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Empirical estimation of nearshore waves from a global deep-water wave model

journal contribution
posted on 06.12.2017, 00:00 by Matthew BrowneMatthew Browne, D Strauss, B Castelle, M Blumenstein, R Tomlinson, C Lane
Global wind-wave models such as the National Oceanic and Atmospheric Administration Wave Watch 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).

History

Volume

3

Issue

4

Start Page

462

End Page

466

Number of Pages

5

ISSN

1545-598X

Location

United States

Publisher

IEEE

Language

en-aus

Peer Reviewed

Yes

Open Access

No

External Author Affiliations

CoastalWatch Australia; Griffith University;

Era Eligible

No

Journal

IEEE geoscience and remote sensing letters.