<p>Do we get a better picture of the world around us if we simultaneously observe many aspects instead of a few? Dense sensing networks are an elaborate way to validate our representation of land surface boundary layer processes commonly derived from single point monitoring stations or a three-dimensional model world. More samples promise unique insights into interactions that occur at different scales, separated in space and time.</p><p>We present a combination of techniques that purvey a) observations of the temperature and wind field in high detail and b) the extraction of information about dynamic interactions near the surface. A field experiment was conducted in complex terrain, in which landscape features dramatically modulate local flow patterns and the atmospheric stability during summer days rapidly transitions on a diurnal scale and between locations. Wind and temperature were simultaneously observed using a network of Doppler lidar, sonic anemometer, fiber-optic temperature sensing (DTS) and thermal imaging velocimetry (TIV) instrumentation, centered around the TERENO/ICOS preAlpine grassland observatory station Fendt, Germany, during the ScaleX Campaigns (https://scalex.imk-ifu.kit.edu). Data analyses relied on signal decomposition and statistical clustering, aimed at the characterization of (non-)turbulent motions and their feedback on turbulent mixing near the surface. The combination of methods offered multiple levels of detail about the development and impact of organized structures in the atmospheric boundary layer.</p><p>The study shows that the exploration of novel micrometeorological and data sciences techniques helps advance our knowledge of fundamental aspects of atmospheric turbulence, and provides new avenues for theoretical and numerical studies of the atmospheric boundary layer.</p>
Characterisation of the Dome C Atmospheric Boundary Layer Turbulence with a Non-Doppler Acoustic Radar
