Determination of vorticity, divergence, and deformation rates from analysis of drogue observations

Ōkubo, Akira; Ebbesmeyer, Curtis C.

doi:10.1016/0011-7471(76)90875-5

Cited by 72 publications

(94 citation statements)

References 3 publications

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“…For our purposes dU\ldx 2 will be taken as the average velocity gradient in the range all < x 2 < 3a/2. The eddy viscosity must therefore correspond to a scale equal to a, and is computed from the values in Table 2 using K' 22 = K 22 (a/3oy) 4 ' 3 , which is consistent with the scale dependence found by Okubo (1971). The above values of U c and a are based on data from the vicinity of the Dl, D2 and D3 experimental sites, which are far removed from the CIPREA experimental site.…”

Section: Itsupporting

confidence: 73%

“…This must be generally true for transverse motion if there is to be no net motion through a plane traversing the cluster. For longitudinal motion this will be true for singularity structures corresponding to nodes or lines of convergence/divergence (Okubo, 1970). Thus we expect g* to be generally negative for large space lags and small time lags.…”

Section: Sr( Ajdmentioning

confidence: 99%

“…Considering a model for drifter motion that consists of advection with the patch centroid, and diffusion relative to the patch centroid, then a time-dependent eddy-diffusivity tensor can be defined in a manner analogous to those of Okubo et al (1976) and Sanderson et al (1988) Kij(t) = \ £ *;"jj" ]/(N -1), i, j = 1,2 (2) L«=i J where x) n is the time derivative of x' jn . Eddy diffusivity tensors calculated using (2) turn out to be rapidly fluctuating functions of time, as illustrated by the representative example in Fig.…”

Section: Calculation Of Eddy Diffusivitiesmentioning

confidence: 99%

“…It is conceivable that the large values of K u are caused by a larger scale deformation of clusters. We therefore compute the diffusivity tensor Kfj{t) from residuals xj'that are left when both the centroid displacement and the displacements due to timevarying spatially uniform Lagrangian deformations are extracted (Okubo et al, 1976;Sanderson et al, 1988).…”

Section: Calculation Of Eddy Diffusivitiesmentioning

confidence: 99%

“…It is quite clear from Okubo ( 1971 ) that in general the relative velocity is not statistically stationary. Nevertheless we might consider it quasi-stationary if the time taken for a particle's relative velocity to become substantially auto-decorrelated is small, compared with the time taken for the statistical properties of the relative velocity to change appreciably.…”

Section: Calculation Of Eddy Diffusivitiesmentioning

confidence: 99%

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Patch diffusion computed from Lagrangian data, with application to the atlantic equatorial undercurrent

Sanderson

Pal

1990

Atmosphere-Ocean

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Section: Itsupporting

confidence: 73%

Section: Sr( Ajdmentioning

confidence: 99%

Section: Calculation Of Eddy Diffusivitiesmentioning

confidence: 99%

Section: Calculation Of Eddy Diffusivitiesmentioning

confidence: 99%

Section: Calculation Of Eddy Diffusivitiesmentioning

confidence: 99%

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Patch diffusion computed from Lagrangian data, with application to the atlantic equatorial undercurrent

Sanderson

Pal

1990

Atmosphere-Ocean

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Drifter observations of submesoscale flow kinematics in the coastal ocean

Ohlmann

Molemaker

Baschek

et al. 2017

Geophysical Research Letters

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Fronts and eddies identified with aerial guidance are seeded with drifters to quantify submesoscale flow kinematics. The Lagrangian observations show mean divergence and vorticity values that can exceed 5 times the Coriolis frequency. Values are the largest observed in the field to date and represent an extreme departure from geostrophic dynamics. The study also quantifies errors and biases associated with Lagrangian observations of the underlying velocity strain tensor. The greatest error results from undersampling, even with a large number of drifters. A significant bias comes from inhomogeneous sampling of convergent regions that accumulate drifters within a few hours of deployment. The study demonstrates a Lagrangian sampling paradigm for targeted submesoscale structures over a broad range of scales and presents flow kinematic values associated with vertical velocities O(10) m h−1 that can have profound implications on ocean biogeochemistry.

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Steepest Ascent Low/Non‐Low‐Frequency Ratio in Empirical Mode Decomposition to Separate Deterministic and Stochastic Velocities From a Single Lagrangian Drifter

Chu

2018

JGR Oceans

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SOund Fixing And Ranging (RAFOS) floats deployed by the Naval Postgraduate School (NPS) in the California Current system from 1992 to 2001 at depth between 150 and 600 m (http://www.oc.nps.edu/npsRAFOS/) are used to study 2‐D turbulent characteristics. Each drifter trajectory is adaptively decomposed using the empirical mode decomposition (EMD) into a series of intrinsic mode functions (IMFs) with corresponding specific scale for each IMF. A new steepest ascent low/non‐low‐frequency ratio is proposed in this paper to separate a Lagrangian trajectory into low‐frequency (nondiffusive, i.e., deterministic) and high‐frequency (diffusive, i.e., stochastic) components. The 2‐D turbulent (or called eddy) diffusion coefficients are calculated on the base of the classical turbulent diffusion with mixing length theory from stochastic component of a single drifter. Statistical characteristics of the calculated 2‐D turbulence length scale, strength, and diffusion coefficients from the NPS RAFOS data are presented with the mean values (over the whole drifters) of the 2‐D diffusion coefficients comparable to the commonly used diffusivity tensor method.

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Determination of vorticity, divergence, and deformation rates from analysis of drogue observations

Cited by 72 publications

References 3 publications

Patch diffusion computed from Lagrangian data, with application to the atlantic equatorial undercurrent

Patch diffusion computed from Lagrangian data, with application to the atlantic equatorial undercurrent

Drifter observations of submesoscale flow kinematics in the coastal ocean

Steepest Ascent Low/Non‐Low‐Frequency Ratio in Empirical Mode Decomposition to Separate Deterministic and Stochastic Velocities From a Single Lagrangian Drifter

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