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A unified high-resolution wind and solar dataset from a rapidly updating numerical weather prediction model

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  • James, Eric P.
  • Benjamin, Stanley G.
  • Marquis, Melinda

Abstract

A new gridded dataset for wind and solar resource estimation over the contiguous United States has been derived from hourly updated 1-h forecasts from the National Oceanic and Atmospheric Administration High-Resolution Rapid Refresh (HRRR) 3-km model composited over a three-year period (approximately 22 000 forecast model runs). The unique dataset features hourly data assimilation, and provides physically consistent wind and solar estimates for the renewable energy industry. The wind resource dataset shows strong similarity to that previously provided by a Department of Energy-funded study, and it includes estimates in southern Canada and northern Mexico. The solar resource dataset represents an initial step towards application-specific fields such as global horizontal and direct normal irradiance. This combined dataset will continue to be augmented with new forecast data from the advanced HRRR atmospheric/land-surface model.

Suggested Citation

  • James, Eric P. & Benjamin, Stanley G. & Marquis, Melinda, 2017. "A unified high-resolution wind and solar dataset from a rapidly updating numerical weather prediction model," Renewable Energy, Elsevier, vol. 102(PB), pages 390-405.
    • Handle: RePEc:eee:renene:v:102:y:2017:i:pb:p:390-405
    DOI: 10.1016/j.renene.2016.10.059
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    References listed on IDEAS

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    1. Draxl, Caroline & Clifton, Andrew & Hodge, Bri-Mathias & McCaa, Jim, 2015. "The Wind Integration National Dataset (WIND) Toolkit," Applied Energy, Elsevier, vol. 151(C), pages 355-366.
    2. Carta, José A. & Velázquez, Sergio & Cabrera, Pedro, 2013. "A review of measure-correlate-predict (MCP) methods used to estimate long-term wind characteristics at a target site," Renewable and Sustainable Energy Reviews, Elsevier, vol. 27(C), pages 362-400.
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    Cited by:

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    3. Nguyen Gia Minh Thao & Kenko Uchida, 2018. "An Improved Interval Fuzzy Modeling Method: Applications to the Estimation of Photovoltaic/Wind/Battery Power in Renewable Energy Systems," Energies, MDPI, vol. 11(3), pages 1-26, February.
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    5. Hodge, Bri-Mathias & Brancucci Martinez-Anido, Carlo & Wang, Qin & Chartan, Erol & Florita, Anthony & Kiviluoma, Juha, 2018. "The combined value of wind and solar power forecasting improvements and electricity storage," Applied Energy, Elsevier, vol. 214(C), pages 1-15.
    6. Gil-García, Isabel C. & Ramos-Escudero, Adela & García-Cascales, M.S. & Dagher, Habib & Molina-García, A., 2022. "Fuzzy GIS-based MCDM solution for the optimal offshore wind site selection: The Gulf of Maine case," Renewable Energy, Elsevier, vol. 183(C), pages 130-147.
    7. deCastro, M. & Salvador, S. & Gómez-Gesteira, M. & Costoya, X. & Carvalho, D. & Sanz-Larruga, F.J. & Gimeno, L., 2019. "Europe, China and the United States: Three different approaches to the development of offshore wind energy," Renewable and Sustainable Energy Reviews, Elsevier, vol. 109(C), pages 55-70.

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