Figure 5
From: Deep neural networks for active wave breaking classification
Examples of statistical properties of breaking waves that can be directly obtained from the proposed method. (a) Probability distribution of the wave breaking duration (\(T_{br}\)) normalized by wave peak period (\(T_p\)). The blue line shows the Gamma fit to the data, the purple dashed line shows the mean value (0.13) and the orange dashed line shows the mode value (0.12). (b) Probability distribution of the wave breaking area (\(A_{br}\)) normalized by wavelength (\(\frac{g}{2\pi }T_p^2\)). The blue line shows the Pareto fit to the data. (c) Probability distribution of the ratio between major and minor axis (a/b). The blue line shows the Beta fit to the data, the purple dashed line shows the mean value (2.4) and the orange dashed line shows the mode value (1.8). (d) Probability distribution of the wave breaking area scaling parameter (\(A_{br}/b^2\)). The blue line shows the Beta fit to the data, the purple dashed line shows the mean value (0.1), the orange dashed line shows the mode value (0.08), and the red line shows Duncan’s constant value (0.11). (e) Evolution of the wave breaking area over time. The black markers show the observed values, the error bars show values binned at 0.15 s intervals, the blue line shows the quadratic regression to the data, the blue swath shows the 95% confidence interval for the regression, and \(r^2\) is the correlation coefficient. (f) Phillips12 \(\Lambda (c)dc\) distributions assuming that the wave breaking speed is the same as the phase speed of the carrier wave (that is, \(c=c_{br}\)) grouped by wind speed. The black dashed line shows the \(c^{-6}\) theoretical decay, the colored markers show the average crest length binned at \(0.1ms^{-1}\) wave speed intervals, and the colored lines show a running average with a window size of 10 bins.
