Wave Hindcast Webinar: High-resolution regional hindcast datasets for wave energy resource characterization in US coastal waters

To support nearshore wave climate research and wave energy development in the U.S., long-term, high-resolution, regional wave hindcast datasets were generated using unstructured-grid Simulating WAves Nearshore (SWAN) models for the U.S. coastal waters, including those bordering U.S. territory islands. This large effort was funded by the US Department of Energy, Water Power Technologies Office and carried out by a joint research team of Pacific Northwest National Laboratories (PNNL), Sandia National Laboratories (SNL), and National Renewable Energy Laboratory (NREL). The model domains resolved the entire Exclusive Economic Zones (EEZ) of East Coast, West Coast, Alaska, Gulf of Mexico, Hawaii, Caribbean Sea and Pacific Islands, with a spatial resolution of approximate 200 m nearshore. The regional SWAN models were driven by global WAVEWATCH III® (WW3) model outputs and run for a 42-year period from 1979 to 2020. Extensive model validation and error characterization were performed using buoy data collected and maintained by the National Data Buoy Center (NDBC), Coastal Data Information Program (CDIP). Wave modeling and resource characterization followed International Electrotechnical Commission (IEC) standards. Hindcast datasets include 3-hour time series of six IEC wave resource parameters (omnidirectional wave power J, significant wave height Hm0, energy period Te, spectral width ϵ0, direction of maximum directionally resolved wave power θJmax, and directionality coefficient d) and hourly frequency- and directionally resolved (2D) wave spectra at selected “virtual buoy” locations. These datasets are publicly disseminated through an Amazon Web Service (AWS) open-data registry to facilitate nearshore wave energy research and a range of wave industry activities, e.g., regional energy planning, wave energy project development and wave energy conversion technology design. While the development of these datasets was motivated to support wave energy research and industry activities, they are also valuable for the offshore wind industry, and coastal engineering and resiliency applications. 

During this webinar, Drs. Zhaoqing Yang and Gabriel García-Medina (PNNL), on behalf of the Marine Energy Resource Characterization Team (PNNL, SNL, and NREL), discussed the overall effort and highlighted some technical details and challenges.

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