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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)

Abstract

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.

Keywords

temperature forecast MOS UM KLAPS genetic programming 

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References

  1. 1.
    Carvalho, J.R.P., Assad, E.D., Pinto, H.S.: Kalman filter and correction of the temperatures estimated by PRECIS model. Atmospheric Research 102, 218–226 (2011)CrossRefGoogle Scholar
  2. 2.
    Glahn, H.R., Lowry, D.A.: The use of model output statistics (MOS) in objective weather forecasting. J. Appl. Meteor. 11, 1203–1211 (1972)CrossRefGoogle Scholar
  3. 3.
    Glahn, B., Gilbert, K., Cosgrove, R., Ruth, D.P., Sheets, K.: The gridding of MOS. Weather and Forecasting 24, 520–529 (2009)CrossRefGoogle Scholar
  4. 4.
    Homleid, M.: Weather dependent statistical adaption of 2 meter temperature forecasts using regression methods and Kalman filter. met. no report, Norwegian Meteorological Institute (2004)Google Scholar
  5. 5.
    Kang, J., Suh, M., Hong, K., Kim, C.: Development of updateable Model Output Statistics (UMOS) System for Air Temperature over South Korea. Asia-Pacific Journal of Atmospheric Sciences 47, 199–211 (2011)CrossRefGoogle Scholar
  6. 6.
    Kim, Y., Park, O., Hwang, S.: Realtime Operation of the Korea Local Analysis and Prediction System at METRI. Asia-Pacific Journal of Atmospheric Sciences 38, 1–10 (2002)Google Scholar
  7. 7.
    Koza, J.R.: Genetic Programming: On the Programming of Computers by Natural Selection. MIT Press, Cambridge (1992)zbMATHGoogle Scholar
  8. 8.
    Lee, Y.H., Park, S.K., Chang, D.-E.: Parameter estimation using the genetic algorithm and its impact on quantitative precipitation forecast. Annales Geophysicae 24, 3185–3189 (2006)CrossRefGoogle Scholar
  9. 9.
    Termonia, P., Deckmyn, A.: Model-inspired predictors for model output statistics. Mon. Wea. Rev. 135, 3496–3505 (2007)CrossRefGoogle Scholar
  10. 10.
    United Kingdom Met Office, http://www.metoffice.gov.uk
  11. 11.
    Ustaoglu, B., Cigizoglu, H.K., Karaca, M.: Forecast of daily mean, maximum and minimum temperature time series by three artificial neural network methods. Meteorological Applications 15, 431–445 (2008)CrossRefGoogle Scholar
  12. 12.
    Vannitsem, S.: Dynamical Properties of MOS Forecasts: Analysis of the ECMWF Operational Forecasting System. Weather and Forecasting 23, 1032–1043 (2008)CrossRefGoogle Scholar
  13. 13.
    Yu, X., Park, S., Lee, K., Ahn, K., Choo, S.: The gridding of MOS for high resolution forecasting. In: The Fifth Korea-Japan-China Joint Conference on Meteorology, pp. 18–21 (2011)Google Scholar
  14. 14.
    Zongker, D., Punch, B.: Lil-GP User’s Manual, Michigan State University (1995)Google Scholar

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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