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Sensitivity analysis of observational nudging methodology to reduce error in wind resource assessment (WRA) in the North Sea

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  • Mylonas, M.P.
  • Barbouchi, S.
  • Herrmann, H.
  • Nastos, P.T.

Abstract

Towards the improvement of the mesoscale modeling for offshore wind application, the real time observational nudging capability of the Weather Research and Forecasting (WRF) model has been implemented aiming for enhanced model performance. Utilizing three different horizontal levels of the offshore meteorological mast, FINO3, in the North Sea, wind speed observations were integrated into the model core. The performance of this modified model was then assessed for three different atmospheric stability conditions. Results from this study, illustrate that for all three stratification cases, there is a significant improvement in model performance when using observational nudging showing a reduction in Root Mean Square Error of up to 27% when compared to the observations from FINO1 platform. This study suggests that observational nudging takes a step towards more accurate simulations in wind resource assessment (WRA).

Suggested Citation

  • Mylonas, M.P. & Barbouchi, S. & Herrmann, H. & Nastos, P.T., 2018. "Sensitivity analysis of observational nudging methodology to reduce error in wind resource assessment (WRA) in the North Sea," Renewable Energy, Elsevier, vol. 120(C), pages 446-456.
    • Handle: RePEc:eee:renene:v:120:y:2018:i:c:p:446-456
    DOI: 10.1016/j.renene.2017.12.088
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    References listed on IDEAS

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    1. Cheng, William Y.Y. & Liu, Yubao & Bourgeois, Alfred J. & Wu, Yonghui & Haupt, Sue Ellen, 2017. "Short-term wind forecast of a data assimilation/weather forecasting system with wind turbine anemometer measurement assimilation," Renewable Energy, Elsevier, vol. 107(C), pages 340-351.
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    3. 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.
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    1. Salvação, Nadia & Bentamy, Abderrahim & Guedes Soares, C., 2022. "Developing a new wind dataset by blending satellite data and WRF model wind predictions," Renewable Energy, Elsevier, vol. 198(C), pages 283-295.
    2. Kim, Ji-Young & Oh, Ki-Yong & Kim, Min-Suek & Kim, Kwang-Yul, 2019. "Evaluation and characterization of offshore wind resources with long-term met mast data corrected by wind lidar," Renewable Energy, Elsevier, vol. 144(C), pages 41-55.

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