Hector Torres (Jet Propulsion Laboratory , USA); Patrice Klein (Caltech, USA); Matthew Archer (Jet Propulsion Laboratory , USA); Eugenio Cutolo (IMT Atlantique, France); J. Thomas Farrar (Woods Hole Oceanographic Institute, USA); Bo Qiu (University of Hawaii , USA); Lee-Lueng Fu (Jet Propulsion Laboratory , USA)

The Surface Water and Ocean Topography (SWOT) mission’s Ka-Band Radar Interferometer (KaRIn) has surpassed prelaunch expectations by 2–4 times, delivering sea surface height (SSH) observations that resolve small-scale oceanic structures down to spatial scales below 10 km. Among these are submesoscale cyclones, with diameters as small as 5 km. This presentation begins with a case study of submesoscale cyclones in the California Current System, analyzing their horizontal and vertical structures using SWOT KaRIn SSH, high-resolution sea surface temperature (SST) from the VIIRS sensor, in-situ mooring data, and synthetic aperture radar (SAR) imagery. We then explore machine learning approaches for global detection and analysis of these submesoscale structures, leveraging SWOT’s high-resolution 2D data. Finally, we discuss challenges and opportunities in using SWOT for studies of sub-10km processes.