Separating Mesoscale and Submesoscale Flows from Clustered Drifter Trajectories

TL;DR

This paper introduces a new method to separate mesoscale and submesoscale ocean flows from clustered drifter trajectories by fitting velocities to a local Taylor expansion, improving flow variability analysis.

Contribution

The paper presents a principled approach to decompose drifter velocities into mesoscale and submesoscale components with statistical uncertainty estimation, enhancing flow analysis accuracy.

Findings

Mesoscale component explains much velocity variability at low frequencies.

Method reduces contamination of submesoscale diffusivity estimates.

Validated through simulations and real drifter data from LatMix.

Abstract

Drifters deployed in close proximity collectively provide a unique observational data set with which to separate mesoscale and submesoscale flows. In this paper we provide a principled approach for doing so by fitting observed velocities to a local Taylor expansion of the velocity flow field. We demonstrate how to estimate mesoscale and submesoscale quantities that evolve slowly over time, as well as their associated statistical uncertainty. We show that in practice the mesoscale component of our model can explain much first and second-moment variability in drifter velocities, especially at low frequencies. This results in much lower and more meaningful measures of submesoscale diffusivity, which would otherwise be contaminated by unresolved mesoscale flow. We quantify these effects theoretically via computing Lagrangian frequency spectra, and demonstrate the usefulness of our methodology through simulations as well as with real observations from the LatMix deployment of drifters. The outcome of this method is a full Lagrangian decomposition of each drifter trajectory into three components that represent the background, mesoscale, and submesoscale flow.