of the Korean earth science society / v.28, no.2, 2007 , pp. 214-226 More about this Journal
Abstract
Because precipitation is influenced by various atmospheric variables, it is highly nonlinear. Although precipitation predicted by a dynamic model can be corrected by using a nonlinear Artificial Neural Network, this approach has limits such as choices of the initial weight, local minima and the number of neurons, etc. In the present paper, we correct simulated precipitation by using a multiple linear regression (MLR) method, which is simple and widely used. First of all, Ensemble hindcast is conducted by the PNU/CME Coupled General Circulation Model (CGCM) (Park and Ahn, 2004) for the period from April to August in 1979-2005. MLR is applied to precipitation simulated by PNU/CME CGCM for the months of June (lead 2), July (lead 3), August (lead 4) and seasonal mean JJA (from June to August) of the Northeast Asian region including the Korean Peninsula $(110^{\circ}-145^{\circ}E,\;25-55^{\circ}N)$. We build the MLR model using a linear relationship between observed precipitation and the hindcasted results from the PNU/CME CGCM. The predictor variables selected from CGCM are precipitation, 500 hPa vertical velocity, 200 hPa divergence, surface air temperature and others. After performing a leave-oneout cross validation, the results are compared with the PNU/CME CGCM's. The results including Heidke skill scores demonstrate that the MLR corrected results have better forecasts than the direct CGCM result for rainfall.
Keywords
Multiple Linear Regression (MLR); Model Output Statistics (MOS); Precipitation over East Asia; Coupled General Circulation Model;
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