Quasi-Objective Eddy Visualization from Sparse Drifter Data

TL;DR

This paper introduces a novel Lagrangian diagnostic tool, the trajectory rotation average, to visualize and detect oceanic eddies from sparse drifter data, outperforming existing methods and revealing smaller-scale features.

Contribution

The paper develops a new eddy detection algorithm based on the trajectory rotation average, improving identification accuracy on sparse data and capturing smaller-scale oceanic vortices.

Findings

The trajectory rotation average outperforms other methods in eddy detection.

The method identifies eddies on scales unresolved by satellite data.

The approach is validated on two large drifter datasets.

Abstract

We employ a recently developed single-trajectory Lagrangian diagnostic tool, the trajectory rotation average $ (\mathrm{\overline{TRA}}) $, to visualize oceanic vortices (or eddies) from sparse drifter data. We apply the $ \mathrm{\overline{TRA}} $ to two drifter data sets that cover various oceanographic scales: the Grand Lagrangian Deployment (GLAD) and the Global Drifter Program (GDP). Based on the $ \mathrm{\overline{TRA}} $, we develop a general algorithm that extracts approximate eddy boundaries. We find that the $ \mathrm{\overline{TRA}} $ outperforms other available single-trajectory-based eddy detection methodologies on sparse drifter data and identifies eddies on scales that are unresolved by satellite-altimetry.