Ordering of trajectories reveals hierarchical finite-time coherent sets in Lagrangian particle data: detecting Agulhas rings in the South Atlantic Ocean
The detection of finite-time coherent particle sets in Lagrangian trajectory data, using data-clustering techniques, is an active research field at the moment. Yet, the clustering methods mostly employed so far have been based on graph partitioning, which assigns each trajectory to a cluster, i.e. t...
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ftdoajarticles:oai:doaj.org/article:107993849b7b4688b05feb012ebf49d6 2023-05-15T18:21:03+02:00 Ordering of trajectories reveals hierarchical finite-time coherent sets in Lagrangian particle data: detecting Agulhas rings in the South Atlantic Ocean D. Wichmann C. Kehl H. A. Dijkstra E. van Sebille 2021-01-01T00:00:00Z https://doi.org/10.5194/npg-28-43-2021 https://doaj.org/article/107993849b7b4688b05feb012ebf49d6 EN eng Copernicus Publications https://npg.copernicus.org/articles/28/43/2021/npg-28-43-2021.pdf https://doaj.org/toc/1023-5809 https://doaj.org/toc/1607-7946 doi:10.5194/npg-28-43-2021 1023-5809 1607-7946 https://doaj.org/article/107993849b7b4688b05feb012ebf49d6 Nonlinear Processes in Geophysics, Vol 28, Pp 43-59 (2021) Science Q Physics QC1-999 Geophysics. Cosmic physics QC801-809 article 2021 ftdoajarticles https://doi.org/10.5194/npg-28-43-2021 2022-12-31T07:00:26Z The detection of finite-time coherent particle sets in Lagrangian trajectory data, using data-clustering techniques, is an active research field at the moment. Yet, the clustering methods mostly employed so far have been based on graph partitioning, which assigns each trajectory to a cluster, i.e. there is no concept of noisy, incoherent trajectories. This is problematic for applications in the ocean, where many small, coherent eddies are present in a large, mostly noisy fluid flow. Here, for the first time in this context, we use the density-based clustering algorithm of OPTICS (ordering points to identify the clustering structure; Ankerst et al. , 1999 ) to detect finite-time coherent particle sets in Lagrangian trajectory data. Different from partition-based clustering methods, derived clustering results contain a concept of noise, such that not every trajectory needs to be part of a cluster. OPTICS also has a major advantage compared to the previously used density-based spatial clustering of applications with noise (DBSCAN) method, as it can detect clusters of varying density. The resulting clusters have an intrinsically hierarchical structure, which allows one to detect coherent trajectory sets at different spatial scales at once. We apply OPTICS directly to Lagrangian trajectory data in the Bickley jet model flow and successfully detect the expected vortices and the jet. The resulting clustering separates the vortices and the jet from background noise, with an imprint of the hierarchical clustering structure of coherent, small-scale vortices in a coherent, large-scale background flow. We then apply our method to a set of virtual trajectories released in the eastern South Atlantic Ocean in an eddying ocean model and successfully detect Agulhas rings. We illustrate the difference between our approach and partition-based k -means clustering using a 2D embedding of the trajectories derived from classical multidimensional scaling. We also show how OPTICS can be applied to the spectral embedding of a ... Article in Journal/Newspaper South Atlantic Ocean Directory of Open Access Journals: DOAJ Articles Nonlinear Processes in Geophysics 28 1 43 59 |
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Directory of Open Access Journals: DOAJ Articles |
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English |
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Science Q Physics QC1-999 Geophysics. Cosmic physics QC801-809 |
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Science Q Physics QC1-999 Geophysics. Cosmic physics QC801-809 D. Wichmann C. Kehl H. A. Dijkstra E. van Sebille Ordering of trajectories reveals hierarchical finite-time coherent sets in Lagrangian particle data: detecting Agulhas rings in the South Atlantic Ocean |
topic_facet |
Science Q Physics QC1-999 Geophysics. Cosmic physics QC801-809 |
description |
The detection of finite-time coherent particle sets in Lagrangian trajectory data, using data-clustering techniques, is an active research field at the moment. Yet, the clustering methods mostly employed so far have been based on graph partitioning, which assigns each trajectory to a cluster, i.e. there is no concept of noisy, incoherent trajectories. This is problematic for applications in the ocean, where many small, coherent eddies are present in a large, mostly noisy fluid flow. Here, for the first time in this context, we use the density-based clustering algorithm of OPTICS (ordering points to identify the clustering structure; Ankerst et al. , 1999 ) to detect finite-time coherent particle sets in Lagrangian trajectory data. Different from partition-based clustering methods, derived clustering results contain a concept of noise, such that not every trajectory needs to be part of a cluster. OPTICS also has a major advantage compared to the previously used density-based spatial clustering of applications with noise (DBSCAN) method, as it can detect clusters of varying density. The resulting clusters have an intrinsically hierarchical structure, which allows one to detect coherent trajectory sets at different spatial scales at once. We apply OPTICS directly to Lagrangian trajectory data in the Bickley jet model flow and successfully detect the expected vortices and the jet. The resulting clustering separates the vortices and the jet from background noise, with an imprint of the hierarchical clustering structure of coherent, small-scale vortices in a coherent, large-scale background flow. We then apply our method to a set of virtual trajectories released in the eastern South Atlantic Ocean in an eddying ocean model and successfully detect Agulhas rings. We illustrate the difference between our approach and partition-based k -means clustering using a 2D embedding of the trajectories derived from classical multidimensional scaling. We also show how OPTICS can be applied to the spectral embedding of a ... |
format |
Article in Journal/Newspaper |
author |
D. Wichmann C. Kehl H. A. Dijkstra E. van Sebille |
author_facet |
D. Wichmann C. Kehl H. A. Dijkstra E. van Sebille |
author_sort |
D. Wichmann |
title |
Ordering of trajectories reveals hierarchical finite-time coherent sets in Lagrangian particle data: detecting Agulhas rings in the South Atlantic Ocean |
title_short |
Ordering of trajectories reveals hierarchical finite-time coherent sets in Lagrangian particle data: detecting Agulhas rings in the South Atlantic Ocean |
title_full |
Ordering of trajectories reveals hierarchical finite-time coherent sets in Lagrangian particle data: detecting Agulhas rings in the South Atlantic Ocean |
title_fullStr |
Ordering of trajectories reveals hierarchical finite-time coherent sets in Lagrangian particle data: detecting Agulhas rings in the South Atlantic Ocean |
title_full_unstemmed |
Ordering of trajectories reveals hierarchical finite-time coherent sets in Lagrangian particle data: detecting Agulhas rings in the South Atlantic Ocean |
title_sort |
ordering of trajectories reveals hierarchical finite-time coherent sets in lagrangian particle data: detecting agulhas rings in the south atlantic ocean |
publisher |
Copernicus Publications |
publishDate |
2021 |
url |
https://doi.org/10.5194/npg-28-43-2021 https://doaj.org/article/107993849b7b4688b05feb012ebf49d6 |
genre |
South Atlantic Ocean |
genre_facet |
South Atlantic Ocean |
op_source |
Nonlinear Processes in Geophysics, Vol 28, Pp 43-59 (2021) |
op_relation |
https://npg.copernicus.org/articles/28/43/2021/npg-28-43-2021.pdf https://doaj.org/toc/1023-5809 https://doaj.org/toc/1607-7946 doi:10.5194/npg-28-43-2021 1023-5809 1607-7946 https://doaj.org/article/107993849b7b4688b05feb012ebf49d6 |
op_doi |
https://doi.org/10.5194/npg-28-43-2021 |
container_title |
Nonlinear Processes in Geophysics |
container_volume |
28 |
container_issue |
1 |
container_start_page |
43 |
op_container_end_page |
59 |
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1766200111155642368 |