SearcharxivSearch

arXiv · 1701.02174

Observation of turbulence in wave-induced oscillatory flows

Abstract

The dynamic and thermal regimes of climate are regulated by an exchange of energy and momentum between the atmosphere and the ocean. The role exerted by surface waves on this interchange is particularly enigmatic. Waves induce turbulence in the upper ocean by breaking and through Langmuir circulations. However, waves can directly inject energy into subsurface layers. This relates to waves not being truly irrotational and therefore the induced orbital motion being turbulent. The existence, extent and properties of this turbulent oscillatory flow still remain uncertain. Here we present measurements of the velocity field of oscillatory flows, which are induced by mechanically generated random wave fields in a large scale experimental facility. Velocities were recorded at a depth sufficiently far from the water surface to rule out effects of wave breaking. We demonstrate that the spectral tail of the velocity field follows a power-law scaling close to $ω^{-5/3}$. The turbulent behaviour is investigated via rigorous statistical analysis of the structure functions to highlight the emergence of intermittency in oscillatory flows. The results show that wave motion is turbulent and can contribute to ocean mixing. By deepening of the mixed layer, wave induced motion affects cyclogenesis and sediment resuspension.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Alberto Alberello, Federico Frascoli, Miguel Onorato, Alessandro Toffoli. 2017-01-09. Observation of turbulence in wave-induced oscillatory flows. https://doi.org/10.1016/j.wavemoti.2018.10.003

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Windowed Envelope Statistics for Time-Domain Significant Wave Height Estimation From HF Radar

Significant wave height (SWH) retrieval from high-frequency (HF) radar typically relies on a weak second-order Doppler continuum that is sensitive to noise, interference, and spectral leakage. This letter presents a Windowed Envelope Statistics Estimator (WESE) that operates directly on beam-formed time-domain voltages. A second-order term obtained from a Neumann expansion of the rough-surface field equation motivates quadratic compensation of localized radar features. WESE extracts the mean, standard deviation, or variance from overlapping windows of the in-phase, quadrature, or envelope-magnitude sequence, followed by quadratic compensation, rank ordering, least-squares regression, and causal smoothing. Evaluation used 335 synchronized hourly observations from a 13.385 MHz, 12-element WERA system at Argentia, Newfoundland and Labrador. The optimal configuration used quadrature variance, a 16-sample window, 896 retained chronological samples, and 30-h smoothing, achieving an RMSE of 0.152 m and a Pearson correlation of 0.978. This represents RMSE reductions of 32.1% and 18.7% relative to previously reported linear and second-order compensated ordered-statistics models, respectively. The results demonstrate robust time-domain SWH estimation without explicit Doppler-spectrum construction.

physics.ao-ph

KiloDA: Reconstructing kilometer-scale near-surface wind states from sparse station observations

Accurate kilometer-scale near-surface winds are important for understanding atmospheric processes over complex terrain, yet remain difficult to reconstruct from sparse and unevenly distributed observations. Here we introduce KiloDA, a diffusion framework for hourly kilometer-scale wind reconstruction from surface stations. KiloDA learns the statistical distribution and spatial structure of wind fields from historical 3-km Weather Research and Forecasting (WRF) model forecasts. At each reconstruction time, no contemporaneous WRF field is used. Instead, station observations provide the only constraints on the current atmospheric state and guide posterior sampling from the learned prior. In idealized WRF experiments, KiloDA recovers localized wind structures when only 0.24% of grid cells are observed and shows an overall advantage over conventional interpolation across terrain conditions and wind speed regimes. This capability largely transfers to real observations. In a fully withheld region, KiloDA reduces the median wind speed root mean square error (RMSE) by 19% relative to ERA5 reanalysis, using only observations outside the region, with the largest improvements over high-elevation and high-relief terrain. A random station holdout further confirms that this advantage extends across different complex-terrain locations and holdout configurations. These results show that historical model archives can provide useful structural knowledge for reconstructing kilometer-scale wind fields from sparse observations without requiring an accurate model estimate of the current atmospheric state.

physics.ao-ph