Sampling, Tracking, and Analyzing Roll Features in Large Eddy Simulations of the Marine Atmospheric Boundary Layer

Decomposing heterogeneous flow features in the marine atmospheric boundary layer (MABL) is pivotal for understanding the heat and momentum exchange between the ocean and atmosphere, including the formation of cloud structures. However, aliasing in airborne and stationary sampling of meteorological variables in the MABL can lead to misunderstanding the collected data. This project aims to replicate various sampling methods within a large eddy simulation (LES) of an idealized marine boundary layer environment. The simulated sampling methods coupled with object-oriented analysis of the coherent turbulent structures within the LES quantifies the impacts of different sampling methods on the data produced. Analysis of the sampled LES data shows paths in both the streamwise and spanwise directions capture a wider range of values, compared to paths that sample in the streamwise direction only. Identical paths starting from the same location that are sampled 100 seconds apart from one another capture different sets of features and ultimately fluxes on different scales. Due to the variations in samples produced using the same method, information about the lifetimes, spacing, shapes, and sizes of the features present in the field is necessary to properly interpret the data. TOBAC, a Python package used for identifying and tracking features, is able to provide key information about these flow characteristics through object-oriented analysis of the turbulent coherent structures in LES space. Together, TOBAC and data mined from the LES demonstrate both the 4D nature of the features in the MABL and how these features appear in data when sampled from an aircraft or point measurement. Real world examples of modeled sampling phenomenon are provided from the recent Moisture and Aerosol Gradients/Physics of Inversion Evolution (MAGPIE) airborne and surface observations.