Accurate typhoon intensity forecasts using a non-iterative spatiotemporal transformer model
This work addresses a critical problem for operational meteorology and disaster preparedness by improving typhoon intensity forecasts, though it is incremental as it builds on existing machine learning approaches.
The paper tackles the challenge of accurately forecasting tropical cyclone intensity, especially during rapid intensification and weakening, by introducing TIFNet, a transformer-based model that reduces forecast error by 29-43% in extreme regimes compared to operational baselines.
Accurate forecasting of tropical cyclone (TC) intensity - particularly during periods of rapid intensification and rapid weakening - remains a challenge for operational meteorology, with high-stakes implications for disaster preparedness and infrastructure resilience. Recent advances in machine learning have yielded notable progress in TC prediction; however, most existing systems provide forecasts that degrade rapidly in extreme regimes and lack long-range consistency. Here we introduce TIFNet, a transformer-based forecasting model that generates non-iterative, 5-day intensity trajectories by integrating high-resolution global forecasts with a historical-evolution fusion mechanism. Trained on reanalysis data and fine-tuned with operational data, TIFNet consistently outperforms operational numerical models across all forecast horizons, delivering robust improvements across weak, strong, and super typhoon categories. In rapid intensity change regimes - long regarded as the most difficult to forecast - TIFNet reduces forecast error by 29-43% relative to current operational baselines. These results represent a substantial advance in artificial-intelligence-based TC intensity forecasting, especially under extreme conditions where traditional models consistently underperform.