Detection, quantification, and tracking of vortices using streamline geometry

Sadarjoen, I. Ari; Post, Frits H.

doi:10.1016/s0097-8493(00)00029-7

Cited by 200 publications

(141 citation statements)

References 3 publications

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“…Several automated eddy detection schemes are found in the literature (Jeong and Hussian 1995;Portela 1997;Sadarjoen et al 1998;Sadarjoen and Post 2000;Isern-Fontanet, García-Ladona, and Font 2006;Chaigneau, Gizolme, and Grados 2008;Nencioli et al 2010;Chelton et al 2007;Chelton, Schlax, and Samelson 2011;Kurian et al 2011;Faghmous et al 2015). However, studies have shown that when these algorithms are applied over the same data set, they provide significant statistical differences (Nencioli et al 2010;Souza, Montegut, and Traon 2011).…”

Section: Eddy Detection Methodsmentioning

confidence: 99%

Monitoring the mesoscale eddies of the Lofoten Basin: importance, progress, and challenges

Raj¹,

Halo

2016

International Journal of Remote Sensing

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The Lofoten Basin is a 'hot spot' of intense mesoscale eddy activity in the Nordic Seas. The mesoscale eddies of the Lofoten Basin can be coupled to the heat transport, local climate, and fisheries of the region. During the past two decades, the European satellite missions European Remote Sensing (ERS) satellite and Environmental Satellite (ENVISAT) have played a major part in delivering continuous altimeter measurements over the Lofoten Basin. In recent years, automated eddy detection and tracking methods have revolutionized the long-term monitoring of eddies in the Lofoten Basin from the gridded altimeter data.

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Section: Eddy Detection Methodsmentioning

confidence: 99%

Monitoring the mesoscale eddies of the Lofoten Basin: importance, progress, and challenges

Raj¹,

Halo

2016

International Journal of Remote Sensing

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“…Despite the implementation of this cutoff, OW has been proved to be rather affected by noise generated in W-fields that increases falsely detected eddies (Souza et al 2011a;D'Ovidio et al 2009). To overcome this issue, some novel approaches based on flow geometry characteristics or additional physical constraints were developed (e.g., Ari Sadarjoen and Post 2000;Turiel et al 2007;Chaigneau et al 2008;Nencioli et al 2010;Ubelmann and Fu 2011;Beron-Vera et al 2013;Halo et al 2014). These methods include additional empirical parameters to identify the correct structures, complicating the eddy identification procedure.…”

Section: Introductionmentioning

confidence: 99%

Benchmarking the mesoscale variability in global ocean eddy-permitting numerical systems

et al. 2017

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The role of data assimilation procedures on representing ocean mesoscale variability is assessed by applying eddy statistics to a state-of-the-art global ocean reanalysis (C-GLORS), a free global ocean simulation (performed with the NEMO system) and an observation-based dataset (ARMOR3D) used as an independent benchmark. Numerical results are computed on a 1/4 • horizontal grid (ORCA025) and share the same resolution with ARMOR3D dataset. This "eddy-permitting" resolution is sufficient to allow ocean eddies to form. Further to assessing the eddy statistics from three different datasets, a global threedimensional eddy detection system is implemented in order to bypass the need of regional-dependent definition of thresholds, typical of commonly adopted eddy detection algorithms. It thus provides full three-dimensional eddy Istituto Nazionale di Geofisica e Vulcanologia (INGV), Bologna, Italy statistics segmenting vertical profiles from local rotational velocities. This criterion is crucial for discerning real eddies from transient surface noise that inevitably affects any twodimensional algorithm. Data assimilation enhances and corrects mesoscale variability on a wide range of features that cannot be well reproduced otherwise. The free simulation fairly reproduces eddies emerging from western boundary currents and deep baroclinic instabilities, while underestimates shallower vortexes that populate the full basin. The ocean reanalysis recovers most of the missing turbulence, shown by satellite products , that is not generated by the model itself and consistently projects surface variability deep into the water column. The comparison with the statistically reconstructed vertical profiles from ARMOR3D show that ocean data assimilation is able to embed variability into the model dynamics, constraining eddies with in situ and altimetry observation and generating them consistently with local environment.

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“…Recent studies (Williams et al, 2011;Petersen et al, 2013) have developed a new R 2 algorithm to eliminate the dependency on threshold choosing, which judges the quality of an eddy based on the similarity of certain functional fits with an idealized Gaussian vortex. Second, the derivation of this physical parameter may bring extra noise that increases falsely detected eddies (Ari Sadarjoen and Post, 2000;Chaigneau et al, 2008;Chelton et al, 2011;Nencioli et al, 2010). Third, the physical criteria may fail to locate the eddies or underestimate the dimensions (Basdevant and Philipovitch, 1994;Doglioli et al, 2007;Henson and Thomas, 2008;Isern-Fontanet et al, 2003a).…”

Section: Introductionmentioning

confidence: 99%

Enhancing the accuracy of automatic eddy detection and the capability of recognizing the multi-core structures from maps of sea level anomaly

et al. 2014

Ocean Sci.

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Abstract. Automated methods are important for automatically detecting mesoscale eddies in large volumes of altimeter data. While many algorithms have been proposed in the past, this paper presents a new method, called hybrid detection (HD), to enhance the eddy detection accuracy and the capability of recognizing eddy multi-core structures from maps of sea level anomaly (SLA). The HD method has integrated the criteria of the Okubo-Weiss (OW) method and the sea surface height-based (SSH-based) method, two commonly used eddy detection algorithms. Evaluation of the detection accuracy shows that the successful detection rate of HD is ∼ 96.6 % and the excessive detection rate is ∼ 14.2 %, which outperforms the OW and those methods using SLA extrema to identify eddies. The capability of recognizing multi-core structures and its significance in tracking eddy splitting or merging events have been illustrated by comparing with the detection results of different algorithms and observations in previous literature.

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Detection, quantification, and tracking of vortices using streamline geometry

Cited by 200 publications

References 3 publications

Monitoring the mesoscale eddies of the Lofoten Basin: importance, progress, and challenges

Monitoring the mesoscale eddies of the Lofoten Basin: importance, progress, and challenges

Benchmarking the mesoscale variability in global ocean eddy-permitting numerical systems

Enhancing the accuracy of automatic eddy detection and the capability of recognizing the multi-core structures from maps of sea level anomaly

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