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

TL;DR

This paper introduces a dynamic multivariate spatial-temporal variable selection method within a Poisson hurdle model to better understand and predict tropical cyclone counts based on sea surface temperature and latent heat flux data.

Contribution

It develops a novel variable selection procedure that accounts for spatial, temporal, and response sharing in a Poisson hurdle model for cyclone prediction.

Findings

Identifies significant SST and LHF features linked to cyclone occurrence.

Estimates seasonal and regional cyclone counts.

Delimits areas with strong SST and LHF correlations to cyclone strength.

Abstract

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.