Communications for Statistical Applications and Methods

The Korean Statistical Society (한국통계학회)
권호 표지
DOI QR코드

DOI QR Code

Binary Forecast of Heavy Snow Using Statistical Models

  • Sohn, Keon-Tae (Department of Statistics, Pusan National University)
  • Published : 2006.08.31
Download PDF
⟨ Previous Next ⟩

Abstract

This Study focuses on the binary forecast of occurrence of heavy snow in Honam area based on the MOS(model output statistic) method. For our study daily amount of snow cover at 17 stations during the cold season (November to March) in 2001 to 2005 and Corresponding 45 RDAPS outputs are used. Logistic regression model and neural networks are applied to predict the probability of occurrence of Heavy snow. Based on the distribution of estimated probabilities, optimal thresholds are determined via true shill score. According to the results of comparison the logistic regression model is recommended.

Keywords

References

  1. 손건태 (2004). 3시간 기상예보를 위한 수치모델 예측치의 통계적 수정. Journal of the Korean Data Analysis Society, 제6권 2호, 453-464
  2. 손건태, 김재환 (2003). 서울지역 난후기에 대한 통계적 강수예보 모델 개발. Journal of the Korean Data Analysis Society, 제5권 1호, 113-126
  3. 손건태, 이정형 (2005a). 3시간 강수량예보를 위한 상태종속모형 개발. Journal of the Korean Data Analysis Society, 제7권 1호, 137-150
  4. 손건태, 이정형, 류찬수 (2005b) 호남지역 강수량 예측을 위한 통계모형 개발. Journal of the Korean Data Analysis Society, 제7권 2호, 507-521
  5. 손건태, 한정임 (2004). 순서형 확률예측 모형에 대한 새로운 예측성 평가측도의 제안. Journal of the Korean Data Analysis Society, 제6권 1호, 267-278
  6. 최준태, 조주영 (2002). PPM을 이용한 객관적 강수확률 예보법. 한국기상학회지, 제38권 2호, 119-127
  7. Glahn, H.R., and Lowry, D.A. (1972). The use of model output statistics (MOS) in Objective weather forecasting. Journal of Applied Meteorology, Vol. 11, 1203-1211 https://doi.org/10.1175/1520-0450(1972)011<1203:TUOMOS>2.0.CO;2
  8. Hans, V.S. and W.Z. Francis (1999). Statistical Analysis in Climate Research Cambridge University Press, Cambridge
  9. Kok, K. and Kruizinga, S. (1992). Updating probabilistic MOS equations. Proceedings of the 12th Conferences on Probability and Statistics in Atmospheric Sciences, American Meteorologist Society, 62-65
  10. Lemcke, C. and Kruizinga, S. (1988). Model output statistics (three years of operational experience in the Netherlands). Monthly Weather Reviews, Vol. 116, 1077-1090 https://doi.org/10.1175/1520-0493(1988)116<1077:MOSFTY>2.0.CO;2
  11. Murphy, A.H. (1993). What is a good forecast? An essay on the nature of goodness in weather forecasting. Weather and Forecasting, Vol. 8, 281-293 https://doi.org/10.1175/1520-0434(1993)008<0281:WIAGFA>2.0.CO;2
  12. Sohn, K.T. (2006). Guidance on the Use of Skill scores for Two-class Forecast. Proceedings of the 6th METRI-IAP Joint Workshop, Jeju, Korea
  13. Sohn, K.T., Lee, J.H, Lee, S.H. and Ryu, C.S. (2005). Statistical Prediction of Heavy Rain in South Korea. Advanced in Atmospheric Sciences, Vol. 22, 703-710 https://doi.org/10.1007/BF02918713
  14. Sohn, K.T., Rha, D.K. and Seo, Y.K. (2003). The 3-hour-interval prediction of ground-level temperature in South Korea using Dynamic linear model. Advanced in Atmospheric Sciences, Vol. 20, 575-582 https://doi.org/10.1007/BF02915500
  15. Ross, G.B. and Studwicke, C.C. (1994). Logistic regression using a Kalman filter within an update able MOS forecasting system. Proceedings of the 13th Conferences on Probability and Statistics in Atmospheric Sciences, American Meteorologist Society, 204-209

Cited by

  1. Guidance on the choice of threshold for binary forecast modeling vol.25, pp.1, 2008, https://doi.org/10.1007/s00376-008-0083-8