Diffusion-Model-Based Downscaling of Observed Sea Surface Height over the Kuroshio Extension Since 2000

Satellite altimetry measurements enable the resolution of ocean variability from basin-scale to mesoscale. However, the spatial resolution is still limited. The two-dimensional map from the merged data for all the available altimetry satellites can resolve mesoscale eddies down to 150 km in mid-latitudes, for example. We introduce a generative diffusion model to downscale a merged altimetry dataset, which is applied to the eddy-rich Kuroshio Extension region from 2000 to 2022. A reanalysis dataset with a high-resolution model at a horizontal scale of approximately 12 km is employed to train the diffusion model. Using the trained generative diffusion model, the merged dataset at a grid size of 1/4^o is downscaled. It was demonstrated that this trained generative diffusion model outperforms the other two high-resolution reanalyses and neural-network-based datasets. The downscaled data reproduce the spatial patterns and power spectra of satellite along-track measurements. The analysis also indicates that eddy kinetic energy at horizontal scales less than 250 km has intensified by 10.14 cm²/s² (2.07%) per decade since 2004 in the Kuroshio Extension region. Our results underscore the potential of generative diffusion models in downscaling satellite altimetry datasets and improving our understanding of ocean dynamics at mesoscales.