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Optimizing the Weather Research and Forecasting (WRF) Model for Mapping the Near-Surface Wind Resources over the Southernmost Caribbean Islands of Trinidad and Tobago

Author

Listed:
  • Xsitaaz T. Chadee

    (Environmental Physics Laboratory, Department of Physics, Faculty of Science and Technology, The University of the West Indies, St. Augustine, Trinidad and Tobago)

  • Naresh R. Seegobin

    (Department of Computing and Information Technology, Faculty of Science and Technology, The University of the West Indies, St. Augustine, Trinidad and Tobago)

  • Ricardo M. Clarke

    (Environmental Physics Laboratory, Department of Physics, Faculty of Science and Technology, The University of the West Indies, St. Augustine, Trinidad and Tobago)

Abstract

Numerical wind mapping is currently the wind power industry’s standard for preliminary assessments for sites of good wind resources. Of the various available numerical models, numerical weather prediction (NWP) models are best suited for modeling mesoscale wind flows across small islands. In this study, the Weather Research and Forecast (WRF) NWP model was optimized for simulating the wind resources of the Caribbean islands of Trinidad and Tobago in terms of spin-up period for developing mesoscale features, the input initial and boundary conditions, and the planetary boundary layer (PBL) parameterizations. Hourly model simulations of wind speed and wind direction for a one-month period were compared with corresponding 10 m level wind observations. The National Center for Environmental Prediction (NCEP)-Department of Energy (DOE) reanalysis of 1.875° horizontal resolution was found to be better suited to provide initial and boundary conditions (ICBCs) over the 1° resolution NCEP final analysis (FNL); 86% of modeled wind speeds were within ±2 m/s of measured values at two locations when the coarse resolution NCEP reanalysis was used as compared with 55–64% of modeled wind speeds derived from FNL. Among seven PBL schemes tested, the Yonsei University PBL scheme with topographic drag enabled minimizes the spatial error in wind speed (mean bias error +0.16 m/s, root-mean-square error 1.53 m/s and mean absolute error 1.21 m/s) and is capable of modeling the bimodal wind speed histogram. These sensitivity tests provide a suitable configuration for the WRF model for mapping the wind resources over Trinidad and Tobago, which is a factor in developing a wind energy sector in these islands.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:10:y:2017:i:7:p:931-:d:103657
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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.
    3. 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).
    4. Jonghoon Jin & Yuzhang Che & Jiafeng Zheng & Feng Xiao, 2019. "Uncertainty Quantification of a Coupled Model for Wind Prediction at a Wind Farm in Japan," Energies, MDPI, vol. 12(8), pages 1-18, April.
    5. Mekalathur B Hemanth Kumar & Saravanan Balasubramaniyan & Sanjeevikumar Padmanaban & Jens Bo Holm-Nielsen, 2019. "Wind Energy Potential Assessment by Weibull Parameter Estimation Using Multiverse Optimization Method: A Case Study of Tirumala Region in India," Energies, MDPI, vol. 12(11), pages 1-21, June.
    6. 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.
    7. Wu, Chunlei & Luo, Kun & Wang, Qiang & Fan, Jianren, 2022. "Simulated potential wind power sensitivity to the planetary boundary layer parameterizations combined with various topography datasets in the weather research and forecasting model," Energy, Elsevier, vol. 239(PB).

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