Extended Abstract (104K)J3.13
A Space-Time Model for Seasonal Hurricane Prediction
Thomas H. Jagger, Florida State Univ., Tallahassee, FL; and X. Niu and J. B. Elsner
The authors explain and apply a space-time count process model to annual North Atlantic hurricane activity. The model uses the best-track data set of historical hurricane positions and intensities together with climate variables to determine local space-time coefficients of a right-truncated Poisson process. The truncated Poisson space-time autoregressive (TPSTAR) model is motivated by first examining a time-series model for the entire domain. Then a Poisson generalized linear model is considered that uses grids boxes within the domain and adds offset factors for latitude and longitude. A natural extension is then made that includes instantaneous local and autoregressive coupling between the grids. A final version of the model is found by backward selection of the predictors based on values of Bayesian and Akiake information criteria. The final model has five nearest neighbors and statistically significant couplings. A single hindcast experiment is performed on the 1994 hurricane season. Results show that, on average, hindcast probabilities are larger in regions in which hurricanes were observed. Although more work is needed to fully verify model skill, results indicate that the TPSTAR model as formulated in this study could become a useful tool for making climate forecasts.
Joint Session 3, Climate Variations and Forecasting (Joint with the 16th Conference Probability and Statistics and the 13th Symposium on Global Change and Climate Variations)
Tuesday, 15 January 2002, 8:30 AM-2:30 PM
Previous paper Next paper