OceanWave Reconstruction Algorithms Based On Spatio-temporal 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 ASC Flash LIDAR 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. In its current design, the camera generates a 64 × 64 matrix of laser rays, providing as many simultaneous measurements of the distance to the ocean surface, every 0.05–0.25 s, in an angular sector of 15–20 by 9–15 deg. (depending on design assumptions). From this data and the camera's location and orientation, the coordinates of the measured surface points can be generated as a function of time; this yields a sample of spatio-temporal wave elevation data. Due to wave shadowing effects, 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 1D and 2D irregular surface models, whose amplitude coefficients are estimated based on 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 based on a directional Phillips or JS 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. In the paper, we develop and validate the 1D surface generation and reconstruction of irregular sea states using Choppy.