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Korean Meteorological Society (한국기상학회)
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Study on the Sea Level Pressure Prediction of Typhoon Period in South Coast of the Korean Peninsula Using the Neural Networks

신경망 모형을 이용한 태풍시기의 남해안 기압예측 연구

  • Park, Jong-Kil (School of Environmental Science Engineering/AEI Center/Dept. of Atmospheric Environment Information Engineering, Graduate School, Inje University) ;
  • Kim, Byung-Soo (Dept. of Data Science, Inje University) ;
  • Jung, Woo-Sik (School of Environmental Science Engineering/AEI Center/Dept. of Atmospheric Environment Information Engineering, Graduate School, Inje University) ;
  • Seo, Jang-Won (Marine Meteorology & Earthquake Research Laboratory, Meteorological Research Institute, KMA) ;
  • Shon, Yong-Hee (Dept. of Data Science, Inje University) ;
  • Lee, Dae-Geun (School of Environmental Science Engineering/AEI Center/Dept. of Atmospheric Environment Information Engineering, Graduate School, Inje University) ;
  • Kim, Eun-Byul (School of Environmental Science Engineering/AEI Center/Dept. of Atmospheric Environment Information Engineering, Graduate School, Inje University)
  • 박종길 (인제대학교 환경공학부/대기환경정보연구센터/대학원 대기환경정보공학과) ;
  • 김병수 (인제대학교 데이터정보학과) ;
  • 정우식 (인제대학교 환경공학부/대기환경정보연구센터/대학원 대기환경정보공학과) ;
  • 서장원 (기상청 기상연구소 해양기상지진연구실) ;
  • 손용희 (인제대학교 데이터정보학과) ;
  • 이대근 (인제대학교 환경공학부/대기환경정보연구센터/대학원 대기환경정보공학과) ;
  • 김은별 (인제대학교 환경공학부/대기환경정보연구센터/대학원 대기환경정보공학과)
  • Received : 2006.01.20
  • Accepted : 2006.03.28
  • Published : 2006.03.30
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Abstract

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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