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Accuracy Comparison of Coastal Wind Speeds between WRF Simulations Using Different Input Datasets in Japan

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  • Takeshi Misaki

    (Graduate School of Maritime Sciences, Kobe University, 5-1-1 Fukae-minami, Higashinada-Ku, Kobe, Hyogo 658-0022, Japan)

  • Teruo Ohsawa

    (Graduate School of Maritime Sciences, Kobe University, 5-1-1 Fukae-minami, Higashinada-Ku, Kobe, Hyogo 658-0022, Japan)

  • Mizuki Konagaya

    (Graduate School of Maritime Sciences, Kobe University, 5-1-1 Fukae-minami, Higashinada-Ku, Kobe, Hyogo 658-0022, Japan
    E&E Solutions Inc., Akihabara UDX Building, 4-14-1 Sotokanda, Chiyoda-Ku, Tokyo 101-0021, Japan)

  • Susumu Shimada

    (National Institute of Advanced Industrial Science and Technology, 2-2-9 Machiikedai, Koriyama, Fukushima 963-0298, Japan)

  • Yuko Takeyama

    (Department of Marine Resources and Energy, Tokyo University of Marine Science and Technology, 4-5-7 Konan, Minato-Ku, Tokyo 108-8477, Japan)

  • Satoshi Nakamura

    (National Institute of Maritime, Port and Aviation Technology, Port and Airport Research Institute, 3-1-1 Nagase, Yokosuka 239-0826, Japan)

Abstract

In order to improve the accuracy of the wind speed simulated by a mesoscale model for the wind resource assessment in coastal areas, this study evaluated the effectiveness of using the Japan Meteorological Agency (JMA)’s latest and finest (2 km × 2 km) grid point value (GPV) data, produced from the local forecast model (LFM) as input data to the mesoscale model. The evaluation was performed using wind lidar measurements at two sites located on the coasts of the Sea of Japan and Pacific Ocean. The accuracy of the LFM–GPV was first compared with that of two products from the JMA Meso Scale Model (MSM) (5 km × 5 km): MSM-GPV and mesoscale analysis (MANAL). Consequently, it was shown that LFM–GPV exhibited the most accurate wind speeds against lidar measurements. Next, dynamical downscaling simulations were performed using the weather research and forecasting model (WRF) forced by the three datasets above, and their results were compared. As compared to the GPVs, it was found that the WRF dynamical downscaling simulation using them as input can improve the accuracy of the coastal wind speeds. This was attributed to the advantage of the WRF simulation to improve the negative bias from the input data, especially for the winds blowing from the sea sectors. It was also found that even if the LFM–GPV is used as an input to the WRF simulation, it does not always reproduce more accurate wind speeds, as compared to the simulations using the other two datasets. This result is partly owing to the tendency of WRF to overestimate the wind speed over land, thus obscuring the higher accuracy of the LFM–GPV. It was also shown that the overestimation tendency cannot be improved by only changing the nudging methods or the planetary boundary layer schemes in WRF. These results indicate that it may be difficult to utilize the LFM–GPV in the WRF wind simulation, unless the overestimation tendency of WRF itself is improved first.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:14:p:2754-:d:249508
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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.
    2. Mattar, Cristian & Borvarán, Dager, 2016. "Offshore wind power simulation by using WRF in the central coast of Chile," Renewable Energy, Elsevier, vol. 94(C), pages 22-31.
    3. Carvalho, D. & Rocha, A. & Gómez-Gesteira, M. & Silva Santos, C., 2014. "WRF wind simulation and wind energy production estimates forced by different reanalyses: Comparison with observed data for Portugal," Applied Energy, Elsevier, vol. 117(C), pages 116-126.
    4. Carvalho, D. & Rocha, A. & Gómez-Gesteira, M. & Silva Santos, C., 2014. "Offshore wind energy resource simulation forced by different reanalyses: Comparison with observed data in the Iberian Peninsula," Applied Energy, Elsevier, vol. 134(C), pages 57-64.
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    Cited by:

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    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.

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