SearcharxivSearch

arXiv · 1912.00358

Evolution of Spectra for Mechanical and Wind Waves in a Large Tank

Abstract

Empirical spectra for mechanical and wind waves measured in a large tank of the First Institute of Oceanography of China are presented. Analysis for the first and the second type of waves is done separately. It is shown that, in the case of mechanical waves with a steepness more than 0.2, the frequency spectra of waves evolve to ones with the tail decay S(f) ~ (f to power -4.2), whilst the shape of spectra at large fetches is self-similar. Numerical solutions of the four-wave kinetic equation, written in the fetch-limited version, result in the same spectra. This allows treating the empirical observations for mechanical waves as the natural evolution of free nonlinear surface waves. In the case of wind waves, the wave spectra evolve to ones with the tail decay S(f) ~ (f to power -4.0) at fetches X greater than 8 meters, under any applied winds. The well-known Toba three-second relation, H ~ (T to power 3/2), between the mean wave height, H, and the peak period, T, is well fulfilled. Though, the intensities of spectra tails do not follow the Toba ratio, S(f) ~ (gu)(f to power -4), rather they better correspond to the ratio , S(f) ~ ((u to power 2)/Xg)(to power p)(gu)(f to power -4) with p = 1/3. Moreover, the shape of the wind-wave spectra is not self-similar. Discussion of the results is presented.

Explore related subjects

Keep this discovery

BibTeXRIS

Vladislav Polnikov, Fangli Qiao, Hongyu Ma, Shumin Jiang. 2019-12-01. Evolution of Spectra for Mechanical and Wind Waves in a Large Tank. https://arxiv.org/abs/1912.00358

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