Communications for Statistical Applications and Methods

The Korean Statistical Society (한국통계학회)
권호 표지
DOI QR코드

DOI QR Code

Statistical Correction of Numerical Model Forecasts for Typhoon Tracks

  • Sohn, Keon-Tae (Department of Statistics, Pusan National University)
  • Published : 2005.08.01
Download PDF
⟨ Previous Next ⟩

Abstract

This paper concentrates on the prediction of typhoon tracks using the dynamic linear model (DLM) for the statistical correction of the numerical model guidance used in the JMA. The DLM with proposed forecast strategy is applied to reduce their systematic errors using the latest observation. All parameters of the DLM are updated dynamically and backward forecasting is performed to remove the effect of initial values.

Keywords

References

  1. Ghil, M. and P. Malanotte-Rizzoli (1991). Data assimilation in meteorology and oceanography, Advanced Geophysics, 33, 141-266 https://doi.org/10.1016/S0065-2687(08)60442-2
  2. Glahn, H. R. and D. A. Lowry (1972). The use of model output statistics (MOS) in objective weather forecasting, Journal of Applied Meteorology, 11, 1203-1211 https://doi.org/10.1175/1520-0450(1972)011<1203:TUOMOS>2.0.CO;2
  3. Homleid, M (1995). Diurnal correction of short-term surface temperature forecasts using the Kalman filter, Weather and Forecast, 10, 689-707 https://doi.org/10.1175/1520-0434(1995)010<0689:DCOSTS>2.0.CO;2
  4. Kalman, R. E. (1960). A new approach to linear filtering and prediction problems, Transaction of the ASME Journal of Basic Engineering, 35-45
  5. Kok, K. and S. Kruizinga (1992). Updating probabilistic MOS equations, Proceedings of the 12th Coriferences on Probability and Statistics in Atmospheric Sciences, American Meteorologist Society, 62-65
  6. Lemcke, C. and S. Kruizinga (1988). Model output statistics (three years of operational experience in the Netherlands), Monthly Weather Reviews, 116, 1077-1090 https://doi.org/10.1175/1520-0493(1988)116<1077:MOSFTY>2.0.CO;2
  7. Persson, A. O. (1991). Kalman filtering - A new approach to adaptive statistical interpretation of numerical meteorological forecasts, WMO TD, 421, xx27-xx37
  8. Report of the fifth WMO International Workshop on Tropical Cyclone (2002). Tropical Meteorology Research Programme Report Series, Report No. 67, Cairns, Australia
  9. Ross, G. H. (1989). Model output statistics using an updateable scheme, Proceedings of the 11th Conferences on Probability and Statistics in Atmospheric Sciences, American Meteorologist Society, 93-97
  10. Ross, G. H. and C. C. Studwicke (1994). Logistic regression using a Kalman filter within an updateable MOS forecasting system, Proceedings of the 13th Conferences on Probability and Statistics in Atmospheric Sciences, American Meteorologist Society, 204-209
  11. Simonsen, C. (1991). Self adaptive model output statistics based on Kalman filtering, WMO TD, 421, xx33-xx37
  12. Sohn, K. T. (2004). Statistical correction of Numerical model forecasts for 3-hour-forecast system, Journal of the Korean Data Analysis Society, Vol. 6, No.2, 453-464
  13. Sohn, K. T., J. Kwon and A. S. Suh (2003). Prediction of Typhoon tracks using dynamic linear models, Advanced Atmospheric Sciences, Vol. 20, No.3, 379-384 https://doi.org/10.1007/BF02690796
  14. Sohn, K. T., D. K. Rha and Y. K. Seo (2003). The 3-hour-interval prediction of ground-level temperature in South Korea using Dynamic linear models, Advanced Atmospheric Sciences, Vol. 20, No.4, 575-582 https://doi.org/10.1007/BF02915500
  15. Verron, J., L. Gourdeau, D. T. Pham, R. Murtugudde and A. J. Busalacchi (1999). An extended Kalman filter to assimilate satellite altimeter data into a nonlinear numerical model of the tropical Pacific Ocean: Method and validation, Journal of Geophysical Research, 104, C3, 5441-5458 https://doi.org/10.1029/1998JC900079
  16. West, M. and J. Harrison (1997). Bayesian forecasting and Dynamic models (2nd ed.), Springer-Verlag, New York