The Effect of Langmuir Circulation on the Surface Boundary Layer.

1996 ◽  
Author(s):  
Robert A. Weller ◽  
Albert J. Plueddemann
Keyword(s):  
Boundary Layer ◽  
Surface Boundary ◽  
Langmuir Circulation ◽  
Surface Boundary Layer

scholarly journals Observations of Turbulence Mixing and Vorticity in a Littoral Surface Boundary Layer

2008 ◽  
Vol 38 (3) ◽  
pp. 648-669 ◽  
Author(s):  
R-C. Lien ◽  
B. Sanford ◽  
W-T. Tsai
Keyword(s):  
Boundary Layer ◽  
Kinetic Energy ◽  
Surface Waves ◽  
Turbulence Kinetic Energy ◽  
Surface Boundary ◽  
Vertical Profiles ◽  
Langmuir Circulation ◽  
Surface Boundary Layer ◽  
Turbulence Mixing ◽  
Shear Turbulence

Abstract Measurements of small-scale vorticity, turbulence velocity, and dissipation rates of turbulence kinetic energy ɛ were taken in a littoral fetch-limited surface wave boundary layer. Drifters deployed on the surface formed convergence streaks with ∼1-m horizontal spacing within a few minutes. In the interior, however, no organized pattern of velocity, vorticity, or turbulence mixing intensity was found at a similar horizontal spatial scale. The turbulent Langmuir number La was 0.6–1.3, much larger than the 0.3 of the typical open ocean, suggesting comparable importance of wind-driven turbulence and Langmuir circulation. Observed ɛ are explained by the wind-driven shear turbulence. The production rate of turbulence kinetic energy associated with the vortex force is about 10−7 W kg−1, slightly smaller than that generated by the wind-driven turbulence. The rms values of the streakwise component of vorticity σζ|| and the vertical component of vorticity σζz have a similar magnitude of ∼0.02 s−1. Vertical profiles of ɛ, σζ||, and σζz showed a monotonic decrease from the surface. Traditionally, surface convergence streaks are regarded as signatures of Langmuir circulation. Two large-eddy simulations with and without Stokes drift were performed. Both simulations produced surface convergence streaks and vertical profiles of ɛ, vorticity, and velocity consistent with observations. The observations and model results suggest that the presence of surface convergence streaks does not necessarily imply the existence of Langmuir circulation. In a littoral surface boundary layer where surface waves are young, fetch-limited, and weak, and La = O(1), the turbulence mixing in the surface mixed layer is primarily due to the wind-driven shear turbulence, and convergence streaks exist with or without surface waves.

Prediction of Atmospheric Turbulence Characteristics for Surface Boundary Layer using Empirical Spectral Methods

2020 ◽  
Author(s):  
Yagya Dutta Dwivedi ◽  
Vasishta Bhargava Nukala ◽  
Satya Prasad Maddula ◽  
Kiran Nair
Keyword(s):  
Boundary Layer ◽  
Time Series ◽  
Surface Roughness ◽  
Wind Speed ◽  
Atmospheric Turbulence ◽  
Surface Boundary ◽  
Low Frequencies ◽  
Surface Boundary Layer ◽  
Power Spectral ◽  
Mean Wind Speed

Abstract Atmospheric turbulence is an unsteady phenomenon found in nature and plays significance role in predicting natural events and life prediction of structures. In this work, turbulence in surface boundary layer has been studied through empirical methods. Computer simulation of Von Karman, Kaimal methods were evaluated for different surface roughness and for low (1%), medium (10%) and high (50%) turbulence intensities. Instantaneous values of one minute time series for longitudinal turbulent wind at mean wind speed of 12 m/s using both spectra showed strong correlation in validation trends. Influence of integral length scales on turbulence kinetic energy production at different heights is illustrated. Time series for mean wind speed of 12 m/s with surface roughness value of 0.05 m have shown that variance for longitudinal, lateral and vertical velocity components were different and found to be anisotropic. Wind speed power spectral density from Davenport and Simiu profiles have also been calculated at surface roughness of 0.05 m and compared with k−1 and k−3 slopes for Kolmogorov k−5/3 law in inertial sub-range and k−7 in viscous dissipation range. At high frequencies, logarithmic slope of Kolmogorov −5/3rd law agreed well with Davenport, Harris, Simiu and Solari spectra than at low frequencies.

scholarly journals A global perspective on Langmuir turbulence in the ocean surface boundary layer

2012 ◽  
Vol 39 (18) ◽  
Author(s):  
Stephen E. Belcher ◽  
Alan L. M. Grant ◽  
Kirsty E. Hanley ◽  
Baylor Fox-Kemper ◽  
Luke Van Roekel ◽  
...  
Keyword(s):  
Boundary Layer ◽  
Ocean Surface ◽  
Global Perspective ◽  
Surface Boundary ◽  
Langmuir Turbulence ◽  
Surface Boundary Layer

LESbrary: A library of large eddy simulation data for the calibration and uncertainty quantification of ocean surface boundary layer turbulence parameterizations

2021 ◽  
Author(s):  
Gregory Wagner ◽  
Andre Souza ◽  
Adeline Hillier ◽  
Ali Ramadhan ◽  
Raffaele Ferrari
Keyword(s):  
Boundary Layer ◽  
Turbulent Mixing ◽  
Large Scale ◽  
Ocean Surface ◽  
Limited Area ◽  
Surface Boundary ◽  
Model Errors ◽  
Surface Boundary Layer ◽  
Large Eddy ◽  
Free Parameters

<p>Parameterizations of turbulent mixing in the ocean surface boundary layer (OSBL) are key Earth System Model (ESM) components that modulate the communication of heat and carbon between the atmosphere and ocean interior. OSBL turbulence parameterizations are formulated in terms of unknown free parameters estimated from observational or synthetic data. In this work we describe the development and use of a synthetic dataset called the “LESbrary” generated by a large number of idealized, high-fidelity, limited-area large eddy simulations (LES) of OSBL turbulent mixing. We describe how the LESbrary design leverages a detailed understanding of OSBL conditions derived from observations and large scale models to span the range of realistically diverse physical scenarios. The result is a diverse library of well-characterized “synthetic observations” that can be readily assimilated for the calibration of realistic OSBL parameterizations in isolation from other ESM model components. We apply LESbrary data to calibrate free parameters, develop prior estimates of parameter uncertainty, and evaluate model errors in two OSBL parameterizations for use in predictive ESMs.</p>

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