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Evaluation Of The Weather Research And Forecasting Model On Forecasting Low-level Jets: Implications For Wind Energy

机译:预报低空喷气机的天气研究和预报模型的评估:对风能的影响

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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.
机译:夜间低空急流(LLJ)事件通常在美国大平原地区观察到,因此使该地区更有利于风能生产。同时,LLJs的存在可显着改变风力涡轮机轮毂高度附近的垂直剪切力和夜间湍流,因此对涡轮机转子产生不利影响。因此,需要精确的数值模拟和LLJ的预测来精确评估风能,可靠地预测发电量以及风力涡轮机的坚固设计。然而,中尺度数值天气预报模型在精确预测低空急流的发展,大小和位置方面面临挑战。这是由于LLJ在夜间稳定边界层中很常见,并且在文献中有一个普遍共识,即我们对这种边界层机制的当代理解和建模能力非常差。在本文中,我们研究了天气研究与预测(WRF)模型在预测西德克萨斯州和堪萨斯州南部的LLJ事件中的潜力。来自这两种情况的详细观测数据用于评估具有不同模型配置的WRF模型的性能。我们的结果表明,WRF模型可以捕获观测到的LLJ的一些基本特征,从而为提高风资源估计和短期风能预测的准确性提供了前景。但是,LLJ的核心往往比所观察到的更高和更慢,为模型性能留有改进的余地。

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