WINDMAP (Wind Intelligent Navigation Data and Models for Aviation Planning) is a NASA University Leadership Initiative with participants from Oklahoma State University, Virginia Tech University, Universities of Kentucky, Nebraska, and Oklahoma, NCAR, and the Choctaw Nation, as well as several industry partners. The project addresses emerging needs in real-time weather forecasting to improve the safety of low altitude aircraft operations through the integration of real-time observations from autonomous systems with numerical weather prediction and flight management and safety systems. In addition to the challenges of traffic congestion and obstacles, critical technology gaps exist for modeling, detecting, and accommodating the dynamic local weather environment and for precision navigation through uncertain weather conditions. The use of small uncrewed aircraft systems (sUAS) as a tool for use in atmospheric observations is well known. However, they will play an increasingly larger role in advanced air mobility (AAM) in the integration of urban air mobility (UAM) solutions as well as unmanned traffic management (UTM) system development and implementation. However, the challenges of urban wind field sensing include high spatial turbulence gradients, high turbulence magnitudes, and degraded position references, all of which complicate the traditional approach. This is particularly true in urban environments where the presence of buildings produce complex, unsteady flow patterns. This paper discusses approaches and field tests the WINDMAP team has utilized over the effort for utilizing UAS to provide real-time data to enable UAM and UTM operations, and the last year of integrated flight campaigns for improving the resolution and accuracy of comprehensive wind and thermodynamic field observation, data assimilation, numerical weather prediction, and system implementation.

The impact of assimilating targeted uncrewed aircraft system (UAS) observations on the prediction of radiation and river valley fog is assessed using observing system experiments (OSEs). Two multirotor UASs were deployed during Frequent in situ Observations above Ground for Modeling and Advanced Prediction of fog (FOGMAP) which took place during the summer of 2022 in northern Kentucky. Targeted UAS missions were fl own to sample the spatiotemporal variability of temperature and moisture in the vicinity of the Cincinnati/Northern Kentucky International Airport. During each mission, the UAS performed near-continuous profiling at two locations between the surface to 120 m AGL throughout the night. Data denial experiments were performed using the ensemble adjustment Kalman fi lter available in NSF NCAR's Data Assimilation Research Testbed (DART) to determine the impact of assimilating UAS observations on the skill of analyses and forecasts issued during potential fog events. Simulations that only assimilated conventional observations tended to have a dry bias in the analyses and forecasts. The dry bias in the analyses was reduced in experiments that assimilated UAS observations leading to improved probabilistic predictions of fog. Sensitivity tests revealed that the ensemble mean analyses were improved when assimilating UAS observations of specific humidity rather than relative humidity (RH) due to the existence of a cold bias near the surface and the negative covariance between RH and temperature. It was also found that either the assumed observation error variance of (1 g kg21)2 or the ensemble spread of the background specific humidity was too large since their sum tended to overestimate the root-mean-square error (RMSE) of the predicted ensemble mean values.

Multiple fixed-wing and multirotor uncrewed aircraft systems were deployed to measure the early morning katabatic flow along a valley as part of the lower atmosphere profiling studies at elevation a remotely-piloted aircraft team experiment (LAPSE-RATE) campaign. The valley’s topography was that of a narrow canyon emerging into a broader shallow-sloped valley, allowing for an assessment of the suitability of one-dimensional approximations for the broad, flat part of the valley. The one-dimensional integral model predicts growth in the katabatic layer with downslope distance, which was not observed in the broader portions of the valley. Instead, observations revealed thinning of the katabatic layer at the valley centreline, coinciding with oscillatory behavior with a period between 30 and 60 min. These features were attributed to strong asymmetry and three-dimensional features initiating in the narrow part of the valley. These features produced initial conditions upstream of the broad slope flow that were not captured by the one-dimensional model.

A review of 1980s to early 2010s research supporting and documenting successful missions for on-orbit assembly of large space structures was conducted to bring together flexible space structure dynamics challenges and progress toward solutions. Research in this period focused on issues ranging from ground validation via unique test beds to in-space modal characterization and model adjustment. The phased assembly of the International Space Station (ISS), along with its evolving structural loads and dynamics, provides the central example, with ground and on-orbit tests of other systems providing contributing and contrasting examples of potential value for researchers facing today’s challenges.

Uncrewed aircraft system (UAS) observations collected during the 2018 Lower Atmospheric Process Studies at Elevation-a Remotely Piloted Aircraft Team Experiment (LAPSE-RATE) field campaign were assimilated into a high-resolution configuration of the Weather Research and Forecasting Model using an ensemble Kalman filter. The benefit of UAS observations was assessed for a terrain-driven (drainage and upvalley) flow event that occurred within Colorado's San Luis Valley (SLV) using independent observations. The analysis and prediction of the strength, depth, and horizontal extent of drainage flow from the Saguache Canyon and the subsequent transition to upvalley and up-canyon flow were improved relative to that obtained both without data assimilation (benchmark) and when only surface observations were assimilated. Assimilation of UAS observations greatly improved the analyses of vertical variations in temperature, relative humidity, and winds at multiple locations in the northern portion of the SLV, with reductions in both bias and the root-mean-square error of roughly 40% for each variable relative to the benchmark run. Despite these noted improvements, some biases remain that were tied to measurement error and/or the impact of the boundary layer parameterization on vertically spreading the observations, both of which require further exploration. The results presented here highlight how observations obtained with a fleet of profiling UAS improve limited-area, high-resolution analyses and short-term forecasts in complex terrain.

Abstract. In July 2018, unmanned aerial systems (UAS) were deployed to measure the properties of lower atmosphere within the San Luis Valley, an elevated valley in Colorado, USA as part of the Lower Atmospheric Profiling Studies at Elevation – a Remotely-piloted Aircraft Team Experiment (LAPSE-RATE). Measurement objectives included detailing boundary-layer transition, canyon cold-air drainage, and convection initiation within the valley. Details of the contribution to LAPSE-RATE made by University of Kentucky are provided here, which include measurements by seven different fixed-wing and rotorcraft UAS totaling over 178 flights with validated data. The data from these coordinated UAS flights consist of thermodynamic and kinematic variables (air temperature, humidity, pressure, wind speed and direction) and include vertical profiles up to 900 m above the ground level and horizontal transects up to 1500 m in length. These measurements have been quality controlled and are openly available in the Zenodo LAPSE-RATE community data repository (https://zenodo.org/communities/lapse-rate/), with the University of Kentucky data available at https://doi.org/10.5281/zenodo.3701845 (Bailey et al., 2020).

ABSTRACTBecause 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.

In July 2018, unmanned aerial systems (UASs) were deployed to measure the properties of the lower atmosphere within the San Luis Valley, an elevated valley in Colorado, USA, as part of the Lower Atmospheric Profiling Studies at Elevation – a Remotely-piloted Aircraft Team Experiment (LAPSE-RATE). Measurement objectives included detailing boundary layer transition, canyon cold-air drainage and convection initiation within the valley. Details of the contribution to LAPSE-RATE made by the University of Kentucky are provided here, which include measurements by seven different fixed-wing and rotorcraft UASs totaling over 178 flights with validated data. The data from these coordinated UAS flights consist of thermodynamic and kinematic variables (air temperature, humidity, pressure, wind speed and direction) and include vertical profiles up to 900 m above the ground level and horizontal transects up to 1500 m in length. These measurements have been quality controlled and are openly available in the Zenodo LAPSE-RATE community data repository (https://zenodo.org/communities/lapse-rate/, last access: 23 July 2020), with the University of Kentucky data available at https://doi.org/10.5281/zenodo.3701845 (Bailey et al., 2020).

Unmanned aircraft systems (UASs) offer innovative capabilities for providing new perspectives on the atmosphere, and therefore atmospheric scientists are rapidly expanding their use, particularly for studying the planetary boundary layer. In support of this expansion, from 14 to 20 July 2018 the International Society for Atmospheric Research using Remotely piloted Aircraft (ISARRA) hosted a community flight week, dubbed the Lower Atmospheric Profiling Studies at Elevation – a Remotely-piloted Aircraft Team Experiment (LAPSE-RATE; de Boer et al., 2020a). This field campaign spanned a 1-week deployment to Colorado's San Luis Valley, involving over 100 students, scientists, engineers, pilots, and outreach coordinators. These groups conducted intensive field operations using unmanned aircraft and ground-based assets to develop comprehensive datasets spanning a variety of scientific objectives, including a total of nearly 1300 research flights totaling over 250 flight hours. This article introduces this campaign and lays the groundwork for a special issue on the LAPSE-RATE project. The remainder of the special issue provides detailed overviews of the datasets collected and the platforms used to collect them. All of the datasets covered by this special issue have been uploaded to a LAPSE-RATE community set up at the Zenodo data archive (https://zenodo.org/communities/lapse-rate/, last access: 3 December 2020).

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.

We use unmanned aerial vehicles to interrogate the surface layer processes during a solar eclipse and gain a comprehensive look at the changes made to the atmospheric surface layer as a result of the rapid change of insolation. Measurements of the atmospheric surface layer structure made by the unmanned systems are connected to surface measurements to provide a holistic view of the impact of the eclipse on the near-surface behaviour, large-scale turbulent structures and small-scale turbulent dynamics. Different regimes of atmospheric surface layer behaviour were identified, with the most significant impact including the formation of a stable layer just after totality and evidence of Kelvin-Helmholtz waves appearing at the interface between this layer and the residual layer forming above it. The decrease in surface heating caused a commensurate decrease in buoyant turbulent production, which resulted in a rapid decay of the turbulence in the atmospheric surface layer both within the stable layer and in the mixed layer forming above it. Significant changes in the wind direction were imposed by the decrease in insolation, with evidence supporting the formation of a nocturnal jet, as well as backing of the wind vector within the stable layer.

This paper discusses results of the CLOUD-MAP(Collaboration Leading Operational UAS Development for Meteorology and Atmospheric Physics) project dedicated to developing, fielding, and evaluating integrated small unmanned aircraft systems (sUAS) for enhanced atmospheric physics measurements. The project team includes atmospheric scientists, meteorologists, engineers, computer scientists, geographers, and chemists necessary to evaluate the needs and develop the advanced sensing and imaging, robust autonomous navigation, enhanced data communication, and data management capabilities required to use sUAS in atmospheric physics. Annual integrated evaluation of the systems in coordinated field tests are being used to validate sensor performance while integrated into various sUAS platforms. This paper focuses on aspects related to atmospheric sampling of thermodynamic parameters with sUAS, specifically sensor integration and calibration/validation, particularly as it relates to boundary layer profiling. Validation of sensor output is performed by comparing measurements with known values, including instrumented towers, radiosondes, and other validated sUAS platforms. Experiments to determine the impact of sensor location and vehicle operation have been performed, with sensor aspiration a major factor. Measurements are robust provided that instrument packages are properly mounted in locations that provide adequate air flow and proper solar shielding.

Small unmanned aircraft (or aerial) systems (sUAS) are poised to revolutionize geospatial data collection for meteorology and atmospheric science, particularly within the lowest portion of the atmosphere, referred to as the atmospheric boundary layer (ABL), where conventional data collection techniques such as satellites, balloons and ground-based weather towers do not provide adequate coverage for monitoring certain types of weather events. The prospects of these autonomous mobile sensing platforms for geospatial data collection in the atmospheric realm are immense, but there has been limited discourse on the broader GIScience-related research challenges and opportunities surrounding data capture and analysis for this field beyond traditional imagery applications. Here, we address the integration of sUAS into meteorology and atmospheric science applications, including severe weather monitoring, from the perspective of the myriad GIScience research challenges and opportunities that exist for collecting and processing alternative types of non-image geospatial data from sUAS. Specifically, we highlight opportunities surrounding data capture and processing, data representations, and societal implications stemming from these applications.

Since 1996, the University of Kentucky (UK) has offered multi-disciplinary aerospace design experiences culminating in microgravity and space systems flight experiments. This paper summarizes these experiments, with a focus on key technical results rather than on the systems engineering educational aspects presented previously. While most participating students joined the aerospace workforce at small or large companies or at national laboratories, several entrepreneurial graduates recently established start-up companies which are applying their educational experiences and entending the technical results from their student experience to contribute to local economic development.

Engineered inflatable systems are designed to precisely control internal or external surfaces, or both, to achieve a desired response or structure. The dynamics and structure of inflatable systems are directly related to the configuration of interfacing boundaries. A design pathfinder for inflatable systems has been developed over the past three years and this paper presents nonlinear finite element (FE) simulations performed to evaluate the performance of the pathfinder and its sensitivity to imposed damage. The static deflection response of the design pathfinder was compared with performance standards for terrestrial and space-based radar reflectors and radar decoys. Further, damage was introduced as thread loss along seams of the internal structure of the design pathfinder. It was found that the design pathfinder met or exceeded all performance standards and the static response remained unchanged under a significant level of damage. The work presented in this paper verifies the development of the design pathfinder. Further, the methods used in development of the pathfinder can be used to improve efficiency of design and analysis techniques for future inflatable structures.

In this paper, an approach is described to implement autonomous (waypoint tracking) flight in a testbed airframe, which uses wing twist for roll control. These flights were performed using an existing commercial autopilot. Aileron effectiveness was identified as a parameter that could be modified to maintain roll control during autonomous flight. A modeling process was then developed to calculate the aileron effectiveness for a wing shaping demonstrator aircraft utilizing numerically determined aerodynamic properties. Simulations and flight tests with the testbed aircraft were performed that demonstrated suitability of the approach for autonomous flight. In-flight aileron doublets were used to validate the aileron effectiveness predicted by the numerical model, which matched within 7%.