基于图深度学习的地中海精确海洋预报
Accurate Mediterranean Sea forecasting via graph-based deep learning
AI总结：
提出基于图的神经网络SeaCast用于高分辨率区域海洋预报，整合外部强迫数据，实验表明其在地中海预报技能上优于传统运营模型。
AI中文摘要：
精确的海洋预报系统对于理解海洋动力学至关重要，海洋动力学在航运、水产养殖、环境监测和沿海风险管理等领域发挥着关键作用。传统的数值求解器虽然有效，但计算成本高昂且耗时。机器学习的最新进展彻底改变了天气预报，提供了快速且节能的替代方案。基于这些进展，我们引入了SeaCast，一种专为高分辨率区域海洋预报设计的神经网络。SeaCast采用基于图的框架来有效处理海洋网格的复杂几何结构，并整合了为区域海洋环境量身定制的外部强迫数据。我们的方法通过使用地中海的运营数值预报系统，结合数值和大气强迫，在高水平分辨率下进行了实验验证。结果表明，SeaCast在预报技能上始终优于运营模型，标志着区域海洋预测的重大进步。
英文摘要：
Accurate ocean forecasting systems are essential for understanding marine dynamics, which play a crucial role in sectors such as shipping, aquaculture, environmental monitoring, and coastal risk management. Traditional numerical solvers, while effective, are computationally expensive and time-consuming. Recent advancements in machine learning have revolutionized weather forecasting, offering fast and energy-efficient alternatives. Building on these advancements, we introduce SeaCast, a neural network designed for high-resolution regional ocean forecasting. SeaCast employs a graph-based framework to effectively handle the complex geometry of ocean grids and integrates external forcing data tailored to the regional ocean context. Our approach is validated through experiments at a high horizontal resolution using the operational numerical forecasting system of the Mediterranean Sea, along with both numerical and data-driven atmospheric forcings. Results demonstrate that SeaCast consistently outperforms the operational model in forecast skill, marking a significant advancement in regional ocean prediction.
