INSPIRE: Understanding Spatiotemporal Extent and Structure of Large Wind Farm Footprint on Weather and Climate by Combining Observational Analysis with Numerical Modeling

INSPIRE：通过观测分析与数值建模相结合，了解大型风电场足迹对天气和气候的时空范围和结构

• 批准号: 1247137
• 负责人: Liming Zhou
• 金额: $ 50万
• 依托单位: SUNY at Albany
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-09-15 至 2017-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1247137&HistoricalAwards=false
• 关键词: INSPIRE; Understanding; Spatiotemporal; Extent; Structure

This INSPIRE award is partially funded by Physical and Dynamic Meteorology program in the Division of Atmospheric and Geospace Sciences in the Directorate for Geosciences, Climate and Large-scale Dynamics program in the Division of Atmospheric and Geospace Sciences in the Directorate for Geosciences, and Environmental Sustainability program in the Division of Chemical, Bioengineering, Environmental, and Transport Systems in the Directorate for Engineering.The global wind industry has experienced a remarkably rapid expansion of capacity in recent years and this fast growth is expected to continue in the future. While converting wind's kinetic energy into electricity, wind turbines modify surface-atmosphere exchanges of energy, momentum, mass and moisture. Given the current installed capacity and the projected installation worldwide, wind farms (WFs) are likely becoming a major driver of manmade land use change on Earth. Hence, understanding WF-atmosphere-environment interactions and assessing potential environmental impacts are of significant societal importance. However, recent studies of WF impacts on meteorology have been primarily in the modeling domain using simplified wind turbine parameterizations (WTPs) due to the lack of observations. Given the availability of high resolution radar and remote sensing data, we believe it is time to begin systematically assessing WF impacts in the U.S.The project will conduct a process-based observational and modeling study to investigate possible impacts on weather, climate and environments due to the rapid development of wind farms (WF) in the Great Plains and Midwest. Specifically, the study will involve analyzing a variety of observational data (near-surface meteorological variables, radar and remote sensed), detecting, quantifying and attributing such impacts over the 17 biggest U.S. WFs, and then performing a series of high resolution mesoscale simulations to evaluate wind turbine parameterizations (WTPs). The investigations will unveil how operational WFs influence the diurnal, seasonal and interannual variations of atmospheric boundary layer (ABL) structures and phenomena, near-surface hydrometeorology, and crop/vegetation growth, and how these changes vary under various meteorological and surface conditions and WF configurations (e.g., elevation, land cover, wind patterns, local climate).Intellectual merit:This research can potentially bridge scientific knowledge gap of (a) atmospheric boundary layer dynamics and thermodynamics within and downwind of operational wind farms and (b) physical processes and mechanisms of wind farm impacts on environment. It will also generate knowledge about the performance of the wind turbine parameterizations and identify model refinements required to simulate wind farms in mesoscale models. It is potentially transformative by providing a comprehensive picture of wind farm footprint on weather and climate, a fundamental step toward projecting the future impacts at large scales, and by challenging conventional wisdom with far more complicated wind farm-atmosphere-environment interactions. Broader Impacts:Wind power supports environmental sustainability and is likely to be part of the solution to the climate change, air pollution and energy security problem. Assessing potential WF impacts is critical for developing efficient adaptation and management strategies to ensure long-term sustainability of wind power. The study will lead to improvements in numerical weather prediction and projection of WF impacts on weather, climate, environments, water cycle and agricultural practices. The obtained knowledge and approaches can be generalized to other studies. In addition, this project has important education and outreach components and will provide learning and training experience for undergraduate and graduate students.

INSPIRE奖的部分资金来自地球科学局大气和地球空间科学部的物理和动态气象计划，地球科学局大气和地球空间科学部的气候和大型动力学计划，以及工程局化学、生物工程、环境和运输系统司的环境可持续发展计划。近年来，全球风电行业经历了显著的快速产能扩张，预计这种快速增长将在未来继续下去。在将风的动能转化为电能的同时，风力涡轮机改变了地面与大气之间的能量、动量、质量和水分的交换。考虑到目前全球的装机容量和预计的装机容量，风力发电场(WFS)很可能成为地球上人为土地利用变化的主要驱动力。因此，了解WF-大气-环境相互作用并评估潜在的环境影响具有重要的社会意义。然而，由于观测资料的缺乏，最近对白云天气对气象学影响的研究主要集中在使用简化的风力机参数(WTP)的模拟领域。鉴于高分辨率雷达和遥感数据的可用性，我们认为现在是开始系统评估美国白云影响的时候了。该项目将进行基于过程的观测和建模研究，以调查大平原和中西部风电场(WF)的快速发展可能对天气、气候和环境造成的影响。具体地说，这项研究将涉及分析各种观测数据(近地表气象变量、雷达和遥感)，检测、量化这些影响并将其归因于美国17个最大的WFS，然后执行一系列高分辨率中尺度模拟来评估风力涡轮机参数化(WTP)。这些研究将揭示运行的WFS如何影响大气边界层(ABL)结构和现象、近地表水文气象和作物/植被生长的日变化、季节变化和年际变化，以及这些变化如何在不同的气象和地表条件以及WF配置(例如海拔、土地覆盖、风型、当地气候)下变化。智力优势：这项研究有望弥合以下科学知识差距：(A)运行中的风电场及其下风的大气边界层动力学和热力学，以及(B)风电场对环境影响的物理过程和机制。它还将生成有关风力涡轮机参数化性能的知识，并确定在中尺度模型中模拟风电场所需的模型改进。它提供了风电场对天气和气候的足迹的全面图景，是在大规模预测未来影响的根本步骤，并通过更复杂的风电场-大气-环境相互作用挑战传统智慧，具有潜在的变革性。更广泛的影响：风力发电支持环境可持续性，很可能成为气候变化、空气污染和能源安全问题的解决方案的一部分。评估潜在的风能影响对于制定有效的适应和管理战略以确保风电的长期可持续性至关重要。这项研究将改进数值天气预报，并预测天气预报对天气、气候、环境、水循环和农业实践的影响。所获得的知识和方法可以推广到其他研究中。此外，该项目还包括重要的教育和宣传部分，将为本科生和研究生提供学习和培训经验。