Early Prediction of Seasonal Atlantic Hurricanes Using Sea Surface Temperature Maps and Neural Networks

The influence of the Sea Surface Temperature (SST) measurements on tropical storm activity has been well studied in the research community. Both tropical Atlantic and tropical mean SSTs have been shown to be key predictors for the upcoming hurricane season strength. In this paper, we propose a novel approach to predicting the strength of the upcoming hurricane season, which exploits the spatial information contained in global SST maps. We design a Convolutional Neural Network (CNN) architecture that uses SST data maps as input images and predicts the Accumulated Cyclone Energy (ACE) index, classified into two classes: high intensity and medium-low intensity. Our experiments show that our novel design based on neural networks is 20% more accurate in predicting the strength of the incoming hurricane season using January SST data maps when compared to the current operational seasonal predictions issued in May by the National Oceanic and Atmospheric Administration (NOAA) for 2000-2019.