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

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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.