AI 中文总结
研究针对现有波浪模型难以处理非结构化网格与长时程的问题，提出基于GNN的WaveGraph模型，实现地中海17年稳定的年代际波浪重建，效果与观测数据相当。
AI 中文摘要
海浪的准确模拟与预测对海岸风险管理和气候研究至关重要。深度学习在波浪建模中已展现出良好效果，但多数方法仍基于规则网格运行且聚焦于短期预报，无法推广到非结构化离散化或长时程场景。本文提出WaveGraph，一种基于图神经网络（GNN）的模型，可直接在非结构化网格上模拟 basin 尺度的波浪动力学，海岸区域分辨率高达2-3公里。该模型在地中海经过偏差校正的模拟数据上训练，采用多尺度架构，将非结构化模型网格与均匀图结合，可同时表征局部海岸相互作用与大尺度波浪动力学。WaveGraph 能有效重建有效波高、平均周期和平均方向的演变，以自回归方式连续应用17年无需重新初始化或出现漂移。通过与浮标及卫星观测数据的验证，其技能水平与输入数据集相当；消融实验表明，风强迫是长期稳定性的主要驱动因素，而波浪历史则改善涌浪驱动及 basin 尺度的动力学过程。这些结果证实，GNN 可在非结构化域上提供稳定高效的谱波浪模型模拟器，实现年代际波浪重建。
英文摘要
Accurate simulation and prediction of ocean waves are essential for coastal risk management and climate studies. Deep learning has shown promising results for wave modeling, but most approaches still operate on regular grids and on forecasting time scales, and do not generalize to unstructured discretization or to long time horizons. Here we present WaveGraph, a model based on Graph Neural Networks (GNNs) that emulates basin-scale wave dynamics directly on unstructured meshes with high resolution along the coasts (up to 2-3 km). Trained on bias-corrected simulation data over the Mediterranean Sea, WaveGraph uses a multiscale architecture combining the unstructured model mesh with a uniform graph, allowing simultaneous representation of local coastal interactions and large-scale wave dynamics. The model reconstructs the evolution of significant wave height, mean period, and mean direction, and is applied autoregressively for a continuous 17-year period without reinitialization or drift. Validation against buoy and satellite observations shows skill comparable to the input data set, and ablation experiments indicate that wind forcing drives most of the long-term stability while wave history improves swell-driven and basin-scale dynamics. These results show that GNNs can provide stable and efficient emulators of spectral wave models on unstructured domains, enabling decadal wave reconstructions.
Comments37 pages, 11+4+2 figures in main+appendix+supplemental material. This work has been submitted to Artificial Intelligence for the Earth Systems
