Surf zone bathymetry and circulation predictions via data assimilation of remote sensing observations

Bathymetry is a major factor in determining nearshore and surf zone wave transformation and currents, yet is often poorly known. This can lead to inaccuracy in numerical model predictions. Here bathymetry is estimated as an uncertain parameter in a data assimilation system, using the ensemble Kalman filter (EnKF). The system is tested by assimilating several remote sensing data products, which were collected in September 2010 as part of a field experiment at the U.S. Army Corps of Engineers Field Research Facility (FRF) in Duck, NC. The results show that by assimilating remote sensing data alone, nearshore bathymetry can be estimated with good accuracy, and nearshore forecasts (e.g., the prediction of a rip current) can be improved. This suggests an application where a nearshore forecasting model could be implemented using only remote sensing data, without the explicit need for in situ data collection. Key Points Field application of a nearshore EnKF data assimilation system Remote sensing data are assimilated to improve model bathymetry After assimilating data, model prediction of a rip current is also improved