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\begin{document}

\title{Topological Feature Tracking for Submesoscale Eddies}



\author[1]{Sam Voisin}%
\author[1]{Jay Hineman}%
\author[2]{James Bruce Polly}%
\author[1]{Gary Koplik}%
\author[1]{Ken Ball}%
\author[1]{Paul Bendich}%
\author[3]{Joseph D`Addezio}%
\author[4]{Gregg A Jacobs}%
\author[5]{Tamay M. M. Ozgokmen}%
\affil[1]{Geometric Data Analytics}%
\affil[2]{Geometric Data Analytics, Inc.}%
\affil[3]{Naval Research Laboratory Ocean Dynamics \& Prediction Branch}%
\affil[4]{Naval Research Laboratory}%
\affil[5]{University of Miami}%


\vspace{-1em}



  
  \date{June 14, 2024}


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\begin{abstract}
Current state-of-the art procedures for studying modeled submesoscale
oceanographic features have made a strong assumption of independence
between features identified at different times. Therefore, all
submesoscale eddies identified in a time series were studied in
aggregate. Statistics from these methods are illuminating but oversample
identified features and cannot determine the lifetime evolution of the
transient submesoscale processes. To this end, the authors apply the
Topological Feature Tracking (TFT) algorithm to the problem of
identifying and tracking submesoscale eddies over time. TFT allows a
user to identify submesoscale eddies as critical points on a set of
time-ordered scalar fields and associate the points between consecutive
timesteps. The procedure yields tracklets which represent
spatio-temporal displacement of eddies. Thus the time-dependent behavior
of submesoscale eddies can be studied. We analyze the submesoscale eddy
dataset produced by TFT, which yields unique, time-varying statistics on
this currently underexplored phenomenon.%
\end{abstract}%



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