Topological Feature Tracking for Submesoscale Eddies
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 identifies critical points on a set of time‐ordered scalar fields and associates those points between consecutive timesteps. The procedure yields tracklets which represent spatio‐temporal displacement of eddies. In this way we study the time‐dependent behavior of submesoscale eddies, which are generated by a 1‐km resolution submesoscale‐permitting model. We summarize the submesoscale eddy data set produced by TFT, which yields unique, time‐varying statistics.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Oct 13, 2022
- Source ID
- 10.1029/2022gl099416
Entities
People
- Gary Koplik
- Gregg A. Jacobs
- James B. Polly
- Jay Hineman
- Joseph M. D’Addezio
- Ken Ball
- Paul Bendich
- Sam Voisin
- Tamay M. Özgökmen
Organizations
- Defense Advanced Research Projects Agency
- Duke University
- Office of Naval Research
- United States Naval Research Laboratory
- University of Miami