Random Errors in the Stable Boundary Layer: Implications for Modern Observational Techniques

Abstract

Abstract For decades, stable boundary layer (SBL) turbulence has proven challenging to measure, parameterize, simulate, and interpret. Uncrewed aircraft systems (UAS) are becoming a reliable method to sample the atmospheric boundary layer, offering new perspectives 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 the extent to which vertical profiles from UAS can represent larger-scale SBL flows. To quantify the representativeness of observations from UAS profiles and eddy-covariance observations within the SBL, we performed a random error analysis using a suite of six large-eddy simulations for a wide range of stabilities. We combine these random error estimates with emulated observations of a UAS and eddy-covariance systems to better inform future observational studies. For each experiment, we estimate relative random errors using the so-called relaxed filtering method for first- and second-order moments as functions of height and averaging time. We show that the random errors can be on the same order of magnitude as other instrument-based errors due to bias or dynamic response. Unlike instrument errors, however, random errors decrease with averaging time. For these reasons, we recommend coupling UAS observations with other ground-based instruments as well as dynamically adjusting the UAS vertical ascent rate to account for how errors change with height and stability. Significance Statement Weather-sensing uncrewed aircraft systems are rapidly being realized as effective tools to collect valuable observations within the atmospheric boundary layer. To fully capitalize on this novel observational technique, it is necessary to develop an understanding of how well their observations can represent the surrounding atmosphere across various spatial and temporal scales. In this study we quantify the representativeness of atmospheric observations in the stable boundary layer by evaluating the random errors for parameters such as temperature, wind speed, and fluxes as estimated from a suite of large-eddy simulations. Our results can better inform future studies utilizing uncrewed aircraft systems by highlighting how random errors in their observations relate to vertical ascent rate, atmospheric stability, and measurement height.