Satellite altimetry has been widely utilized to monitor global sea surface dynamics, enabling investigation of upper ocean variability from basin-scale to localized eddy ranges. However, the sparse spatial resolution of observational altimetry limits our understanding of oceanic submesoscale variability, prevalent at horizontal scales below 0.25o resolution. Here, we introduce a state-of-the-art generative diffusion model to train high-resolution sea surface height (SSH) reanalysis data and demonstrate its advantage in observational SSH downscaling over the eddy-rich Kuroshio Extension region. The diffusion-based model effectively downscales raw satellite-interpolated data from 0.25o resolution to 1/16o, corresponding to approximately 12-km wavelength. This model outperforms other high-resolution reanalysis datasets and neural network-based methods. Also, it successfully reproduces the spatial patterns and power spectra of satellite along-track observations. Our diffusion-based results indicate that eddy kinetic energy at horizontal scales less than 250 km has intensified significantly since 2004 in the Kuroshio Extension region. These findings underscore the great potential of deep learning in reconstructing satellite altimetry and enhancing our understanding of ocean dynamics at eddy scales.

本研究针对卫星测高数据在解析海洋亚微观变动方面的局限性，提出了一种先进的生成扩散模型，通过将原始卫星插值数据从0.25度分辨率下分辨率至1/16度（约12公里波长）来增强观测的海面高度（SSH）重分析数据。研究结果表明，自2004年以来黑潮延伸区的涡旋动能在250公里以下的水平尺度上显著增强，强调了深度学习在重构卫星测高数据及提升对海洋动态理解方面的巨大潜力。

基于生成扩散模型的2000年以来黑潮延伸区海面高度下分辨率处理