Abstract
In this study, we explore the applicability of a wavelet-entropy based segmentation technique in reduction of motion-induced contaminations in time-domain from subsurface turbulence measurements made by a moving shear probe. After the quality screening of data, the Shannon entropy procedure is combined with a time-dependent adaptive wavelet thresholding method to split each 60-s long shear segment into a number of motion-reduced subblocks. The wavelet-entropy strategy leads to preventing the false detection effect caused by applying either wavelet de-noising or Shannon entropy alone for conditions where the turbulence (strongly) overlap with scales induced by waves or platform motions. The longest stationary subblock, with a size greater than 16-s, is then used to extract the Turbulent Kinetic Energy (TKE) dissipation rate, varepsilon. Efficiency of the proposed method is verified by comparing with varepsilon measurements made by a nearby free-falling microstructure profiler. While the quality of observations is constrained by a number of factors such as sensors’ angle of attack, and the wave kinematical and dynamical effects, results demonstrate significant improvements, by approximately a factor of 5–10, compared with varepsilon measurements from each 60-s segment using the Goodman et al. [13] method. Furthermore, the magnitudes of the motion-corrected varepsilon using the proposed method is largely consistent with the scaling suggested by Terray et al. [30].
Highlights
The study of oceanic turbulence near the sea surface is of great importance in order to understand the exchange processes of heat, momentum, energy, mass, and gas transfer rate taking place across the air-sea interface
The velocities induced by the movement of sensors can be properly removed/reduced using the data from the inertial motion package affixed to the Moored Autonomous Turbulence System (MATS) [11], the application of the method to our data cannot be addressed with confidence
We discard all segments with substantial roll angle greater than 1 o (Fig. 9b) and small values of the ratio between the mean flow and the wave-induced flow, i.e. R < 1.15 in which values of σare extracted from the integration of the Acoustic Doppler Velocimeter (ADV)’s velocity spectra within the wave-affected frequency band (Fig. 9c)
Summary
The study of oceanic turbulence near the sea surface is of great importance in order to understand the exchange processes of heat, momentum, energy, mass, and gas transfer rate taking place across the air-sea interface. Near the wavy sea surface is still a challenging problem due to the diverse sources of disturbance from the wave orbital velocities (in particular for the weak mean current), the wave-induced platform instabilities (large Angle-ofAttack, AOA), sensor limitations to operate appropriately in such energetic environment, and the lack of adequate statistically reliable signal processing tools to separate wave-related contaminations from the turbulent fluctuating motions [10]. Waves can directly inject energy to the upper ocean boundary layer when they are not truly irrotational so that their orbital motions can induce turbulence (i.e. stochastic wave effect)
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