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A preliminary sensitivity study of Planetary Boundary Layer parameterisation schemes in the weather research and forecasting model to surface winds in coastal Ghana

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  • Dzebre, Denis E.K.
  • Adaramola, Muyiwa S.

Abstract

There is growing interest in the use of Weather Researching and Forecasting (WRF) model for assessment of wind energy potential. The influence of parameterisation schemes in these models depends on meteorological processes, which tend to differ with geographic regions. In this paper, we test the sensitivity of surface winds in an area in Ghana, to 11 of the Planetary Boundary Layer schemes available in WRF. Thirty-six days were simulated with the schemes. Hourly simulated wind speeds and directions were compared with measurements taken at 40, 50, and 60m above ground level, and the schemes ranked according to a prediction skill score calculated according to how well their predictions compared to observations. The local closure MYNN schemes offered consistently good performance; often predicting the average wind speed with a Root Mean Square Error of less than <2 m/s, indicating good performance. However, the GBM and UW schemes produced relatively better results for days selected from a period in which monthly average winds at this location are highest. Based on our results, we recommend the MYNN3 (and the GBM, depending on the season of the year) for wind simulations in this area, and areas with similar topography and climate in Ghana.

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  • Dzebre, Denis E.K. & Adaramola, Muyiwa S., 2020. "A preliminary sensitivity study of Planetary Boundary Layer parameterisation schemes in the weather research and forecasting model to surface winds in coastal Ghana," Renewable Energy, Elsevier, vol. 146(C), pages 66-86.
    • Handle: RePEc:eee:renene:v:146:y:2020:i:c:p:66-86
    DOI: 10.1016/j.renene.2019.06.133
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    References listed on IDEAS

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    1. 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.
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    Cited by:

    1. Jafarzadeh Ghoushchi, Saeid & Manjili, Sobhan & Mardani, Abbas & Saraji, Mahyar Kamali, 2021. "An extended new approach for forecasting short-term wind power using modified fuzzy wavelet neural network: A case study in wind power plant," Energy, Elsevier, vol. 223(C).
    2. Denis E.K. Dzebre & Muyiwa S. Adaramola, 2019. "Impact of Selected Options in the Weather Research and Forecasting Model on Surface Wind Hindcasts in Coastal Ghana," Energies, MDPI, vol. 12(19), pages 1-16, September.
    3. Wang, Qiang & Luo, Kun & Yuan, Renyu & Wang, Shuai & Fan, Jianren & Cen, Kefa, 2020. "A multiscale numerical framework coupled with control strategies for simulating a wind farm in complex terrain," Energy, Elsevier, vol. 203(C).
    4. He, Yuhang & Han, Xingxing & Xu, Chang & Cheng, Zhe & Wang, Jincheng & Liu, Wei & Xu, Dong, 2023. "Sensitivity of simulated wind power under diverse spatial scales and multiple terrains using the weather research and forecasting model," Energy, Elsevier, vol. 285(C).
    5. Duarte Jacondino, William & Nascimento, Ana Lucia da Silva & Calvetti, Leonardo & Fisch, Gilberto & Augustus Assis Beneti, Cesar & da Paz, Sheila Radman, 2021. "Hourly day-ahead wind power forecasting at two wind farms in northeast Brazil using WRF model," Energy, Elsevier, vol. 230(C).
    6. Wu, Chunlei & Luo, Kun & Wang, Qiang & Fan, Jianren, 2022. "Simulated potential wind power sensitivity to the planetary boundary layer parameterizations combined with various topography datasets in the weather research and forecasting model," Energy, Elsevier, vol. 239(PB).

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