Genetic Programming-Based Model Output Statistics for Short-Range Temperature Prediction

  • Kisung Seo
  • Byeongyong Hyeon
  • Soohwan Hyun
  • Younghee Lee
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7835)


This paper introduces GP (Genetic Programming) based robust compensation technique for temperature prediction in short-range. MOS (Model Output Statistics) is a statistical technique that corrects the systematic errors of the model. Development of an efficient MOS is very important, but most of MOS are based on the idea of relating model forecasts to observations through a linear regression. Therefore it is hard to manage complex and irregular natures of the prediction. In order to solve the problem, a nonlinear and symbolic regression method using GP is suggested as the first attempt. The purpose of this study is to evaluate the accuracy of the estimation by GP based nonlinear MOS for the 3 days temperatures for Korean regions. This method is then compared to the UM model and shows superior results. The training period of summer in 2007-2009 is used, and the data of 2010 summer is adopted for verification.


temperature forecast MOS UM KLAPS genetic programming 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Kisung Seo
    • 1
  • Byeongyong Hyeon
    • 1
  • Soohwan Hyun
    • 2
  • Younghee Lee
    • 3
  1. 1.Dept. of Electronic EngineeringSeokyeong UniversitySeoulKorea
  2. 2.Hyundai Heavy Industries Research InstituteYonginKorea
  3. 3.National Institute of Meteorological Research/Korea Meteorological AdministrationSeoulKorea

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