Nonlinear Ocean Wave Reconstruction Algorithms Based on Spatio - temporal Data 2 Acquired by a Flash LIDAR Camera

5 We report on the development of free surface reconstruction algorithms to predict ocean waves, based on spatial 6 observations made with a high frequency Flash LIDAR camera. We assume that the camera is mounted on a vessel, 7 in a forward looking position, and is pointing at some distance ahead of its path yielding a sample of spatio-temporal 8 wave elevation data. Due to the geometry, the density of measurement points gradually decreases (i.e., becomes sparse) 9 with the distance to the camera. Free surface reconstruction algorithms were first developed and validated for linear 10 1D and 2D irregular surface models, whose amplitude coefficients are estimated based on minimizing the mean square 11 error of simulated surface elevations to measurements, over space and time (for a specified time initialization period). 12 In the validation tests reported here, irregular ocean surfaces are generated based on a directional Pierson-Moskowitz 13 or Elfouhaily spectrum, and simulated LIDAR data sets are constructed by performing geometric intersections of laser 14 rays with each generated surface. Once a nowcast of the ocean surface is estimated from the (simulated) LIDAR data, 15 a forecast can be made of expected waves ahead of the vessel, for a time window that depends both on the initialization 16 period and the resolved wavenumbers in the reconstruction. The process can then be repeated for another prediction 17 window, and so forth. To reconstruct severe sea states, however, nonlinear effects must be included in the sea surface 18 representation. This is done, here, by representing the ocean surface using the efficient Lagrangian “Choppy wave 19 model”([1]). 20