提出EddyFlow框架，实现海温高精度下放并保持中尺度结构。
Transferable Dual-Stream Representations for Mesoscale-Preserving Sea Surface Temperature Downscaling
- 双流表示学习融合多尺度信息，提升跨区域泛化能力。
- 零样本下RMSE降低21%，未见区域技能达85.6%。
- 适合关注海洋动力学与物理一致性建模的研究者。
科学时空下放的深度学习模型常因仅最小化重建误差而忽略物理上合理的多尺度结构。对于海表温度预测，这会导致数值合理但过于平滑的结果，丢失对区域海洋动力至关重要的中尺度变化。现有方法多依赖像素级目标或单一上下文条件，限制了谱保真度和跨区域泛化能力。为此，我们提出EddyFlow，一种面向千米级海表温度下放的表示学习框架，兼顾预测精度、尺度相关结构与区域泛化性。该模型在圣劳伦斯湾训练，在芬迪湾和墨西哥湾进行零样本与少样本评估。EddyFlow表明，物理引导的表示学习使零样本RMSE降低21%，在未见域相对持久性预测达到最高85.6%的技能得分，并保持近乎理想的谱保真度，功率谱密度比约为1.00。
原文摘要 · Abstract (English)
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 $\approx 1.00$.
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