Communications for Statistical Applications and Methods

The Korean Statistical Society (한국통계학회)
권호 표지
DOI QR코드

DOI QR Code

Bayesian Typhoon Track Prediction Using Wind Vector Data

  • Han, Minkyu (Department of Statistics, Seoul National University) ;
  • Lee, Jaeyong (Department of Statistics, Seoul National University)
  • Received : 2014.12.27
  • Accepted : 2015.05.07
  • Published : 2015.05.31
Download PDF
⟨ Previous Next ⟩

Abstract

In this paper we predict the track of typhoons using a Bayesian principal component regression model based on wind field data. Data is obtained at each time point and we applied the Bayesian principal component regression model to conduct the track prediction based on the time point. Based on regression model, we applied to variable selection prior and two kinds of prior distribution; normal and Laplace distribution. We show prediction results based on Bayesian Model Averaging (BMA) estimator and Median Probability Model (MPM) estimator. We analysis 8 typhoons in 2006 using data obtained from previous 6 years (2000-2005). We compare our prediction results with a moving-nest typhoon model (MTM) proposed by the Korea Meteorological Administration. We posit that is possible to predict the track of a typhoon accurately using only a statistical model and without a dynamical model.

Keywords

References

  1. Johnstone, I. M. and Silverman, B. W. (2005). Empirical Bayes selection of wavelet thresholds, The Annals of Statistics, 33, 1700-1752. https://doi.org/10.1214/009053605000000345
  2. Kim, J. H., Choo, G. M., Kim, B. J., Won, S. H. and Kwon, H. J. (2007). Performance of MTM in 2006 typhoon forecast, Atmosphere, 17, 207-216.
  3. Lee, J. and Oh, H. S. (2013). Bayesian regression based on principal components for high-dimensional data, Journal of Multivariate Analysis, 117, 175-192. https://doi.org/10.1016/j.jmva.2013.02.002
  4. Rozanova, O. S., Yu, J. L. and Hu, C. K. (2010). Typhoon eye trajectory based on a mathematical model: Comparing with observational data, Nonlinear Analysis: Real World Applications, 11, 1847-1861. https://doi.org/10.1016/j.nonrwa.2009.04.011
  5. Sohn, K. T. and Kim, S. D. (2001). Typhoon track prediction using dynamic linear model, Atmo-sphere, 11, 125-128.
  6. Song, H. J., Huh, S. H., Kim, J. H., Ho, C. H. and Park, S. K. (2005). Typhoon track prediction by a support vector machine using data reduction methods. In Y. Hao, et al. (Eds.), Lecture Notes in Computer Science: Vol. 3801. Computational Intelligence and Security (pp. 503-511), Springer, Berlin.