Improving tropical cyclone intensity forecasts with a simple AI correction

Summary

Tropical cyclones (TCs) are among the most destructive weather systems. Improving forecasts of their track and intensity is critical for helping communities to prepare for severe winds, storm surge and flooding. Forecasting TCs has been one of the more demanding tests for machine-learning weather models. Artificial intelligence (AI) weather models have made remarkable progress in predicting the tracks of TCs. However, forecasting their intensity poses a much greater challenge, with models exhibiting a persistent tendency to underestimate storm strength. Much of this difficulty stems from the need to accurately represent the compact, often rapidly evolving inner core, where small-scale processes can strongly influence storm evolution and trigger rapid intensification. A new collaboration with Anna Allen, Wessel Bruinsma and Richard Turner at the University of Cambridge explores a simple way to address this problem. Rather than building a new TC forecasting model from scratch, the approach starts with the existing Artificial Intelligence Forecasting System (AIFS) forecast and learns how to correct its intensity errors. The result is a system that, when tested on TCs from the 2025 season that were not used in training, achieves performance comparable to both leading operational guidance and AI state-of-the-art systems in forecasting maximum wind speed and minimum central pressure. Correcting the intensity weakness rather than rebuilding the model The approach, called AIFS-TC, is based on a straightforward idea. The AIFS forecast provides an initial estimate of a storm's future intensity. A second machine-learning system then learns the errors that AIFS tends to make and predicts a correction to that forecast. The correction uses information already contained in the AIFS forecast. This includes the predicted track and intensity of the storm, as well as three-dimensional atmospheric fields such as wind, temperature, geopotential and humidity in a region around the cyclone. The correction is then applied to the original AIFS intensity forecast. This is particularly useful because the underlying problem is clear. At around 0.25° resolution, the AIFS grid is too coarse to fully resolve the inner core of a TC, and the deterministic training methods further prevent AIFS from skilful TC intensity predictions. The resulting forecast tends to smooth the strongest winds…