Bayesian Forecasting of Seasonal Typhoon Activity: A Track-Pattern-Oriented Categorization Approach

Abstract

Abstract A new approach to forecasting regional and seasonal tropical cyclone (TC) frequency in the western North Pacific using the antecedent large-scale environmental conditions is proposed. This approach, based on TC track types, yields probabilistic forecasts and its utility to a smaller region in the western Pacific is demonstrated. Environmental variables used include the monthly mean of sea surface temperatures, sea level pressures, low-level relative vorticity, vertical wind shear, and precipitable water of the preceding May. The region considered is the vicinity of Taiwan, and typhoon season runs from June through October. Specifically, historical TC tracks are categorized through a fuzzy clustering method into seven distinct types. For each cluster, a Poisson or probit regression model cast in the Bayesian framework is applied individually to forecast the seasonal TC activity. With a noninformative prior assumption for the model parameters, and following Chu and Zhao for the Poisson regression model, a Bayesian inference for the probit regression model is derived. A Gibbs sampler based on the Markov chain Monte Carlo method is designed to integrate the posterior predictive distribution. Because cluster 5 is the most dominant type affecting Taiwan, a leave-one-out cross-validation procedure is applied to predict seasonal TC frequency for this type for the period of 1979–2006, and the correlation skill is found to be 0.76.