Wyatt, L.R. and Green, J.J. (2024) Swell and wind-sea partitioning of HF radar directional spectra. Journal of Operational Oceanography, 17 (1). pp. 28-39. ISSN 1755-876X
Abstract
Partitioning is a process used to separate wind-sea and swell contributions in an ocean wave directional spectrum to simplify, and hence make more useful, the interpretation of the spectrum for users of wave data. HF radar systems can measure the wave spectrum over regions of the coastal ocean from the coast to over 100 km offshore with good spatial and temporal resolution depending on the operating frequency and bandwidth. Such systems can measure hundreds of directional spectra across the field of view of the radar, so there is very strong motivation to reduce the dimensionality of the data set for practical applications using partitioning. For similar reasons partitioning methods are increasingly being used for wave model and satellite-measured spectra. A partitioning method, which extends the method of Waters J, Wyatt LR, Wolf J, Hines A. [2013. Data assimilation of partitioned HF radar wave data into Wavewatch III. Ocean Model. 72:17–31.] for HF radar data, is described, assessed using buoy data and used to demonstrate the spatial variability of both swell and wind waves in three coastal regions. The results are very encouraging. HF radar systems could therefore provide very useful data for wave model and satellite partitioning validations in coastal waters where model and satellite measurements are most challenged by wave-current interactions, coastal topography and bathymetry.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2022 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited, and is not altered, transformed, or built upon in any way. |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Science (Sheffield) > School of Mathematics and Statistics (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 15 Sep 2022 12:42 |
Last Modified: | 25 Sep 2024 12:23 |
Status: | Published |
Publisher: | Taylor & Francis |
Refereed: | Yes |
Identification Number: | 10.1080/1755876x.2022.2127232 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:190689 |
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