零样本预测台风路径与强度，性能远超传统方法
TC-Next: Zero-Shot Multimodal Cyclone Forecasting
- 融合气象场与卫星图像，用基础模型生成预报数据
- 路径误差降15%-44%，强度误差降低3-6倍
- 无需重训即可跨模型应用，适合气象预警场景
我们提出TropicalCycloneNext（TC-Next），一种多模态深度学习模型，通过利用基础模型生成的气流运动和热力学场以及GridSat红外卫星图像，对西太平洋区域热带气旋的路径与强度进行6-24小时的预测。该模型仅在GraphCast预测场上训练，但仅依赖通用大气变量。相比传统的规则型追踪器TempestExtremes，TC-Next在路径误差上降低15%-44%，强度误差降低3-6倍；在未重新训练的情况下应用于Pangu-Weather和IFS HRES的预报场，仍显著优于后者。在2025年西太平洋季风季节的WeatherNext Cyclones数据上，零样本应用下，其强度误差始终更低，路径误差也更低或相当，优于该模型专有的直接追踪器。消融实验表明，多模态输入在所有预报时长下均提升路径预测性能，在长时距下显著改善强度预测。
原文摘要 · Abstract (English)
We present TropicalCycloneNext (TC-Next), a multimodal deep learning model that forecasts tropical cyclone track and intensity at $6$-$24$ h leads by leveraging a foundation model's forecast fields of atmospheric kinematic and thermodynamic fields and GridSat infrared satellite imagery. Trained only on GraphCast forecasts over the Western Pacific (WP), yet reliant only on generic atmospheric variables, TC-Next on GraphCast lowers track error by $15$-$44\%$ and intensity error by a factor of $3$-$6$ relative to a conventional, rule-based tracker, TempestExtremes; applied without retraining to the forecast fields of Pangu-Weather and IFS HRES, it stays ahead of TempestExtremes on both. Applied zero-shot to the generic weather fields of WeatherNext Cyclones on the 2025 WP season, TC-Next attains lower intensity error at every lead time, and lower or comparable track error, compared to that model's specialized direct tracker in a deterministic comparison. Our ablation studies show that our multimodal model is able to utilize the additional modality to improve performance in tracking errors at every lead time and in intensity prediction at longer lead times.
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