Insitu Sensing and Analysis of Turbulence - Investigation to Enhance Fine-Structure Turbulence Observation Capabilities of Autonomous Aircraft Systems

The advent of mobile observation platforms made possible by advances in small Unmanned Aircraft Systems (sUAS) technology has revolutionized the landscape of Observational Meteorology. sUAS are proven to be safe, reliable, economical, and easy-to-deploy atmospheric sensing platforms. Unmanned aircraft can sample on flexible flight trajectories, have increased observation range, and are increasingly equipped with miniaturized high-resolution sensing instruments, making them instrumental for targeted observations of atmospheric turbulence. The principal objectives of the investigations described in this dissertation were twofold. First, to improve the accuracy and reliability of current turbulence sensing capabilities of the University of Colorado DataHawk UAS. We explore the current landscape of observational turbulence and develop novel strategies to advance fixed-wing UAS turbulence measurement capabilities. Second, to obtain insightful turbulence data products to further our understanding of complex, small-scale turbulence processes. Challenges in turbulence data processing, analysis strategies, and interpretation of UAS sensed data are discussed. Turbulence measurements made using DataHawk UAS during the Instabilities, Dynamics, and Energetics accompanying Atmospheric Layering (IDEAL) observational campaign are the basis for all investigations presented in this document

Insitu Sensing and Analysis of Turbulence - Investigation to Enhance Fine-Structure Turbulence Observation Capabilities of Autonomous Aircraft Systems

The advent of mobile observation platforms made possible by advances in small Unmanned Aircraft Systems (sUAS) technology has revolutionized the landscape of Observational Meteorology. sUAS are proven to be safe, reliable, economical, and easy-to-deploy atmospheric sensing platforms. Unmanned aircraft can sample on flexible flight trajectories, have increased observation range, and are increasingly equipped with miniaturized high-resolution sensing instruments, making them instrumental for targeted observations of atmospheric turbulence. The principal objectives of the investigations described in this dissertation were twofold. First, to improve the accuracy and reliability of current turbulence sensing capabilities of the University of Colorado DataHawk UAS. We explore the current landscape of observational turbulence and develop novel strategies to advance fixed-wing UAS turbulence measurement capabilities. Second, to obtain insightful turbulence data products to further our understanding of complex, small-scale turbulence processes. Challenges in turbulence data processing, analysis strategies, and interpretation of UAS sensed data are discussed. Turbulence measurements made using DataHawk UAS during the Instabilities, Dynamics, and Energetics accompanying Atmospheric Layering (IDEAL) observational campaign are the basis for all investigations presented in this document.</p

Turbulence Kinetic Energy Dissipation Rates Estimated from Concurrent UAV and MU Radar Measurement s

We tested models commonly used for estimating turbulence kinetic energy dissipation rates ε from very high frequency stratosphere–troposphere radar data. These models relate the root-mean-square value σ of radial velocity fluctuations assessed from radar Doppler spectra to ε. For this purpose, we used data collected from the middle and upper atmosphere (MU) radar during the Shigaraki unmanned aerial vehicle (UAV)—radar experiment campaigns carried out at the Shigaraki MU Observatory, Japan, in June 2016 and 2017. On these occasions, UAVs equipped with fast-response and low-noise Pitot tube sensors for turbulence measurements were operated in the immediate vicinity of the MU radar. Radar-derived dissipation rates ε estimated from the various models at a range resolution of 150 m from the altitude of 1.345 km up to the altitude of ~ 4.0 km, a (half width half power) beam aperture of 1.32° and a time resolution of 24.6 s, were compared to dissipation rates (εU) directly obtained from relative wind speed spectra inferred from UAV measurements. Firstly, statistical analysis results revealed a very close relationship between enhancements of σ and εU for εU≳10⁻⁵m²s⁻³, , indicating that both instruments detected the same turbulent events with εU above this threshold. Secondly, εU was found to be statistically proportional to σ³, whereas a σ² than the longitudinal and transverse dimensions of the radar sampling volume. The σ³ dependence was found even after excluding convectively generated turbulence in the planetary boundary layer and below clouds. The best agreement between εU and radar-derived ε was obtained with the simple formulation based on dimensional analysis ε=σ³ /Lc where LC ≈ 50–70 m. This empirical expression constitutes a simple way to estimate dissipation rates in the lower troposphere from MU radar data whatever the sources of turbulence be, in clear air or cloudy conditions, consistent with UAV estimates

The DataHawk2 uncrewed aircraft system for atmospheric research

The DataHawk2 (DH2) is a small, fixed-wing, uncrewed aircraft system, or UAS, developed at the University of Colorado (CU) primarily for taking detailed thermodynamic measurements of the atmospheric boundary layer. The DH2 weighs 1.7&thinsp;kg and has a wingspan of 1.3&thinsp;m, with a flight endurance of approximately 60&thinsp;min, depending on configuration. In the DH2's most modern form, the aircraft carries a Vaisala RSS-421 sensor for pressure, temperature, and relative humidity measurements, two CU-developed infrared temperature sensors, and a CU-developed fine-wire array, in addition to sensors required to support autopilot function (pitot tube with pressure sensor, GPS receiver, inertial measurement unit), from which wind speed and direction can also be estimated. This paper presents a description of the DH2, including information on its design and development work, and puts the DH2 into context with respect to other contemporary UASs. Data from recent field work (MOSAiC, the Multidisciplinary drifting Observatory for the Study of Arctic Climate) is presented and compared with radiosondes deployed during that campaign to provide an overview of sensor and system performance. These data show good agreement across pressure, temperature, and relative humidity as well as across wind speed and direction. Additional examples of measurements provided by the DH2 are given from a variety of previous campaigns in locations ranging from the continental United States to Japan and northern Alaska. Finally, a look toward future system improvements and upcoming research campaign participation is given. &nbsp;</p

Intercomparison of Small Unmanned Aircraft System (sUAS) Measurements for Atmospheric Science during the LAPSE-RATE Campaign

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&mdash;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 &plusmn;2.6∘ C and 0.22 &plusmn;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.</p

Observing the Central Arctic Atmosphere and Surface with University of Colorado uncrewed aircraft systems

AbstractOver 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.</jats:p

University of Colorado and Black Swift Technologies RPAS-based measurements of the lower atmosphere during LAPSE-RATE

Between 14 and 20&nbsp;July&nbsp;2018, small remotely piloted aircraft systems (RPASs) were deployed to the San Luis Valley of Colorado (USA) together with a variety of surface-based remote and in situ sensors as well as radiosonde systems as part of the Lower Atmospheric Profiling Studies at Elevation &ndash; a Remotely-piloted Aircraft Team Experiment (LAPSE-RATE). The observations from LAPSE-RATE were aimed at improving our understanding of boundary layer structure, cloud and aerosol properties, and surface&ndash;atmosphere exchange and provide detailed information to support model evaluation and improvement work. The current paper describes the observations obtained using four different types of RPASs deployed by the University of Colorado Boulder and Black Swift Technologies. These included the DataHawk2, the Talon and the TTwistor (University of Colorado), and the S1 (Black Swift Technologies). Together, these aircraft collected over 30&thinsp;h of data throughout the northern half of the San Luis Valley, sampling altitudes between the surface and 914&thinsp;m&thinsp;a.g.l. Data from these platforms are publicly available through the Zenodo archive and are co-located with other LAPSE-RATE data as part of the Zenodo LAPSE-RATE community (https://zenodo.org/communities/lapse-rate/, last access: 27&nbsp;May&nbsp;2021). The primary DOIs for these datasets are https://doi.org/10.5281/zenodo.3891620 (DataHawk2, de Boer et al., 2020a, e), https://doi.org/10.5281/zenodo.4096451 (Talon, de Boer et al., 2020d), https://doi.org/10.5281/zenodo.4110626 (TTwistor, de Boer et al., 2020b), and https://doi.org/10.5281/zenodo.3861831 (S1, Elston and Stachura, 2020). &nbsp;</p

Intercomparison of Small Unmanned Aircraft System (sUAS) Measurements for Atmospheric Science During the LAPSE-RATE Campaign

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

Atmospheric observations made at Oliktok Point, Alaska, as part of the Profiling at Oliktok Point to Enhance YOPP Experiments (POPEYE) campaign

Between 1 July and 30 September 2018, small unmanned aircraft systems (sUAS), tethered balloon systems (TBSs), and additional radiosondes were deployed at Oliktok Point, Alaska, to measure the atmosphere in support of the second special observing period for the Year of Polar Prediction (YOPP). These measurements, collected as part of the Profiling at Oliktok Point to Enhance YOPP Experiments (POPEYE) campaign, targeted quantities related to enhancing our understanding of boundary layer structure, cloud and aerosol properties and surface&ndash;atmosphere exchange and providing extra information for model evaluation and improvement work. Over the 3-month campaign, a total of 59 DataHawk2 sUAS flights, 52 TBS flights, and 238 radiosonde launches were completed as part of POPEYE. The data from these coordinated activities provide a comprehensive three-dimensional data set of the atmospheric state (air temperature, humidity, pressure, and wind), surface skin temperature, aerosol properties, and cloud microphysical information over Oliktok Point. These data sets have been checked for quality and submitted to the US Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) program data archive (http://www.archive.arm.gov/discovery/, last access: July 2019) and are accessible at no cost by all registered users. The primary dataset DOIs are https://doi.org/10.5439/1418259 (DataHawk2 measurements; Atmospheric Radiation Measurement Program, 2016), https://doi.org/10.5439/1426242 (TBS measurements; Atmospheric Radiation Measurement Program, 2017) and https://doi.org/10.5439/1021460 (radiosonde measurements; Atmospheric Radiation Measurement Program, 2013a).</p