标题： 使用非迭代时空变换器模型的准确台风强度预测 显示英文标题

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

摘要： 准确预测热带气旋（TC）强度——特别是在快速增强和快速减弱期间——仍然是业务气象学的挑战，对灾害准备和基础设施韧性具有高风险影响。 机器学习的最新进展在TC预测方面取得了显著进展；然而，大多数现有系统提供的预测在极端情况下迅速退化，并且缺乏长期一致性。 在这里，我们介绍了TIFNet，这是一种基于Transformer的预测模型，通过将高分辨率全球预测与历史演变融合机制相结合，生成非迭代的5天强度轨迹。 TIFNet在再分析数据上进行训练，并使用业务数据进行微调，在所有预测时间范围内始终优于业务数值模型，为弱、强和超级台风类别提供了稳健的改进。 在长期被视为最难预测的快速强度变化情况下，TIFNet相对于当前业务基准将预测误差降低了29-43%。 这些结果代表了基于人工智能的TC强度预测的重大进展，尤其是在传统模型持续表现不佳的极端条件下。 显示英文摘要

摘要： 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.