Enhancing Near Real Time AI-NWP Hurricane Forecasts: Improving Explainability and Performance Through Physics-Based Models and Land Surface Feedback
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| Autores principales: | , , , , , , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| author | Sudharsan, Naveen Singh, Manmeet Talukdar, Sasanka Mohanty, Shyama Kamath, Harsh Osuri, Krishna K. Dashtian, Hassan Young, Michael Yang, Zong-Liang Dawson, Clint Leung, L. Ruby Gopalakrishnan, Sundararaman Mehra, Avichal Tallapragada, Vijay Niyogi, Dev |
| author_facet | Sudharsan, Naveen Singh, Manmeet Talukdar, Sasanka Mohanty, Shyama Kamath, Harsh Osuri, Krishna K. Dashtian, Hassan Young, Michael Yang, Zong-Liang Dawson, Clint Leung, L. Ruby Gopalakrishnan, Sundararaman Mehra, Avichal Tallapragada, Vijay Niyogi, Dev |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_01797 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Enhancing Near Real Time AI-NWP Hurricane Forecasts: Improving Explainability and Performance Through Physics-Based Models and Land Surface Feedback Sudharsan, Naveen Singh, Manmeet Talukdar, Sasanka Mohanty, Shyama Kamath, Harsh Osuri, Krishna K. Dashtian, Hassan Young, Michael Yang, Zong-Liang Dawson, Clint Leung, L. Ruby Gopalakrishnan, Sundararaman Mehra, Avichal Tallapragada, Vijay Niyogi, Dev Atmospheric and Oceanic Physics Geophysics 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. |
| title | Enhancing Near Real Time AI-NWP Hurricane Forecasts: Improving Explainability and Performance Through Physics-Based Models and Land Surface Feedback |
| topic | Atmospheric and Oceanic Physics Geophysics |
| url | https://arxiv.org/abs/2502.01797 |