用多任务学习提升台风概率预报精度，兼顾速度与可解释性。
CycloneMAE: A Scalable Multi-Task Learning Model for Global Tropical Cyclone Probabilistic Forecasting
- 基于台风结构感知的掩码自编码器，从多模态数据中学习通用表征
- 全球五大洋盆测试中，120小时风压预测和24小时路径预测优于主流数值模型
- 首次实现短时依赖核心结构、长时关注环境因素的可解释预报机制
热带气旋是最具破坏性的自然灾害之一，其预报面临根本性权衡：数值天气预报（NWP）模型计算成本高且难以利用历史数据，而现有深度学习模型多为单变量、确定性，无法跨变量泛化。本文提出CycloneMAE，一种可扩展的多任务预报模型，通过台风结构感知的掩码自编码器，从多模态数据中学习可迁移的台风表征。结合离散概率网格机制与预训练/微调范式，该模型同时输出确定性预报与概率分布。在五个全球洋盆上评估，CycloneMAE在120小时内风速与气压预报、24小时内路径预报上超越领先NWP系统。通过集成梯度归因分析揭示：短期预报主要依赖卫星图像中的内部对流结构，长期预报则逐步转向外部环境因素。本框架为业务化台风预报提供了可扩展、概率化且可解释的新路径。
原文摘要 · Abstract (English)
Tropical cyclones (TCs) rank among the most destructive natural hazards, yet their forecasting faces fundamental trade-offs: numerical weather prediction (NWP) models are computationally prohibitive and struggle to leverage historical data, while existing deep learning (DL)-based intelligent models are variable-specific and deterministic, which fail to generalize across different forecasting variables. Here we present CycloneMAE, a scalable multi-task forecasting model that learns transferable TC representations from multi-modal data using a TC structure-aware masked autoencoder. By coupling a discrete probabilistic gridding mechanism with a pre-train/fine-tune paradigm, CycloneMAE simultaneously delivers deterministic forecasts and probability distributions. Evaluated across five global ocean basins, CycloneMAE outperforms leading NWP systems in pressure and wind forecasting up to 120 hours and in track forecasting up to 24 hours. Attribution analysis via integrated gradients reveals physically interpretable learning dynamics: short-term forecasts rely predominantly on the internal core convective structure from satellite imagery, whereas longer-term forecasts progressively shift attention to external environmental factors. Our framework establishes a scalable, probabilistic, and interpretable pathway for operational TC forecasting.
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