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The WRF Model Forecast-Derived Low-Level Wind Shear Climatology over the United States Great Plains

Author

Listed:
  • Brandon Storm

    (Wind Science and Engineering Research Center, Texas Tech University, Lubbock, TX, USA)

  • Sukanta Basu

    (Atmospheric Science Group, Department of Geosciences, Texas Tech University, Lubbock, TX, USA)

Abstract

For wind resource assessment projects, it is common practice to use a power-law relationship (U( z ) ~ z α ) and a fixed shear exponent (α = 1=7) to extrapolate the observed wind speed from a low measurement level to high turbine hub-heights. However, recent studies using tall-tower observations have found that the annual average shear exponents at several locations over the United States Great Plains (USGP) are significantly higher than 1=7. These findings highlight the critical need for detailed spatio-temporal characterizations of wind shear climatology over the USGP, where numerous large wind farms will be constructed in the foreseeable future. In this paper, a new generation numerical weather prediction model—the Weather Research and Forecasting (WRF) model, a fast and relatively inexpensive alternative to time-consuming and costly tall-tower projects, is utilized to determine whether it can reliably estimate the shear exponent and the magnitude of the directional shear at any arbitrary location over the USGP. Our results indicate that the WRF model qualitatively captures several low-level wind shear characteristics. However, there is definitely room for physics parameterization improvements for the WRF model to reliably represent the lower part of the atmospheric boundary layer.

Suggested Citation

  • Brandon Storm & Sukanta Basu, 2010. "The WRF Model Forecast-Derived Low-Level Wind Shear Climatology over the United States Great Plains," Energies, MDPI, vol. 3(2), pages 1-19, February.
    • Handle: RePEc:gam:jeners:v:3:y:2010:i:2:p:258-276:d:7191
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    Citations

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    Cited by:

    1. Sward, J.A. & Ault, T.R. & Zhang, K.M., 2023. "Spatial biases revealed by LiDAR in a multiphysics WRF ensemble designed for offshore wind," Energy, Elsevier, vol. 262(PA).
    2. Gunnell, Yanni & Mietton, Michel & Touré, Amadou Abdourhamane & Fujiki, Kenji, 2023. "Potential for wind farming in West Africa from an analysis of daily peak wind speeds and a review of low-level jet dynamics," Renewable and Sustainable Energy Reviews, Elsevier, vol. 188(C).
    3. Xsitaaz T. Chadee & Naresh R. Seegobin & Ricardo M. Clarke, 2017. "Optimizing the Weather Research and Forecasting (WRF) Model for Mapping the Near-Surface Wind Resources over the Southernmost Caribbean Islands of Trinidad and Tobago," Energies, MDPI, vol. 10(7), pages 1-23, July.
    4. Jonghoon Jin & Yuzhang Che & Jiafeng Zheng & Feng Xiao, 2019. "Uncertainty Quantification of a Coupled Model for Wind Prediction at a Wind Farm in Japan," Energies, MDPI, vol. 12(8), pages 1-18, April.
    5. Rebecca J. Barthelmie & Tristan J. Shepherd & Jeanie A. Aird & Sara C. Pryor, 2020. "Power and Wind Shear Implications of Large Wind Turbine Scenarios in the US Central Plains," Energies, MDPI, vol. 13(16), pages 1-21, August.
    6. Gibson, Peter B. & Cullen, Nicolas J., 2015. "Synoptic and sub-synoptic circulation effects on wind resource variability – A case study from a coastal terrain setting in New Zealand," Renewable Energy, Elsevier, vol. 78(C), pages 253-263.

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