Multi-Decade High-Resolution Regional Hindcasts for Wave Energy Resource Characterization in U.S. Coastal Waters

Zhaoqing Yang, Gabriel Garcia Medina, Vincent Neary, Seongho Ahn, Levi Kilcher, Aidan Bharath

Research output: Contribution to journalArticlepeer-review

2 Scopus Citations

Abstract

Long-term, high-resolution, regional wave hindcast datasets were generated using unstructured-grid Simulating WAves Nearshore (SWAN) models for the U.S. coastal waters to support nearshore wave energy development in the U.S. including those bordering U.S. territorial islands. The model domains resolved the entire U.S. exclusive economic zones, with a spatial resolution of approximately 200 m nearshore. The regional SWAN models were driven by the global WAVEWATCH III® model outputs and run for a 42-year period from 1979 to 2020. Extensive model validations were performed using buoy observations and altimeter data. Regional resource characterization was performed based on hindcast data points at 2 km from shore and along the 100 m isobath. Aggregations of wave resource parameters were produced, and spatial and seasonal variations were analyzed for all the regions. Wave resource metrics recommended by international standards, including a 3-h time series of six resource parameters, hourly frequency- and directionally resolved wave spectra at selected “virtual buoy” locations, and average-annual values of omni-directional wave power, significant wave height, and energy period are publicly disseminated through an Amazon Web Service and a Marine Energy Atlas web application tool to facilitate wave energy research and a wide range of coastal ocean applications.

Original languageAmerican English
Pages (from-to)803-817
Number of pages15
JournalRenewable Energy
Volume212
DOIs
StatePublished - 2023

Bibliographical note

Publisher Copyright:
© 2023 Elsevier Ltd

NREL Publication Number

  • NREL/JA-5700-86041

Keywords

  • High-resolution
  • Long-term simulations
  • Nearshore
  • Resource characterization
  • Wave energy
  • Wave modeling

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