Sensitivity and robustness of Lagrangian coherent structures in coastal water systems
Anusmriti Ghosh, K. A. Suara, Scott W. McCue, Richard J. Brown

TL;DR
This study evaluates how mesh resolution, interpolation, and noise affect the reliability of Lagrangian Coherent Structures in coastal water models, highlighting their robustness and limitations in complex environments.
Contribution
It provides the first systematic analysis of LCS sensitivity to data errors and grid resolution in realistic coastal water simulations.
Findings
LCSs are robust to interpolation errors in data conversion.
Random errors of 1-10% can disrupt LCS ridge continuity.
LCS applicability is limited by flow divergence and data noise.
Abstract
In coastal water systems, horizontal chaotic dispersion plays a significant role in the distribution and fate of pollutants. Lagrangian Coherent Structures (LCSs) provide useful tools to approach the problem of the transport of pollutants and have only recently been applied to coastal waters. While the fundamentals of the LCS approach using idealised analytical flow fields are well established in the literature, there are limited studies on their practical implementations in coastal waters where effects of boundaries and bathymetry frequently become significant. Due to their complex bathymetry and boundaries, unstructured grid systems are commonly used in modelling of coastal waters. For convenient derivation of LCS diagnostics, structured grids are commonly used. Here we examine the effect of mesh resolution, interpolation schemes and additive random noise on the LCS diagnostics in…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsOceanographic and Atmospheric Processes · Tropical and Extratropical Cyclones Research · Ocean Waves and Remote Sensing
