On the Trajectory Dependence of Atmospheric Boundary Layer Turbulence Sensing using Small Unmanned Vehicles
Balaji Jayaraman, Sam Allison, He Bai

TL;DR
This paper explores the potential of using small unmanned aerial vehicles to better sample and understand atmospheric boundary layer turbulence, addressing challenges in trajectory optimization and data interpretation.
Contribution
It introduces the concept of UAS-based turbulence sensing, highlighting the potential and challenges for improved boundary layer turbulence measurement.
Findings
UAS can provide high-resolution 3D turbulence data.
Optimal trajectories are crucial for representative turbulence sampling.
Sparse measurements' representativeness remains a key challenge.
Abstract
Atmospheric turbulence, especially in the near-surface boundary layer is known to be under-sampled due to the need to capture a wide separation in length and time-scales and limitation in the number of sensors. Over the past decade, the use if Unmanned Aircraft Systems (UAS) technology is approaching ubiquitous proportions for a wide variety of applications, especially with the recent FAA relaxation of flying restrictions. From a geophysical sciences perspective, such technology would allow for sensing of large-scale atmospheric flows, particularly, atmospheric boundary layer (ABL) turbulence, air quality monitoring in urban settings where multitude of small, minimally-invasive and mobile sensors can drastically alter our ability to study such complex phenomena. Currently available observational data of atmospheric boundary layer physics is so sparse and infrequent which significantly…
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Taxonomy
TopicsFluid Dynamics and Turbulent Flows · Atmospheric aerosols and clouds · Meteorological Phenomena and Simulations
