发表机构
Dalhousie University（达尔豪斯大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对海面温度降尺度中易丢失中尺度特征的问题，提出EddyFlow框架，在圣劳伦斯湾训练后，于芬迪湾、墨西哥湾的零样本/少样本评估中，实现误差降低、技能得分提升与高光谱保真度。
AI 中文摘要
面向科学时空降尺度的深度学习模型常以最小化重构误差为目标，却难以保留具有物理意义的多尺度结构。对于海面温度预测，这会生成数值看似合理但过于平滑的输出，缺失对区域海洋动力学至关重要的中尺度变异性。现有方法多关注像素级目标或单一上下文条件，限制了其保留光谱保真度及跨区域泛化的能力。为解决该问题，我们提出EddyFlow，一种用于千米级海面温度降尺度的表示学习框架，可平衡预测精度、尺度依赖结构与区域泛化能力。EddyFlow在圣劳伦斯湾接受训练，并在芬迪湾和墨西哥湾的零样本与少样本设置中进行评估。结果显示，基于物理知识的表示学习使EddyFlow的零样本均方根误差降低21%，在未见过的域上相对于持续预测达到最高85.6%的技能得分，且保持接近理想的光谱保真度，功率谱密度比约为1.00。
英文摘要
Deep learning models for scientific spatio-temporal downscaling often minimize reconstruction error while failing to preserve physically meaningful multi-scale structure. For sea surface temperature prediction, this can yield outputs that are numerically plausible yet overly smooth, missing mesoscale variability critical to regional ocean dynamics. Existing methods often focus on pixel-wise objectives or single-context conditioning, which limits their ability to preserve spectral fidelity and generalize across regions. To address this, we propose EddyFlow, a representation learning framework for kilometer-scale sea surface temperature downscaling that balances predictive accuracy, scale-dependent structure, and regional generalization. EddyFlow is trained on the Gulf of St.~Lawrence and evaluated in zero-shot and few-shot settings on the Bay of Fundy and the Gulf of Mexico. EddyFlow demonstrates that physics-informed representation learning reduces zero-shot RMSE by 21%, achieves up to 85.6% skill relative to persistence on unseen domains, and maintains near-ideal spectral fidelity with a PSD ratio of $\approx 1.00$.
