Empirical Estimation of Nearshore Waves From a Global Deep-Water Wave Model

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Title Empirical Estimation of Nearshore Waves From a Global Deep-Water Wave Model
Author Browne, Matthew; Strauss, Darrell; Castelle, Bruno Olivier; Blumenstein, Michael Myer; Tomlinson, Rodger Benson; Lane, Chris
Journal Name IEEE Geoscience and Remote Sensing Letters
Editor William J. Emery
Year Published 2006
Place of publication New York
Publisher Institute of Electrical and Electronic Engineers
Abstract Global wind-wave models such as the National Oceanic and Atmospheric AdministrationWaveWatch 3 (NWW3) play an important role in monitoring the world's oceans. However, untransformed data at grid points in deep water provide a poor estimate of swell characteristics at nearshore locations, which are often of significant scientific, engineering, and public interest. Explicit wave modeling, such as the Simulating Waves Nearshore (SWAN), is one method for resolving the complex wave transformations affected by bathymetry, winds, and other local factors. However, obtaining accurate bathymetry and determining parameters for such models is often difficult. When target data is available (i.e., from in situ buoys or human observers, empirical alternatives such artificial neural networks (ANNs) and linear regression may be considered for inferring nearshore conditions from offshore model output. Using a sixfold cross-validation scheme, significant wave height Hs and period were estimated at one onshore and two nearshore locations. In estimating Hs at the shoreline, the validation performance of the best ANN was r = 0.91, as compared to those of linear regression (0.82), SWAN (0.78), and the NWW3 Hs baseline (0.54).
Peer Reviewed Yes
Published Yes
Publisher URI http://ieeexplore.ieee.org/Xplore/dynhome.jsp
Alternative URI http://dx.doi.org/10.1109/LGRS.2006.876225
Copyright Statement Copyright 2006 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
Volume 3
Issue Number 4
Page from 462
Page to 466
ISSN 1545-598X
Date Accessioned 2007-03-15
Language en_AU
Research Centre Griffith Centre for Coastal Management; Institute for Integrated and Intelligent Systems
Faculty Faculty of Science, Environment, Engineering and Technology
Subject PRE2009-Neural Networks, Genetic Alogrithms and Fuzzy Logic; PRE2009-Oceanography
URI http://hdl.handle.net/10072/14365
Publication Type Journal Articles (Refereed Article)
Publication Type Code c1

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