发表机构
College of Computer Science and Technology, National University of Defense Technology（国防科技大学计算机学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
OceanLight框架结合几何自适应非结构化网格分词与GNN主干，在提升海洋预报精度的同时降低计算成本，为可扩展数据驱动海洋学建立了可推广范式。
AI 中文摘要
可靠的全球海洋预报对气候监测、海上导航和极端事件预警至关重要。基于物理的海洋预报模型计算成本极高，而现有深度学习方法主要依赖结构化网格架构，会在被陆地覆盖的单元上产生不必要的计算，且无论局部流动复杂性如何，都在动态异质的海洋区域采用统一分辨率。本文提出OceanLight，一种高效的全球海洋预报框架，创新性地将几何自适应非结构化网格分词与图神经网络（GNN）主干结合。OceanLight的逐点预报精度和动能谱保真度均超过业务数值分析和最先进的基于AI的模型，且在地转平衡一致性上优于所有基于AI的海洋模型。此外，OceanLight展现出可靠的中尺度涡旋表示能力，能捕捉超越逐点统计优化的连贯海洋结构。与结构化网格基线相比，其GPU内存消耗降低62%，浮点运算量（FLOPs）降低70%。本文的非结构化网格表示为可扩展的数据驱动海洋学建立了可推广的范式。
英文摘要
Reliable global ocean forecasting is critical for climate monitoring, marine navigation, and extreme event early warning. Physics-based ocean forecasting models impose prohibitive computational costs, while existing deep learning approaches predominantly rely on structured-grid architectures, incurring unnecessary computation on masked land cells and enforcing uniform resolution across dynamically heterogeneous ocean regions regardless of local flow complexity. Here we present OceanLight, an efficient global ocean forecasting framework innovatively combining geometry-adaptive unstructured mesh tokenization with a graph neural network (GNN) backbone. OceanLight achieves pointwise forecast accuracy and kinetic energy spectral fidelity exceeding both operational numerical analyses and state-of-the-art AI-based models, while surpassing all AI-based ocean models in geostrophic balance consistency. Furthermore, OceanLight demonstrates reliable mesoscale eddy representation, capturing coherent ocean structures beyond pointwise statistical optimization. These capabilities are delivered with a 62% reduction in GPU memory consumption and 70\% reduction in FLOPs relative to structured-grid baselines. Our unstructured mesh representation establishes a generalizable paradigm for scalable data-driven oceanography.
Comments35 pages, 21 figures
