CAIG: Leveraging AI to Unravel Marine Heatwave Impacts on Hurricane Evolution and Coastal Resilience Against Compound Flooding

Tropical cyclones are becoming more destructive as the climate warms, especially when they pass over unusually warm ocean waters that can cause storms to strengthen rapidly. These fast-changing storms leave coastal communities with less time to prepare against the ever-increasing risk of flooding. This project will improve understanding of how these events combine to create greater flood hazards and will develop new tools to better anticipate their impacts. The research will support more informed emergency planning and coastal resilience by providing actionable risk information for decision-makers. It will also train graduate students and a postdoctoral researcher in artificial intelligence and coastal hazard science while making all data, models, and software openly available to encourage future research and innovation. This project will investigate how prolonged ocean warming events influence the rapid intensification of tropical cyclones and the resulting compound coastal flooding hazards. Using Mobile Bay, Alabama, and Galveston Bay, Texas, as complementary study regions, the research will quantify changes in storm surge, rainfall, and wind associated with tropical cyclones that intensify over unusually warm waters, or marine heatwaves. The project will develop Bayesian deep learning methods to quantify predictive uncertainty and integrate physics-informed artificial intelligence with process-based numerical models to enable accurate and computationally efficient near-real-time hazard assessment. A tiered modeling framework will benchmark advanced machine learning approaches against operational and nonlinear baseline models to ensure robust, reproducible, and transferable predictions across diverse coastal environments. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.