Modeling of low-level jets over the Great Plains: Implications for wind energy.
Storm, Brandon Allen
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Low-level jets (LLJs), wind maximums centered 100 - 1000 m above the ground, are common features observed over the Great Plains of the United States. An accurate understanding of LLJs has many implications for the wind power industry. For example, LLJs can increase wind speeds at turbine heights, which in turn leads to an increase in energy. However, these same high speed winds created by LLJs can create large amounts of stress on the turbines, causing fatigue issues over time. Without a proper understanding of the LLJ, accurate estimates of the wind speeds at hub height, which are essential for wind resource assessment and forecasting projects, are very difficult to obtain. When assessing a particular location for placement of a wind farm, it is common within the wind power industry to use towers that do not reach the height of the turbine hubs to estimate the hub height wind speeds. Therefore, the hub height wind speeds are estimated by using lower-level wind speed measurements (60 m or lower) and assuming a simple power law relationship. However, to obtain an accurate estimate of the hub height and higher wind speeds, one has to know what shear exponent value to assume. The presence of LLJs causes the shear exponent to be significantly higher than what the industry currently assumes. The Weather Research and Forecasting (WRF) model simulates the low-level wind speed over a majority of the Great Plains in a manner that could be used to estimate the shear exponent over the Great Plains. However, a comparative investigation between LLJ climatologies developed from WRF model output and observed profiler data indicates that the WRF model struggles in forecasting the frequency, speed and height of LLJs. For regions with strong and frequent LLJs (e.g., southern Kansas), the underestimation in LLJs results in lower predicted shear exponents than observed. Detailed investigations of two LLJ events reveal similar problems in accurately forecasting the heights and speeds of LLJs, as well as sensitivity to boundary layer parameterizations. To determine if accurate WRF based wind power resource assessments can be accomplished, a framework was developed to downscale the coarse WRF model output with a wind resource analysis tool commonly used within the industry, the Wind Atlas Analysis and Application Program (WAsP). The dynamic downscaling accounts for fine scale topography and surface roughness features that can have a large impact on low-level wind fields. It was found that the WRF model can be used as input into WAsP, and in the future could possibly be a replacement to tower observations when completing preliminary resource assessment projects. This would allow the wind power industry to complete site assessment projects in a timely and economically efficient manner.