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Alex P Encinas-Bartos1, Nikolas O Aksamit1, George Haller1

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Summary
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We introduce a new tool, trajectory rotation average (TRA), to visualize ocean eddies using sparse drifter data. This method effectively identifies eddy boundaries, even at scales too small for satellite observation.

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Area of Science:

  • Oceanography
  • Fluid Dynamics
  • Data Analysis

Background:

  • Oceanic vortices (eddies) play a crucial role in ocean dynamics and heat transport.
  • Observing and characterizing these eddies is challenging, especially with sparse observational data.
  • Existing methods for eddy detection from drifter data have limitations in resolution and accuracy.

Purpose of the Study:

  • To introduce and validate a novel Lagrangian diagnostic tool, the trajectory rotation average (TRA), for visualizing oceanic vortices from sparse drifter data.
  • To develop a general algorithm for extracting approximate eddy boundaries based on the TRA.
  • To compare the performance of TRA against existing single-trajectory-based eddy detection methods.

Main Methods:

  • Application of the trajectory rotation average (TRA) tool to two distinct drifter datasets: the Grand Lagrangian Deployment and the Global Drifter Program.
  • Development of a general algorithm to identify approximate eddy boundaries using TRA.
  • Comparative analysis of TRA with other single-trajectory eddy detection techniques.

Main Results:

  • The TRA tool successfully visualizes oceanic vortices from sparse drifter data.
  • The developed algorithm effectively extracts approximate eddy boundaries.
  • TRA demonstrates superior performance compared to existing methods for sparse drifter data.
  • TRA identifies eddies at scales not resolvable by satellite altimetry.

Conclusions:

  • The trajectory rotation average (TRA) is a powerful and effective tool for analyzing oceanic vortices from sparse drifter data.
  • The TRA-based algorithm provides a robust method for eddy boundary detection.
  • This approach enhances our ability to study ocean dynamics at finer scales than previously possible with remote sensing.