Hyperlocal weather predictions are often necessary in order to determine whether a particular sUAS route will be safe to fly. The General Urban area Microclimate Predictions tool (GUMP) seeks to provide such predictions through the use of machine learning (ML) models and computational fluid dynamics (CFD) simulations. The computed wind flow field is converted into an intuitive risk map for sUAS operators through the use of appropriate thresholds on wind velocities.
Adverse weather conditions, particularly, high winds, can have a highly adverse impact on small unmanned aircraft system (sUAS) operations. These conditions can vary significantly within a small area (particularly, in an urban environment); thus, hyperlocal weather predictions are often necessary in order to determine whether a particular sUAS route will be safe to fly. The General Urban area Microclimate Predictions tool (GUMP) seeks to provide such predictions through the use of machine learning (ML) models and computational fluid dynamics (CFD) simulations. Specifically, ML models are trained to ingest mesoscale forecasts from the National Oceanic and Atmospheric Administration (NOAA) and output refined forecasts for some specific location, typically, a weather station that serves as a source of ground truth data during training. At the same time, CFD simulations over 3D models of structures (e.g., buildings) are utilized to extend the refined forecast to other points within the area of interest surrounding the location. Because it is difficult to perform such simulations in real-time, they are executed offline under a wide range of boundary conditions, generating a broad set of resulting wind flow fields. During deployment, GUMP retrieves the wind flow field that is most consistent with the ML model’s forecast. The wind flow field can be converted into an intuitive risk map for sUAS operators through the use of appropriate thresholds on wind velocities.
Funding Agency: National Aeronautics and Space Administration
The synergy between moderate resolution satellite imagery and fine resolution drone imagery, LiDAR data, and meteorological data, along with generally available GIS data, must be identified and optimized. These data will be integrated to produce a variety of products that help identify what tools, inputs, and management strategies most effectively contribute to an increase in the productivity and resilience of an important agricultural system to a major weather or climate related disturbance.
Satellite imagery has been used in agriculture for some time and the increasing implementation of drones into agriculture and agriculture science holds unique promise. However, the synergy between moderate resolution satellite imagery, fine resolution drone imagery, fine resolution LiDAR (Light Detection And Ranging) data, fine-resolution meteorological data, and generally available GIS (Geographic Information Systems) data must be identified and optimized. To be most useful, this fusion of data should help provide estimates in the health and yield of agriculture systems as well as insight into the microclimate and ecosystem variation within a farm site. These data will be integrated to produce a variety of fine-resolution maps that can be analyzed to identify what tools, inputs, and management strategies most effectively contribute to an increase in productivity, agroecological system health, and resiliency or restoration (typically in response to weather or climatic disturbance) of a given farming operation and site. This research will apply these data science methods and tools to varying farm types in Puerto Rico. We expect new insight into how the fusing of a multitude of data can be effectively integrated into an agriculture operation and, subsequently, determine which outputs are most valuable to the varied farm types, practices, and locations. This investigation will also provide critical information on the resistance and resilience of an important agricultural system to major weather or climate-related disturbances and, subsequently, inform management decisions related to climate change adaptation.
Funding Agency: United States Department of Agriculture
LADME: Low-Altitude Drone Monsoon Experiment
The LADME projects seeks to investigate sources of convective initiation and suppression within the orographic planetary boundary layer over Arizona during the North American Monsoon using a unique suite of meteorological instruments mounted onto both unmanned and manned ERAU aircraft. This low-altitude drone monsoon experiment (LADME) will obtain focused meteorological quantities in locations of known thunderstorm initiation and sample the fine-scale atmospheric flows that pre-condition the atmosphere and promote updrafts that trigger convection. This research features interdisciplinary research between ERAU’s Daytona Beach and Prescott campuses.
Project Lead: Dr. Ronny Schroeder, Embry-Riddle Aeronautical University, Prescott, AZ
Using Stationary and Mobile Low-Cost Sensors to Assess Air Quality
The goal of this work is to (1) improve the accessibility of air quality sensors, (2) increase the number of air quality observations, and (3) increase the spatial resolution of air quality sensors using low-cost sensors (LCSs). Particulate matter (PM) has been recognized as a detrimental pollutant in the atmosphere causing several adverse impacts on human health as well as on the environment. In the United States, similar to many other countries, concentrations of PM are monitored and enforced by regulations based on the National Ambient Air Quality Standards (NAAQS). However, measuring atmospheric PM using the existing infrastructure is designed to measure PM pollution on urban/regional-scales. Nonetheless, these conventional monitoring technologies are limited in capturing the spatial and temporal variations in atmospheric PM concentrations at fine scales. Additionally, observations from these technologies possess inherent limitations due to relatively high cost and size. Hence, these constraints bring about the need for more sustainable methods to monitor air pollution in order to devise preventive and/or corrective measures to protect the environment in highly polluted urban - where monitoring is not spatially
sufficient- as well as remote areas - where monitoring is often underestimated. This project is designed to characterize and compare the performance of three different commercially available PM LCSs. The innovative aspect of this proposed design is our ability to measure horizontal and vertical profiles of PM in the atmosphere using three different multi-dimensional modes of operation: (1) stationary (1D), (2) mobile on manned vehicles (2D), and (3) mobile on an unmanned aerial vehicles (3D). This study has the potential to produce viable systems to be used by public, and systems to be implemented in manned and unmanned vehicles. As a renowned aeronautical university, Embry-Riddle Aeronautical University (ERAU) has extensive experience with manned and unmanned vehicles, and more recently, the university has been giving special attention to research in air quality and sustainability as part of its strategic plan.
Project Lead: Dr. Marwa El-Sayed, Sustainability and Environmental Engineering Lab (SEEL), Embry-Riddle Aeronautical University
MoVE: A Mobility Virtual Environment for Planning, Rehearsing, Collecting and Visualizing Atmospheric Observations Using Multiple Coordinated Unmanned Aerial Vehicles
Unmanned Aircraft Systems (UAS) have become prevalent in a wide variety of meteorological investigations. UAS afford the ability to fill an important atmospheric observational gap, namely observations in the domain between the reach of ground-based sensors and the altitudes that manned aircraft can safely operate at. Fixed-wing UAS offer an opportunity to cover vast horizontal and vertical distances in a continuous manner with high spatial resolution. Multirotor UAS possess the ability to launch and recover in small spaces, fly at slow airspeeds, hover, accomplish vertical profiles, and probe obstacle laden environments while making spatially dense observations. Each of these UAS categories offer a new observational strategy that is efficient, reusable, durable, repeatable, has a much lower cost barrier, requires minimal infrastructure, and renders superior spatial flexibility, range, and resolution.
Swarms of meteorologically instrumented UAS provide an opportunity to further capitalize on these advantages. However, any given UAS flight must remain within visual line of sight (VLOS) of the remote pilot. Therefore, in order to observe a large geographical area that spans beyond VLOS, multiple UAS must be simultaneously flown. Likewise, to accomplish more spatially dense observations in an immediate area, multiple UAS must undertake concurrent observations. Each of these strategies increase the complexity of the operation and present a challenge in tying together disparate measurements.
To assist in multi-vehicle data collection, as previously described, the open-source, publicly available Mobility Virtual Environment (MoVE) has been developed. This software is designed to first rehearse multi-vehicle scenarios in simulation and then collect real data in real time using a cellular network. In simulation, MoVE can be used to select waypoint routes that ensure safety, are appropriate for the objectives of the atmospheric investigation, and fit within the performance envelope of the involved pilots and unmanned aircraft (UA). Once observational strategies are reasonably well prepared in the virtual environment, real pilots with real UA can rehearse or undertake flight plans with uninstrumented or instrumented UA. Through MoVE, all data is brought together in time and geo-tagged at suitable frequencies making it easy to combine individual UA data together into a single data set. In this presentation, the ability of MoVE to streamline the planning, execution, post-processing and visualization of data in multi-vehicle field campaigns is explored. The benefits MoVE affords the atmospheric science community can also translate to the broader scientific and engineering communities.
Project Lead: Dr. Marc Compere, Embry-Riddle Aeronautical University
Real-Time Urban Weather Observations for Aviation
Urban air mobility (UAM) is expected to be an integral component of cities of the future. However, the urban environment is a new setting for sustained aviation operations. The lower mass, more limited thrust and slower speeds of these vehicles increase their sensitivity to the spatially and temporally dynamic urban environment. Exacerbating this situation is the fact that traditional aviation weather products for observations and forecasts on the outskirts of a metropolitan area do not necessarily translate well to the urban setting. The initial and continuing costs associated with a dense meteorological observation network, required for the heterogeneous nature of the urban environment, make the creation of one in every participating metropolitan area across the country unrealistic. This project explores a variety of potential data sources and proposes a cyber-physical system (CPS) architecture, including an incentive-based crowdsensing application, for real-time aviation observations.
Project Lead: Dr. M. Ilhan Akbas, Embry-Riddle Aeronautical University
TEAMx: Multi-scale Transport and Exchange processes in the Atmosphere over Mountains – program and eXperiment
Embry-Riddle is one of 27 international partnering organizations.
TOPO: Thunderstorm Orographic Preconditioning and Organization
Analysis of Near-Surface Meteorology in Wind Turbine Array Boundary Layers Using Instrumented Unmanned Aerial Systems and Large-Eddy Simulation
Simulation and modeling have shown that wind farms have an impact on the near-surface atmospheric boundary layer as turbulent wakes generated by the turbines enhance vertical mixing. While a few observational data sets that focus on near-surface temperature changes exist, these studies lack high spatial resolution and neglect the combined effect of these temperature changes with an altered humidity profile. With a large portion of wind farms hosted within an agricultural context, changes to humidity can potentially have secondary impacts, such as to the productivity of crops. The goal of this study was to gather high-resolution in situ field measurements in the wake of wind turbines in order to differentially map downstream changes to humidity. Measurements, obtained by instrumented unmanned aerial systems, are complemented by numerical experiments conducted using large-eddy simulation. Observations and numerical results are in good general agreement and show that downstream relative humidity is differentially altered in all directions.
Project Lead: Dr. Kevin A. Adkins, Embry-Riddle Aeronautical University
Green Infrastructure Prioritization and Conditions Assessment for Climate Change Adaptation
Implement comprehensive, decadal scenarios of land cover change associated with sea-level rise
Develop suitability assessments and spatial optimizations/prioritizations for implementing green infrastructure within the IRL watershed
Deploy multi-sensor UAS missions on near-term priority sites identified in Objective 2 for the purpose of developing high resolution site condition assessments of hydrologic connectivity, extant vegetation quality, and associated feasibility for sustainable utilization in a green infrastructure mosaic
Development of specific policy and, as appropriate, project recommendations for potential implementation by governmental and non-governmental entities, with a special focus on underserved and underrepresented communities
Provide frequent and diverse opportunities for direct public engagement and technical outreach, including with elected officials and historically underrepresented communities
Provide professional development support for undergraduate students and a post-doctoral researcher through a project that provides technical advancement, public outreach, and concrete policy outcomes
Project Lead: Stetson University; Dr. Dan Macchiarella , Embry-Riddle Aeronautical University
Windtl: Automated Wind Observing System for Fire Weather
The complexity of wildfires dictate the use of semi-empirical physically-based models to predict wildfire spread rates and direction. It has long been recognized that processes in the lower atmospheric boundary layer (ABL) significantly influence the behavior of the wildfires, such as modifying their rate of spread (Schroeder, 1970; Beer, 1991). Recent laboratory and field experiments, along with numerical simulation, have shown that fine-scale atmospheric processes, such as sweeps and ejections, play an important role in fire spread (Bebieva et al., 2021). Although the National Weather Service (NWS) provides regular weather forecasts, including enhanced model development (such as the High-Resolution Rapid Refresh Ensemble, HRRRE, Kalina et al. (2021)), it is still incapable of capturing all of the small physical processes that determine the fire spread rates. In the absence of fires, a recent study evaluating weather nowcasting against Doppler LiDAR data showed that winds generated by surface heating are poorly predicted by the model (Banta et al., 2021). In the presence of fires, buoyant flame dynamics induce extra wind (i.e., the wind generated by the fires themselves) that is impossible to predict using the weather forecasting models. Yet, this extra wind controls the convective heating process that ignites fuel ahead and yields fire spread (Finney et al., 2015).
The new generation of wildfire models combine combustion fire models (either physical or empirical) together with a numerical weather prediction (NWP) model or a computational fluid dynamics (CFD) model (see e.g. Bakhshaii and Johnson, 2019, for the full review). One of the important parameters in the weather model is wind characteristics. Currently there are no observations of fire generated wind patterns around fire fronts and, consequently, a new observational system is needed. The overarching goal of this project is to develop a fully automated wind observing system, ”Windtl”, that allows a better prediction of wind and turbulence events in the lower ABL. At the heart of this system is an algorithm that estimates local wind velocity based on aircraft state measurements without the need for hosted wind sensors.
Funding Agency: Improving Aviation, Ms. Rocio Frej Vitalle
Associated Publications, Presentations and Media
Adkins, K. and Sescu, A. (2022). Wind farms and humidity, Energies, 15(7):2603. doi.org/10.3390/en15072603.
Akbas, M., Adkins, K., and Compere, M. (2021). Real-Time Urban Observations for Aviation, Proceedings of the 2021 AIAA Aviation Forum. doi: 10.2514/6.2021-2359.
Adkins, K., Albelo, J., and Bowyer, S. (2021). Exploring the Impact of Early Exposure to Research on Dual Enrollment Students: A Qualitative Single-Case Study, Journal of Applied Learning & Teaching, 4(1). doi.org/10.37074/jalt.2021.4.1.2.
Adkins, K., Akbas, M., and Compere, M. (2020). Real-Time Urban Weather Observations for Urban Air Mobility, International Journal of Aviation, Aeronautics, and Aerospace, 7(4). doi.org/10.15394/ijaaa.2020.1540.
Adkins, K., Wambolt, P., Sescu, A., *Swinford, C., and Macchiarella, N.D. (2020). Observational Practices for Urban Microclimates Using Meteorologically Instrumented Unmanned Aircraft Systems, Atmosphere, 11, 1008. doi.org/10.3390/atmos11091008.
Adkins, K., *Swinford, C., Wambolt, P., and Bease, G. (2020). Development of a Sensor Suite for Atmospheric Boundary Layer Measurement with a Small Multirotor Unmanned Aerial System, International Journal of Aviation, Aeronautics, and Aerospace, 7(1). doi.org/10.15394/ijaaa.2020.1433.
Macchiarella, D., Adkins, K., and Wallace, R. (2020). Using Small Unmanned Aircraft Systems for Remote Sensing and Data Collection: Aerospace Education and Service Learning. Proceedings of the 2020 AIAA SciTech Forum. doi.org/10.2514/6.2020-1771.
Haywood, J., Sescu, A. and Adkins, K. (2019). Large-Eddy Simulation Study of the Humidity Variation in the Shadow of a Large Wind Farm in Stable Conditions, Wind Energy, 2019. doi.org/10.1002/we.2434.
Compere, M., Adkins, K., *Legon, O., and Currier, P. (2019). MoVE: A Mobility Virtual Environment for Testing Multi-Vehicle Scenarios, Proceedings of the 2019 NDIA Ground Vehicle Systems Engineering and Technology Symposium. (Best Paper Award)
Adkins, K. (2019). Urban Flow and Small Unmanned Aerial System Operations in the Built Environment, International Journal of Aviation, Aeronautics, and Aerospace, vol. 6.1. doi.org/10.15394/ijaaa.2019.1312.
Adkins, K. and Sescu, A. (2018). Analysis of Near-Surface Relative Humidity in a Wind Turbine Array Boundary Layer Using an Instrumented Unmanned Aerial System and Large-Eddy Simulation, Wind Energy, 21:1155-1168. doi.org/10.1002/we.2220.
Adkins, K., *Bailey, C., and *Taylor, A. (2018). Integrating Unmanned Aerial Systems into the Crop Production System through On-Farm Research, Journal of Agricultural Science, vol. 10, no. 7. doi:10.5539/jas.v10n7p10.
Adkins, K., *Bailey, C., and *Taylor, A. (2018). The Next Step Beyond Identifying Field Variability: Integrating Unmanned Aerial Systems Into the Farm Management Workflow, Proceedings of the Association for Unmanned Vehicle Systems International Xponential 2018, AUVSI, Denver, CO.
Adkins, K. and Sescu, A. (2017). Observations of Relative Humidity in the Near-Wake of a Wind Turbine Using an Instrumented Unmanned Aerial System, International Journal of Green Energy, vol. 14, pp. 845-860. doi.org/10.1080/15435075.2017.1334661.
Adkins, K., Olds, J., and Ellis, C. (2017). Development, Testing and Use of an Instrumented Unmanned Aerial System to Investigate Changes to the Near-Surface Meteorology within a Wind Farm, Proceedings of the Association for Unmanned Vehicle Systems International Xponential 2017, AUVSI, Dallas, TX.
Adkins, K. (2007). Quantifying the Aerodynamic Forces on Objects During a Vault, SOARce: The Society of Accident Reconstructionists, SOARce.
Adkins, K. (2017). “The Elements” in Mirot, A.J. (ed), Unmanned Aircraft Systems and Safety. Orlando FL: Unmanned Safety Institute (ARGUS International), ch. 7, pp. 135-156. ISBN-10: 0998729515; ISBN-13: 978-0998729510.
Online Training Courses
Adkins, K. (2017). Online sUAS Safety Certification Course for commercial operators, Unit 8: UAS and the Elements. Unmanned Safety Institute (ARGUS International). www.unmannedsafetyinstitute.org. 2017.
*Bowles, J., Compere, M., Adkins, K., and El-Sayed, M. (2022). Developing a Novel Sensor Technology for Measuring Particulate Matter on Unmanned Aircrafts, Air Sensors International Conference, Pasadena, CA.
Adkins, K. (2022). Using Drones for Aerial Measurements, invited lecture, College of Engineering and Natural Sciences, University of Tulsa, Tulsa, OK.
Cortright, B., Schroeder, R., Adkins, K., James, C., Kaplan, M., Kaplan, M., Koch, S., [and 11 others]. (2021). Detection of Convective Initiation and Suppression in Northern Arizona’s Complex Terrain with Uncrewed and Crewed Aerial Systems, Fall Meeting, AGU, New Orleans, LA.
Adkins, K. (2021). Are We Clear for Launch: Preparations for the Coming of Advanced Air Mobility, DeLand Sport Aviation Showcase, Deland, FL.
Adkins, K. (2021). Weather Alert: Tech Needs for AAM and UAS, National Business Aviation Association (NBAA) Business Aviation Convention & Exhibition (BACE), discussion panelist, Las Vegas, NV.
*Bowles, J., Compere, M., Adkins, K., and El-Sayed, M. (2021). Multi-dimensional Characterization of Particulate Matter Low-Cost Sensors, 39th Annual American Association for Aerosol Research Conference, Urban Aerosols Working Group, Albuquerque, NM.
Adkins, K., Akbus, I., and Compere, M. (2021). Real-Time Urban Observations for Aviation, AIAA Aviation 2021, Washington D.C.
Adkins, K., Compere, M., and *Krishnan, A. (2021). MoVE: A Mobility Virtual Environment for Planning, Rehearsing, Collecting and Visualizing Atmospheric Observations Using Multiple Mobile Sensors, 21st Symposium on Meteorological Observation and Instrumentation, American Meteorological Society Annual Meeting 2021, New Orleans, LA.
Adkins, K., Albelo, J., and Harrison, S. (2020). Exploring the Impact of Early Exposure to Research on Dual Enrollment Students, STEMinar 2020.
Adkins, K., Sescu, A., Wambolt, P., *Swinford, C., and *Rentzke, N. (2020). High-Resolution In Situ Measurement of an Urban Boundary Layer Using Instrumented Unmanned Aerial Systems, 15th Symposium on the Urban Environment, American Meteorological Society Annual Meeting 2020, Boston, MA.
Macchiarella, D., Adkins, K., and Wallace, R. (2020). Using Small Unmanned Aircraft Systems for Remote Sensing and Data Collection: Aerospace Education and Service Learning, American Institute of Aeronautics and Astronautics SciTech, Orlando, FL.
Adkins, K., Sescu, A., *Swinford, C., and *Rentzke, N. (2019). Nocturnal Observations of Thermodynamic and Kinematic Properties in a Wind Turbine Array Boundary Layer Using an Instrumented Unmanned Aerial System, Fall Meeting, AGU, San Francisco, CA.
Compere, M., Adkins, K., *Legon, O., and Currier, P. (2019). MoVE: A Mobility Virtual Environment for Testing Multi-Vehicle Scenarios, 2019 Ground Vehicle Systems Engineering and Technology Symposium, Warren, MI.
Adkins, K., *Swinford, C., and Wambolt, P. (2019). Development of a Meteorological Sensor Suite for Atmospheric Boundary Layer Measurement Using a Small Multirotor Unmanned Aerial System, International Society for Atmospheric Research using Remotely-piloted Aircraft World Congress, Lugo, Spain.
Adkins, K. and Wambolt, P. (2019). UAS Flight Operations in Complex Terrain: Assessing the Agricultural Impact from Hurricane Maria in the Central Mountainous Region of Puerto Rico and Developing New Data Science Tools, Xponential 2019, AUVSI, Chicago, IL.
Haywood, J., Sescu, A., and Adkins, K. (2018). Humidity Variation in the Shadow of a Large Wind Farm: an LES Investigation, Division of Fluid Dynamics, APS, Atlanta, GA.
Adkins, K., *Bailey, C., and *Taylor, A. (2018). The Next Step Beyond Identifying Field Variability: On-Farm Investigations Using an Unmanned Aerial System to Accurately Diagnose Crop Disease, Xponential 2018, AUVSI, Denver, CO.
Adkins, K. (2017). Application of UAS for Environmental Sustainability, anchor lecture for multiday Informatics Days event hosted by the School for Environment & Sustainability at The University of Michigan, Ann Arbor. (invited guest lecturer)
Adkins, K., Olds, J., and Ellis, C. (2017). Development, Testing and Use of an Instrumented Unmanned Aerial System to Investigate Changes to the Near-Surface Meteorology within a Wind Farm, Xponential 2017, AUVSI, Dallas, TX.
Adkins, K. (2016). Observations of Near-Surface Relative Humidity in a Wind Turbine Array Boundary Layer Using an Instrumented Unmanned Aerial System, Fall Meeting, AGU, San Francisco, CA.
Adkins, K., and Elfajri, O. (2016). Analysis of Near-Surface Relative Humidity in a Wind Turbine Array Boundary Layer Using an Instrumented Unmanned Aerial System and Large-Eddy Simulation, Division of Fluid Dynamics, APS, Portland, OR.
Adkins, K. (2016). UAS: An Overview and Case For, Civil Air Patrol: Florida Field Leadership Exercise (FLeX). (invited guest lecturer)
Adkins, K. (2015). Effect of Derating and Shutting Down of Turbines on the Extracted Power of Large Wind Farms in Thermally-Stratified Atmospheric Boundary Layer, Fall Meeting, AGU, San Francisco, CA.
Faculty mentored student presentations:
*Andres, M., Compere, M., Adkins, K., and El-Sayed, M. (faculty advisor). (2021). Low-Cost Particulate Matter Sensors and Their Future Use with Unmanned Vehicles, Embry-Riddle Aeronautical University Student Research Symposium, Daytona Beach, FL.
*Bowles, J., *Krishnan, A. M., Compere, M. El-Sayed, M., and Adkins, K. (faculty advisor). (2021). New Air Quality Measurement Method: Low-Cost Sensors on UAV’s, Embry-Riddle Aeronautical University Discovery Day, Daytona Beach, FL.
*Bowles, J., *Krishnan, A. M., Compere, M. El-Sayed, M., and Adkins, K. (faculty advisor). (2021). The New Air Quality Measurement Method: Low-Cost Sensors on UAV’s, National Conference on Undergraduate Research, Asheville, NC.
*Bowles, J., *Krishnan, A. M., Compere, M. El-Sayed, M., and Adkins, K. (faculty advisor). (2021). The New Air Quality Measurement Method: Low-Cost Sensors on UAV’s, Florida Undergraduate Research Conference, FL.
*Bowles, J., *Krishnan, A. M., Compere, M. El-Sayed, M., and Adkins, K. (faculty advisor). (2020). Low-cost Sensors on Unmanned Aerial Vehicles: an Advancement in Air Quality Measurement, Embry-Riddle Aeronautical University Student Research Symposium, Daytona Beach, FL.
*Krishnan, A. M., Adkins, K., and Compere, M. (graduate faculty advisor). (2020). Real Time Data Downlink Device for Live Telemetry from Instrumented Vehicles, Embry-Riddle Aeronautical University Student Research Symposium, Daytona Beach, FL. (graduate student project winner)
*Swinford, C., *Oreo, A., and Adkins, K. (faculty advisor). (2020). Development of a Three-Dimensional Wind Measurement Sensor Hosted on a Small Unmanned Aerial System, Embry-Riddle Aeronautical University Discovery Day, Daytona Beach, FL.
*Swinford, C., *Rentzke, N., and Adkins, K. (faculty advisor). (2020). Nocturnal Observations of Thermodynamic and Kinematic Properties in a Wind Turbine Array Boundary Layer Using an Instrumented Unmanned Aerial System, Embry-Riddle Aeronautical University Discovery Day, Daytona Beach, FL.
*Caputo, A., *Oreo, A., *Alterizio, V., and Adkins, K. (faculty advisor). (2020). Development of a Meteorologically Instrumented Small Transition Unmanned Aerial System For Urban Boundary Layer Investigations, Embry-Riddle Aeronautical University Discovery Day, Daytona Beach, FL.
*Swinford, C., *Oreo, A., and Adkins, K. (faculty advisor). (2019). Development of a Three-Dimensional Wind Measurement Solution Hosted on a Small Unmanned Aerial System, Florida Undergraduate Research Conference, Jacksonville, FL.
*Oreo, A., *Swinford, C., and Adkins, K. (faculty advisor). (2019). Development of a Telemetry System for a Meteorologically Instrumented Small Unmanned Aerial System, Embry-Riddle Aeronautical University Discovery Day, Daytona Beach, FL.
*Swinford, C., *Oreo, A., and Adkins, K. (faculty advisor). (2019). Development of a Three-Dimensional Wind Measurement Solution Hosted on a Small Unmanned Aerial System, Embry-Riddle Aeronautical University Discovery Day, Daytona Beach, FL.
*Swinford, C., and *Oreo, A. (2019). Development of a Meteorologically-Instrumented Small Unmanned Aerial System, Embry-Riddle Aeronautical University Industry Expo Reception, Daytona Beach, FL.
SELECTED MEDIA AND PUBLIC OUTREACH
‘Chasing Air Taxis: Eagles Simulate Drone Routes to Advance NASA Research’, ERAU News, February 2022.
‘Modeling Monsoons: Low-altitude flights help Eagle researchers predict storm formation’, Embry-Riddle Lift Magazine, Fall/Winter 2021. https://lift.erau.edu/modeling-monsoons
‘Dr. Kevin Adkins Joins NBAA Panel,’ ERAU News, October 2021. https://ernie.erau.edu/Pages/announcementsDetail.aspx?itemID=6652
‘Monsoon Research: An Anemoment Use Case,’ Anemoment, September 2021. https://anemoment.com/use-cases/monsoon-research
‘University Deploys Drone Technology to Help Improve Monsoon Predictions in Arizona,’ Meteorological Technology International, September 2021. https://www.meteorologicaltechnologyinternational.com/news/rainfall/university-deploys-drone-technology-to-help-improve-monsoon-predictions-in-arizona.html
‘Embry-Riddle Aeronautical University Leverages UAS Technology to Improve Predictions of the North American Monsoon,’ UAS Weekly, September 2021. https://uasweekly.com/2021/09/29/embry-riddle-aeronautical-university-leverages-uas-technology-to-improve-predictions-of-the-north-american-monsoon
'New research being done to better predict damaging, destructive storms', ABC15 Phoenix, AZ, August 2021.
‘Scientists Study Inner Workings of Thunderstorms,’ National Public Radio, KNAU Arizona Public Radio, August 2021. https://www.knau.org/post/prescott-scientists-study-inner-workings-thunderstorms
‘Low-Altitude Flight to Help Eagles Predict Monsoons,’ ERAU News, August 2021. https://news.erau.edu/headlines/low-altitude-flight-to-help-eagles-predict-monsoons
'Unique Embry-Riddle Assets May Vastly Improve Weather and Air-Quality Data,' ERAU News, July 2021. https://news.erau.edu/headlines/unique-embry-riddle-assets-may-vastly-improve-weather-and-air-quality-data
‘Embry-Riddle Studies Hyperlocal Weather Prediction for Drone Operations,’ UAS Vision, November 2020.
‘Ubers With Altitude? "Jetsons" Project Aims to Bring Electric "Flying Cars" to Florida,’ SpectrumNews13, November 2020.
‘Embry-Riddle and Northrop Grumman Survey Sea Turtles,’ UAS Vision, July 2020. https://www.uasvision.com/2020/07/30/embry-riddle-and-northrop-grumman-survey-sea-turtles
‘Sea Turtle Conservation Flies Forward Thanks to New Drone Project’, ERAU News, July 2020. https://news.erau.edu/headlines/sea-turtle-conservation-flies-forward-thanks-to-new-drone-project
‘On the Horizon: Studying the Urban Boundary Layer’, Embry-Riddle Lift Magazine, Spring 2020. https://lift.erau.edu/issue/spring-2020
‘UAS Operational Applications’. Session Moderator. National Training Aircraft Symposium (NTAS). March, 2020.
‘Drone-Mounted Wind Sensors in Urban Environments’, Unmanned Systems Technology (print and broadcast), January 2020. https://www.unmannedsystemstechnology.com/2020/01/case-study-drone-mounted-wind-sensors-in-urban-environments
‘Urban air mobility: Kosovo’, FT Technologies Featured Case Study, January 2020. https://fttechnologies.com/case-studies/urban-air-mobility-kosovo
‘Farmers Facing Destructive Weather Events Have a New Tool’, Airborne Unmanned in partnership with AUVSI (broadcast), September 2019.
‘Drones in Cities: The Future of Urban Air Mobility’, Pix4D Featured Case Study, September 2019.
‘New Drone Study to Help Growers Brace for Climate Change’, ERAU News, August 2019.
‘Urban Drone Flight over Kosovo Offer Rare Insights’, sUAS News, August 2019.
‘Q&A: Embry-Riddle Stands at the Forefront of Booming Drone Business’, ERAU News, May 2019.
‘Urban Flow and Small Unmanned Aerial System Operations in the Built Environment’, Unmanned Safety Institute (USI) February Safety Brief Featured Article, February 2019.
‘Drones Measure Climate Change’s Impact on Agriculture’, Pix4D Featured Case Study, December 2018.
‘UAS Research Supports Puerto Rican Coffee Growers’, ERAU News, August 2018.
‘Strategic Partnerships: University and School District Collaboration’, St. Lucie County Chamber of Commerce Leadership Academy, May 2018.
‘Embry-Riddle sUAS Study Effects of Wind Turbines on Farmland’, UAS Vision, UAS Magazine, September 2017.
‘Wind Turbines Affect Humidity Over Crop Fields, New UAS Measurements Confirm’, ERAU News, September 2017.
‘Helping Students Realize Success’, Embry-Riddle Aeronautical University Conference for State of Florida Dual-Enrollment Educators, September 2016.
‘Setting Appropriate Expectations in the College Classroom’, Embry-Riddle Aeronautical University Conference for State of Florida Dual-Enrollment Educators, August 2015.
‘Think Like an Engineer’, St. Lucie County Chamber of Commerce Leadership Academy, May 2015.
‘Aerospace Education: A New Model’, National Public Radio (NPR) WQCS, January 2014.
‘A History of Leadership in Aerospace’, St. Lucie County Chamber of Commerce Leadership Academy, May 2013.
‘The State-of-the-Art in Aerospace Education’, Experimental Aircraft Association, Chapter #908, April 2012.