Spatial Structure of Thermodynamic Variables in the Atmospheric Boundary Layer

Recent technological advancements in unmanned and autonomous systems have fostered the increasing use of small unmanned aircraft systems (sUAS) to sample thermodynamic variables for meteorology and atmospheric sciences. The versatility of sUAS make them well suited for filling spatial and temporal sensing gaps in the atmospheric boundary layer (ABL), which is the portion of the atmosphere in direct contact with the surface of the Earth and a critical area for weather development including severe local storms. However, we currently do not have established sampling methodologies or a universal understanding of the scales associated with the atmospheric processes operating within this region to guide those sampling schemes. The intensity of the spatial disbursement of scaler quantities, such as temperature, in a turbulent flow can be quantified using the structure function. A similar measure used in other geosciences is known as the variogram. This research employs variogram analysis to determine the optimal spatial sampling scales for thermodynamic variables in the ABL using sUAS. Furthermore, this research combines the statistical methodologies of variogram analysis with small-scale turbulence theory to characterize the spatial structure of temperature in the ABL during a variety of mixing conditions in the San Luis Valley, Colorado in an effort to confirm the universality of these functions across a variety of environment types and atmospheric conditions. A better understanding of coherent turbulent structures, as captured through variation in temperature, will allow for better informed sampling, enhanced mission planning, and improved scientific analyses.