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Abstract. This study analyzes turbulent energy fluxes in the Arctic atmospheric boundary layer (ABL) using measurements with a small uncrewed aircraft system (sUAS). Turbulent fluxes constitute a major part of the atmospheric energy budget and influence the surface heat balance by distributing energy vertically in the atmosphere. However, only few in situ measurements of the vertical profile of turbulent fluxes in the Arctic ABL exist. The study presents a method to derive turbulent heat fluxes from DataHawk2 sUAS turbulence measurements, based on the flux gradient method with a parameterization of the turbulent exchange coefficient. This parameterization is derived from high-resolution horizontal wind speed measurements in combination with formulations for the turbulent Prandtl number and anisotropy depending on stability. Measurements were taken during the MOSAiC (Multidisciplinary drifting Observatory for the Study of Arctic Climate) expedition in the Arctic sea ice during the melt season of 2020. For three example cases from this campaign, vertical profiles of turbulence parameters and turbulent heat fluxes are presented and compared to balloon-borne, radar, and near-surface measurements. The combination of all measurements draws a consistent picture of ABL conditions and demonstrates the unique potential of the presented method for studying turbulent exchange processes in the vertical ABL profile with sUAS measurements. 

Abstract. The DataHawk2 (DH2) is a small, fixed-wing, uncrewed aircraft system, or UAS,developed at the University of Colorado (CU) primarily for taking detailedthermodynamic measurements of the atmospheric boundary layer. The DH2 weighs1.7 kg and has a wingspan of 1.3 m, with a flight endurance of approximately60 min, depending on configuration. In the DH2's most modern form, theaircraft carries a Vaisala RSS-421 sensor for pressure, temperature, andrelative humidity measurements, two CU-developed infrared temperaturesensors, and a CU-developed fine-wire array, in addition to sensors requiredto support autopilot function (pitot tube with pressure sensor, GPSreceiver, inertial measurement unit), from which wind speed and directioncan also be estimated. This paper presents a description of the DH2,including information on its design and development work, and puts the DH2 intocontext with respect to other contemporary UASs. Data from recent field work(MOSAiC, the Multidisciplinary drifting Observatory for the Study of ArcticClimate) is presented and compared with radiosondes deployed during thatcampaign to provide an overview of sensor and system performance. These datashow good agreement across pressure, temperature, and relative humidity aswell as across wind speed and direction. Additional examples of measurementsprovided by the DH2 are given from a variety of previous campaigns inlocations ranging from the continental United States to Japan and northernAlaska. Finally, a look toward future system improvements and upcomingresearch campaign participation is given. 

Abstract. The Instabilities, Dynamics, and Energetics accompanying Atmospheric Layering (IDEAL) program was conceived to improve understanding of the dynamics of thin strongly stratified “sheet” and deeper weakly stratified “layer” (S&L) structures in the lower troposphere under strongly stable conditions. The field portion of the IDEAL program was conducted from 24 October to 15 November 2017 at Dugway Proving Ground, Utah, to target nighttime lower troposphere S&L conditions. It employed a synergistic combination of observations by multiple simultaneous DataHawk-2 (DH2) small unmanned aircraft systems (sUASs) and concurrent ground-based profiling by the NCAR Earth Observing Laboratory Integrated Sounding System (ISS) comprising a wind profiler radar and hourly high-resolution radiosonde soundings. DH2 measurement intervals as well as vertical (∼ 2–4 km) and horizontal (∼ 5–10 km) flight trajectories were chosen based on local high-resolution weather forecasting and guided by near-real-time ISS measurements. These flights combined simultaneous vertical and slant-path profiling, and/or horizontal racetrack sampling, spanning several hours before sunrise. High-spatial- and temporal-resolution data were downlinked in real time to enable near-real-time changes in DH2 flight paths based on observed flow features. The IDEAL field program performed 70 DH2 flights on 16 d, coordinated with 93 high-resolution radiosonde soundings. In this paper, raw and derived measurements from this campaign are outlined, and preliminary analyses are briefly described. This data set, along with “quick look” figures, is available for access by other researchers, as described herein. 

Over a five-month time window between March and July 2020, scientists deployed two small uncrewed aircraft systems (sUAS) to the central Arctic Ocean as part of legs three and four of the MOSAiC expedition. These sUAS were flown to measure the thermodynamic and kinematic state of the lower atmosphere, including collecting information on temperature, pressure, humidity and winds between the surface and 1 km, as well as to document ice properties, including albedo, melt pond fraction, and open water amounts. The atmospheric state flights were primarily conducted by the DataHawk2 sUAS, which was operated primarily in a profiling manner, while the surface property flights were conducted using the HELiX sUAS, which flew grid patterns, profiles, and hover flights. In total, over 120 flights were conducted and over 48 flight hours of data were collected, sampling conditions that included temperatures as low as −35 °C and as warm as 15 °C, spanning the summer melt season.

 

This dataset includes unprocessed raw data from DataHawk2 fixed-wind uncrewed aircraft system (UAS) flights that were conducted in the central Arctic Ocean over sea ice during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition. Synchronized and quality controlled data are available in the Arctic Data Center at doi:10.18739/A22Z12Q8X for data provided at their native frequency logged on board the aircraft’s secure digital (SD) card (A1 level files), or at doi:10.18739/A2Z60C34R for data interpolated to a common 10 hertz (Hz) clock (B1 level files). Users are encouraged to primarily use the B1 level data for analysis. Please contact the authors if you plan to use this dataset. More information on data collection with the DataHawk2 can be found in de Boer, G. R. Calmer, G. Jozef, J. Cassano, J. Hamilton, D. Lawrence, S. Borenstein, A. Doddi, C. Cox, J. Schmale, A. Preußer and B. Argrow (2022): Observing the Central Arctic Atmosphere and Surface with University of Colorado Uncrewed Aircraft Systems, Nature Scientific Data, submitted. 

Abstract Under stably stratified conditions, the dissipation rate ε of turbulence kinetic energy (TKE) is related to the structure function parameter for temperature , through the buoyancy frequency and the so-called mixing efficiency. A similar relationship does not exist for convective turbulence. In this paper, we propose an analytical expression relating ε and in the convective boundary layer (CBL), by taking into account the effects of nonlocal heat transport under convective conditions using the Deardorff countergradient model. Measurements using unmanned aerial vehicles (UAVs) equipped with high-frequency response sensors to measure velocity and temperature fluctuations obtained during the two field campaigns conducted at Shigaraki MU observatory in June 2016 and 2017 are used to test this relationship between ε and in the CBL. The selection of CBL cases for analysis was aided by auxiliary measurements from additional sensors (mainly radars), and these are described. Comparison with earlier results in the literature suggests that the proposed relationship works, if the countergradient term γ D in the Deardorff model, which is proportional to the ratio of the variances of potential temperature θ and vertical velocity w , is evaluated from in situ (airplane and UAV) observational data, but fails if evaluated from large-eddy simulation (LES) results. This appears to be caused by the tendency of the variance of θ in the upper part of the CBL and at the bottom of the entrainment zone to be underestimated by LES relative to in situ measurements from UAVs and aircraft. We discuss this anomaly and explore reasons for it. 

This dataset is derived from DataHawk2 fixed-wind uncrewed aircraft system (UAS) flights that were conducted in the central Arctic Ocean over sea ice during the MOSAiC expedition. The data include Universal Coordinated Time (UTC), aircraft position and attitude, atmospheric thermodynamic conditions (pressure, temperature, humidity) from various sensors, approximate brightness temperature of the surface and overlying atmosphere, and estimated horizontal winds. A flight flag is included to indicate when the aircraft is in flight. All the data have been synchronized and quality controlled, and are provided at their native frequency logged on board the aircraft’s secure digital (SD) card. Data interpolated to a common 10 hertz (Hz) clock are provided in the B1 level files, and are available in the Arctic Data Center at doi:10.18739/A2Z60C34R. Users are encouraged to primarily use the B1 level data for analysis. More information on the data and methods used for synchronization and quality control can be found in de Boer, G. R. Calmer, G. Jozef, J. Cassano, J. Hamilton, D. Lawrence, S. Borenstein, A. Doddi, C. Cox, J. Schmale, A. Preußer and B. Argrow (2021): Observing the Central Arctic Atmosphere and Surface with University of Colorado Uncrewed Aircraft Systems, Nature Scientific Data, in prep. 

This dataset is derived from DataHawk2 fixed-wind uncrewed aircraft system (UAS) flights that were conducted in the central Arctic Ocean over sea ice during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition. The data include Coordinated Universal Time (UTC), aircraft position and attitude, atmospheric thermodynamic conditions (pressure, temperature, humidity) from various sensors, approximate brightness temperature of the surface and overlying atmosphere, and estimated horizontal winds. A flight flag is included to indicate when the aircraft is in flight. All the data have been synchronized, quality controlled, and interpolated at 10 hertz (Hz). Data at their native frequency are provided in the A1 level files, and are available in the Arctic Data Center at doi:10.18739/A22Z12Q8X. The purpose of this dataset is to provide information on the thermodynamic and kinematic states of the lower atmosphere, and provide detailed observations of turbulence between the surface and one kilometer. Two flight patterns were implemented during the campaign with the DataHawk2: an orbital profile extending from the ice surface to 1000 meter(m) or cloud base if lower, and a “racetrack” pattern where the aircraft was held at a constant altitude while sampling horizontally between two circles. The latter was used to collect data on the spatial variability of thermodynamic properties over the ice surface, particularly over inhomogeneities in the surface such as leads. Displaying latitude, longitude and altitude will help users to identify the flight pattern. Thermodynamic and kinematic measurements have been validated with radiosonde-based measurements. More information on the data and methods used for synchronization and quality control can be found in de Boer, G. R. Calmer, G. Jozef, J. Cassano, J. Hamilton, D. Lawrence, S. Borenstein, A. Doddi, C. Cox, J. Schmale, A. Preußer and B. Argrow (2021): Observing the Central Arctic Atmosphere and Surface with University of Colorado Uncrewed Aircraft Systems, Nature Scientific Data, in prep. 

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.