Abstract

Abstract. The Genealogical Evolution Model (GEM) presented here is an efficient logical model used to track dynamic evolution of mesoscale eddies in the ocean. It can distinguish between different dynamic processes (e.g., merging and splitting) within a dynamic evolution pattern, which is difficult to accomplish using other tracking methods. To this end, the GEM first uses a two-dimensional (2-D) similarity vector (i.e., a pair of ratios of overlap area between two eddies to the area of each eddy) rather than a scalar to measure the similarity between eddies, which effectively solves the “missing eddy” problem (temporarily lost eddy in tracking). Second, for tracking when an eddy splits, the GEM uses both “parent” (the original eddy) and “child” (eddy split from parent) and the dynamic processes are described as the birth and death of different generations. Additionally, a new look-ahead approach with selection rules effectively simplifies computation and recording. All of the computational steps are linear and do not include iteration. Given the pixel number of the target region L, the maximum number of eddies M, the number N of look-ahead time steps, and the total number of time steps T, the total computer time is O(LM(N + 1)T). The tracking of each eddy is very smooth because we require that the snapshots of each eddy on adjacent days overlap one another. Although eddy splitting or merging is ubiquitous in the ocean, they have different geographic distributions in the North Pacific Ocean. Both the merging and splitting rates of the eddies are high, especially at the western boundary, in currents and in “eddy deserts”. The GEM is useful not only for satellite-based observational data, but also for numerical simulation outputs. It is potentially useful for studying dynamic processes in other related fields, e.g., the dynamics of cyclones in meteorology.

Highlights

  • Eddies are ubiquitous in the ocean, and they move from one place to another (Chelton and Schlax, 1996; Chelton et al, 2007)

  • The eddy centers (SLA extrema of eddy snapshots) on each day are counted on each 1◦ × 1◦ grid

  • We find that the look-ahead time N should be larger than 0; otherwise, the risks of eddy jumping and eddy missing are too great

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Summary

Introduction

Eddies are ubiquitous in the ocean, and they move from one place to another (Chelton and Schlax, 1996; Chelton et al, 2007). Eddies in the ocean can cause large-scale transports of heat, salt, and other tracers (Bennett and White, 1986; Chelton et al, 2011a; Dong et al, 2014; McGillicuddy et al, 2011) by trapping these passive tracers inside the eddies. Such transports may have important impacts on the environment and climate of the ocean (Dong et al, 2014). Several automated identification and tracking algorithms have been developed for eddies in the ocean (Chelton et al, 2011b; Ienna et al, 2014; Mason et al, 2014; Yi et al, 2015)

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