AIFS-TC: A simple correction competitive with the operational frontier for tropical cyclone intensity forecasting
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Physics > Atmospheric and Oceanic Physics
Title:AIFS-TC: A simple correction competitive with the operational frontier for tropical cyclone intensity forecasting
Abstract:AI weather models are in the process of revolutionising weather forecasting. While these models have been shown to achieve superior performance to physics-based NWP in forecasting tropical cyclone (TC) tracks, they dramatically underestimate intensity. Here we present AIFS-TC, a simple correction to the AIFS-Single model that is competitive with the operational state-of-the-art for forecasting maximum wind speed and minimum central pressure at lead times of 12 h to seven days. This performance also holds for rapid intensification events. Notably, the entire system was autonomously designed and built by a large language model (Claude Fable 5) in a few hours, directed through a small number of natural-language prompts by a single domain scientist. That the operational frontier can be reached with an open-source AI forecast model (AIFS-Single) and relatively simple, cheap post-processing is significant for TC science, and points to agentic coding as a route to rapid exploration and progress in life-saving early-warning systems in other domains.
| Comments: | 6 pages, 5 figures, 2 tables |
| Subjects: | Atmospheric and Oceanic Physics (physics.ao-ph); Machine Learning (cs.LG) |
| Cite as: | arXiv:2608.09959 [physics.ao-ph] |
| (or arXiv:2608.09959v1 [physics.ao-ph] for this version) | |
| https://doi.org/10.48550/arXiv.2608.09959
arXiv-issued DOI via DataCite
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