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ABSTRACT
Automated eddy detection methods are fundamental tools to analyze eddy activity from the large datasets derived from satellite measurements and numerical model simulations. Existing methods are either based on the distribution of physical parameters usually computed from velocity derivatives or on the geometry of velocity streamlines around minima or maxima of sea level anomaly. A new algorithm was developed based exclusively on the geometry of the velocity vectors. Four constraints characterizing the spatial distribution of the velocity vectors around eddy centers were derived from the general features associated with velocity fields in the presence of eddies. The grid points in the domain for which these four constraints are satisfied are detected as eddy centers. Eddy sizes are computed from closed contours of the streamfunction field, and eddy tracks are retrieved by comparing the distribution of eddy centers at successive time steps. The results were validated against manually derived eddy fields. Two parameters in the algorithm can be modified by the users to optimize its performance. The algorithm is applied to both a high-resolution model product and high-frequency radar surface velocity fields in the Southern California Bight.
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1. Introduction
Mesoscale eddies are ubiquitous features in the world's oceans, and they play a major role in ocean circulation as well as in heat and mass transport (e.g., McWilliams 2008). They can have a profound influence on biological productivity, upper ocean ecology and biogeochemistry, and thus in elemental cycling and fluxes (i.e., Falkowski et al. 1991; McGillicuddy et al. 1998; McNeil et al. 1999; Benitez-Nelson et al. 2007). In the past decade, several studies have focused on the statistical characterization of mesoscale eddy activity within specific regions through the analysis of satellite measurements or results from numerical models (i.e., Isern-Fontanet et al. 2003; Morrow et al. 2004; Chelton et al. 2007; Chaigneau et al. 2008; Doglioli et al. 2007).
A sultable definition of an eddy and the implementatlon of an algorithm to automatically identify and track mesoscale and submesoscale features are fundamental to study eddy activity from large datasets. A few methods have been proposed, based either on the physical or geometrical characteristics of the flow field. Methods based on physical characteristics identify eddies using the values of a specified parameter...