IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v12y2019i14p2754-d249508.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/12/14/2754/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/12/14/2754/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Mizuki Konagaya & Teruo Ohsawa & Toshinari Mito & Takeshi Misaki & Taro Maruo & Yasuyuki Baba, 2022. "Estimation of Nearshore Wind Conditions Using Onshore Observation Data with Computational Fluid Dynamic and Mesoscale Models," Resources, MDPI, vol. 11(11), pages 1-18, October.
    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Carvalho, D. & Rocha, A. & Gómez-Gesteira, M. & Silva Santos, C., 2017. "Offshore winds and wind energy production estimates derived from ASCAT, OSCAT, numerical weather prediction models and buoys – A comparative study for the Iberian Peninsula Atlantic coast," Renewable Energy, Elsevier, vol. 102(PB), pages 433-444.
    2. Tuy, Soklin & Lee, Han Soo & Chreng, Karodine, 2022. "Integrated assessment of offshore wind power potential using Weather Research and Forecast (WRF) downscaling with Sentinel-1 satellite imagery, optimal sites, annual energy production and equivalent C," Renewable and Sustainable Energy Reviews, Elsevier, vol. 163(C).
    3. González-Alonso de Linaje, N. & Mattar, C. & Borvarán, D., 2019. "Quantifying the wind energy potential differences using different WRF initial conditions on Mediterranean coast of Chile," Energy, Elsevier, vol. 188(C).
    4. 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.
    5. de Assis Tavares, Luiz Filipe & Shadman, Milad & Assad, Luiz Paulo de Freitas & Estefen, Segen F., 2022. "Influence of the WRF model and atmospheric reanalysis on the offshore wind resource potential and cost estimation: A case study for Rio de Janeiro State," Energy, Elsevier, vol. 240(C).
    6. Boudia, Sidi Mohammed & Santos, João Andrade, 2019. "Assessment of large-scale wind resource features in Algeria," Energy, Elsevier, vol. 189(C).
    7. de Assis Tavares, Luiz Filipe & Shadman, Milad & de Freitas Assad, Luiz Paulo & Silva, Corbiniano & Landau, Luiz & Estefen, Segen F., 2020. "Assessment of the offshore wind technical potential for the Brazilian Southeast and South regions," Energy, Elsevier, vol. 196(C).
    8. Alain Ulazia & Ander Nafarrate & Gabriel Ibarra-Berastegi & Jon Sáenz & Sheila Carreno-Madinabeitia, 2019. "The Consequences of Air Density Variations over Northeastern Scotland for Offshore Wind Energy Potential," Energies, MDPI, vol. 12(13), pages 1-18, July.
    9. Ulazia, Alain & Sáenz, Jon & Ibarra-Berastegi, Gabriel & González-Rojí, Santos J. & Carreno-Madinabeitia, Sheila, 2019. "Global estimations of wind energy potential considering seasonal air density changes," Energy, Elsevier, vol. 187(C).
    10. Dhunny, A.Z. & Timmons, D.S. & Allam, Z. & Lollchund, M.R. & Cunden, T.S.M., 2020. "An economic assessment of near-shore wind farm development using a weather research forecast-based genetic algorithm model," Energy, Elsevier, vol. 201(C).
    11. Carvalho, D. & Rocha, A. & Gómez-Gesteira, M. & Silva Santos, C., 2014. "Sensitivity of the WRF model wind simulation and wind energy production estimates to planetary boundary layer parameterizations for onshore and offshore areas in the Iberian Peninsula," Applied Energy, Elsevier, vol. 135(C), pages 234-246.
    12. Ulazia, Alain & Saenz, Jon & Ibarra-Berastegui, Gabriel, 2016. "Sensitivity to the use of 3DVAR data assimilation in a mesoscale model for estimating offshore wind energy potential. A case study of the Iberian northern coastline," Applied Energy, Elsevier, vol. 180(C), pages 617-627.
    13. Santos, F. & Gómez-Gesteira, M. & deCastro, M. & Añel, J.A. & Carvalho, D. & Costoya, Xurxo & Dias, J.M., 2018. "On the accuracy of CORDEX RCMs to project future winds over the Iberian Peninsula and surrounding ocean," Applied Energy, Elsevier, vol. 228(C), pages 289-300.
    14. Yuan, Renyu & Ji, Wenju & Luo, Kun & Wang, Jianwen & Zhang, Sanxia & Wang, Qiang & Fan, Jianren & Ni, MingJiang & Cen, Kefa, 2017. "Coupled wind farm parameterization with a mesoscale model for simulations of an onshore wind farm," Applied Energy, Elsevier, vol. 206(C), pages 113-125.
    15. Majidi Nezhad, Meysam & Neshat, Mehdi & Piras, Giuseppe & Astiaso Garcia, Davide, 2022. "Sites exploring prioritisation of offshore wind energy potential and mapping for wind farms installation: Iranian islands case studies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 168(C).
    16. Ulazia, Alain & Sáenz, Jon & Ibarra-Berastegui, Gabriel & González-Rojí, Santos J. & Carreno-Madinabeitia, Sheila, 2017. "Using 3DVAR data assimilation to measure offshore wind energy potential at different turbine heights in the West Mediterranean," Applied Energy, Elsevier, vol. 208(C), pages 1232-1245.
    17. Salvação, N. & Guedes Soares, C., 2018. "Wind resource assessment offshore the Atlantic Iberian coast with the WRF model," Energy, Elsevier, vol. 145(C), pages 276-287.
    18. Gualtieri, G., 2022. "Analysing the uncertainties of reanalysis data used for wind resource assessment: A critical review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 167(C).
    19. Ammara Kanwal & Zia ul Rehman Tahir & Muhammad Asim & Nasir Hayat & Muhammad Farooq & Muhammad Abdullah & Muhammad Azhar, 2023. "Evaluation of Reanalysis and Analysis Datasets against Measured Wind Data for Wind Resource Assessment," Energy & Environment, , vol. 34(5), pages 1258-1284, August.
    20. Chen, Xinping & Foley, Aoife & Zhang, Zenghai & Wang, Kaimin & O'Driscoll, Kieran, 2020. "An assessment of wind energy potential in the Beibu Gulf considering the energy demands of the Beibu Gulf Economic Rim," Renewable and Sustainable Energy Reviews, Elsevier, vol. 119(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:12:y:2019:i:14:p:2754-:d:249508. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.