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Separating Mesoscale and Submesoscale Flows from Clustered Drifter Trajectories

Research output: Contribution to Journal/Magazine › Journal article › peer-review

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Separating Mesoscale and Submesoscale Flows from Clustered Drifter Trajectories. / Oscroft, Sarah; Sykulski, Adam M.; Early, Jeffrey J.
In: Fluids, Vol. 6, No. 1, 14, 31.12.2020.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Oscroft, S, Sykulski, AM & Early, JJ 2020, 'Separating Mesoscale and Submesoscale Flows from Clustered Drifter Trajectories', Fluids, vol. 6, no. 1, 14. https://doi.org/10.3390/fluids6010014

APA

Oscroft, S., Sykulski, A. M., & Early, J. J. (2020). Separating Mesoscale and Submesoscale Flows from Clustered Drifter Trajectories. Fluids, 6(1), Article 14. https://doi.org/10.3390/fluids6010014

Vancouver

Oscroft S, Sykulski AM, Early JJ. Separating Mesoscale and Submesoscale Flows from Clustered Drifter Trajectories. Fluids. 2020 Dec 31;6(1):14. doi: 10.3390/fluids6010014

Author

Oscroft, Sarah ; Sykulski, Adam M. ; Early, Jeffrey J. / Separating Mesoscale and Submesoscale Flows from Clustered Drifter Trajectories. In: Fluids. 2020 ; Vol. 6, No. 1.

Bibtex

@article{5366be96fb774668ad52aafd0ec4956e,
title = "Separating Mesoscale and Submesoscale Flows from Clustered Drifter Trajectories",
abstract = "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.",
keywords = "drifters, mesoscale, submesoscale, diffusivity, strain, vorticity, divergence, Lagrangian, frequency spectra, bootstrap, uncertainty quantification, splines",
author = "Sarah Oscroft and Sykulski, {Adam M.} and Early, {Jeffrey J.}",
year = "2020",
month = dec,
day = "31",
doi = "10.3390/fluids6010014",
language = "English",
volume = "6",
journal = "Fluids",
issn = "2311-5521",
publisher = "Multidisciplinary Digital Publishing Institute (MDPI)",
number = "1",

}

RIS

TY - JOUR

T1 - Separating Mesoscale and Submesoscale Flows from Clustered Drifter Trajectories

AU - Oscroft, Sarah

AU - Sykulski, Adam M.

AU - Early, Jeffrey J.

PY - 2020/12/31

Y1 - 2020/12/31

N2 - 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.

AB - 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.

KW - drifters

KW - mesoscale

KW - submesoscale

KW - diffusivity

KW - strain

KW - vorticity

KW - divergence

KW - Lagrangian

KW - frequency spectra

KW - bootstrap

KW - uncertainty quantification

KW - splines

U2 - 10.3390/fluids6010014

DO - 10.3390/fluids6010014

M3 - Journal article

VL - 6

JO - Fluids

JF - Fluids

SN - 2311-5521

IS - 1

M1 - 14

ER -