标题： 拉图：增强动力学的深度学习用于涡旋分辨率海洋预报 显示英文标题

标题： LanTu: Dynamics-Enhanced Deep Learning for Eddy-Resolving Ocean Forecasting

摘要： 中尺度涡旋主导了海洋的时空多尺度变化，它们对全球海洋能量级串的影响不容忽视。涡旋解析的海洋预报为渔业和航行安全提供了更可靠的保障，但也给传统数值模型带来了显著的科学挑战和高昂的计算成本。基于人工智能（AI）的天气和海洋预报系统正成为一种平衡预报性能与计算效率的强大工具。然而，海洋动力系统的复杂多尺度特征使得AI模型在中尺度涡旋预报（特别是在区域建模方面）仍面临许多挑战。 在这里，我们开发了LanTu，这是一种基于增强动力学的深度学习的区域涡旋解析海洋预报系统。我们将跨尺度相互作用纳入LanTu，并构建多尺度物理约束以优化LanTu，通过利用涡旋动力学的知识来提高LanTu对中尺度演化的预报技巧。结果显示，LanTu在温度、盐度、海面异常和流速预测方面优于现有的先进业务数值海洋预报系统（NOFS）和基于AI的海洋预报系统（AI-OFS），预报时效超过10天。我们的研究强调，增强动力学的深度学习（LanTu）可以成为涡旋解析海洋预报的一个强大范式。 显示英文摘要

摘要： Mesoscale eddies dominate the spatiotemporal multiscale variability of the ocean, and their impact on the energy cascade of the global ocean cannot be ignored. Eddy-resolving ocean forecasting is providing more reliable protection for fisheries and navigational safety, but also presents significant scientific challenges and high computational costs for traditional numerical models. Artificial intelligence (AI)-based weather and ocean forecasting systems are becoming powerful tools that balance forecast performance with computational efficiency. However, the complex multiscale features in the ocean dynamical system make AI models still face many challenges in mesoscale eddy forecasting (especially regional modelling). Here, we develop LanTu, a regional eddy-resolving ocean forecasting system based on dynamics-enhanced deep learning. We incorporate cross-scale interactions into LanTu and construct multiscale physical constraint for optimising LanTu guided by knowledge of eddy dynamics in order to improve the forecasting skill of LanTu for mesoscale evolution. The results show that LanTu outperforms the existing advanced operational numerical ocean forecasting system (NOFS) and AI-based ocean forecasting system (AI-OFS) in temperature, salinity, sea level anomaly and current prediction, with a lead time of more than 10 days. Our study highlights that dynamics-enhanced deep learning (LanTu) can be a powerful paradigm for eddy-resolving ocean forecasting.