Deep Learning Methods for Inference of Sea Surface Kinematics from SWOT Altimetry

Observations of sea surface height (SSH) provided by the Surface Water and Ocean Topography (SWOT) satellite capture features at length scales smaller than the scales on which geostrophic balance is a valid approximation. At these smaller length scales (less than order 100 km) the SSH signature of submesoscale balanced motions are comparable with the signatures of internal gravity waves (IGW). In addition to these non-dynamical features in the SWOT SSH observations, measurements from the SWOT satellite contain noise and errors from a variety of sources. These issues complicate the usual approximation of surface velocity using geostrophic balance as instrument noise and non-dynamical features need to be filtered out. To address these challenges, a recent study [1] has demon-strated the effectiveness of a machine learning approach to infer vorticity from SSH using model data on structured grids. There remain a number of actions needed to extend this method to SWOT measurements. The dimensions and structure of the SWOT observation data differs significantly from the regularly structured model data used in [1]. We have created tools that transform SWOT observations into data structures that are compatible with the existing UNet developed in [1], which was not designed to handle SWOT observations. We revisit the model selection and model architecture of this UNet. We have created methods for handling gaps in SWOT observations including the nadir gap, missing/bad data, and the shape of the swath. These tools also provide some interpolation techniques so that a region of interest larger than the swath may be populated with observations from multiple orbital passes. We present results summarizing the effectiveness of these tools, and discuss the next steps in scaling out these workflows to support global SWOT observations, including validation and fine-tuning.