SEASONAL PREDICTION MODELS FOR NORTH-ATLANTIC BASIN HURRICANE LOCATION

Using multivariate discriminant analysis techniques, statistically sig nificant and skillful models are developed for making extended-range f orecasts of hurricane activity within specific locations of the North Atlantic basin. These forecasts predict the presence or absence of hur ricane activity and not the actual number of storms that will occur wi thin a region. Successful models are developed for predicting intense hurricane activity in both the Gulf of Mexico and the Caribbean subbas ins 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 coas t 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 feas ible due, respectively, to the relative lack and abundance of hurrican e activity. Cross-validated forecast accuracies range from 78% to 81% for the regions in which successful models can be developed. An all-po ssible subsets selection algorithm is used to identify the predictor m odels. while bootstrap techniques are used to assess model significanc e. Statistical tests using normal approximations are employed to compa re cross-validated (hindcast) forecast accuracy to climatology.