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.

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Published in Sensors, v. 19, no. 9, 2179, p. 1-32.

© 2019 by the authors. Licensee MDPI, Basel, Switzerland.

This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

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This research was funded by general support from the US National Science Foundation grant number AGS 1807199, and the US Department of Energy grant number DE-SC0018985, in the form of travel support for early career participants. Partial support for this work was provided by the US National Science Foundation grant number CBET-1351411 and by US National Science Foundation grant number 1539070, Collaboration Leading Operational UAS Development for Meteorology and Atmospheric Physics (CLOUD-MAP). Institutional participation and data used in this paper were supported by grants to: D.G.S., S.D.R., and H.F from the Institute for Critical Technology and Applied Science at Virginia Tech; D.G.S and S.D.R from the US National Science Foundation grant number AGS 1520825; O.K. from the US National Science Foundation grant number AGS 1665456; D.L. from the National Science Foundation grant number AGS 1632829; S.T.K’s from the ISOBAR project funded by the Research Council of Norway under the FRINATEK scheme project number 251042/F20; L.B. from the University of Vermont’s REACH program.