Improving Understanding of Non-Equilibrium Surface-Layer Stress-Strain-Rate Relationships Using a Novel Stationarity-Analysis Technique and Large-Eddy Simulation

该奖项全部或部分由《2021年美国救援计划法案》（公法117-2）资助。大气边界层是大气的最底层，由于空气运动的不同尺度以及气流与地球表面的相互作用，大气边界层的数值天气模拟非常复杂。科学家们试图使用几种技术来模拟边界层中的流动，其中一些技术可能会占用大量的计算能力。在这个项目中，研究人员将研究一种简化的边界层建模技术，该技术需要更少的计算资源并更好地拟合现有的观测数据。该项目有可能影响边界层的数值模拟，特别是与对流尺度过程和近地面风预测有关。该项目还包含一个强有力的教育组成部分，将为下一代科学家提供培训活动。这个奖项是为了一个研究项目，其目标是减少湍流大气流动数值模拟中的误差。这些错误是由对未解湍流通量和已解场之间的平衡关系的不切实际的假设引起的。目前，解决这个问题最直接的方法是通过计算未解析通量的时间演化，这被称为二阶闭包。然而，这种方法有多个问题，包括大量的计算费用。研究人员计划使用这种方法的简化版本，称为伪二阶闭包模型。提案的主要研究目标是：1)通过将多传感器平稳性分析技术（MSATv2）应用于现有的现场活动数据，减少湍流统计现场数据估计中的不确定性；2)通过对现场活动数据进行LES运行，研究使大涡模拟（LES）能够再现现场数据的外部驱动力；3)利用现场和LES数据评估和完善伪二阶闭合模型。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2).Numerical weather modeling of the atmospheric boundary layer, which is the lowest layer of the atmosphere, is extremely complex due to the different scales of motion of the air and the interaction of flow with the Earth’s surface. Scientists attempt to simulate flows in the boundary layer using several techniques, some of which can take up enormous amounts of computing power. In this project, the researcher will investigate a simplified technique for modeling the boundary layer that requires fewer computational resources and better fits existing observational data. The project has the potential to impact numerical modeling of the boundary layer, which is especially relevant to convective scale processes and near-surface wind predictions. The project also contains a strong educational component which will provide training activities for the next generation of scientists.This award is for a research project with the goal of reducing errors in numerical simulations of turbulent atmospheric flows. These errors are induced by unrealistic assumptions of equilibrium relationships between unresolved turbulent fluxes and resolved fields. Currently, the most straightforward approach to addressing this problem is by computing the time-evolution of the unresolved fluxes, which is known as the second-order closure. However, this method has multiple issues, including significant computational expense. The researcher plans to work with a simplified version of this approach, known as the pseudo-second-order closure model. The major research objectives in the proposal are to: 1) Reduce uncertainties in field-data estimates of turbulence statistics by applying a multi-sensor stationarity-analysis technique (MSATv2) to existing field campaign data, 2) Investigate external driving forces that enable Large Eddy Simulation (LES) to reproduce field data by conducting LES runs of field campaign data, and 3) Evaluate and refine pseudo-second-order closure models using the field and LES data.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.