Evaluation of the weather research and forecasting model on forecasting low-level jets: Implications for wind energy

Brandon Storm, Jimy Dudhia, Sukanta Basu, Andy Swift, Ian Giammanco

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    166 Scopus citations


    Nocturnal low-level jet (LLJ) events are commonly observed over the Great Plains region of the USA, thus making this region more favorable for wind energy production. At the same time, the presence of LLJs can significantly modify vertical shear and nocturnal turbulence in the vicinities of wind turbine hub height, and therefore has detrimental effects on turbine rotors. Accurate numerical modeling and forecasting of LLJs are thus needed for precise assessment of wind resources, reliable prediction of power generation and robust design of wind turbines. However, mesoscale numerical weather prediction models face a challenge in precisely forecasting the development, magnitude and location of LLJs. This is due to the fact that LLJs are common in nocturnal stable boundary layers, and there is a general consensus in the literature that our contemporary understanding and modeling capability of this boundary-layer regime is quite poor. In this paper, we investigate the potential of the Weather Research and Forecasting (WRF) model in forecasting LLJ events over West Texas and southern Kansas. Detailed observational data from both cases were used to assess the performance of the WRF model with different model configurations. Our results indicate that the WRF model can capture some of the essential characteristics of observed LLJs, and thus offers the prospect of improving the accuracy of wind resource estimates and short-term wind energy forecasts. However, the core of the LLJ tended to be higher as well as slower than what was observed, leaving room for improvement in model performance.

    Original languageEnglish
    Pages (from-to)81-90
    Number of pages10
    JournalWind Energy
    Issue number1
    StatePublished - Jan 2009


    • Atmospheric boundary layer
    • Low-level jet
    • Numerical weather prediction
    • Resource assessment
    • Short-term wind prediction


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