Ordering of trajectories reveals hierarchical finite-time coherent sets in Lagrangian particle data: detecting Agulhas rings in the South Atlantic Ocean

Wichmann, David; Kehl, Christian; Dijkstra, Henk A.; van Sebille, Erik

doi:https://doi.org/10.5194/npg-28-43-2021

Articles | Volume 28, issue 1

https://doi.org/10.5194/npg-28-43-2021

© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/npg-28-43-2021

© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 28, issue 1

Research article

|

19 Jan 2021

Research article |

| 19 Jan 2021

Ordering of trajectories reveals hierarchical finite-time coherent sets in Lagrangian particle data: detecting Agulhas rings in the South Atlantic Ocean

David Wichmann, Christian Kehl, Henk A. Dijkstra, and Erik van Sebille

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Final revised paper (published on 19 Jan 2021)
Preprint (discussion started on 29 Jun 2020)

Interactive discussion

Status: closed

AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment

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- Supplement

RC1: 'Referee comments', Anonymous Referee #1, 25 Aug 2020
- AC1: 'Answer to Referee #1', David Wichmann, 19 Oct 2020
RC2: 'Wichmann et al manuscript', Anonymous Referee #2, 23 Sep 2020
- AC2: 'Answer to Referee #2', David Wichmann, 19 Oct 2020

Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision

AR by David Wichmann on behalf of the Authors (19 Oct 2020) Author's response Manuscript

ED: Publish as is (03 Nov 2020) by Juan Restrepo

AR by David Wichmann on behalf of the Authors (06 Nov 2020)

Short summary

Fluid parcels transported in complicated flows often contain subsets of particles that stay close over finite time intervals. We propose a new method for detecting finite-time coherent sets based on the density-based clustering technique of ordering points to identify the clustering structure (OPTICS). Unlike previous methods, our method has an intrinsic notion of coherent sets at different spatial scales. OPTICS is readily implemented in the SciPy sklearn package, making it easy to use.