Surface Wave Processes on the Continental Shelf and Beach

LONG-TERM GOALS: Wind waves and swell dominate the hydrodynamic and sediment transport processes on many continental shelves and beaches, affect underwater acoustics, and play an important role in remote sensing applications. Wave prediction in coastal environments is a challenging task because waves are affected by many processes, including scattering by seafloor topography, strong nonlinear interactions, wave breaking, and friction in the bottom boundary layer. Several of these processes are poorly understood and existing wave prediction models rely on parameterizations and empirical calibration to represent them. The long term goals of this research are to obtain a better understanding of the physical processes that affect ocean surface waves in the coastal environment and develop improved wave prediction capability.Award Numbers: N0001408WR20154, N0001408WR20003, N00014-07-1-0365, N00014-07-1-040

The California coastal wave monitoring and prediction system

AbstractA decade-long effort to estimate nearshore (20m depth) wave conditions based on offshore buoy observations along the California coast is described. Offshore, deep water directional wave buoys are used to initialize a non-stationary, linear, spectral refraction wave model. Model hindcasts of spectral parameters commonly used in nearshore process studies and engineering design are validated against nearshore buoy observations seaward of the surfzone. The buoy-driven wave model shows significant skill at most validation sites, but prediction errors for individual swell or sea events can be large. Model skill is high in north San Diego County, and low in the Santa Barbara Channel and along the southern Monterey Bay coast. Overall, the buoy-driven model hindcasts have relatively low bias and therefore are best suited for quantifying mean (e.g. monthly or annual) nearshore wave climate conditions rather than extreme or individual wave events. Model error correlation with the incident offshore wave energy, and between neighboring validation sites, may be useful in identifying sources of regional modeling errors