Isolating Balanced Ocean Dynamics in SWOT Data

Abstract

The Surface Water and Ocean Topography (SWOT) mission provides two-dimensional sea surface height anomaly (SSHA) maps at unprecedented resolution, but its signal is a combination of balanced meso- and submesoscale turbulence, unbalanced motions such as internal waves, and small-scale noise. Interpreting the meso- and submesoscale flow features captured by SWOT requires careful isolation of the balanced signal. We present a statistical method to do so in regions where internal tides make a negligible contribution to the SSHA signal, such as western boundary current regions and the Southern Ocean. Our method assumes Gaussian statistics as a Bayesian prior for both the balanced flow and the remainder of the signal, and we infer their respective covariance functions by fitting parametric models to the observed SSHA wavenumber spectrum. Using these inferred covariance functions, we perform a Bayesian inversion to produce swath-aligned SSHA maps that isolate the balanced signal and fill the nadir gap. We evaluate the method using synthetic data from a high-resolution simulation with realistic SWOT-like small-scale noise added. Comparisons with the underlying model data show that our inversion successfully removes the unbalanced small-scale signal while preserving meso- and submesoscale eddies, fronts, and filaments down to a spatial feature scale (the inverse angular wavenumber) of about 6 km in the subtropical Northwest Atlantic region used for illustration. This comparison also demonstrates that the posterior uncertainty is a reliable estimate of the error. While our focus is on balanced dynamics, the balanced extraction can be extended to include tides, internal waves, and non-Gaussian priors.

arXiv comment41 pages, 11 figures, submitted to AGU Earth and Space Science