Published online in Wiley InterScience (www.interscience.wiley.com). DOI:10.1002/joc.755 A SPACE–TIME MODEL FOR SEASONAL HURRICANE PREDICTION

• Main Authors: Thomas H. Jagger, Xufeng Niua, James B. Elsnerb
• Other Authors: The Pennsylvania State University CiteSeerX Archives
• Format: Text
• Language: English
• Published: 2001
• Subjects: KEY WORDS; North Atlantic Ocean; space–time Poisson regression; US hurricanes; ENSO; NAO; African rainfall; North Atlantic
• Online Access: http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.500.8132; http://myweb.fsu.edu/jelsner/PDF/Research/JaggerNiuElsner2002.pdf

A space–time count process model is explained and applied 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 grid 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 neighbours and statistically significant couplings. Hindcasts are performed on the hurricane seasons from 1994 to 1997. Results show that, on average, model forecast probabilities are larger in regions in which hurricanes occurred. Quantitative skill assessment indicates some useful skill above climatology — currently the default leading candidate. The TPSTAR model could be a valuable guidance product when issuing seasonal hurricane forecasts. Copyright 2002 Royal Meteorological Society.