Hurricane track forecasting remains a significant challenge due to the complex interactions between the atmosphere, land, and ocean. Although AI-based numerical weather prediction models, such as Google Graphcast operation, have significantly improved hurricane track forecasts, they currently function as atmosphere-only models, omitting critical land and ocean interactions. To investigate the impact of land feedback, we conducted independent simulations using the physics-based Hurricane WRF experimental model to assess how soil moisture variations influence storm trajectories. Our results show that land surface conditions significantly alter storm paths, demonstrating the importance of land-atmosphere coupling in hurricane prediction. Although recent advances have introduced AI-based atmosphere-ocean coupled models, a fully functional AI-driven atmosphere-land-ocean model does not yet exist. Our findings suggest that AI-NWP models could be further improved by incorporating land surface interactions, improving both forecast accuracy and explainability. Developing a fully coupled AI-based weather model would mark a critical step toward more reliable and physically consistent hurricane forecasting, with direct applications for disaster preparedness and risk mitigation.
@article{arxiv.2502.01797,
title = {Enhancing Near Real Time AI-NWP Hurricane Forecasts: Improving Explainability and Performance Through Physics-Based Models and Land Surface Feedback},
author = {Naveen Sudharsan and Manmeet Singh and Sasanka Talukdar and Shyama Mohanty and Harsh Kamath and Krishna K. Osuri and Hassan Dashtian and Michael Young and Zong-Liang Yang and Clint Dawson and L. Ruby Leung and Sundararaman Gopalakrishnan and Avichal Mehra and Vijay Tallapragada and Dev Niyogi},
journal= {arXiv preprint arXiv:2502.01797},
year = {2025}
}