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RISS 인기검색어
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- 저자 : 손용희(Yong-Hee Shon) (검색결과 1 건)
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박종길(Jong-Kil Park),김병수(Byung-Soo Kim),정우식(Woo-Sik Jung),서장원(Jang-Won Seo),손용희(Yong-Hee Shon),이대근(Dae-Geun Lee),김은별(Eun-Byul Kim) 한국기상학회 2006 대기 Vol.16 No.1
The purpose of this study is to develop the statistical model to predict sea level pressure of typhoon period in south coast of the Korean Peninsula. Seven typhoons, which struck south coast of the Korean Peninsula, are selected for this study, and the data for analysis include the central pressure and location of typhoon, and sea level pressure and location of 19 observing site. Models employed in this study are the first order regression, the second order regression and the neural network. The dependent variable of each model is a 3-hr interval sea level pressure at each station. The cause variables are the central pressure of typhoon, distance between typhoon center and observing site, and sea level pressure of 3 hrs before, whereas the indicative variable reveals whether it is before or after typhoon passing. The data are classified into two groups - one is the full data obtained during typhoon period and the other is the data that sea level pressure is less than 1000 hPa. The stepwise selection method is used in the regression model while the node number is selected in the neural network by the Schwarz's Bayesian Criterion. The performance of each model is compared in terms of the root-mean square error. It turns out that the neural network shows better performance than other models, and the case using the full data produces similar or better results than the case using the other data.
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