REMOTE SENSING AND DATA ASSIMILATION FOR SURF ZONE BATHYMETRIC INVERSION

Abstract

We demonstrate the implementation and validation of a surf zone forecasting system, which uses remote sensing observations to control errors in surf zone bathymetry. This system uses ensemble-based sequential data assimilation techniques, which are adaptable to arbitrary geophysical observations, and/or arbitrary improvements to model physics. The system is validated using data from a 2010 field experiment at Duck, NC (U.S.A.), and is shown to produce accurate corrections to bathymetry, leading to improvements in prediction of currents.