Isolating Balanced Ocean Dynamics in SWOT Data

TL;DR

This paper introduces a statistical Bayesian method to isolate balanced ocean surface signals from SWOT satellite data, effectively removing noise while preserving mesoscale features in specific ocean regions.

Contribution

A novel Bayesian inversion approach that separates balanced ocean signals from noise in SWOT data, validated with synthetic high-resolution simulation data.

Findings

Successfully removes small-scale noise from synthetic SWOT-like data.

Preserves mesoscale and submesoscale features down to 10 km.

Posterior uncertainty reliably estimates reconstruction error.

Abstract

The Surface Water and Ocean Topography (SWOT) mission provides two-dimensional sea surface height (SSH) maps at unprecedented resolution, but its signal is a combination of balanced meso- and submesoscale turbulence, unbalanced internal waves, and small-scale noise. Interpreting the meso- and submesoscale flow features captured by SWOT requires a careful isolation of the balanced signal. We present a statistical method to do so in regions where internal-wave signals are negligible, such as western boundary current regions and the Southern Ocean. Our method assumes Gaussian statistics for both the balanced flow and the noise, which we infer by fitting parametric models to the observed SSH wavenumber spectrum. Using these inferred parameters, we perform a Bayesian inversion to reconstruct swath-aligned SSH maps that fill the nadir gap. We evaluate the method using synthetic data from a high-resolution simulation with realistic SWOT-like noise added. Comparisons with the underlying model data show that our reconstruction successfully removes small-scale noise while preserving meso- and submesoscale eddies, fronts, and filaments down to a feature scale of 10km. The comparison also demonstrates that the posterior uncertainty is a reliable estimate of the error.