用物理引导的深度学习模型，提升极端海温事件10天预测精度。
Improved Forecasts of Global Extreme Marine Heatwaves Through a Physics-guided Data-driven Approach
- 基于大气驱动和集合预报思想设计双模块，融合物理机制与数据驱动。
- 对极端海温事件预测准确率显著高于传统数值模型，计算成本更低。
- 揭示风力是海温演变主因，可解释性强，适合气候预警与生态研究。
异常温暖的海表温度事件称为海洋热浪（MHWs），对海洋生态系统影响深远。准确预测极端MHWs具有重要科学与经济价值。然而，现有方法在最极端事件上仍存在局限。本研究基于MHWs的物理特性，构建了一种新型深度学习神经网络，实现10天期的高精度极端MHW预测。框架通过两个受数值模型启发的特殊模块——耦合器与概率数据增强——显著提升预测能力。耦合器模拟大气对MHW的驱动作用，概率数据增强借鉴集合预报思想，大幅提高极端事件预测性能。相比传统数值预测，该框架在准确性上显著更优，且所需计算资源更少。可解释性AI分析表明，风强迫是MHW演变的主要驱动因素，并揭示其与气海热交换的关系。整体上，该模型为理解MHW驱动机制及未来业务化预测提供了新范式。
原文摘要 · Abstract (English)
The unusually warm sea surface temperature events known as marine heatwaves (MHWs) have a profound impact on marine ecosystems. Accurate prediction of extreme MHWs has significant scientific and financial worth. However, existing methods still have certain limitations, especially in the most extreme MHWs. In this study, to address these issues, based on the physical nature of MHWs, we created a novel deep learning neural network that is capable of accurate 10-day MHW forecasting. Our framework significantly improves the forecast ability of extreme MHWs through two specially designed modules inspired by numerical models: a coupler and a probabilistic data argumentation. The coupler simulates the driving effect of atmosphere on MHWs while the probabilistic data argumentation approaches significantly boost the forecast ability of extreme MHWs based on the idea of ensemble forecast. Compared with traditional numerical prediction, our framework has significantly higher accuracy and requires fewer computational resources. What's more, explainable AI methods show that wind forcing is the primary driver of MHW evolution and reveal its relation with air-sea heat exchange. Overall, our model provides a framework for understanding MHWs' driving processes and operational forecasts in the future.
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