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Abstract This work provides a rare quantification of lateral spreading from Lagrangian measurements in a buoyant river plume by comparing four methods. Drifter motions, including along‐stream shear and rotation, can be incorrectly interpreted as lateral spreading. This work aims to improve estimates of lateral spreading by identifying additional motions in drifter trajectories. The techniques applied are first evaluated and compared using an idealized group of drifters undergoing specific types of motion, and then applied to in situ data from 27 surface Lagrangian drifters released in the Merrimack River plume (Massachusetts) under a variety of different environmental conditions. The techniques tested include two methods using the standard deviation of drifter position with respect to various interpretations of mean drifter direction and two methods using a rotating elliptical coordinate reference frame. The idealized trajectories are modeled analytically with each type of motion (i.e., spreading, rotation, and shear) separately, then in different combinations, to identify the method that best resolves and isolates lateral spreading. The idealized experiments demonstrate that three of the methods are sensitive to shear and rotational motion in various combinations. The most robust method resolving lateral spreading is the “time‐step” method, which applies a reference frame that follows the mean flow at each time step, calculated as the average direction of the drifters between two time steps. This method also successfully identifies lateral spreading in observations, which is maximized in classic bulge‐shaped plume deployments. This work is applicable to other river plume systems as well as other propagating oceanographic phenomena.
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Separating Mesoscale and Submesoscale Flows from Clustered Drifter Trajectories
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.
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- Award ID(s):
- 1658564
- PAR ID:
- 10215028
- Date Published:
- Journal Name:
- Fluids
- Volume:
- 6
- Issue:
- 1
- ISSN:
- 2311-5521
- Page Range / eLocation ID:
- 14
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.3390/fluids6010014
