Influence of irradiance and mixing layer height on the vertical trace matter distribution in the lower planetary boundary layer – drone-based investigation

Abstract

Drone-based atmospheric measurements allow time-resolved investigation of stratification in the lower atmosphere of many atmospherically relevant variables. Measuring a variety of variables can be used, e.g., for mixing layer (height, MLH) identification and examination of the representativity of ground-based measurements in the lowermost boundary layer. We present drone-based vertical profiling during two 2.5-week summer field campaigns in rural Germany to investigate the vertical distribution of trace matter and meteorological variables in the lowermost troposphere. Night-time vertical profiling of temperature, humidity, wind speed, and trace substances (CO 2 , O 3 , particle mass and number concentrations) in the lowest 120 m revealed a multiple-layer fine-scale stratification. Trace substances surpassed several traditional meteorological variables in sensitivity for MLH detection. Among all variables, O 3 and potential temperature were the most reliable MLH markers, highlighting combining trace matter and meteorological measurements to understand stratification processes. Using a gradient-based approach for diurnal vertical profiles up to 500 m above ground, we evaluate under which conditions and to which degree ground-based measurements, used world-wide in networks or during individual field campaigns, can be taken as representative for the lowermost mixing layer, and how strong radiative-driven mixing reduces vertical gradients. Location-specific sources and sinks affect the trace matter distribution in the ML much stronger than irradiance. However, homogeneity of aerosol particle concentrations was strongly promoted under high-radiative conditions, even at low MLHs. This suggests distinguishing between high- and low-irradiation conditions in the planetary boundary layer could improve parameterization for vertical mixing and should be considered when evaluating ground-based data.