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Verification of the Reliability of Offshore Wind Resource Prediction Using an Atmosphere–Ocean Coupled Model

Author

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  • Minhyeop Kang

    (Multidisciplinary Graduate School Program for Wind Energy, Jeju National University, 102 Jejudaehakro, Jeju 63243, Korea)

  • Kyungnam Ko

    (Faculty of Wind Energy Engineering, Jeju National University, 102 Jejudaehakro, Jeju 63243, Korea)

  • Minyeong Kim

    (National Typhoon Center, Jeju 63614, Korea)

Abstract

An atmosphere–ocean coupled model is proposed as an optimal numerical prediction method for the offshore wind resource. Meteorological prediction models are mainly used for wind speed prediction, with active studies using atmospheric models. Seawater mixing occurring at sea due to solar radiation and wind intensity can significantly change the sea surface temperature (SST), an important variable for predicting wind resources and energy production, considering its wind effect, within a short time. This study used the weather research forecasting and ocean mixed layer (WRF-OML) model, an atmosphere–ocean coupled model, to reflect time-dependent SST and sea surface fluxes. Results are compared with those of the WRF model, another atmospheric model, and verified through comparison with observation data of a meteorological mast (met-mast) at sea. At a height of 94 m, the wind speed predicted had a bias and root mean square error of 1.09 m/s and 2.88 m/s for the WRF model, and −0.07 m/s and 2.45 m/s for the WRF-OML model, respectively. Thus, the WRF-OML model has a higher reliability. In comparing to the met-mast observation data, the annual energy production (AEP) estimation based on the predicted wind speed showed an overestimation of 15.3% and underestimation of 5.9% from the WRF and WRF-OML models, respectively.

Suggested Citation

  • Minhyeop Kang & Kyungnam Ko & Minyeong Kim, 2020. "Verification of the Reliability of Offshore Wind Resource Prediction Using an Atmosphere–Ocean Coupled Model," Energies, MDPI, vol. 13(1), pages 1-15, January.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:1:p:254-:d:305068
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    References listed on IDEAS

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