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 (CO2, O3, 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, O3 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.