Small unmanned aircraft systems (sUAS) are rapidly transforming atmospheric research. With the advancement of the development and application of these systems, improving knowledge of best practices for accurate measurement is critical for achieving scientific goals. We present results from an intercomparison of atmospheric measurement data from the Lower Atmospheric Process Studies at Elevation—a Remotely piloted Aircraft Team Experiment (LAPSE-RATE) field campaign. We evaluate a total of 38 individual sUAS with 23 unique sensor and platform configurations using a meteorological tower for reference measurements. We assess precision, bias, and time response of sUAS measurements of temperature, humidity, pressure, wind speed, and wind direction. Most sUAS measurements show broad agreement with the reference, particularly temperature and wind speed, with mean value differences of 1.6 ± 2.6 ∘ C and 0.22 ± 0.59 m/s for all sUAS, respectively. sUAS platform and sensor configurations were found to contribute significantly to measurement accuracy. Sensor configurations, which included proper aspiration and radiation shielding of sensors, were found to provide the most accurate thermodynamic measurements (temperature and relative humidity), whereas sonic anemometers on multirotor platforms provided the most accurate wind measurements (horizontal speed and direction). We contribute both a characterization and assessment of sUAS for measuring atmospheric parameters, and identify important challenges and opportunities for improving scientific measurements with sUAS.

Traditional configurations for mounting Temperature–Humidity (TH) sensors on multirotor Unmanned Aerial Systems (UASs) often suffer from insufficient radiation shielding, exposure to mixed and turbulent air from propellers, and inconsistent aspiration while situated in the wake of the UAS. Descent profiles using traditional methods are unreliable (when compared to an ascent profile) due to the turbulent mixing of air by the UAS while descending into that flow field. Consequently, atmospheric boundary layer profiles that rely on such configurations are bias-prone and unreliable in certain flight patterns (such as descent). This article describes and evaluates a novel sensor housing designed to shield airborne sensors from artificial heat sources and artificial wet-bulbing while pulling air from outside the rotor wash influence. The housing is mounted above the propellers to exploit the rotor-induced pressure deficits that passively induce a high-speed laminar airflow to aspirate the sensor consistently. Our design is modular, accommodates a variety of other sensors, and would be compatible with a wide range of commercially available multirotors. Extensive flight tests conducted at altitudes up to 500 m Above Ground Level (AGL) show that the housing facilitates reliable measurements of the boundary layer phenomena and is invariant in orientation to the ambient wind, even at high vertical/horizontal speeds (up to 5 m/s) for the UAS. A low standard deviation of errors shows a good agreement between the ascent and descent profiles and proves our unique design is reliable for various UAS missions.

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Abstract. This paper describes the data collected by the University of Nebraska-Lincoln (UNL) as part of the field deployment during the Lower Atmospheric Process Studies at Elevation – a Remotely-piloted Aircraft Team Experiment (LAPSE-RATE) flight campaign in July 2018. UNL deployed two multirotor unmanned aerial systems (UASs) at various sites in the San Luis Valley (Colorado, USA) for data collection in support of three science missions: convection-initiation, boundary layer transition, and cold air drainage flow. We conducted 172 flights resulting in over 1300 minutes of cumulative flight time. Our novel design for the sensor housing onboard the UAS was employed in these flights to meet the aspiration and shielding requirements of the temperature/humidity sensors, and attempt to separate them from the mixed turbulent airflow from the propellers. Data presented in this paper include time-stamped temperature and humidity data collected from the sensors, along with the three-dimensional position and velocity of the UAS. Data are quality controlled and time-synchronized using a zero-order-hold interpolation without additional post processing. The full dataset is also made available for download at (https://doi.org/10. 5281/zenodo.4306086 (Islam et al. , 2020)).

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