Hierarchical Bayesian modeling of Atlantic storms based on the sea surface temperature field

The increasing intensity and frequency of Atlantic tropical storms and hurricanes in recent decades have underscored the need for a deeper understanding of the factors influencing these events. The complex interplay between oceanic and atmospheric conditions, particularly sea surface temperatures (SSTs), has been widely recognized as a key driver of hurricane activity. In this paper, we introduce a novel statistical model that explores the relationship between Atlantic tropical storm occurrences and climate factors, with a particular focus on the spatial variability inherent in the climate system. By employing a hierarchical Bayesian modeling framework and incorporating key climate predictors such as global surface temperature, the North Atlantic Oscillation, and the Atlantic Multidecadal Oscillation, we aim to capture the dynamic and spatially heterogeneous nature of the factors influencing hurricane activity. Our model, utilizing climate data from 1900 to 2002, demonstrates the ability to explain a significant portion of the trends and variability in Atlantic tropical storm activity. The results highlight the importance of considering spatial dynamics in understanding and predicting hurricane Occurrences.