A Parallel SLA-Based Algorithm for Global Mesoscale Eddy Identification

Abstract

This paper proposes a new algorithm for parallel identification of mesoscale eddies from global satellite altimetry data. By simplifying the recognition process and the sea level anomaly (SLA) contours’ search range, the method improves identification efficiency compared with the previous SSH-based method even in the single-threaded process. The global SLA map is divided into several regions. These regions are identified simultaneously with a new SSH-based method. All the eddy identification results of these regions are merged seamlessly into a global eddy map. A β-plane approximation is used to calculate the geostrophic speed in the equatorial band. Compared with the computation complexity of the previous SSH-based method, which is , the computation complexity of the new method is , where K is the number of threads and L is the number of regional SLA maps. When applying the new method to the global SLA map, the computation is ~100 times faster than the previous SSH-based method on an average computer. The new method characterizes an eddy structure by radius, amplitude, eddy core, closed SLA contour, and closed SLA contour with maximum average geostrophic speed. In situ data and another global eddy dataset are applied to validate the reliability of eddies detected by the new algorithm. Global eddy mean properties, variability, and the geographical distribution of both datasets are analyzed to demonstrate the performance of this new method and to help understand eddy activities on a global scale.

Characterization of Sea Surface Temperature and Air‐Sea Heat Flux Anomalies Associated With Mesoscale Eddies in the South China Sea

“…These methods can be categorized into the three types: the physical parameter‐based methods that utilize the physical characteristics (i.e., vorticity) of the flow field (Chaigneau et al, 2008; Doglioli et al, 2007; Isern‐Fontanet et al, 2003), the flow geometry‐based methods that identify eddies based on the shape or curvature of the streamlines (Nencioli et al, 2010), and the SSH‐based methods (Chelton et al, 2011; Faghmous et al, 2015) that identify eddies by finding closed contours of SSH encircling a set of grid points. This study used the SSH‐based approach and implemented a parallel computation to improve the computational processing speed and efficiency (Liu et al, 2016).…”

“…Once selected, the SSHA contours were computed from minimum (maximum) SSHA values to maximum (minimum) SSHA values at 0.25‐cm intervals. The following five criteria were applied to determine whether an eddy could be established (Liu et al, 2016): (1) The SSHA contours contain no more than one local maxima/minima. (2) The SSHA contours are located in areas where water depths are greater than 200 m to minimize the impacts of data errors near the coastal shallow water region (Cheng & Qi, 2010).…”

An Improved Automatic Algorithm for Global Eddy Tracking Using Satellite Altimeter Data

“…The method for detecting mesoscale eddies adopted here was from Liu et al [36] and similar to that used in Mason et al [21]. The detection procedures are as follows.…”

“…SLA contours are computed and searched in 0.25 cm intervals for levels from the minimum/maximum SLA value of all the possible cyclonic/anticyclonic cores. Four criteria are assigned to test each "searching" closed contour [36]. First, they must contain only one eddy core.…”

Contrasting Short‐Lived With Long‐Lived Mesoscale Eddies in the Global Ocean

“…But a major breakthrough is that it allows for the first time an oceanic eddy to be individually tracked during its lifetime. Taking advantage of such an unprecedented data product, a number of automated procedures have been developed for identifying and tracking mesoscale eddies globally and/or regionally (e.g., Chelton et al, 2011;Isern-Fontanet et al, 2003;Liu et al, 2016;Mason et al, 2014;Penven et al, 2005;Sun et al, 2017).…”