Large Eddy Simulations of Stable Boundary Layers and Implications for UAS Sampling

The stable boundary layer (SBL) has significant societal impacts ranging from pollution dispersion to wind energy production. It is therefore vital for numerical weather prediction models to accurately predict the diurnal cycle of low-level temperature and wind fields. The issue at present with this need is that operational numerical weather prediction models typically have effective resolutions too coarse to resolve turbulent processes in the SBL, thereby requiring the effects of turbulent transport of heat, moisture, and momentum to be parameterized. Studies suggest that a large limiting factor in the efficacy of these model parameterizations is related to a poor understanding of the physical processes occurring in the SBL. SBL turbulence is notoriously difficult 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. For increasing stability, turbulence can also become intermittent in space and time, resulting in poor convergence of temporally-averaged turbulence statistics. Fortunately, in recent years weather sensing uncrewed aircraft systems (WxUAS) have advanced to the point of being a routinely reliable method to sample the atmospheric boundary layer, 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. LES is therefore a powerful tool in establishing a baseline framework to understand how singular vertical profiles from WxUAS can represent larger-scale SBL flows. By leveraging a combination of rotary-wing uncrewed aircraft system data from the ISOBAR18 field campaign and LES output of the SBL, this presentation will address the following questions: 1) What considerations are necessary to appropriately extend the application of a gradient-based turbulence scaling framework to UAS vertical profile data? 2) How well can LES represent SBL turbulence for horizontal grid spacings (and therefore filter scales) ranging from 3.1 m ≤ Δx ≤ 8.4 m when using a scale-dependent Lagrangian subgridmodel? and 3) How do the integral timescales of various first-order parameters (i.e., U and theta) impact the ability of UAS to adequately represent the ensemble mean when collecting observations in vertical profiles?