| Summary: | The physical processes governing stable atmospheric boundary layer (SBL) dynamics have significant societal impacts ranging from pollution dispersion and wind energy production to polar sea ice loss. For decades, SBL turbulence has proven challenging to measure, parameterize, simulate, and interpret for a variety of reasons. For example, turbulence intensity in the SBL is often orders of magnitude smaller than in the convective boundary layer as thermal stratification suppresses vertical motions. As atmospheric stability increases, turbulence can also become intermittent in space and time, resulting in poor convergence of temporally-averaged turbulence statistics. Characteristic turbulent motions within the SBL can also be considerably smaller than the grid spacings employed by operational numerical weather prediction (NWP) models. These NWP models therefore need to parameterize turbulent energy exchange within the SBL, which can result in significant errors in near-surface temperature and wind speed forecasts due to the imperfect nature of parameterization schemes. It has been shown that improvements in SBL forecasting skill have been hindered by a relative lack in knowledge of fundamental SBL processes, which in turn is partially due to a dearth in routine and spatially dense thermodynamic and kinematic observations within the SBL. To address this so-called data gap, uncrewed aircraft systems (UAS) are proving the ability to reliably sample the atmospheric boundary layer (ABL), offering a new perspective for understanding the SBL. Moreover, continual computational advances have enabled the use of large-eddy simulations (LES) to simulate the atmosphere at ever-smaller scales. This dissertation therefore seeks to synergize UAS observations and large-eddy simulations to explore the underlying processes governing SBL dynamics. In the first component of this dissertation, we explore the potential of a new method for the estimation of profiles of turbulence statistics in the SBL. By applying gradient-based scaling ...
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