An optimized multicopter UAV sounding technique (MUST) for probing comprehensive atmospheric variables

The unique maneuverability, ease of deployment, simplicity in logistics, and relatively low costs of multicopters render them effective vehicles for low atmospheric research. While many efforts have contributed to the fundamental success of atmospheric applications of multicopters in the past, several challenges remain, including limited measurable variables, possible response-delay in real-time observations, insufficient measurement accuracy, endurance of harsh conditions and tolerance towards interferences. To address these challenges and further fortify the applicability in diversified research disciplines, this study developed an optimized multicopter UAV sounding technique (MUST). The MUST serves as an integrated platform by combining self-developed algorithms, optimized working environments for sensors/monitors, and retrofitted sampling devices to probe a comprehensive set of atmospheric variables. These variables of interest include meteorological parameters (temperature, relative humidity, pressure, wind direction and speed), the chemical composition (speciated VOCs, CO, CO2, CH4, CO2 isotopologues, O3, PM2.5, and black carbon), and the radiation flux, as well as visible and thermal images. The aim of this study is to achieve the following objectives: 1. to easily probe a comprehensive set of near-surface atmospheric variables; 2. to improve data quality by correcting for sensors’ delay in real-time observations and minimizing environmental interferences; and 3. to enhance the versatility and applicability of aerial measurements by incorporating necessary hardware and software. Field launching cases from the surface to a maximum height of 1000 m were conducted to validate the robustness of the integrated MUST platform with sufficient speed, accuracy and resolution for the target variables. [Display omitted] •Versatile observation techniques are integrated with an agile multicopter.•A comprehensive set of near-surface atmospheric variables can be probed with ease.•Data quality is improved by optimizing sensing/sampling and reducing interferences.•Potential for environmental and climate studies is demonstrated.