Accurate typhoon intensity forecasts using a non-iterative spatiotemporal transformer model

• URL: http://arxiv.org/abs/2509.21349v1
• Date: Thu, 18 Sep 2025 20:50:17 GMT
• Title: Accurate typhoon intensity forecasts using a non-iterative spatiotemporal transformer model
• Authors: Hongyu Qu, Hongxiong Xu, Lin Dong, Chunyi Xiang, Gaozhen Nie,
• Abstract summary: Accurate forecasting of tropical cyclone (TC) intensity remains a challenge for operational meteorology.<n>Recent advances in machine learning have yielded notable progress in TC prediction.<n>Here we introduceNet, a transformer-based forecasting model that generates non-iterative, 5-day intensity trajectories.
• Score: 3.468838035344738
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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