Collaborative Research: Understanding Interactions between Mesoscale and Microscale Flows in the Stable Boundary Layer over Shallow Terrain

不稳定条件(如晴天)下的大气流动比通常在夜间发生的稳定条件下的大气流动更容易理解。如果地形不是完全平坦的，或者地表长满了植物，水流就变得更加难以理解。这种不确定性意味着夜间天气预报可能缺乏准确性。在美国中部大部分地区可以看到的略微倾斜的地形上，情况尤其如此。这个项目将回答存在于地球大部分地区稳定条件下的基本物理问题，包括：植物、地形和海拔的变化如何影响大气流动？受影响的大气气流如何与来自其他区域的气流相互作用？缓坡地区有什么特殊的气流(如向下的沟谷冷空气流动；气流的碰撞)？这个项目将使用以前收集的实验数据来推导出定义这些现象可能发生的时间、地点和原因的经验关系。这种对现有理论框架的完善，将进一步加深对夜间污染物迁移转化的理解。公众健康和安全可以受益于对化学或生物危险污染的运输的改进的定量预测。农业界将受益于增加对控制田间温度的物理过程的了解，包括影响作物健康的过程以及导致霜冻和随后的作物损失的过程，这是确保全球粮食安全的一项重大挑战。利用美国国家科学基金会资助的稳定大气变率和输送(SAVANT)活动期间进行的独特观测，这项工作将有助于更好地理解浅层复杂地形对稳定边界层的力学和热力学性质的影响，并填补在环境和局部流动之间的尺度相互作用方面的知识空白，特别是关于发生在浅层地形中的流动模式。Savant在2018年秋季的两个密集月中收集了并行的现场和遥感观测数据。现场装置的设计是为了研究浅沟冷空气排水(下沟)流动的原因和影响。用多个激光雷达(光探测和测距)系统跟踪的示踪剂羽流释放提供了在多个空间尺度上检查相互作用的流动的独特机会。复杂地形中稳定层湍流混合的两个关键因素，风切变和稳定层结，将被用于分层观测，以利用主沟内和主沟上方的塔数据，并通过来自激光雷达的空间切变观测来增强能量最强的湍流涡旋。我们将研究湍流涡旋的垂直尺度因子和排水水流的影响。将详细介绍导致排水水流不均匀的因素(即脉动、曲折和汇聚水流)。塔式和3-D激光雷达观测的独特组合将允许改进能量和质量交换的参数化，这将提高预测模型能力。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Atmospheric flows under unstable conditions (e.g., sunny days) are better understood than that under stable conditions which often occur at night. If the terrain is not perfectly flat or the surface is vegetated, flows become even more difficult to understand. This uncertainty means nighttime weather forecasts can lack accuracy. This is particularly true in slightly sloping topography as seen through much of the central US. This project will answer fundamental physics questions that exist for stable conditions over much of the planet including: How do plant, terrain, and elevation changes impact atmospheric flows? How do the impacted atmospheric flows interact with flows from other regions? What special flows (like down gully cold air flow; flows colliding) exist in gently sloped areas? This project will use previously collected experimental data to deduce empirical relationships defining when, where, and why these phenomena are likely to occur. This improvement of current theoretical frameworks will further understanding of nighttime pollutant transport and transformation. Public health and safety can benefit from improved quantitative prediction of transport of chemical or biological hazardous pollution. The agricultural community will benefit from increased knowledge of physical processes controlling field-scale temperature, including those affecting crop health and those leading to patches of frost and subsequent crop loss, a major challenge in ensuring global food security. Using unique observations taken during the NSF funded Stable Atmospheric Variability ANd Transport (SAVANT) campaign, this work will lead to an improved understanding of impacts of shallow complex terrain on mechanical and thermodynamic properties of the stable boundary layer and to fill the knowledge gap in scale interactions between environmental and local flows, specifically with respect to flow patterns that occur in shallow topography. SAVANT collected concurrent in-situ and remote sensing observations during two intensive months in the fall of 2018. The field setup was designed to investigate causes and effects of cold air drainage (down gully) flow in a shallow gully. Tracer plume releases tracked with multiple lidar (Light Detection and Ranging) systems offer the unique opportunity to examine interacting flows at multiple spatial scales. Two critical factors for stable-layer turbulent mixing in complex terrain, wind shear and the stable stratification, will be used to stratify observations to describe the most energetic turbulence eddies using the tower data within and above the main gully, augmented by spatial shear observations from lidars. Vertical scaling factors for turbulent eddies and the influences of drainage flows will be investigated. Factors leading to non-uniform drainage flows (i.e., pulsing, meandering, and converging flows) will be detailed. The unique combination of tower and 3-D lidar observations will allow for improved parameterizations of energy and mass exchanges, which will improve predictive model capabilities.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.