用机器学习分解海表高数据中的平衡与非平衡运动，提升多尺度精度。
Multi-scale decomposition of sea surface height snapshots using machine learning
- 引入ZCA白化与数据增强，解决多尺度分解难题
- 在低数据量下仍保持跨尺度稳定性，优于传统深度学习方法
- 适合海洋环流研究、气候预测及SWOT卫星数据处理人员
海洋环流知识对理解天气气候和管理蓝色经济至关重要，可通过海表高（SSH）观测估算。但需将SSH分解为平衡运动（BMs）与非平衡运动（UBMs）的贡献。这一任务对新型SWOT卫星尤为重要，因其提供了前所未有的空间分辨率。本研究目标是将瞬时SSH分解为BMs与UBMs。尽管已有深度学习方法将其视为图像到图像的翻译任务并取得进展，但在不同空间尺度上表现不佳，且依赖大量训练数据，而该领域数据稀缺。此类挑战普遍存在于需要多尺度保真的问题中。本文表明，通过使用零相位成分分析（ZCA）白化与数据增强，可有效克服上述局限，使该方法在跨尺度分解中具备可行性。
原文摘要 · Abstract (English)
Knowledge of ocean circulation is important for understanding and predicting weather and climate, and managing the blue economy. This circulation can be estimated through Sea Surface Height (SSH) observations, but requires decomposing the SSH into contributions from balanced and unbalanced motions (BMs and UBMs). This decomposition is particularly pertinent for the novel SWOT satellite, which measures SSH at an unprecedented spatial resolution. Specifically, the requirement, and the goal of this work, is to decompose instantaneous SSH into BMs and UBMs. While a few studies using deep learning (DL) approaches have shown promise in framing this decomposition as an image-to-image translation task, these models struggle to work well across a wide range of spatial scales and require extensive training data, which is scarce in this domain. These challenges are not unique to our task, and pervade many problems requiring multi-scale fidelity. We show that these challenges can be addressed by using zero-phase component analysis (ZCA) whitening and data augmentation; making this a viable option for SSH decomposition across scales.
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