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Tropical cyclone and sea surface temperature data have been used in severalstudies to forecast the total number of hurricanes in the Atlantic Basin. Seasurface temperature (SST) and latent heat flux (LHF) are correlated withtropical cyclone occurrences, but this correlation is known to vary withlocation and strength of the storm. The objective of this article is toidentify features of SST and LHF that can explain the spatial-temporalvariation of tropical cyclone counts, categorized by their strength. We developa variable selection procedure for multivariate spatial-temporally varyingcoefficients, under a Poisson hurdle model (PHM) framework, which takes intoaccount the zero-inflated nature of the counts. The method differs from currentspatial-temporal variable selection techniques by offering a dynamic variableselection procedure, that shares information between responses, locations, timeand levels in the PHM context. The model is used to study the associationbetween SST and LHF and the number of tropical cyclones of different strengthsin 400 locations in the Atlantic Basin over the period of 1950-2013. Resultsshow that it is possible to estimate the number of tropical storms by seasonand region. Furthermore, the model delimits areas with a significantcorrelation between SST and LHF features and the occurrence and strength of TCsin the North Atlantic Basin.

Multivariate Spatial-Temporal Variable Selection with Applications to Seasonal Tropical Cyclone Modeling