Advancing the theory and applications of Lagrangian Coherent Structures methods for oceanic surface flows

Filippi, Margaux

doi:10.1575/1912/24337

Cited by 2 publications

(2 citation statements)

References 58 publications

(91 reference statements)

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“…In the Martha's Vineyard case study, since the flow south of the Vineyard has a strong tidal component, the resulting coherent clusters are qualitatively similar over the same phase of the tide on different days and even different years, whereas coherent clusters for the opposite tidal phase are qualitatively different. The strong dependence of the cluster geometry on the tidal phase has been also observed in tidally driven flow over an experiment in Scott Reef, Australia [43]. Comparing the model-based clusters south of Martha's Vineyard to the motion of real drifters, for most of the identified clusters, drifters deployed within a cluster stayed within the same cluster for 6 h, which was the time interval chosen for our analysis.…”

Section: Discussionmentioning

confidence: 60%

An Optimized-Parameter Spectral Clustering Approach to Coherent Structure Detection in Geophysical Flows

Filippi

Rypina

Hadjighasem

et al. 2021

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In Lagrangian dynamics, the detection of coherent clusters can help understand the organization of transport by identifying regions with coherent trajectory patterns. Many clustering algorithms, however, rely on user-input parameters, requiring a priori knowledge about the flow and making the outcome subjective. Building on the conventional spectral clustering method of Hadjighasem et al. (2016), a new optimized-parameter spectral clustering approach is developed that automatically identifies optimal parameters within pre-defined ranges. A noise-based metric for quantifying the coherence of the resulting coherent clusters is also introduced. The optimized-parameter spectral clustering is applied to two benchmark analytical flows, the Bickley Jet and the asymmetric Duffing oscillator, and to a realistic, numerically generated oceanic coastal flow. In the latter case, the identified model-based clusters are tested using observed trajectories of real drifters. In all examples, our approach succeeded in performing the partition of the domain into coherent clusters with minimal inter-cluster similarity and maximum intra-cluster similarity. For the coastal flow, the resulting coherent clusters are qualitatively similar over the same phase of the tide on different days and even different years, whereas coherent clusters for the opposite tidal phase are qualitatively different.

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Section: Discussionmentioning

confidence: 60%

An Optimized-Parameter Spectral Clustering Approach to Coherent Structure Detection in Geophysical Flows

Filippi

Rypina

Hadjighasem

et al. 2021

Fluids

Self Cite

View full text Add to dashboard Cite

show abstract

“…In The Martha's Vineyard case study, since the flow south of the Vineyard has a strong tidal component, the resulting coherent clusters are qualitatively similar over the same phase of the tide on different days and even different years, whereas coherent clusters for the opposite tidal phase are qualitatively different. The strong dependence of the cluster geometry on the tidal phase has been also observed in tidally-driven flow over an experiment in Scott Reef, Australia [18]. Comparing the model-based clusters south of Martha's Vineyard to the motion of real drifters, for most of the identified clusters, drifters deployed within a cluster stayed within the same cluster for 6 hours, which was the time interval chosen for our analysis.…”

Section: Discussionmentioning

confidence: 62%

A Parameter-Free Spectral Clustering Approach to Coherent Structure Detection in Geophysical Flows

Filippi¹,

Rypina²,

Hadjighasem³

et al. 2020

Preprint

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In Lagrangian dynamics, the detection of coherent clusters can help understand the organization of transport by identifying regions with coherent trajectory patterns. Many clustering algorithms, however, rely on user-input parameters, requiring a priori knowledge about the flow and making the outcome subjective. Building on the conventional spectral clustering method of Hadjighasem et al (2016), a new parameter-free spectral clustering approach is developed that automatically identifies parameters and does not require any user-input choices. A noise-based metric for quantifying the coherence of the resulting coherent clusters is also introduced. The parameter-free spectral clustering is applied to two benchmark analytical flows, the Bickley Jet and the asymmetric Duffing oscillator, and to a realistic, numerically-generated oceanic coastal flow. In the latter case, the identified model-based clusters are tested using observed trajectories of real drifters. In all examples, our approach succeeded in performing the partition of the domain into coherent clusters with minimal inter-cluster similarity and maximum intra-cluster similarity. For the coastal flow, the resulting coherent clusters are qualitatively similar over the same phase of the tide on different days and even different years, whereas coherent clusters for the opposite tidal phase are qualitatively different.

show abstract

Advancing the theory and applications of Lagrangian Coherent Structures methods for oceanic surface flows

Abstract: Advancing the theory and applications of Lagrangian Coherent Structures methods for oceanic surface flows

Cited by 2 publications

References 58 publications

An Optimized-Parameter Spectral Clustering Approach to Coherent Structure Detection in Geophysical Flows

An Optimized-Parameter Spectral Clustering Approach to Coherent Structure Detection in Geophysical Flows

A Parameter-Free Spectral Clustering Approach to Coherent Structure Detection in Geophysical Flows

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