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Surf zone bathymetry and circulation predictions via data assimilation of remote sensing observations
Author(s) -
Wilson G. W.,
ÖzkanHaller H. T.,
Holman R. A.,
Haller M. C.,
Honegger D. A.,
Chickadel C. C.
Publication year - 2014
Publication title -
journal of geophysical research: oceans
Language(s) - English
Resource type - Journals
eISSN - 2169-9291
pISSN - 2169-9275
DOI - 10.1002/2013jc009213
Subject(s) - bathymetry , data assimilation , rip current , surf zone , remote sensing , ensemble kalman filter , current (fluid) , kalman filter , geology , field (mathematics) , meteorology , environmental science , oceanography , geography , computer science , extended kalman filter , mathematics , artificial intelligence , pure mathematics , shore
Abstract 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.