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Impact of Selected Options in the Weather Research and Forecasting Model on Surface Wind Hindcasts in Coastal Ghana

Author

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  • Denis E.K. Dzebre

    (Faculty of Environmental Sciences and Natural Resources, Norwegian University of Life Sciences (NMBU), 1432 Akershus, Norway
    Department of Mechanical Engineering, Kwame Nkrumah University of Science and Technology (KNUST), Kumasi 00000, Ghana
    The Brew-Hammond Energy Centre, KNUST, Kumasi 00000, Ghana)

  • Muyiwa S. Adaramola

    (Faculty of Environmental Sciences and Natural Resources, Norwegian University of Life Sciences (NMBU), 1432 Akershus, Norway)

Abstract

This paper examines the impacts of five planetary boundary layer (PBL) parameterization schemes paired with several compatible surface layer (SL) parameterization schemes in the Weather Research and Forecasting Model on wind hindcasts for resource assessment purposes in a part of Coastal Ghana. Model predictions of hourly wind speeds at 3 × 3 km 2 and 9 × 9 km 2 grid boxes were compared with measurements at 40 m, 50 m, and 60 m. It was found that the Mellor-Yamada Nakanishi and Niino Level 3 (MYNN3) PBL scheme generally predicted winds with a relatively better combination of error metrics, irrespective of the SL scheme it was paired with. When paired with the Eta surface layer scheme, it often produced some of the relatively fewest errors in estimated mean wind power density (WPD) and Weibull cumulative density. A change in the simulation grid size did not have a significant impact on the conclusions of the relative performance of the PBL-SL pairs that were tested. The results indicate that the MYNN3 PBL and Eta SL pair is probably best for wind speed and energy assessments for this part of coastal Ghana.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:19:p:3670-:d:270648
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    References listed on IDEAS

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    1. Camilo Carrillo & José Cidrás & Eloy Díaz-Dorado & Andrés Felipe Obando-Montaño, 2014. "An Approach to Determine the Weibull Parameters for Wind Energy Analysis: The Case of Galicia (Spain)," Energies, MDPI, vol. 7(4), pages 1-25, April.
    2. Frederick Armah & Justice Odoi & Genesis Yengoh & Samuel Obiri & David Yawson & Ernest Afrifa, 2011. "Food security and climate change in drought-sensitive savanna zones of Ghana," Mitigation and Adaptation Strategies for Global Change, Springer, vol. 16(3), pages 291-306, March.
    3. 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.
    4. Mohammadpour Penchah, Mohammadreza & Malakooti, Hossein & Satkin, Mohammad, 2017. "Evaluation of planetary boundary layer simulations for wind resource study in east of Iran," Renewable Energy, Elsevier, vol. 111(C), pages 1-10.
    5. Takeshi Misaki & Teruo Ohsawa & Mizuki Konagaya & Susumu Shimada & Yuko Takeyama & Satoshi Nakamura, 2019. "Accuracy Comparison of Coastal Wind Speeds between WRF Simulations Using Different Input Datasets in Japan," Energies, MDPI, vol. 12(14), pages 1-20, July.
    6. 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.
    7. Dongbum Kang & Kyungnam Ko & Jongchul Huh, 2018. "Comparative Study of Different Methods for Estimating Weibull Parameters: A Case Study on Jeju Island, South Korea," Energies, MDPI, vol. 11(2), pages 1-19, February.
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