Nonlinear Ocean Wave Reconstruction Algorithms Based on Simulated Spatiotemporal Data Acquired by a Flash LIDAR Camera

We report on the development of free surface reconstruction algorithms to predict ocean waves, based on spatial observations made with a high-frequency Flash light detection and ranging camera. We assume that the camera is mounted on a vessel, in a forward looking position, and is pointing at some distance ahead of its path yielding a sample of spatiotemporal wave elevation data. Because of the geometry, the density of measurement points gradually decreases (i.e., becomes sparse) with the distance to the camera. Free surface reconstruction algorithms were first developed and validated for linear 1-D and 2-D irregular surface models, whose amplitude coefficients are estimated on the basis of minimizing the mean square error of simulated surface elevations to measurements, over space and time (for a specified time initialization period). In the validation tests reported here, irregular ocean surfaces are generated on the basis of a directional Pierson-Moskowitz or Elfouhaily spectrum, and simulated LIDAR data sets are constructed by performing geometric intersections of laser rays with each generated surface. Once a nowcast of the ocean surface is estimated from the (simulated) LIDAR data, a forecast can be made of expected waves ahead of the vessel, for a time window that depends both on the initialization period and the resolved wavenumbers in the reconstruction. The process can then be repeated for another prediction window, and so forth. To reconstruct severe sea states, however, nonlinear effects must be included in the sea surface representation. This is done, here, by representing the ocean surface using the efficient Lagrangian choppy wave model .