Next Article in Journal
Analysis and Modelling of the Commutation Error
Next Article in Special Issue
An Optimized-Parameter Spectral Clustering Approach to Coherent Structure Detection in Geophysical Flows
Previous Article in Journal
Numerical Investigation of Two-Phase Flows in Corrugated Channel with Single and Multiples Drops
Previous Article in Special Issue
Eulerian and Lagrangian Comparison of Wind Jets in the Tokar Gap Region
Article

Separating Mesoscale and Submesoscale Flows from Clustered Drifter Trajectories

1
STOR-i Centre for Doctoral Training, Lancaster University, Lancaster LA1 4YW, UK
2
Department of Mathematics and Statistics, Lancaster University, Lancaster LA1 4YW, UK
3
NorthWest Research Associates, Redmond, WA 98052, USA
*
Author to whom correspondence should be addressed.
Fluids 2021, 6(1), 14; https://doi.org/10.3390/fluids6010014
Received: 28 November 2020 / Revised: 24 December 2020 / Accepted: 25 December 2020 / Published: 31 December 2020
(This article belongs to the Special Issue Lagrangian Transport in Geophysical Fluid Flows)
Drifters deployed in close proximity collectively provide a unique observational data set with which to separate mesoscale and submesoscale flows. In this paper we provide a principled approach for doing so by fitting observed velocities to a local Taylor expansion of the velocity flow field. We demonstrate how to estimate mesoscale and submesoscale quantities that evolve slowly over time, as well as their associated statistical uncertainty. We show that in practice the mesoscale component of our model can explain much first and second-moment variability in drifter velocities, especially at low frequencies. This results in much lower and more meaningful measures of submesoscale diffusivity, which would otherwise be contaminated by unresolved mesoscale flow. We quantify these effects theoretically via computing Lagrangian frequency spectra, and demonstrate the usefulness of our methodology through simulations as well as with real observations from the LatMix deployment of drifters. The outcome of this method is a full Lagrangian decomposition of each drifter trajectory into three components that represent the background, mesoscale, and submesoscale flow. View Full-Text
Keywords: drifters; mesoscale; submesoscale; diffusivity; strain; vorticity; divergence; Lagrangian; frequency spectra; bootstrap; uncertainty quantification; splines drifters; mesoscale; submesoscale; diffusivity; strain; vorticity; divergence; Lagrangian; frequency spectra; bootstrap; uncertainty quantification; splines
Show Figures

Figure 1

MDPI and ACS Style

Oscroft, S.; Sykulski, A.M.; Early, J.J. Separating Mesoscale and Submesoscale Flows from Clustered Drifter Trajectories. Fluids 2021, 6, 14. https://doi.org/10.3390/fluids6010014

AMA Style

Oscroft S, Sykulski AM, Early JJ. Separating Mesoscale and Submesoscale Flows from Clustered Drifter Trajectories. Fluids. 2021; 6(1):14. https://doi.org/10.3390/fluids6010014

Chicago/Turabian Style

Oscroft, Sarah, Adam M. Sykulski, and Jeffrey J. Early. 2021. "Separating Mesoscale and Submesoscale Flows from Clustered Drifter Trajectories" Fluids 6, no. 1: 14. https://doi.org/10.3390/fluids6010014

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop