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Evaluation of the Operational Multi-scale Environment model with Grid Adaptivity (OMEGA) for use in Wind Energy Applications in the Great Basin of Nevada.

机译:内华达州大盆地风能应用中具有网格适应性(OMEGA)的可操作多尺度环境模型的评估。

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摘要

In order to further assess the wind energy potential for Nevada, the accuracy of a computational meteorological model, the Operational Multi-scale Environment model with Grid Adaptivity (OMEGA), was evaluated by comparing simulation results with data collected from a wind monitoring tower near Tonopah, NV. The state of Nevada is characterized by high mountains and low-lying valleys, therefore, in order to determine the wind potential for the state, meteorological models that predict the wind must be able to accurately represent and account for terrain features and simulate topographic forcing with accuracy. Topographic forcing has a dominant role in the development and modification of mesoscale flows in regions of complex terrain, like Tonopah, especially at the level of wind turbine blade heights (~80 m). Additionally, model factors such as horizontal resolution, terrain database resolution, model physics, time of model initialization, stability regime, and source of initial conditions may each affect the ability of a mesoscale model to forecast winds correctly.;The observational tower used for comparison was located at Stone Cabin, Nevada. The tower had both sonic anemometers and cup anemometers installed at heights of 40 m, 60 m, and 80 m above the surface. During a previous experiment, tower data were collected for the period February 9 through March 10, 2007 and compared to model simulations using the MM5 and WRF models at a number of varying horizontal resolutions. In this previous research, neither the MM5 nor the WRF showed a significant improvement in ability to forecast wind speed with increasing horizontal grid resolution.;The present research evaluated the ability of OMEGA to reproduce point winds as compared to the observational data from the Stone Cabin Tower at heights of 40 m, 60 m, and 80 m. Unlike other mesoscale atmospheric models, OMEGA incorporates an unstructured triangular adaptive grid which allows for increased flexibility and accuracy in characterizing areas of complex terrain. Model sensitivity to horizontal grid resolution, initial conditions, and time of initialization were tested. OMEGA was run over three different horizontal grid resolutions with minimum horizontal edge lengths of: 18 km, 6 km, and 2 km. For each resolution, the model was initialized using both the Global Forecasting System (GFS) and North American Regional Reanalysis (NARR) to determine model sensitivity to initial conditions. For both the NARR and GFS initializations, the model was started at both 0000 UTC and 1200 UTC to determine the effect of start time and stability regime on the performance of the model. An additional intensive study into the model's performance was also conducted by a detailed evaluation of model results during two separate 24-hour periods, the first a period where the model performed well and the second a period where the model performed poorly, to determine which atmospheric factors most affect the predictive ability of the OMEGA model. The statistical results were then compared with the results from the MM5 and WRF simulations to determine the most appropriate model for wind energy potential studies in complex terrain.
机译:为了进一步评估内华达州的风能潜力,通过将模拟结果与从托诺帕附近的风监测塔收集的数据进行比较,评估了计算气象模型(具有网格自适应性的多尺度运行环境模型)的准确性。内华达州。内华达州以高山和低谷为特征,因此,为了确定该州的风势,预测风的气象模型必须能够准确表示和解释地形特征,并模拟地形强迫。准确性。地形强迫在像Tonopah这样复杂地形区域的中尺度水流的发展和变化中起着主要作用,尤其是在风力涡轮机叶片高度(〜80 m)的水平上。此外,诸如水平分辨率,地形数据库分辨率,模型物理学,模型初始化时间,稳定状态和初始条件的来源等模型因素都可能会影响中尺度模型正确预测风的能力。位于内华达州的斯通小屋。该塔同时在地面上方40 m,60 m和80 m的高度安装了声波风速计和杯形风速计。在先前的实验中,收集了2007年2月9日至2007年3月10日期间的塔架数据,并与使用MM5和WRF模型的模型模拟在许多不同的水平分辨率下进行了比较。在先前的研究中,MM5和WRF均未显示出水平网格分辨率提高时风速预测能力的显着改善。;与石舱的观测数据相比,本研究评估了OMEGA再现点风的能力。塔高分别为40 m,60 m和80 m。与其他中尺度大气模型不同,OMEGA结合了非结构化的三角形自适应网格,可在表征复杂地形区域时提高灵活性和准确性。测试了模型对水平网格分辨率,初始条件和初始化时间的敏感性。 OMEGA在三种不同的水平网格分辨率上运行,最小水平边缘长度为:18 km,6 km和2 km。对于每种分辨率,均使用全球预测系统(GFS)和北美区域再分析(NARR)初始化模型,以确定模型对初始条件的敏感性。对于NARR和GFS初始化,都在0000 UTC和1200 UTC上启动模型,以确定启动时间和稳定性对模型性能的影响。还通过对两个单独的24小时周期内的模型结果进行详细评估来对模型的性能进行进一步的深入研究,第一个是模型运行良好的时期,第二个是模型运行不良的时期,以确定哪个大气因素最会影响OMEGA模型的预测能力。然后将统计结果与MM5和WRF模拟的结果进行比较,以确定最适合于复杂地形中风能潜力研究的模型。

著录项

  • 作者

    King, Kristien C.;

  • 作者单位

    University of Nevada, Reno.;

  • 授予单位 University of Nevada, Reno.;
  • 学科 Atmospheric Sciences.
  • 学位 M.S.
  • 年度 2010
  • 页码 150 p.
  • 总页数 150
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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