用非迭代Transformer模型精准预测台风强度变化，尤其擅长快速增强/减弱期。
Accurate typhoon intensity forecasts using a non-iterative spatiotemporal transformer model
- 基于Transformer构建非迭代模型，融合历史演变与全球预报数据。
- 在5天预报周期内，对强、超强台风误差降低29%~43%。
- 特别适合需要高精度台风强度预测的防灾与应急决策场景。
准确预测热带气旋（TC）强度——尤其是快速增强和快速减弱阶段——仍是业务气象学的重大挑战，直接影响灾害应对与基础设施韧性。尽管机器学习取得进展，但现有系统在极端条件下性能迅速下降，且缺乏长程一致性。本文提出TIFNet，一种基于Transformer的非迭代预报模型，通过融合高分辨率全球预报与历史演变信息，生成5天强度轨迹。该模型在再分析数据上训练，并用业务数据微调，显著优于所有业务数值模型，在各预报时长下表现稳健，尤其在弱、强及超强台风类别中均实现提升。在长期被认为最难预测的快速强度变化阶段，相比当前业务基准，误差降低29%-43%。该成果标志着人工智能在极端条件下的台风强度预报取得实质性突破。
原文摘要 · Abstract (English)
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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