Seasonal Prediction Models for North Atlantic Basin Hurricane Location

Abstract

Using multivariate discriminant analysis techniques, statistically significant and skillful models are developed for making extended-range forecasts of hurricane activity within specific locations of the North Atlantic basin. These forecasts predict the presence or absence of hurricane activity and not the actual number of storms that will occur within a region. Successful models are developed for predicting intense hurricane activity in both the Gulf of Mexico and the Caribbean subbasins separately. Extended-range forecasts of all hurricane activity are also possible within the Caribbean Sea. More significantly, lead-time forecasts of landfalling hurricanes on the southeastern Atlantic coast of the United States are possible and show a substantial improvement over climatology. Extended-range forecasts of hurricane activity for the northeastern United States and for the Gulf of Mexico are not feasible due, respectively, to the relative lack and abundance of hurricane activity. Cross-validated forecast accuracies range from 78% to 81% for the regions in which successful models can be developed. An all-possible subsets selection algorithm is used to identify the predictor models, while bootstrap techniques are used to assess model significance. Statistical tests using normal approximations are employed to compare cross-validated (hindcast) forecast accuracy to climatology.