{"ID":2869567,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.21349","arxiv_id":"2509.21349","title":"Accurate typhoon intensity forecasts using a non-iterative spatiotemporal transformer model","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.","short_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 no...","url_abs":"https://arxiv.org/abs/2509.21349","url_pdf":"https://arxiv.org/pdf/2509.21349v1","authors":"[\"Hongyu Qu\",\"Hongxiong Xu\",\"Lin Dong\",\"Chunyi Xiang\",\"Gaozhen Nie\"]","published":"2025-09-18T20:50:17Z","proceeding":"physics.ao-ph","tasks":"[\"physics.ao-ph\",\"cs.LG\"]","methods":"[\"Transformer\"]","has_code":false}