Kinematics and Statistics of Breaking Waves Observed Using SWIFT Buoys

Abstract

Surface wave instrumentation floats with tracking were deployed by helicopter ahead of five large storms off the Oregon coast. The buoys drifted freely with the wave motions, surface currents, and wind. The buoys use a 9-DoF inertial measurement unit that fuses the measurements of accelerometers, magnetometers, and gyroscopes to measure acceleration in the global North-West-Up reference frame. Rapid sampling (25 Hz) allows for the observation of both propagating wave motions and wave breaking events. Bulk wave parameters and wave spectra are calculated from the motion of the buoys using conventional methods, and breaking wave impacts are identified in the raw acceleration data using a new algorithm based on a short-time Fourier transform. The number of breaking waves is used to infer breaker fraction, which is found to depend on bulk wave steepness as previously shown in the literature. The magnitude and duration of acceleration during breaking is used in a new quantification of breaker intensity, which increases with wave height, period, and steepness. There is significant variance of breaker intensity in a given wave field, such that intense breakers still occur in relatively mild wave fields. The buoy observations are compared to the output of the WaveWatch III forecast model, with evaluation of an empirical breaker prediction scheme applied to WaveWatch III output.

Breaking waves in deep water: measurements and modeling of energy dissipation

“…Video imaging data confirms that waves were breaking particularly at the beginning of each deployment. For a more qualitative analysis of whitecaps, we obtained breaker fraction estimates from a companion publication: Brown et al (2018). The time series of breaker fraction estimates were found to correlate well with the image counts of breakers and possible-breakers identified visually, but more so for the April 2017 deployment.…”

“…Breaking waves were identified via a new method introduced in Brown et al (2018) using the same SWIFT data as in this study. The algorithm involves computing the Short-Time Fourier Transform (STFT) of acceleration time series to highlight rapid magnitude changes as a sign of breaking.…”

“…where N b is the number of observed breakers during a period of observation τ, and T is the corresponding wave period. In Brown et al (2018), values of Q b were computed for each data burst (τ = 12 min). In this study, breaker fraction bursts were averaged over each hour of each deployment.…”

“…Meanwhile, false breakers identified as possibly breaking may make breaker fraction values appear too low. It is also inherently the case, because of instrumentation size limitations, that sufficiently small breaking waves fail to excite the SWIFT acceleration enough to indicate breaking in the Brown et al (2018) algorithm. Furthermore, in the selection of the breaking threshold obtained from Brown et al (2018), only the images corresponding to indicated breakers were checked, and not the entire image dataset as was done for this study.…”

“…It is also inherently the case, because of instrumentation size limitations, that sufficiently small breaking waves fail to excite the SWIFT acceleration enough to indicate breaking in the Brown et al (2018) algorithm. Furthermore, in the selection of the breaking threshold obtained from Brown et al (2018), only the images corresponding to indicated breakers were checked, and not the entire image dataset as was done for this study. As such, it is possible that the resulting breaker fraction values may have been underestimated in the case of small whitecaps, which are reasonably expected to occur during low wind conditions and growing seas.…”