A robust minimization-based framework for cyclogeostrophic ocean surface current retrieval

Vadim Bertrand

doi:10.5194/os-22-241-2026

A robust minimization-based framework for cyclogeostrophic ocean surface current retrieval

2026-01-31

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Victor Vianna Zaia De Almeida

DOI：10.5194/os-22-241-2026

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Abstract. Estimations of surface currents at submesoscales (1–50 km) are crucial for operational applications and environmental monitoring; yet accurately deriving them from satellite observations remains a challenge. While the geostrophic approximation has long been used to infer ocean surface currents from Sea Surface Height (SSH); it neglects nonlinear advection; which can become significant at submesoscales. To address this limitation; we present a robust and efficient minimization-based method for inverting the cyclogeostrophic balance equation; implemented in the open-source Python library jaxparrow. Unlike the traditional fixed-point approach; our method reformulates the inversion as a minimization problem; providing stable estimates even in regions where a cyclogeostrophic solution may not exist. Using a submesoscale-permitting model simulation and both DUACS and the high-resolution NeurOST SSH products; we demonstrate that cyclogeostrophic corrections become increasingly relevant at finer spatial scales. Validation against drifter-derived velocities shows that our approach consistently improves current estimates in energetic regions; reducing errors by up to 20 % compared to geostrophy alone in energetic regions of the global ocean. These results support the systematic inclusion of cyclogeostrophic inversion in the analysis of high-resolution SSH fields.

A robust minimization-based framework for cyclogeostrophic ocean surface current retrieval

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