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 $\pm 2.6{}^{\circ }$ C and 0.22 $\pm 0.59$ 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.

Because unmanned aircraft systems (UAS) offer new perspectives on the atmosphere, their use in atmospheric science is expanding rapidly. In support of this growth, the International Society for Atmospheric Research Using Remotely-Piloted Aircraft (ISARRA) has been developed and has convened annual meetings and “flight weeks.” The 2018 flight week, dubbed the Lower Atmospheric Profiling Studies at Elevation–A Remotely-Piloted Aircraft Team Experiment (LAPSE-RATE), involved a 1-week deployment to Colorado’s San Luis Valley. Between 14 and 20 July 2018 over 100 students, scientists, engineers, pilots, and outreach coordinators conducted an intensive field operation using unmanned aircraft and ground-based assets to develop datasets, community, and capabilities. In addition to a coordinated “Community Day” which offered a chance for groups to share their aircraft and science with the San Luis Valley community, LAPSE-RATE participants conducted nearly 1,300 research flights totaling over 250 flight hours. The measurements collected have been used to advance capabilities (instrumentation, platforms, sampling techniques, and modeling tools), conduct a detailed system intercomparison study, develop new collaborations, and foster community support for the use of UAS in atmospheric science.

The aim of the research project “Innovative Strategies for Observations in the Arctic Atmospheric Boundary Layer (ISOBAR)” is to substantially increase the understanding of the stable atmospheric boundary layer (SBL) through a combination of well-established and innovative observation methods as well as by models of different complexity. During three weeks in February 2017, a first field campaign was carried out over the sea ice of the Bothnian Bay in the vicinity of the Finnish island of Hailuoto. Observations were based on ground-based eddy-covariance (EC), automatic weather stations (AWS) and remote-sensing instrumentation as well as more than 150 flight missions by several different Unmanned Aerial Vehicles (UAVs) during mostly stable and very stable boundary layer conditions. The structure of the atmospheric boundary layer (ABL) and above could be resolved at a very high vertical resolution, especially close to the ground, by combining surface-based measurements with UAV observations, i.e., multicopter and fixed-wing profiles up to 200 m agl and 1800 $\mathrm{m}$ agl, respectively. Repeated multicopter profiles provided detailed information on the evolution of the SBL, in addition to the continuous SODAR and LIDAR wind measurements. The paper describes the campaign and the potential of the collected data set for future SBL research and focuses on both the UAV operations and the benefits of complementing established measurement methods by UAV measurements to enable SBL observations at an unprecedented spatial and temporal resolution.

Leading edge erosion (LEE) of wind turbine blades causes decreased aerodynamic performance leading to lower power production and revenue and increased operations and maintenance costs. LEE is caused primarily by materials stresses when hydrometeors (rain and hail) impact on rotating blades. The kinetic energy transferred by these impacts is a function of the precipitation intensity, droplet size distributions (DSD), hydrometeor phase and the wind turbine rotational speed which in turn depends on the wind speed at hub-height. Hence, there is a need to better understand the hydrometeor properties and the joint probability distributions of precipitation and wind speeds at prospective and operating wind farms in order to quantify the potential for LEE and the financial efficacy of LEE mitigation measures. However, there are relatively few observational datasets of hydrometeor DSD available for such locations. Here, we analyze six observational datasets from spatially dispersed locations and compare them with existing literature and assumed DSD used in laboratory experiments of material fatigue. We show that the so-called Best DSD being recommended for use in whirling arm experiments does not represent the observational data. Neither does the Marshall Palmer approximation. We also use these data to derive and compare joint probability distributions of drivers of LEE; precipitation intensity (and phase) and wind speed. We further review and summarize observational metrologies for hydrometeor DSD, provide information regarding measurement uncertainty in the parameters of critical importance to kinetic energy transfer and closure of data sets from different instruments. A series of recommendations are made about research needed to evolve towards the required fidelity for a priori estimates of LEE potential.

The micro-RPAS (remotely piloted aircraft system) SUMO (Small Unmanned Meteorological Observer) equipped with a five-hole-probe (5HP) system for turbulent flow measurements was operated in 49 flight missions during the BLLAST (Boundary-Layer Late Afternoon and Sunset Turbulence) field campaign in 2011. Based on data sets from these flights, we investigate the potential and limitations of airborne velocity variance and TKE (turbulent kinetic energy) estimations by an RPAS with a take-off weight below 1 kg. The integration of the turbulence probe in the SUMO system was still in an early prototype stage during this campaign, and therefore extensive post-processing of the data was required. In order to be able to calculate the three-dimensional wind vector, flow probe measurements were first synchronized with the autopilot's attitude and velocity data. Clearly visible oscillations were detected in the resulting vertical velocity, w, even after correcting for the aircraft motion. The oscillations in w were identified as the result of an internal time shift between the inertial measurement unit (IMU) and the GPS sensors, leading to insufficient motion correction, especially for the vertical wind component, causing large values of σ_w. Shifting the IMU 1–1.5 s forward in time with respect to the GPS yields a minimum for σ_w and maximum covariance between the IMU pitch angle and the GPS climb angle. The SUMO data show a good agreement to sonic anemometer data from a 60 m tower for σ_u, but show slightly higher values for σ_v and σ_w. Vertical TKE profiles, obtained from consecutive flight legs at different altitudes, show reasonable results, both with respect to the overall TKE level and the temporal variation. A thorough discussion of the methods used and the identified uncertainties and limitations of the system for turbulence measurements is included and should help the developers and users of other systems with similar problems.

For atmospheric boundary-layer (ABL) studies, unmanned aircraft systems (UAS) can provide new information in addition to traditional in-situ measurements, or by ground- or satellite-based remote sensing techniques. The ability of fixed-wing UAS to transect the ABL in short time supplement ground-based measurements and the ability to extent the data horizontally and vertically allows manifold investigations. Thus, the measurements can provide many new possibilities for investigating the ABL. This study presents the new mark of the Multi-Purpose Airborne Sensor Carrier (MASC-3) for wind and turbulence measurements and describes the subsystems designed to improve the wind measurement, to gain endurance and to allow operations under an enlarged range of environmental conditions. The airframe, the capabilities of the autopilot Pixhawk 2.1, the sensor system and the data acquisition software, as well as the post-processing software, provide the basis for flight experiments and are described in detail. Two flights in a stable boundary-layer and a close comparison to a measurement tower and a Sodar system depict the accuracy of the wind speed and direction measurements, as well as the turbulence measurements. Mean values, variances, covariance, turbulent kinetic energy and the integral length scale agree well with measurements from a meteorological measurement tower. MASC-3 performs valuable measurements of stable boundary layers with high temporal resolution and supplements the measurements of meteorological towers and sodar systems.