Gradient-Based Turbulence Estimates from Multicopter Profiles in the Arctic Stable Boundary Layer


We explore the potential of a new method for the estimation of profiles of turbulence statistics in the stable boundary layer (SBL). By applying gradient-based scaling to multicopter unoccupied aircraft system (UAS) profiles of temperature and wind, sampled over sea-ice during the 2018 Innovative Strategies for Observations in the Arctic Atmospheric Boundary Layer (ISOBAR18) field campaign, turbulence profiles can be derived. We first validate this method by scaling turbulence observations from three levels on a 10-m mast with the corresponding scaling parameters, and compare the resulting non-dimensional parameters to the semi-empirical similarity functions proposed for this scaling scheme. The scaled data of turbulent fluxes and variances from the three levels collapse to their corresponding similarity functions. After the successful validation, we estimate turbulence statistics from UAS profiles by computing profiles of the gradient Richardson number to which we then apply the similarity functions. These UAS profiles are processed from raw time-series data by applying low-pass filters, time-response corrections, altitude corrections, and temporal averaging across successive flights. We present three case studies covering a broad range of SBL conditions to demonstrate the validity of this approach. Comparisons against turbulence statistics from the 10-m mast and a sodar indicate the broad agreement and physically meaningful results of the method. Successful implementation of the method thus offers a powerful diagnostic tool that requires only a multicopter UAS with a simple thermodynamic sensor payload.

Boundary-Layer Meterology, 183, 321–353
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Brian Greene
Brian Greene
Ph.D. Candidate

My research focuses on developing and utilizing uncrewed aircraft systems (UAS) to collect observations in the planetary boundary layer. This application includes instrumentation, electronics, flight mechanics, thermodynamics, fluid mechanics, and signal processing. I also model the stable boundary layer using large-eddy simulations to better understand how observations from UAS can best represent turbulence across the simulated domain.