Improving Wind Energy Forecasting through Numerical Weather Prediction Model Development
Improving Wind Energy Forecasting through Numerical Weather Prediction Model Development
复制标题
通过数值天气预报模型开发改进风能预测
DOI：
--
复制
发表时间：
2019
期刊：
影响因子：
--
通讯作者：
J. Cline
中科院分区：
文献类型：
--
作者：
J. Olson;J. Kenyon;I. Djalalova;L. Bianco;D. Turner;Y. Pichugina;A. Choukulkar;M. D. Toy;John M. Brown;W. Angevine;Elena Akish;J. Bao;P. Jiménez;B. Kosović;K. A. Lundquist;C. Draxl;J. Lundquist;J. McCaa;K. McCaffrey;K. Lantz;C. Long;J. Wilczak;R. Banta;M. Marquis;S. Redfern;L. Berg;W. Shaw;J. Cline
第二个风力预测改进项目（WFIP 2）的主要目标是提高复杂地形下风能预测的最新水平。为实现这一目标，在哥伦比亚河流域地区开展了为期18个月的全面实地测量活动。这些观测结果被用来诊断和量化在风能预测特别关注的天气事件期间业务高分辨率快速刷新（HRRR）模型中的系统预测误差。此类事件的例子包括冷池、间隙流、热槽/海洋推力、山波和地形尾流。WFIP 2模式的开发重点是边界层和表面层方案，云辐射相互作用，与次网格尺度地形相关的阻力表示，以及HRRR中风电场的表示。此外，数值方法的改进有助于改善一些常见的预测误差模式，特别是与山谷冷池早期侵蚀相关的高风速偏差。本研究描述了WFIP 2期间进行的模型开发和测试，并展示了预测改进。具体而言，WFIP 2发现，通过改善湍流混合长度、水平扩散和重力波阻力，冬季转子层风速预报的平均绝对误差可以降低5%-20%。WFIP 2中所做的模型改进也适用于复杂地形以外的区域。还将讨论模型开发中正在面临的和未来的挑战。
The primary goal of the Second Wind Forecast Improvement Project (WFIP2) is to advance the state-of-the-art of wind energy forecasting in complex terrain. To achieve this goal, a comprehensive 18-month field measurement campaign was conducted in the region of the Columbia River basin. The observations were used to diagnose and quantify systematic forecast errors in the operational High-Resolution Rapid Refresh (HRRR) model during weather events of particular concern to wind energy forecasting. Examples of such events are cold pools, gap flows, thermal troughs/marine pushes, mountain waves, and topographic wakes. WFIP2 model development has focused on the boundary layer and surface-layer schemes, cloud–radiation interaction, the representation of drag associated with subgrid-scale topography, and the representation of wind farms in the HRRR. Additionally, refinements to numerical methods have helped to improve some of the common forecast error modes, especially the high wind speed biases associated with early erosion of mountain–valley cold pools. This study describes the model development and testing undertaken during WFIP2 and demonstrates forecast improvements. Specifically, WFIP2 found that mean absolute errors in rotor-layer wind speed forecasts could be reduced by 5%–20% in winter by improving the turbulent mixing lengths, horizontal diffusion, and gravity wave drag. The model improvements made in WFIP2 are also shown to be applicable to regions outside of complex terrain. Ongoing and future challenges in model development will also be discussed.