  Tropical cyclone and sea surface temperature data have been used in several
studies to forecast the total number of hurricanes in the Atlantic Basin. Sea
surface temperature (SST) and latent heat flux (LHF) are correlated with
tropical cyclone occurrences, but this correlation is known to vary with
location and strength of the storm. The objective of this article is to
identify features of SST and LHF that can explain the spatial-temporal
variation of tropical cyclone counts, categorized by their strength. We develop
a variable selection procedure for multivariate spatial-temporally varying
coefficients, under a Poisson hurdle model (PHM) framework, which takes into
account the zero-inflated nature of the counts. The method differs from current
spatial-temporal variable selection techniques by offering a dynamic variable
selection procedure, that shares information between responses, locations, time
and levels in the PHM context. The model is used to study the association
between SST and LHF and the number of tropical cyclones of different strengths
in 400 locations in the Atlantic Basin over the period of 1950-2013. Results
show that it is possible to estimate the number of tropical storms by season
and region. Furthermore, the model delimits areas with a significant
correlation between SST and LHF features and the occurrence and strength of TCs
in the North Atlantic Basin.
