Advancing Hurricane Forecasting With AI Models for Track and Intensity Prediction

Hurricane forecasting has traditionally relied on numerical weather prediction (NWP) models. However, advancements in artificial intelligence (AI) offer new opportunities to improve forecasting accuracy. This study presents a novel evaluation of the FourCastNet model, trained on MERRA-2 and ERA5 data sets. We perform a comprehensive comparison between the FourCastNet model forecasts and those simulated by the Weather Research and Forecating (WRF) model, a NWP model, assessing both the accuracy and radial distribution of hurricane structure. This comparison provides their representation of hurricane dynamics, including differences in track prediction and intensity forecasts. Additionally, the study addresses the challenge of bias in hurricane intensity forecasts. To overcome this, this study presents a comprehensive assessment of three hurricane intensity estimation models, HxUnet, HxCNN, and HxGNN. Our results demonstrate that HxUnet consistently outperforms the other models, achieving up to a 79% reduction in maximum sustained wind speed errors and a 59% reduction in Mean Sea Level Pressure errors. This significant improvement underscores the potential of AI models to enhance the precision of hurricane intensity forecasts. This research advances the application of AI in meteorology and establishes a foundation for future studies aimed at improving hurricane prediction and mitigation efforts.