CLOUD-MAP:Collabora1onLeadingOpera1onalUASDevelopmentfor

MeteorologyandAtmosphericPhysics

2017AnnualReportNSFEPSCoRRIITrackIIFECAwardNumber:1539070

CLOUD-MAP: Collaboration Leading Operational UASDevelopment for Meteorology and Atmospheric Physics

Jamey Jacob, PI - Oklahoma State UniversityPhil Chilson, Co-PI - University of Oklahoma

Adam Houston, Co-PI - University of NebraskaSuzanne Weaver Smith, Co-PI - University of Kentucky

Compiled May 4, 2017. v. 1.1.

This work is supported by the National Science Foundation under Grant No. 1539070, Collaboration Leading OperationalUAS Development for Meteorology and Atmospheric Physics (CLOUD-MAP ), to Oklahoma State University in partnershipwith the Universities of Oklahoma, Nebraska-Lincoln and Kentucky. Any opinions, findings, and conclusions or recommen-dations expressed in this material are those of the authors and do not necessarily reflect the views of the National ScienceFoundation. Questions or concerns regarding content should be directed to the CLOUD-MAP PI, Prof. Jamey Jacob atjdjacob@okstate.edu.

This document was prepared in LATEXpdfeTeX, Version 3.141592-1.30.4-2.2.

CLOUD-MAP:Collaboration Leading Operational UAS

Development for Meteorology andAtmospheric Physics

Annual ReportNSF EPSCoR RII Track II FEC

Award Number: 1539070Project Duration: 8/1/2015 – 7/30/2019

Reporting Period: 3/16/2016 – 3/15/2017

May 1, 2017

Executive Summary

The period of this report covers from March 16, 2016 to March 15, 2017. In the year-longperformance period, the team accomplished all of its objectives, including progress towardsresearch goals, organization of the research and workforce development efforts, meeting withand generating input from the community of stakeholders, completion of the first flightcampaign, and planning for the second flight campaign, in addition to many individualtechnical and outreach related tasks. These items are detailed in the report.

The overarching goal of the project is to develop integrated small unmanned aircraftsystems (SUAS) capabilities for enhanced atmospheric physics measurements. This teamincludes atmospheric scientists, meteorologists, engineers, computer scientists, geographers,and chemists necessary to evaluate the needs and develop the advanced sensing and imag-ing, robust autonomous navigation, enhanced data communication, and data managementcapabilities required to use SUAS in atmospheric physics. Annual integrated evaluation ofthe systems in coordinated field tests also requires advancing public policy related to adop-tion of SUAS technology and integration of unmanned aircraft into the airspace. CLOUD-MAP builds on the team members’ and combined partners’ existing expertise and capabilitiesin atmospheric and meteorological observations, SUAS development, and STEM outreachand education. A primary long-term impact expected from CLOUD-MAP will be the indeli-ble multidisciplinary scientific and educational collaboration of the early-career faculty whoare involved. In the short duration of the project to date, new collaborations have alreadydeveloped among team members leading to increased collaborative proposal developmentand subsequent collaborative publications.

Contents

1 Overview 11.1 Research Highlights . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.2 2016 Flight Campaign . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31.3 Research Productivity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61.4 RII Track-2 FEC Impacts on University Programs . . . . . . . . . . . . . . . 8

1.4.1 Oklahoma State University . . . . . . . . . . . . . . . . . . . . . . . . 81.4.2 University of Oklahoma . . . . . . . . . . . . . . . . . . . . . . . . . 121.4.3 University of Kentucky . . . . . . . . . . . . . . . . . . . . . . . . . . 151.4.4 University of Nebraska-Lincoln . . . . . . . . . . . . . . . . . . . . . 16

2 Research Program 182.1 Objective 1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

2.1.1 Task 1-1: Mentorship . . . . . . . . . . . . . . . . . . . . . . . . . . . 192.2 Objective 2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

2.2.1 Task 2-1: Convection Initiation . . . . . . . . . . . . . . . . . . . . . 242.2.2 Task 2-2: Storm Microphysics . . . . . . . . . . . . . . . . . . . . . . 292.2.3 Task 2-3: Airborne Soil Hydrology . . . . . . . . . . . . . . . . . . . 322.2.4 Task 2-4: Local-Scale Spatiotemporal Climate Variations Measurements 342.2.5 Task 2-5: Airborne Sampling Systems . . . . . . . . . . . . . . . . . . 372.2.6 Task 2-6: Atmospheric Infrasonic Sensing . . . . . . . . . . . . . . . . 392.2.7 Task 2-7: Multi-Scale GIS Correlation . . . . . . . . . . . . . . . . . 52

2.3 Objective 3 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 552.3.1 Task 3-1: Cooperative Control of Small UAS Formations For Dis-

tributed Measurement . . . . . . . . . . . . . . . . . . . . . . . . . . 552.3.2 Task 3-2: Integration of Spatially Distributed Data from Moving Sen-

sor Platforms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 582.3.3 Task 3-3: Heterogenous Robot Control . . . . . . . . . . . . . . . . . 602.3.4 Task 3-4: Multi-agent UAS Simulators . . . . . . . . . . . . . . . . . 632.3.5 Task 3-5: Robust Conformal Antennas for UAS Communication . . . 64

2.4 Objective 4 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 742.4.1 Task 4-1: Public Perception . . . . . . . . . . . . . . . . . . . . . . . 742.4.2 Task 4-2: UAS Workshops . . . . . . . . . . . . . . . . . . . . . . . . 822.4.3 Task 4-3: Rapid Dissemination of Risk Information . . . . . . . . . . 85

3 Additional Major Project Elements 873.1 Interjurisdictional Collaborations . . . . . . . . . . . . . . . . . . . . . . . . 873.2 Early Career Faculty Advancement . . . . . . . . . . . . . . . . . . . . . . . 883.3 Public Outreach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89

References 90

4 Appendix - Data Outcomes Portal Information 95

iv

List of Tables

1 CLOUD-MAP tasks, task leads, and participation in Year 1 Campaign.. . . . 32 PI and early career faculty involvement in the various objectives and tasks of

the CLOUD-MAP program. . . . . . . . . . . . . . . . . . . . . . . . . . . . 183 Overview of the quantity of events that have occurred since the initial micro-

phone deployment. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 434 Impedence and radiation efficiency trends. . . . . . . . . . . . . . . . . . . . 665 Modal significance. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 676 Impedence and radiation efficiency trends with confirmation. . . . . . . . . . 687 Inductor loaded crossed dipole antenna parameters. . . . . . . . . . . . . . . 718 Parameter comparison for antennas evaluated. . . . . . . . . . . . . . . . . . 729 Scenarios by focus group. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7610 Intra- and inter-jurisdictional collaborations among CLOUD-MAP researchers. 8711 Early career faculty promotions. . . . . . . . . . . . . . . . . . . . . . . . . . 88

List of Figures

1 CLOUD-MAP program objectives. . . . . . . . . . . . . . . . . . . . . . . . . 22 CLOUD-MAP Year 1 flight campaign and typical mission profiles for rotary

wing and fixed wing SUAS. . . . . . . . . . . . . . . . . . . . . . . . . . . . 43 CLOUD-MAP Year 1 flight campaign operations. . . . . . . . . . . . . . . . 54 CLOUD-MAP Year 1 flight campaign data samples. . . . . . . . . . . . . . . 65 Research productivity for early-career researchers and senior researchers in

years 1 and 2 of program. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76 USRI model. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97 USIL - Unmanned Systems Innovation Laboratory. . . . . . . . . . . . . . . 98 OSU/GE Raven methane sensing UAS. . . . . . . . . . . . . . . . . . . . . . 119 OSU Presidents Cup Promoting Creative Interdisciplinarity. . . . . . . . . . 1210 Undergraduate students developing low-cost platforms for citizen science and

third world applications. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1211 3D Mesonet concept. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1412 Low-cost detect and avoid radar housed at OU’s Radar Innovations Laboratory. 1513 UK team at CLOUD-MAP 2016 field campaign. . . . . . . . . . . . . . . . . 1614 3D Mesonet concept will allow extended regular ABL measurements. . . . . 1915 Integrative capacity model for diverse team science [8]. . . . . . . . . . . . . 2016 Inter-jurisdictional network. . . . . . . . . . . . . . . . . . . . . . . . . . . . 2117 Biographs of self-perceived collaboration connections in CLOUD-MAP : before

CLOUD-MAP (left) and during Year-1 of CLOUD-MAP (right). . . . . . . . 2218 Biographs of self-perceived research connections in CLOUD-MAP : during

Year-1 of CLOUD-MAP (left) and during Year-2 of CLOUD-MAP (right).. . 2219 Research publications for early-career researchers and senior researchers in

years 1 and 2 of program. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23

v

20 Surface transects of θe (K) collected from the 20 June 2016 MAHTE in aboundary relative frame of reference. Positive (negative) distances indicateareas on the cool (warm) side of the thermal boundary. . . . . . . . . . . . . 25

21 Modeled surface θe at 22 UTC. There is a sharp discontinuity in θe, with thehighest values located in a small region along the boundary steadily decreasingto the north. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26

22 Vertical cross section of modeled vertical velocities (fill) and θe (contours) at22 UTC show that vertical mixing is stronger and deeper in the warm sectorand suppressed on the cool side of the boundary. The location of the higheste within the MAHTE is indicated by the arrow. . . . . . . . . . . . . . . . . 27

23 Conceptual illustration of the relationship between boundary-parallel shearand a wavy boundary. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28

24 LES solution of a convective boundary layer (top) and airmass boundary(bottom). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29

25 Moisture index performance for a) bare silt loam soil and b) wheat stalk residue 3226 STS-UV/VIS/NIR spectrometers and a Raspberry Pi embedded computer

mounted in a 3D-printed enclosure. . . . . . . . . . . . . . . . . . . . . . . . 3327 The two CO2 sensing systems contained within 3-D printed box. Pen for scale. 3428 CO2 concentrations (ppm) from two CO2 sensing systems. Lines indicate 20

second averages. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3529 Left: OU Bix3 with Pitot tube (CO2 sensor not visible). Right: OU Bix3 in

flight. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3630 Illustration of the overall analytical setup for task 2-5 displaying (A) a top

profile picture of the Skywalker X8; (B) the instrument bed; and (C) the cus-tomized sensor array (CSA). The labeled components are (1) battery pack; (2)Pixhawk; (3) Arduino board with data logging shield; (4) CSA; (5) NH3/CO/NO2multichannel gas sensor; (6) CH4 sensor; (7) CO2 sensor; (8) O3 sensor; (9)pressure sensor; (10) logic shifter; (11) temperature/relative humidity sensor. 38

31 Example of data gathered during a flight with a Skywalker X8 and the firstgeneration sensor array during the first CLOUD MAP campaign in Oklahoma.Site name: Marena site. Date: June 29, 2016. Start time: 9:05 am CST. . . 39

32 (upper left) Satellite image (Google) of the location of the infrasonic arraywith the microphone denoted with the red Xs. (upper right) Arnesha Threat(MS student) posing with microphone 2 after its initial deployment. (lowerleft) Picture of the infrasonic microphone as mounted within the white dome.(lower right) Aerial view of the infrasonic microphone mounted on the roof ofthe Fabrication Laboratory. . . . . . . . . . . . . . . . . . . . . . . . . . . . 40

33 The noise reduction with use of a windscreen plotted versus the frequency ofa known source supplied with a subwoofer. . . . . . . . . . . . . . . . . . . . 41

34 Testing with the first generation of the pulsed gas-combustion infrasonic source. 4235 (top left) Schematic of the second generation pulsed gas-combustion infrasonic

system. (top right) Picture of the pulsed gas-combustion torch in use. (bottomleft) Picture of the system being setup during the 2016 CLOUD-MAP FieldDemonstration. (bottom right) Power spectrum generated when the systemwas pulsed at 5 Hz. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42

vi

36 (top) Spectra from a storm on October 4-5, 2016. Times shown are local(CST). (bottom) Radar images of the storm close to the corresponding spectrashown above. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44

37 (left) Radar data (gis.ncdc.noaa.gov) of a storm that hit on October 6, 2016and (right) the corresponding spectra it produced. . . . . . . . . . . . . . . . 45

38 (left) NASA satellite image of wildfires in Kansas, Oklahoma, and Texas takenon March 7, 2017. Red areas indicate heat and the orange star is the infrasonicarray location. (right) Spectra from the 3-microphones during the wildfire.Dashed circle highlights peaks that were present for several days during thewildfire that decreased as the fire got under control. . . . . . . . . . . . . . . 45

39 (left) Locations of the earthquake epicenter relative to the infrasonic array.(right) Spectra produced before, during, and after the earthquake occurred. . 46

40 Spectrum acquired before and during a 3.1 magnitude earthquake (OGS ID24119). The epicenter of this earthquake was 203 km from the infrasonic array. 47

41 (left) Arnesha Threatt pictured with NASA designed infrasonic windscreen.(right) Close up image of the windscreen (orange ball) next to the portablecase used for data acquisition of the NASA infrasound microphones. . . . . . 48

42 (top) Picture from Dr. Shams field testing station at NASA Langley ResearchCenter. (bottom left) Picture of Arnesha Threatt and Dr. Elbing touringLaRC center. (bottom right) Picture of Drs. Shams and Elbing visitinginfrasonic measurement sites at LaRC. . . . . . . . . . . . . . . . . . . . . . 51

43 Sample semivariogram data of temperature and humidity from the CLOUD-MAP 2016 field campaign. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53

44 CTF causes the inter-vehicle distance to oscillate, whereas DTF forces vehiclesto a 1-m formation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56

45 Indoor DTF experiments. The red circles highlight the UAVs. From left toright: a UAV is released to join the formation (left); the UAV autonomouslyjoins the formation (center left and center right); the 3 UAVs maintain atriangle formation and follow a circular leader trajectory (right). . . . . . . 57

46 Custom 0.25-m diameter rotorcraft (top), and Intel RealSense camera (bottom). 5747 Task penalties in manual vs. autonomous assistance modes across 34 test

subjects. p < 0.05 in both instances. . . . . . . . . . . . . . . . . . . . . . . 6048 A team of heterogenous UAS with meteorological sensors share a single factor

graph representation of their shared intentions. . . . . . . . . . . . . . . . . . 6149 Joint distribution of intention by simulated UAVs after factor graph convergence. 6250 System architecture. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6351 Simulated UAS investigating injected smoke phenomenon. . . . . . . . . . . 6452 Depiction of angle of curvature at θ = 120 degrees (left) and fully curved

antenna with an angle of curvature of θ = curveAng = 180 degrees (right). . 6553 Antenna far-field pattern. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6654 Reflection coefficient for discrete curvatures for a 915MHz design where curveAng

denotes the degree of an arc that one half of the antenna covers. . . . . . . . 6755 E and H Plane radiation patterns for the 915MHz curved folded dipole antenna. 6756 First three current modes and corresponding normalized radiation patterns. . 67

vii

57 Depiction of the conformal angle at φ = 60 degrees (left) and the half-cylindrical bending geometry with a conformal angle φ = bendAng = 90degrees. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68

58 Reflection coefficient for discrete conformal curvatures for a 915MHz designwhere bendAng denotes the degree of an arc that half of the antenna coverson a cylinder. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69

59 Loaded bent crossed dipole geometry. . . . . . . . . . . . . . . . . . . . . . . 7060 LBCD radiation pattern. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7161 Skew Planar Geometry. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7262 Skew Planar Radiation Pattern. . . . . . . . . . . . . . . . . . . . . . . . . . 7363 Fabricated circularly-curved folded dipole antenna on Rogers 5880 substrate. 7364 Ratings of support and trustworthiness (TW) and distrustworthiness (dTW)

across scenarios.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7865 CLOUD-MAP web-site. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8366 NCAR/EOL Workshop: Unmanned Aircrat Systems for Atmospheric Research. 8467 Mesonet seminar flyer - one of a set of seminars presented to the weather and

mesonet community. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8568 Severe storm risk regions. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8669 Research publications for early-career researchers and senior researchers in

years 1 and 2 of program. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87

viii

1 Overview

1.1 Research Highlights

During the first full year of the program, the collaborative research team has achieved allof their planned objectives and are on schedule for achieving the remaining objectives insubsequent project years. At a high level, these include:

• Mentorship/traineeship of early career faculty and students including multi-jurisdictionalcollaboration on proposals and publications through team science;

• Better physical understanding of atmospheric physics and meteorological processeswithin the Earth’s lower boundary layer;

• Improved instrumentation for measurement of meteorological and atmospheric physics(MAP) related phenomena;

• Development of and testing better small unmanned aircraft systems (SUAS) for use inMAP applications;

• Enhanced understanding of operational and logistical requirements to operate SUASwithin the US for MAP applications;

• Knowledge of public perception of SUAS for scientific and other applications withinthe US; and

• Enhancing public awareness of the scientific and broader benefits of SUAS in weatherand atmospheric studies.

These are summarized below and a full description of specific research tasks is provided in§2.

The goal of the CLOUD-MAP project is to develop and test systems for remote and insitu sensing of Earths atmosphere with an associate scientific goal of enhanced understandingof physical processes within the Earth’s atmsophere. As such, primary objectives have beenorganized into four specific threads as shown in Figure 1. In particular, systems will focus onthe lower ABL. This region is beyond the height where data is obtained by surface observingstations, but below that sensed by most airborne weather systems, including balloons, aircraftand satellites. The importance of accurate data in this region is well understood - this regionis a major factor in the development of many meteorological phenomena, not the least ofwhich include severe storms.[1]

The project leverages key expertise across the institutions, including unmanned air-craft systems, atmospheric measurement, robotics and autonomous control, and weatheranalysis and modeling. Each of these areas is critical for the research to be successful. Basicquestions include the following: How can local data acquired by SUAS be used to betterunderstand larger weather phenomena? Can SUAS be used to measure large-scale patternsand trends found in the atmosphere? What advancements in operational requirements arenecessary to provide routine capabilities and confidence to use SUAS as a meteorologicaldiagnostic tool?

1

Figure 1: CLOUD-MAP program objectives.

Research Statement CLOUD-MAP is a multi-disciplinary team of four senior science andengineering faculty researchers and thirteen early-career faculty researchers from OklahomaState University (OSU), the University of Oklahoma (OU), the University of Nebraska-Lincoln (UNL), and the University of Kentucky (UK). Participating faculty from the insti-tutions include researchers across the following departments: Aerospace Engineering (AE),Biosystems and Agricultural Engineering (BAE), Chemistry (CHE), Computer Science (CS),Earth and Atmospheric Sciences (EAS), Electrical and Computer Engineering (ECE), Ge-ography (GEO), Mechanical Engineering (ME) and Meteorology (MET). Although not ex-plicitly listed above, another project goal is to provide the more than 60 involved studentswith opportunities to develop skills in the use of SUAS for atmospheric studies, as well ascollaborative research skills. To that end, graduate and undergraduate students play centralroles in all aspects of the project.

Specific tasks under the objectives (Table 1) are aimed at answering the four ba-sic questions categorized under atmospheric measurement and sensing, unmanned systemsdevelopment and operations, and public policy, as well as evaluating the development ofcollaboration among the researchers. Early-career faculty oversee achieving the majority ofthe science and engineering goals. Each task has a lead researcher responsible for success-fully implementing resources, training involved students, and organizing the research. Theexecutive committee consisting of the lead/senior faculty from each institution facilitatescollaborations among the faculty and trainee participants, as well as with outside partnersthrough workshops hosted in conjunction with the flight campaigns.

Among the senior faculty of the CLOUD-MAP team, previous collaborative experi-ences have been positive, and similarly positive previous collaborations existed among thedistinct university and disciplinary subgroups, so knowledge of negative experiences primar-ily served to inform organizational strategies to head off similar issues. Each of the tasks wasestablished with researchers from other institutions, but moving from planned collaborationsto functional and ultimately productive ones is the goal of the annual CLOUD-MAP FlightCampaign, as well as being the venue for demonstrating and evaluating performance of keytechnical developments in sensing, platforms or cooperative control.

2

Table 1: CLOUD-MAP tasks, task leads, and participation in Year 1 Campaign..

1.2 2016 Flight Campaign

The first-year field campaign was conducted at sites including reference sensing such asMesonet locations and in-ground agricultural sensors. Plans for a day of flights at theDepartment of Energy (DOE) Atmospheric Radiation Measurement (ARM) Southern GreatPlains (SGP) site in northern OK were not realized in 2016. Flights are now ongoing therein the interim; the site will be included in the campaign in 2017. The ARM SGP site hasa unique suite of atmospheric measurements useful for comparison with measurements fromUAS platform sensors. At this initial stage of technology development, comparison of flightmeasurements to “ground truth” is essential.

The planned flight campaign areas shown in Figure 2 include the OSU Unmanned Air-craft Flight Station (UAFS), the Marena Mesonet site, and the DOE Southern Great Plains(SGP) Atmospheric Radiation Measurement (ARM) site. All are within driving distanceof the OSU campus. Note that obtaining proper FAA authorizations, DOE permission ap-proval, and dealing with evolving regulations are a significant regulatory and administrativebarrier in addition to the typical multi-disciplinary collaboration barriers.

The OSU UAFS allowed testing under controlled conditions and provided operatorswith network, power, runway, and hangar access. This first stop in the campaign wasused to evaluate platforms, sensors, communication systems, and protocols prior to movingto the field sites. The Marena Mesonet, in addition to providing a dedicated Mesonettower, also houses the Marena, Oklahoma In Situ Sensor Testbed (MOISST). MOISSTwas established in 2010 to evaluate and compare existing and emerging in situ and proximalsensing technologies for soil moisture monitoring.[2]

The DOE ARM SGP site consists of in situ and remote-sensing instrument clustersarrayed across approximately 143,000 square kilometers in north-central Oklahoma and is

3

Figure 2: CLOUD-MAP Year 1 flight campaign and typical mission profiles for rotary wingand fixed wing SUAS.

the largest and most extensive climate research field site in the world, making it an invaluableresource for CLOUD-MAP researchers.

CLOUD-MAP UAS platforms are typically equipped with high-precision and fast-response atmospheric sensors to be evaluated for their suitability for carrying out a varietyof sensing objectives for the study of ABL properties. Team members have considerable expe-rience in designing, building, and flight-testing such UAS platforms, including custom-builtand commercial off the shelf, rotorcraft and fixed-wing platforms. CLOUD-MAP Year-1 cam-paign flight tasks focused on operations to collect thermodynamic, air chemistry, and winddata to compare with measurements from surface stations within the Oklahoma Mesonetand team-owned stationary and mobile sensor towers. Mesonet measurements are availablein general with an update time of 5 min.[3, 4]

OSU operated fixed-wing and vertical takeoff and landing (VTOL) platforms witha variety of sensors supporting multiple CLOUD-MAP tasks. OU flew VTOL platformsacquiring frequent repeated atmospheric measurements starting before dawn to capture theonset and development of the daily ABL cycle. UK flew three fixed-wing aircraft for chemicaland atmospheric turbulence sensing, along with various rotorcraft supporting a focus onoperations to measure soil conditions, to evaluate integration of spatially distributed data

4

Figure 3: CLOUD-MAP Year 1 flight campaign operations.

from moving sensor platforms, and for multi-vehicle UAS operations. Soil measurementswere included to examine new remote sensing systems for early detection of water stress.UNL flew prescribed rotorcraft flight patterns to evaluate novel identification algorithms andbrought a new tracker/scout vehicle equipped as a mobile mesonet as a reference system.The overall campaign leveraged the infrastructure of these sites to demonstrate the potentialof extending the conventional surface Mesonet concept to include vertical profiling.

Flight totals for the campaign indicated an unexpectedly successful first year. The3-day total flight time exceeded 25 hours for 241 total flights, comprised of 187 rotary and 54fixed-wing flights. Figure 3 includes representative images and Figure 4 shows sample datasets. The composite data set from the second full day at the Marena Mesonet site shows OUprofile trajectories to 1,000 ft (red vertical) among other flight trajectories that day. Co-ordinated flight trajectories for turbulence measurement comparisons, temperature profilesand temperature/humidity/time/altitude maps are included. Data evaluation, reductionand ABL characterization analyses are underway by the scientists participating in the 2016campaign, with presentations, publications, theses and dissertations expected though 2017.Witte, for example, developed a fixed-wing SUAS sensing platform and data reduction tomeasure and characterize ABL turbulence and validated its performance in comparison tomeasurements using a portable tower-based sonic anemometer.[5]

5

Figure 4: CLOUD-MAP Year 1 flight campaign data samples.

1.3 Research Productivity

Research productivity among the project participants has been high with second year Figure5a and 5b shows research number of research proposals by award year, including total,collaborative between award researchers, and non-collaborative proposals, including thosespecifically submitted to and awarded by NSF. Researchers have submitted 47 total proposalswith a total request amount of $35,592,100 and 16 have been funded over the period, witha total award amount of $4,441,041, an award rate of over 1 out of 3. 21 proposals weresubmitted to NSF by the program participants with a total request of $29,053,933. Thus,while most of the proposals were submitted to other agencies, most of there were small effortsand the greatest dollar amount by a very large margin were submitted as large NSF programrequests. Of the proposals submitted to NSF over the program period, 5 were funded with atotal award amount of $2,187,245, roughly half of the total funded amount. Approximately1 in 4 proposals submitted to NSF were funded, which is high by typical NSF standards. Todate, 20 publications have been published or are pending publication, including 3 journalarticles, 15 conference proceedings, and 1 each book and book chapter.

6

(a) Proposals, including collaborative proposals. (b) NSF proposals.

Figure 5: Research productivity for early-career researchers and senior researchers in years1 and 2 of program.

7

1.4 RII Track-2 FEC Impacts on University Programs

A major focus of the Research Infrastructure Improvement Track-2: Focused EPSCoR Col-laborations (RII Track-2 FEC) is to add significant value to increase scientific competitivenessat the national or regional level. RII Track-2 FEC funding enables this by supporting com-petitive collaborative teams of EPSCoR investigators and providing a mechanism to coalesceinvestigator expertise into a critical mass for a sustained, effective research and educationpartnership. This approach can help overcome impediments posed by limited infrastructureor human capital within a single jurisdiction and can enable broad engagement at the fron-tiers of discovery and innovation in science and engineering.[6] As such, the impacts of theRII Track-2 FEC funding can have long-lasting impacts on the funded institutions. Theseimpacts on the individual partner universities are discussed below.

1.4.1 Oklahoma State University

Spurred by the NSF RII Track-2 FEC award, OSU established the Unmanned Systems Re-search Institute (USRI) in late 2015 with operations beginning in early 2016. The purposeof USRI is to bring together multidisciplinary research talent from across OSUs campusesto collaborate on the design, testing, evaluation and application of unmanned technologiesto a wide variety of research problems. Building on its recognized expertise in developing avariety of applications for unmanned aerial vehicles, the USRI will apply this proficiency todesign unmanned vehicles across a range of environmental conditions. Creating the instituteis the latest example of OSU as a leading comprehensive research university. The NSF RIITrack-2 FEC award highlightted the need for an interdisciplinary research organization. Ad-ministratively housed in the College of Engineering, Architecture and Technology (CEAT),as shown in Figure 6, the nature of the institute is to be cross-discipliary and provide supportacross the entire campus.

USRI is initially housed at OSUs Richmond Hills Research Complex on the northside of Stillwater, however a new building devoted to to USRI is in the final planning stages,with construction scheduled for completion in spring 2018. The Unmanned Systems In-novation Laboratory (USIL) will house engineers and scientists at both the graduate andundergraduate level and be devoted to R&D in UAS. The OSU CLOUD-MAP operationswill be housed here. The current design is shown in Figure 7. USRI includes facilities de-voted to commercializing technologies developed through the institute. As part of USRI,this facility concentrates on focused UAS platform development and integration capabilityfor government and commercial customers, including public and civilian aspects missions, toprovide state-of-the-art research and development for UAS and manage a centralized venuefor commercial, academic, and government entities to advance the overall UAS industrysoperational and technical advancements. Specifically this facilitates

• UAS RDT&E - Providing relevant research topics for industry and academia

• Graduate student and faculty research support

• Full design/build/test capabilities for small vehicles and components of larger vehicles

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Figure 6: USRI model.

An example of this collaborative academic-industry approach is shown in Figure 8 with amethane sensing UAS developed in partnership with the General Electric Oil & Gas Technol-ogy Center located in Oklahoma City. The goal of this system is to reduce the environmentalimpact of oil and gas operations across the entire process range from extraction to productionand is suitable for both research and industrial activities.

The overall goal of these collaborations is to develop and sustain a synergistic, inter-disciplinary, campus-wide research and education program involving faculty, students, andstaff that highlights multiple aspects of UAS. This multifaceted effort focuses on (1) develop-ment, design, and implementation of UAS, (2) legal operation and integration of UAS into

Figure 7: USIL - Unmanned Systems Innovation Laboratory.

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the National Airspace System (NAS) as data collection devices, (3) use of UAS-captureddata to analyze spatial processes and aid decision-making in a number of application areas,and (4) education and training to satisfy the growing number of professionals needed tosupport this emerging economic sector within and outside of Oklahoma. Our broad aim is tomake OSU the leading university in the U.S. for UAS research and instruction. We seek toinspire, encourage, and develop the initial generations of UAS industry persons, academics,and entrepreneurs.

Team members from different disciplinary backgrounds bring a diverse array of ex-pertise to the program. The project is supported by several on-campus entities, which helpto ensure continued success: MAE houses aerodynamics and flight dynamics laboratoriesand has led efforts to obtain FAA clearance to fly under specified restrictions; OSU owns anairfield dedicated to UAS flight that provides a controlled, legal environment within whichto operate UAS for flight testing, training, data collection, etc.; the Unmanned SystemsResearch Institute (USRI), directed by team member Jacob, offers certified UAS pilots, air-craft, and additional resources; GEOGs Center for Applications of Remote Sensing (CARS)has a full suite of hardware and software to process UAS-collected data; BAE has accessto Agricultural Experiment Station land where flights have already occurred (e.g., monitor-ing prescribed burns) and is involved with the 120-station Oklahoma Mesonet where UASprototype flights will occur.

Basic and applied research on UAS (e.g., the technology itself, use of the technology,and use of information gathered by the technology) remains in its infancy. The NationalScience Foundation (NSF, 2016) recently recognized the need for interdisciplinary researchon UAS and allotted $35M for UAS projects. Team members have already secured fund-ing from the NSF in several related research areas: (1) emergency management in wildfireresponse, (2) integration of UAS into the NAS, and (3) land cover dynamics from UAS-captured imagery. With respect to (1), since 2014 members of the team have submittedtwo $1.5M proposals to FEMA and one $2.8M proposal to NSF on using UAS for wildfireresponse and mitigation. These proposals include development of UAS for wildfire dynamicsinvestigations as well as for emergency responder information and decision-making. Regard-ing (2), Aviation Education (AVED) is collaborating with USRI to establish a predictiveUAS platform visibility model for pilots operating under visual meteorological conditions todetect and avoid midair collisions with manned aircraft. MAE is developing an experimentalapparatus to evaluate the effects of a UAS platform collision with a general aviation aircraftstructure. Geology (GEOG) is leading efforts surrounding (3) to use UAS-captured aerialimagery for accurate terrain modeling and land cover mapping.

A limited number of universities nationwide have established a formal UAS curricu-lum, and the team is working to develop a comprehensive, interdisciplinary UAS curriculumthat will uniquely position OSU as a nationwide leader in UAS instruction. The curriculumwill incorporate courses across departments and colleges to enable students to successfullycompete for a variety of jobs in this emerging industry (e.g., UAS pilots, engineers, map-ping specialists, etc.). The team is developing UAS-specific interdisciplinary (cross-listed)courses and outreach courses. The applied nature of the UAS project greatly supports OSUsland-grant mission. The UAS team works directly with state and federal agencies and stateand local first responders on applications related to severe storm detection, forecasting, andwarning, emergency management (i.e., wildfire monitoring, field operations, and prepara-

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Figure 8: OSU/GE Raven methane sensing UAS.

tion), agricultural monitoring, oil and gas monitoring (i.e., asset inspection), and naturalresource management.

The team has successfully implemented several program offerings. MAE offers a UASoption for its MS and PhD degrees. AVED established a UAS Pilot Minor in 2015, provid-ing students the ability to specialize in UAS flight operations. In 2016, GEOG introduceda BS in Geospatial Information Science that will incorporate UAS applications. MAE offerstwo graduate-level UAS courses (Unmanned Aerial Systems Design and Analysis and Un-manned Aerial Systems Propulsion) where students focus on UAS design. AVED offers anUnmanned Aircraft Pilot Laboratory for teaching UAS operational aspects. GEOG recentlyproposed Geospatial Applications for UAS to prepare students for processing and analyzingUAS-captured data. The team is working to build stronger interdisciplinary linkages be-tween courses and round out degree options with specific topics. In addition, the team isdeveloping outreach courses to train UAS- interested persons off-campus. Team memberscontinue to hold workshops and participate in events such as GIS Day at the Capitol to fosterpositive perceptions of UAS. This collaborative effort is designed to provide the foundationfor current and future UAS scholarship and education at OSU. Importantly, considering theinterdisciplinary nature of the team effort, a plethora of other departments across campusare becoming involved in UAS projects in specific application areas (e.g., Plant and SoilSciences, Horticulture) as well as the social aspects of UAS (e.g., Sociology, Psychology).OSU recognized the team for Interdisciplinary Synergies for Unmanned Aerial System Inno-vation & Advancement with the OSU Presidents Cup Promoting Creative Interdisciplinarity(Figure 9).

The program has utilized undergraduate researchers in the lab at a large level em-ploying them across all tasks. This includes both integrated involvement in research projects(under direct mentorship of a graduate student) and with class-related and outreach projects.An example of one of the projects is shown in Figure 10 where collaborative teams of un-dergraduate students are developing low-cost platforms for citizen science and third worldapplications. Regular meetings among all team members are held weekly for planning pur-poses and include presentations by student researchers and guest speakers.

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Figure 9: OSU Presidents Cup Promoting Creative Interdisciplinarity.

Figure 10: Undergraduate students developing low-cost platforms for citizen science andthird world applications.

1.4.2 University of Oklahoma

The University of Oklahoma has a long history of promoting and supporting aviation researchand education. The primary focus has been on manned flight; however, in 2008 interestsbegan to expand into unmanned aircraft operations as well. It was at this time that ateam of researchers decided to explore the use of unmanned aircraft systems as a meansof investigating the lower atmosphere for the purpose of meteorological study. The teambegan laying the foundations for an research program at OU, but initial progress was slowdue to limited funding. The NSF RII Track-2 FEC award (CLOUD-MAP ) has played asignificant role in catalyzing these activities and has been a true game changer with respect

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to UAS atmospheric research at OU, in particular, and autonomous sensing and sampling,in general.

Through CLOUD-MAP , OU has hired 20 undergraduate research assistants (11 malesand 9 females, 1 minority) and 2 graduate research assistants (2 males). Of these, 16 ofthe students come from meteorology, 3 from electrical and computer engineering, 1 fromaerospace engineering, and 1 from aviation. This has provided the first research opportunityfor most of the students. The funding has also enabled the OU CLOUD-MAP team topurchase and develop vehicles and instrumentation to enable atmospheric research.

Through CLOUD-MAP , OU has also been able to strengthen its relations with NOAAin the area of for atmospheric monitoring. OU was already working closely with the NationalSevere Storms Laboratory (NSSL) and Storm Prediction Center (SPC); however, now newstrategic partnerships are developing. NSSL, along with OU, the University of Colorado inBoulder, and Meteomatics, a company in Switzerland that manufactures and operates smallUAS for atmospheric monitoring, were awarded funding from the NOAA UAS ProgramOffice to begin integrating UAS into operational weather forecasting. The official title ofthe proposal is Three-Dimensional Profiling of the Severe-Weather Environment but it isoperating under the name of EPIC (Environmental Profiling and Initiation of Convection).The study is unique in that researchers are being directed by the National Weather Service,who is deciding when and where the UAS vehicles will be deployed.

Additionally, OU has begun working with other state and federal agencies, universi-ties, private companies, and Native American tribes. To foster collaboration in the develop-ment of UAS of meteorological applications, OU has been hosting bi-weekly conference callsbetween itself and NSSL, Oklahoma State University, Atlantic Oceanographic and Meteoro-logical Laboratory, NOAA UAS Program Office, and University of Virginia, with interactionswith NASA Ames and the Army Research Laboratory. These interactions allow participantsto share their findings and experiences and to exchange ideas on how to best move thisemerging research field forward.

Motivated in part by the grant, OU has created the Center for Autonomous Sensingand Sampling (CASS) and named the OU PI for CLOUD-MAP as the Director. CASSsmission is to explore, advance, and develop complete adaptive and autonomous sensing andsampling systems for use in the atmosphere, on the ground, and in the water, and to helpfacilitate the integration of this technology across various disciplines and institutions. To doso, CASS will leverage the States and Universitys strengths in aviation, atmospheric science,robotics, and remote sensing development to create innovative solutions to pressing societalneeds and collaborate with industry to develop and transfer technology for commercial ap-plications. The goal of CASS is to establish itself as a recognized global leader in research,education, and development involving autonomous sensing and sampling solutions to ad-dress science and technology driven needs, fostering an environment for trans-disciplinaryapplications of this technology, and helping to promote the effective transfer of knowledgeand technology to academia, government, and industry.

The growing momentum in research in meteorology and atmospheric physics has en-abled OU researchers to begin undertaking more ambitious projects. For example, OU hasbeen partnering with the Oklahoma Mesonet and Oklahoma State University as it developsa concept that would allow extending surface meteorological observations vertically a con-cept being called the 3D Mesonet. The current Oklahoma Mesonet consists of about 120

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Figure 11: 3D Mesonet concept.

stations across the state, with each station producing real-time observations every 5 minutesof pressure, temperatures at 1.5 and 9 m, wind speed at 2 and 10 m, wind direction at 10m, incoming and outgoing solar radiation, atmospheric pressure, humidity, rainfall amounts,along with soil moisture and temperature at 5 and 10 cm below the surface. By addingsmall UAS to the sites, pressure, temperature, humidity, and wind data could be collectedup to a height still to be determined, but maybe on the order of 5000. These data wouldlikely prove invaluable in predicting the onset of high impact weather events. Moreover, thedata could be used to better characterize the atmospheric boundary layer and assimilatedinto weather forecast models. Leveraging funding provided by the NSF, OU has committedfunds to begin developing a prototype of a 3D Mesonet station. A schematic is provided inthe figure below.

The platform being considered for the 3D Mesonet prototype is a rotary-wing vehiclewith 8 propellers an equipped with differential GPS, a hybrid altimetry system (pressure,GPS, and lidar), and a beacon navigation system for precision landing. It provides thermo-dynamic and kinematic atmospheric measurements and can operate in winds up to 50 knots(25 m/s). The system is called CopterSonde and was developed by OU for the NOAA EPICproject.

To establish a reliable safety case for the FAA to allow unattended operation ofUAS as part of the 3D Mesonet, the OU CLOUD-MAP team has been working with OUsAdvanced Radar Research Center on the development of a low-cost detect and avoid radar,which would be used in connection with geo-fenced UAS operations. That is, the radar wouldbe used to detect general aviation aircraft that entered the prescribed operation area of a3D Mesonet station and in such a case, send a notification to the UAS to abort operations.Although being developed for the 3D Mesonet, the radar could be used for a variety of UASoperations. The radar is in the early phases of real-time testing. Shown in the figure is the

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Figure 12: Low-cost detect and avoid radar housed at OU’s Radar Innovations Laboratory.

radar on the roof of the Radar Innovations Laboratory.

1.4.3 University of Kentucky

The UK CLOUD-MAP team consists of five faculty leading separate efforts, along with staff,graduate students and undergraduates in Mechanical Engineering (ME), Chemistry (Chem)and Biosystems and Agricultural Engineering (BAE). The faculty typically meet biweeklyto coordinate activities and initiatives. Students interact working in the laboratory, duringweekly planning meetings of the UAV laboratory, and during weekly flight testing whenweather allows.

Initiatives during the past year include submission of an internal proposal for forma-tion of a new research center, the Center for Autonomous Systems Research (CASR), consist-ing of six overlapping networks led by a core of CLOUD-MAP researchers with key areas ofsensing (Bailey/Renfro), data interpretation (Jacobs/Seales), and control (Hoagg), agricul-ture (Dvorak/Sama), atmospheric science (Guzman/Smith), and transportation (Dadi/Gibson).Center activities will be facilitated by the Director (Smith) and Executive Director (Sama)to concentrate on fostering and enabling collaborations to establish UK expertise in foun-dational areas as a basis for strategic pursuit of larger initiatives. Guzman organized andwill edit a Special Issue of the journal Atmosphere, titled ”Atmospheric Measurements withUnmanned Aerial Systems (UAS).” Plans are underway for the 2018 CLOUD-MAP flight

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Figure 13: UK team at CLOUD-MAP 2016 field campaign.

campaign with site selection planned enable collaborative research to answer an atmosphericscience question.

1.4.4 University of Nebraska-Lincoln

Research at UNL funded through CLOUD-MAP has contributed both directly and indi-rectly to research proposals and cross-jurisdictional collaborations that have been outlinedelsewhere in the report. The focus of this summary is on broader programmatic benefitsdirectly connected to the work supported by this project.

Research executed as part of Task 4-1 (Public Perception of Drones for AtmosphericScience) has involved three members of the Nebraska Public Policy Center (PPC): Dr. LisaPytlikZillig, Ms. Janell Walther, and Mr. Jake Kawamoto. Their work has built capacity atPPC and facilitated additional work related to public engagement during technology devel-opment. For example, the NSF-funded National Robotics Initiative Fire-Drones project, onwhich Drs. Detweiler and PytlikZillig serve as co-PIs, now utilizes scales and methods fash-ioned off those developed through the Task 4-1 research. The CLOUD-MAP work has alsobuoyed a programmatic emphasis at the Nebraska Public Policy Center on the investigationof trust, from a focus on facets of trustworthiness, to take into account the interactive effectsof different targets of trust.

CLOUD-MAP has influenced efforts at UNL to secure the resources necessary toconstruct a large indoor/outdoor flight training and evaluation facility. Not only does UNLsparticipation in CLOUD-MAP serve as leverage for this effort, but Dr. Detweiler (one of theleaders of the initiative) has gained valuable information through project field experimentshosted at similar facilities of partner institutions.

Finally, the fabrication of the second Integrated Mesonet and Tracker (IMeT-2), whichis funded through an equipment allocation within the CLOUD-MAP award, has yielded a

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formal relationship between UNL and NOAA/NSSL. IMeTs fuse the capabilities of a UAStracker vehicle, used to maintain compliance with FAA policies on UAS operation in the USNational Airspace System, with the capabilities of a mobile mesonet. Like IMeT-1, IMeT-2is a Ford Explorer with a dual moonroof (to enable the observer in the second row of seats tosee the aircraft and airspace) and will have a full meteorological sensor suite forward mountedto avoid obstructing the view of the sky from within the vehicle. NSSL is supporting Dr.Sean Waugh’s contributions to the fabrication of IMeT-2.

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2 Research Program

The primary technical and scientific challenge is to develop highly reliable and robust toolsthat can routinely perform regular atmospheric measurements in a variety of weather con-ditions, including day or night operation and during hazardous weather, to improve ourunderstanding of meteorological and atmospheric processes within the Earth. The team wasassembled based on the requirement to accomplish this challenging goal. The team membershave considerable experience with designing, building, and flight-testing such UAS platforms,as well as development of sensors, algorithms, and communication systems. We will con-duct research and development on multiple platform types (custom built and commercial offthe shelf, including both rotary wing aircraft and fixed-wing platforms) and sensor suites.These systems will be equipped with high-precision and fast-response atmospheric sensors,as discussed in detail below. We will focus on boundary layer conditions and developmentand how to best utilize data determine atmospheric stability indices and the likelihood fordevelopment of severe weather. We will adapt miniaturized high-precision and fast-responseatmospheric sensors to the profiling UAS platforms. Additionally, we will compare fixed-wingand rotary wing aircraft vehicles as to their suitability for carrying a variety of sensors forthe study of ABL properties. Because various properties of the atmosphere are being sensed,the UAS aircraft, its movements, outgassing, thermal profile, downwash, wake, and otherproperties have the potential to affect sensor data. This study will allow us to determine theproper aircraft, sensor position, and sensor suite to use in further research with the eventualgoal of being able to use a heterogeneous system of autonomous vehicles to map criticalfeatures of the ABL through both space and time, allowing for a better understanding ofthis critical set of related atmospheric phenomena. A final potential outcome of the effort isshown in Fig. 14, where an autonomous system is coupled with an existing weather tower toprovide extended profiling capabilities that increase the range of the ABL regularly probed.The specific objectives to achieve these goals are outlined in detail below. Participants oneach task are detailed in Table 2.

Table 2: PI and early career faculty involvement in the various objectives and tasks of theCLOUD-MAP program.

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Figure 14: 3D Mesonet concept will allow extended regular ABL measurements.

2.1 Objective 1

Develop a strong mentoring program and intellectual center of gravity in thearea of UAS in Weather and develop joint efforts for future funding and the de-velopment of a national center in use of UAS in Atmospheric Science, ultimatelyresulting in a NSF ERC proposal.

Workforce development impacts students and programs at all partner universitieswith a focus on measurable and transferable improvements. University Programs includeresearch experience on complex systems to retain students and improve intuitive engineeringskills. Student-industry internships will be emphasized as well. Cross-pollination betweenresearchers, industry and end users will foster new ideas and innovations. This objective isadministered by the executive committee consisting of the PIs from each of the institutions.

Metrics include publication rates, proposal activity, awards, and trainee development,both within the institution and multi-jurisdictional.

2.1.1 Task 1-1: Mentorship

Team Science Development Research from a number of scientific and engineering disci-plines must be well-integrated to begin to address the challenges represented by the CLOUD-MAP vision. Salazar, et.al. [8] focused on how innovation is facilitated in diverse teamscience by defining a model of integrative capacity to overcome barriers and understand cat-alysts for knowledge creation and novel integrated discovery. Three pathways are illustratedin Figure 15 that connect the team social integration processes with cognitive integrationprocesses, reinforcing and increasing integrative capacity as a result.

Diversity in the composition of science teams is an essential asset for interdisciplinarydiscovery, but deep differences in characteristics between team members may impact theintegrative processes. Visible differences of age, gender, and race occur and are overcomewith many teams. Salazar, et.al.4 reviewed and characterized compositional diversity fac-tors for teams under knowledge, social and demographic headings, then listed documentedscience team inhibitors. CLOUD-MAP is a diverse science team of 16 faculty and more than100 trainees in multiple disciplines, so knowledge, social and demographic diversity are all

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factors. Of nine listed inhibitors, CLOUD-MAP has the potential for five to a large degree(diverse sub-goals, unstructured problem, distinct technical languages, geographically dis-tant, different time zones, and minimal team familiarity), and two to some degree (negativeprevious collaborative experiences and strong subgroup identities).

Among the senior faculty of the CLOUD-MAP team, previous collaborative experi-ences have been positive, and similarly positive previous collaborations existed among thedistinct university and disciplinary subgroups, so knowledge of negative experiences primarilyserved to inform organizational strategies to head off similar issues. A notional representationof the inter-institutional collaboration under Task 2-1 is seen in Figure 16 including depic-tion of faculty (institutional circles), graduate students (solid circles), and undergraduates(open circles). Each of the tasks was established with researchers from other institutions,but moving from planned collaborations to functional and ultimately productive ones is thegoal of the annual CLOUD-MAP Flight Campaign, as well as being the venue for demon-strating and evaluating performance of key technical developments in sensing, platforms orcooperative control.

While traditional metrics and program reviews are used for technical evaluation, thenew NSF data entry portal was developed for assessment of faculty and student researchercollaborations on proposals, presentations, papers and patents. Self-assessment of projectcollaborations is possible using the integrative capacity framework. In Figure 15, social inte-gration processes initiate the emergence of integrative capacity. The CLOUD-MAP projectleadership established communication practices, shared goals, and social (task) networks thatsupported the distributed knowledge diversity of the original team formation. Our sharedgovernance document included in the Year-1 Report addressed publication protocols, amongother organizational foundations.

One aspect of the CLOUD-MAP project that was not explicitly mentioned as a poten-tial barrier in the summary of prior inter-organizational research is the evolving regulatoryenvironment for operating UAS platforms. Each of the universities had to secure separateFAA flight Certificates of Authorization (CoAs) for the campaign. In late March 2016,the FAA announced that nationwide blanket research CoAs could be requested for researchflights up to altitudes of 400 ft that adhere to other limitations including continuous visibil-ity and having dedicated pilots-in-command, among others. Under the new Part 107 SmallUAS Rules, a significant number of research flights can be accomplished without special au-thorizations, although flights to higher-altitude, occurring overnight, or beyond line-of-sight

Figure 15: Integrative capacity model for diverse team science [8].

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Figure 16: Inter-jurisdictional network.

still require FAA authorization via a CoA. CLOUD-MAP also includes flight safety trainingand briefings, as well as severe-storm training for participants at the annual campaigns inOklahoma.

Another approach for self-assessment of developing collaborations is a self-perceptionsurvey of research collaborations among the CLOUD-MAP faculty. The survey was com-pleted in April 2016, within about eight months of the project start. Faculty were askedwhich others on the team they had collaborated with prior to CLOUD-MAP on research,proposals or papers, and also since CLOUD-MAP was underway.

Biographs were constructed of the connections to illustrate the development of theCLOUD-MAP research social network as well as to annually evaluate changes in connections.On the left in Figure 17 is the network of self-perceived research connections before CLOUD-MAP . The four institutional PIs are highlighted, so that it is clear that a previous pairingbetween UK and OSU became connected to previous pairings of OSU with OU and UNLvia the project PI at OSU circled in red. Two researchers were not connected to any others.

During Year 1, 27 additional inter-institutional connections were identified among thefour categories of research, papers, conference presentations, and proposals to add to the 45total connections primarily internal - before CLOUD-MAP. During Year 2, 11 additionalexternal (inter-institutional) connections were identified. Additionally the network evolvedwith new strongly-connected participants emerging as indicated in Figure 18. Strongly- andweakly-connected participants can be identified in the annual snapshot, while comparisonsbetween surveys show emerging research relationships that become strong in a later year.

Evidence of this team science approach through collaborative research efforts is shownin Figure 19, where research publications are shown for early-career researchers and seniorresearchers in years 1 and 2 of program. While only 6% of the researchers published pro-grammatic related research (one article was published) in year 1 of the program, over 2/3 oftotal researchers published at least one article in year 2 of the effort. It should be noted thatthese numbers only count approximately a year and a half of effort and not a full 2 yearssince the first year only counted from August 2015 to March 2016. Likewise the number ofcollaborative publications is quite large this early in the program, with nearly 1/3 of all re-searchers participating in a collaborative article (Figure 19b). These numbers are indicativeof successful team science collaboration and demonstrate success of this objective. These

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Figure 17: Biographs of self-perceived collaboration connections in CLOUD-MAP : beforeCLOUD-MAP (left) and during Year-1 of CLOUD-MAP (right).

Figure 18: Biographs of self-perceived research connections in CLOUD-MAP : during Year-1of CLOUD-MAP (left) and during Year-2 of CLOUD-MAP (right)..

results have been discussed in a conference paper at the AIAA annual meeting in 2017.[9]

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(a) Number of researcher publications by award year: total, collaborativebetween award researchers, and non-collaborative.

(b) Normalized number of researcher publications per researcher for all researchers, early-careerresearchers, and senior researchers by award year.

Figure 19: Research publications for early-career researchers and senior researchers in years1 and 2 of program.

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2.2 Objective 2

Create and demonstrate UAS capabilities needed to support UAS operatingin the extreme conditions typical in atmospheric sensing, including the sens-ing, control, planning, asset management, learning, control and communicationstechnologies. Accordingly, develop test- beds and related analysis tools for better un-derstanding of the atmosphere using small UAS and perform experiments to inform UAScapabilities and acquire data for atmospheric physics and improved weather forecasts andmodeling.

There is a persistent and pressing need to collect better observations of the ABL.Having a better understanding of the kinematic and thermodynamic structure of the ABL,especially at small mesoscale time and space scales, impacts many areas of meteorology,such as improvements to: numerical weather prediction modeling through better ABL pa-rameterization; our ability to forecast the development and evolution of severe storms andassessments of air quality in and around urban areas; the quality of information provided tothe wind energy sector; and so forth. It has been clearly stated in such recent reports as thoseprovided by the National Research Council and instrumentation workshops, that observingsystems capable of providing detailed profiles of temperature, moisture, and winds withinthe ABL are needed to monitor the lower atmosphere and help determine the potential forsevere weather development. [10, 11] Unfortunately, operationally available observations ofABL variability of the scope and across the scales needed by the meteorological communityare currently not available. The foundational goal of this objective is on the development,evaluation and application of complete UAS system packages capable of acquiring neededmeteorological and atmospheric data miniaturized, high-precision, and fast-response atmo-spheric sensors for wind and thermodynamic measurements along with measurements of airchemistry soil moisture, etc. relevant to climate science as a whole.

2.2.1 Task 2-1: Convection Initiation

Research Accomplishments The goals of this task are as follows:

1. Advance understanding of the mesoscale processes responsible for deep convection ini-tiation (CI) and define the observable environmental conditions that regulate theseprocesses.

2. Establish guidance for the system capabilities and deployment strategies required tomaximize the impact of UAS on numerical weather prediction model skill.

3. Develop and test a system for coordinating multiple-UAS for a future CI-focused fieldcampaign.

The research promises to advance the state of knowledge, integrate multi-disciplinary re-search conducted by atmospheric scientists and engineers, and transition research to oper-ations and thereby directly benefit agencies such as the National Oceanic and AtmosphericAdministration (NOAA).

The following are accomplishments achieved during the second year of this award.

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Figure 20: Surface transects of θe (K) collected from the 20 June 2016 MAHTE in a boundaryrelative frame of reference. Positive (negative) distances indicate areas on the cool (warm)side of the thermal boundary.

Mesoscale Airmasses with High Equivalent Potential Temperature Cur-rent UNL M.S. candidate Wolfgang Hanft has undertaken an observational and mesoscalemodeling analysis of the formation and evolution of Mesoscale Airmasses with High Equiv-alent Potential Temperature (MAHTEs). MAHTEs are airmasses formed through synopticprocesses (e.g., large-scale advection) or mesoscale processes (e.g., thunderstorm outflow) andconstitute the cooler/denser side of an airmass boundary but are characterized by mesoscaleregions, typically near the boundary, for which the equivalent potential temperature (θe)and convective available potential energy (CAPE) are higher than the air mass on the warmside of the boundary. By virtue of their enhanced CAPE in proximity to airmass bound-aries, MAHTEs are locations favorable for CI. Moreover, their spatial scale often falls belowthe resolution of the meteorological observing network. This work aims to make progresstowards Goal 1.

One component of this project has been to investigate the surface characteristics ofMAHTEs through the collection of surface observations with an Integrated Mesonet andTracker (IMeT). Surface transects were collected across a MAHTE in northern KS on 20June 2016 that formed along a weak synoptic cold front. Surface observations showed thehighest values of θe in all transects were within the first 8 km of the cool side of the boundary(Figure 20). θe values in the MAHTE were 10–15 K higher than what was observed in thewarm sector.

The 20 June 2016 case has also been modeled using the Weather Research and Fore-casting (WRF-ARW) model to investigate the processes responsible for MAHTE formationand evolution (Figure 21). It was found that differences in vertical mixing contributed mostsignificantly to MAHTE formation, as suppressed vertical mixing on the cool side of theboundary allowed dew point temperatures to remain higher near the boundary (Figure 22).Analysis indicates that, contrary to previous hypotheses, surfaces fluxes may not play avital role in MAHTE. Efforts are underway to simulate MAHTEs associated with thunder-storm outflow to assess whether the mechanisms differ from those associated with MATHE

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Figure 21: Modeled surface θe at 22 UTC. There is a sharp discontinuity in θe, with thehighest values located in a small region along the boundary steadily decreasing to the north.

formation along synoptic boundaries.This work has been presented at the 2016 American Meteorological Society (AMS)

Conference on Severe Local Storms [12] and the 2017 AMS Special Symposium on SevereLocal Storms [13] and will be presented at the upcoming AMS Mesoscale Conference. It willbe disseminated in Wolfgang Hanft’s M.S. thesis and in 1-2 peer-reviewed articles submittedto the AMS’s Weather Forecasting or Monthly Weather Review.

The role of vertical wind shear in airmass boundary evolution and CI oc-currence. Research has been initiated by UNL M.S. student Alexander Krull to studythe sensitivity of upward motion along boundaries to the boundary-normal and boundary-parallel components of the vertical wind shear. This work aims to make progress towardsGoal 1 above. Previous research has attributed (horizontal) wave development along bound-aries to the normal component of the vertical shear. Moreover, boundary-normal shear willregulate the magnitude of upward motion along boundaries. However, the role of boundary-parallel shear in wave development and upward motion has been largely overlooked.

In contrast to a linear boundary, for which ambient boundary-parallel shear is every-where parallel to the boundary, a wavy boundary will exhibit localized regions where theshear vector crosses the boundary even if the shear is parallel to the average (linear) bound-ary orientation (Figure 23). Since upward motion depends on the local boundary-normalcomponent of the shear, a wavy boundary in the presence of ambient boundary-parallel shearcould produce localized regions of enhanced upward motion and, therefore, regions that arelocally favorable for CI.

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Figure 22: Vertical cross section of modeled vertical velocities (fill) and θe (contours) at 22UTC show that vertical mixing is stronger and deeper in the warm sector and suppressed onthe cool side of the boundary. The location of the highest e within the MAHTE is indicatedby the arrow.

Idealized numerical simulations (using Cloud Model 1) will be conducted to gaininsight into the sensitivity of upward motion along boundaries to the vertical wind shear. Theparameter space will include both boundary-normal and boundary-parallel ambient shear aswell as boundary strength (magnitude of the density perturbation behind the boundary).

This work will be disseminated in Alexander Krull’s M.S. thesis and in a peer-reviewedarticle in the AMS’s Monthly Weather Review.

Observed Sensor Response CI forecasts depend on accurate characterization ofthe thermodynamics and wind fields within the planetary boundary layer (PBL). Numericalweather prediction (NWP) model guidance on PBL structure is prone to well-documentederrors that could theoretically be mitigated with supplemental observations. UAS are well-suited to this task but large gradients in temperature and moisture associated with pre-existing airmass boundaries (which often serve as the loci for CI), near-surface sources ofpotential energy (associated with spatially-variable surface fluxes), and top-of-the-PBL cap-ping inversions, must be faithfully represented. As such, UAS-mounted instruments needsufficiently fast sensor responses. As part of our efforts to establish guidance for the systemcapabilities required to maximize the impact of UAS on NWP model skill (Goal 2 above),we are developing an experiment for execution during the summer 2017 CLOUD-MAP field

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deployment in Oklahoma.

Figure 23: Conceptual illustra-tion of the relationship betweenboundary-parallel shear and awavy boundary.

Specific aims of this experiment are as follows:

• Evaluate the sensor response characteristics of abroad suite of temperature and humidity sensors.

• Evaluate the robustness of several aspirating strate-gies on rotary-wing aircraft.

The experiment will be conducted in and near theNOAA National Severe Storms Laboratory vehicle bay (inNorman, OK). Pseudo-step-function changes in tempera-ture and moisture will be created by moving instruments(both on and off parent platforms) from inside the climate-controlled bay to outside. A similar experiment design hasbeen adopted in the past but this is the first time thatUAS-borne instruments will be tested in this manner.

The current plan is to conduct two sets of tests. Thefirst set of tests (toward aim A listed above) will involvethe placement of many sensors on a single cart that willbe moved across the temperature/humidity change. Thistest will enable valuable benchmark intercomparisons of allinstruments involved. The second set of tests will involveflights of rotary-wing SUAS across the step-change.

Results of these tests will be disseminated in 1-2peer-reviewed articles (likely submitted to the AMS Jour-nal of Atmospheric and Oceanic Technology) jointly au-thored by scientists from all four participating universities.

Required Sensor Response Further explo-ration of the importance of sensor response for characterizing the PBL is being conductedby addressing the question, what sensor response is required to represent key meteorologicalphenomena germane to the accurate prediction of CI? Large-eddy simulations (LES) of aconvective boundary layer and airmass boundary (Figure 24) are being used for the simula-tion of SUAS data collection. Specifically, thermodynamic state variables developed usingLES serve as the nature run for offline aircraft models that represent the flight of SUAS pro-filing the PBL and transecting airmass boundaries. The experiment parameter space alsoincludes air speed (ascent/descent rates) for fixed-wing (rotary-wing) aircraft since the largegradients that characterize these phenomena might be better represented at lower air speed(ascent/descent rates). However, when instantaneous representation of a rapidly evolvingphenomenon is required, slower air speeds may ultimately degrade the accuracy of in-situobservations. This tradeoff will be quantified in this research.

Results from these experiments will presented at the 5th Conference of the Inter-national Society for Atmospheric Research using Remotely-piloted Aircraft. It will also besubmitted to either the Multidisciplinary Digital Publishing Institute’s (MDPI) Atmosphereor AMS’s Journal of Atmospheric and Oceanic Technology.

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Figure 24: LES solution of a convectiveboundary layer (top) and airmass boundary(bottom).

Workforce Development The award issupporting the training of UNL M.S. stu-dents Wolfgang Hanft and Alexander Krull.

2.2.2 Task 2-2: Storm Microphysics

Research Accomplishments Researchtask 2-2 focuses on learning about storm-scale microphysics using UAS measure-ments. Research undertaken in this taskwill generate new datasets which will serveas a baseline for what cloud physics mea-surements can be obtained in real time usingUAS. These datasets will be validated withpolarimetric radar observations from thenational Weather Surveillance Radar-1988Doppler (WSR-88D) network, and methodswill be developed to use UAS-based cloudphysics measurements to derive estimates ofvariables that can serve as input to numer-ical models. Additional scientists currentlycollaborating on this research task includeBailey (UK), Chilson (OU), Elbing (OSU),Jacob (OSU), and Houston (UNL). Threetechnical goals were initially planned underTask 2-2, which are described here, alongwith how the goals have changed in the past year:

• Gathering in-situ liquid drop measurements in the rear and forward flank regions of asample of classic supercell storms. These regions contain unique drop size distributions(DSDs), and characteristics of the DSDs in these regions are thought to convey impor-tant information about storm-scale evolution and near-term severe weather potential.Being able to collect such observations from a UAS platform could improve severeweather predictability and increase scientific understanding of microphysics and ob-served polarimetric radar signatures in these regions. This initial goal remains in placeas an ideal data collection mission; however, given the relative scarcity of isolatedsupercell storms, especially sufficiently near WSR-88D radars, we are now planningto initially deploy instrumentation in any precipitation which falls sufficiently near aWSR-88D radar so that a good comparison can be made with the radar variables.

• Retrieving representative values of the polarimetric radar variables at low levels in thesevere storm environment. Using a liquid water content sensor, UAS may provide arelatively simple way to retrieve values of the radar variables reflectivity factor (ZHH),differential reflectivity (ZDR), and specific differential phase (KDP) from regions ofprecipitation. These fields would be especially valuable since they would be at lowlevels, where radar observations from the WSR-88D network are scarce. Thus, with

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validation, such measurements may be able to fill in or supplement the standard polari-metric radar fields. These supplemental data could then be assimilated into mesoscalemodels, improving near-term severe storm and precipitation rate predictability. It isanticipated that most of the work supported by this grant will focus on validatingUAS-obtained estimates of the radar variables. This research goal has not changedover the past year.

• Retrieving aerosol information from near-storm environments and storm inflow lay-ers. Many aerosol species serve as cloud condensation nuclei (CCN) or ice nuclei (IN),and the distribution of aerosols in the near-storm environment can thus strongly in-fluence DSDs in convection. Differing DSDs, in turn, may support a spectrum ofpossible precipitation and severe weather outcomes. Knowing the aerosol distributionin the near-convective environment may help nowcasters anticipate storm behavior andthreats more accurately than is currently possible. These data would also be valuableinput to mesoscale weather models. This research goal has not changed in the pastyear.

Work toward these three technical goals has mostly centered on determining appro-priate sensors to collect the observations, procuring funding to obtain these instruments, anddeveloping a field logistics plan to carry out initial sensor testing. Specifically, toward eachtechnical goal listed above:

• It has been determined that no DSD sensor (disdrometer) currently available is readilyable to be deployed using a drone platform, though a custom fixed-wing design holdspromise. The OTT Parsivel disdrometer from OTT Hydromet has been determined tobe the optimal sensor for this work, since it collects drop measurements over a largerange of drop diameters with reasonably good spatial resolution of drop size. Thus,we have moved forward with requesting funding for an OTT Parsivel disdrometer. Itis the investigators understanding that this funding will be awarded, but the exactamount of funding is not yet known since the NSF budget is currently on continuingresolution. The cost of this sensor is relatively low, so the likelihood of obtaining itduring spring 2017 is very high. Once the instrument has been obtained, we will takeinitial test measurements near (from 4.6-25.4 km away from) a WSR-88D radar; thisdistance allows the measurements to be comparable to radar measurements, whichare at relatively low altitude and out of ground clutter. Deployments during springand early summer 2017 are likely to be centered around KOAX, the WSR-88D radarat Valley, Nebraska. We plan to take field measurements with this instrument incoordination with the summer 2017 CLOUD-MAP field campaign during June 2017, ifprecipitation occurs. These data collection efforts would not need to be near a WSR-88D radar if sufficient test cases are obtained which are collected near a radar. In thefuture, it would be ideal to mount this instrument to a fixed-wing platform, and weare still investigating potential options for this.

• Retrieving values of the polarimetric radar variables from precipitation can be achievedusing liquid water content (LWC) sensors, which are sufficiently small to be UAV-borne. The LWC sensors we plan to utilize work by determining how much current is

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required to keep a wire or coil at the same temperature. We have identified a sensormanufactured by Droplet Measurement Technologies which is sufficiently small andlightweight, and have requested funding for this instrument as well. The status ofthis request is the same as that of the disdrometer (above)we are waiting for NSF toprogress beyond the current continuing resolution funding situation to know an exactamount of our supplemental funding. This is a relatively expensive instrument, so thelikelihood of obtaining it may be smaller than for the disdrometer. Many of the samescience objectives can be obtained with either instrument, but it would be ideal tohave both instruments collecting data so there are multiple independent estimates ofthe parameters we hope to derive, and to determine which instrument yields the bestresults under particular precipitation regimes. Once this sensor is obtained, the datacollection plan is the same as for the disdrometer (noted above). We hope to collectinitial test data during spring and early summer 2017, and to collect data in the fieldin collaboration with the CLOUD-MAP summer 2017 field campaign.

• We plan to retrieve cloud concentration nucleus (CCN) concentration from convectiveinflow regions using a CCN particle counter. We have identified an instrument whichcan be de-packaged in order to be more readily drone-borne, from TSI Inc. A fundingrequest has been submitted to cover the purchase of this instrument also (describedabove). The CCN counter is relatively inexpensive, so we have high confidence that wewill be able to obtain this instrument during spring 2017. Initial testing will be carriedout in spring and early summer 2017. In addition, we plan to work in collaborationwith the Nebraska Intelligent Mobile Unmanned Systems (NIMBUS) Lab at UNL toloft this instrument for the purpose of sampling a deeper portion of the boundary layer.Initial data collection with this instrument is not as constrained by distance from aradar, so it is anticipated that numerous measurements will be taken in the vicinity ofconvective clouds.

Further meaningful work toward developing our methods of data collection will require de-tailed discussion with other scientists on this NSF grant, who will be able to provide moretechnical input about possible platforms and instrument mountings. This additional col-laboration is expected to take place in late June 2017 when the project group gathers inOklahoma for a field campaign, and when the lead task investigator has the instruments inhand. Additional work toward the project goals will consist of continuing the investigatorsresearch on understanding how radar signatures vary in convective storms across a spectrumof environments characterized by different wind and moisture conditions.

Workforce Development The task lead (Van Den Broeke) has extended two GraduateResearch Assistant offers to students to join the Storm Microphysics research group at theM.S. level starting in fall 2017. Likelihood is high that one M.S. student will join the projectthen.

The task leads research group at UNL includes several students (2 M.S. and 1 Ph.D.)whose research supports the CLOUD-MAP objectives by examining how radar signaturesvary in convection across storm-scale environments and in response to storm interactionswith atmospheric boundaries.

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One M.S. student who supported the research objectives of CLOUD-MAP (LenaHeuscher) graduated in December 2016 and has moved to the University of Alabama-Huntsville as a Ph.D. student.

Leveraged Opportunities and Activities The technical goals of the Storm Micro-physics task align well with some portions of the task leads research activities outside ofthis NSF project. He studies polarimetric radar signature variability in severe storms asa function of environment, and how this radar signature variability may be predictive ofnear-term future severe weather occurrence (currently focused mostly on tornadoes). He haspublished one manuscript in this area over the past year, and has two additional manuscriptsin revision. His ongoing work continues to look at related questions of being able to differen-tiate storms with differing threat profiles, and he plans to submit an additional manuscriptor two in this area over the next year. These results will be leveraged into the develop-ment of hypotheses and research questions for this NSF project. He also plans to submita proposal related to this work, looking at environmental influences on tornadoes in linearconvective modes. These efforts are anticipated to lead to new understanding which can beincorporated into the operational severe weather warning workflow.

2.2.3 Task 2-3: Airborne Soil Hydrology

Figure 25: Moisture index performance for a)bare silt loam soil and b) wheat stalk residue

Research Accomplishments The over-all objective of this task is to determine thefeasibility of applying a novel remote sensingtechnology towards early detection of wa-ter stress in production agriculture. Thisobjective is being addressed with two spe-cific aims: (1) to develop a novel passivenarrow-band single-pixel multispectral sen-sor for measuring visible and near-infrared(NIR) reflectance used to compute NDVIand NDWI and to determine its spectral re-sponse; (2) to test the hypothesis that thespatial patterns in land surface hydrologicalbehavior are reflected in (a) low-altitude at-mospheric humidity measurements and (b)NDWI measurements of bare soil, and thatthe pattern is related to the spatial vari-ability in crop water supply as measured byNDVI/NDVI of the crop during the subse-quent vegetative period.

Progress on the first specific aim isnear completion. Preliminary results fromlaboratory testing revealed that the index-based method proposed worked well onbare soils but was not satisfactory for crop

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Figure 26: STS-UV/VIS/NIR spectrometers and a Raspberry Pi embedded computermounted in a 3D-printed enclosure.

residues (Figure 25) [14]. Consequently, the raw spectral data collected was processed usingseveral machine learning algorithms on the entire dataset to determine the ability to predictmoisture content. Prediction was successful on over 95% of the soil samples and 90% ofthe stalk residue samples tested. Therefore, a different sensor approach was chosen beforeproceeding to field studies. Two sets of miniature spectrometers covering the UV to NIRrange were fitted with lenses for remote sensing and solar irradiance. The spectrometers wereinstrumented using an embedded computer, which was programmed to trigger spectrometermeasurements based on a PWM output signal from a UAS autopilot. The solar irradianceset is shown in Figure 25.

Work on the second specific aim will begin in May, 2017. A test plot in Princeton,KY will be established over an irrigated corn field. A matrix of 46 soil water potentialsensors will be installed at three depths (20, 40, and 60 cm) as ground reference points.A multirotor UAS carrying the remote sensing spectrometers will sample the reflectancespectra at the ground reference points throughout the growing season and the spectral datawill be processed using the methods developed in specific aim 1. Further work is planned atthe second CLOUD-MAP Test Campaign to compare the reflectance spectra measured fromthe UAS with ground-based spectral measurements.

Workforce Development Ali Hamidisepehr (Ph.D. Candidate, Biosystems and Agricul-tural Engineering, University of Kentucky) is the primary student working on this researchproject. He is being trained to develop custom UAS payloads that operate autonomouslyand will conduct the field work needed to complete specific aim 2.

Aaron Turner (Engineer Associate and Ph.D. Student, Biosystems and AgriculturalEngineering, University of Kentucky) worked on developing MATLAB scripts for processingspectrometer reflectance data.

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Figure 27: The two CO2 sensing systems contained within 3-D printed box. Pen for scale.

Christopher Good (M.S. Student, Biosystems and Agricultural Engineering, Univer-sity of Kentucky) worked on sensor instrumentation using an embedded computer.

Surya Saket Dasika (M.S. Student, Biosystems and Agricultural Engineering, Uni-versity of Kentucky) worked on a ground-based test fixture for sensor testing prior to UASintegration.

Leveraged Opportunities and Activities Several proposals are under considerationthat have been developed as a result of the efforts on this project including an NSF NRTproposal with OSU, an internal UK proposal to establish a Center for Autonomous SystemsResearch, and multiple NSF and USDA research proposals involving UAS agricultural andatmospheric sciences.

2.2.4 Task 2-4: Local-Scale Spatiotemporal Climate Variations Measurements

Research Accomplishments The third National Climate Assessment [15] highlightsevents such as winter storms, drought, flood-producing rainfall, and severe storms as fu-ture climate relevant challenges in the Great Plains. From the perspective of agriculture,changes in crop growth cycles and irrigation needs due to timing and magnitude of rainfallevents, as well as warming winters will have major impacts. As such, the long-term goalof this task is to establish a UAS climate monitoring network across the region. This net-work will improve our understanding of spatial and temporal CO2 concentrations, provideinformation regarding precipitation type in winter weather and assist with early detection ofdroughts and pluvials through the interaction of the climate and the atmospheric boundarylayer [16, 17].

The three specific objectives in this task are (1) establish CO2 monitoring using UAS;(2) identify local-scale characteristics of winter climate; and (3) advance understanding ofthe role of the large-scale climate on local conditions. Research accomplishments towardsthese three objectives are described below.

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Figure 28: CO2 concentrations (ppm) from two CO2 sensing systems. Lines indicate 20second averages.

CO2 Monitoring Vertical profiles of CO2 over different vegetation types in differentclimate conditions are of high relevance to climate change but are very limited globally,especially at the lowest levels [18, 19, 20]. Vertical profile measurements (up to 6km aboveground level) are routine at the DOE ARM SGP site in Oklahoma [21]. However, these flightsare not high resolution in the boundary layer, are fixed spatially, and require considerableresources (manned aircraft, air/CO2 collection in flasks). CO2 measurements on towersare more cost effective, but sampling is still limited to tower locations and fixed levelsin the vertical. CO2 concentration monitoring by UAS can fill the critical gap of local,high resolution CO2 concentrations in the boundary layer. These measurements are criticalfor providing constraints on local emissions and uptake of CO2 by vegetation, agriculture,and human sources and for providing ground based validation of modeling experiments andsatellite measurements, such as the new NASA GeoCARB mission [23].

Building on work from the prior year, through collaboration with OSU and priorwork conducted at the University of Maryland (Martin et al. 2016) undergraduate studentSantiago Mazuera has developed two CO2 sensor systems. Each sensor suite consists of aCO2 sensor, the K-30 Fast Response, purchased from CO2meter.com, which is connected toa GPS unit. The sensor outputs its information to an Arduino teensy via I2C. The statedaccuracy of the sensors is ±30 ppm (or ±3% of reading) and measures at 2 Hz. Figure27 shows the two sensor systems contained within individual 3-D printed boxes designedspecifically to house the sensor systems.

Testing of the sensors has occurred to establish biases between the sensors and to testthe 30ppm accuracy prior to flying the sensors. Several long tests (24 hours of continuousmeasurements) have been completed so far and one example is shown in Figure 28. Anoffset is observed between the two sensors but the agreement between the sensors in termsof variability is very high, increasing our confidence in the CO2 fluctuations. Initial testingsuggests that the offset between the two sensors is constant over long periods (i.e. no driftfrom each other) but additional testing is necessary to determine if this is true in differentenvironmental conditions. This testing will be performed at the Kessler Field Site where

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Figure 29: Left: OU Bix3 with Pitot tube (CO2 sensor not visible). Right: OU Bix3 inflight.

measurements can be calibrated against a LICOR open path gas analyzer designed for eddycovariance measurements.

The CO2 sensors will be flown on board a fixed wing UAS. The OU CO2 Bix3(N925UA) is a fixed high wing pusher aircraft available from HobbyKing under the productname of Bix3, which is being modified to allow autonomous flight in addition to its defaultmanual flight mode. The on-board autopilot is the Pixhawk (PX4) running the APM Planesoftware. The inertial measurement unit and GPS is built into the Pixhawk. The OU CO2

Bix3 can maintain stable and safe flight in case of engine failure. Figure 29 shows the OUCO2 Bix3. Based on initial flights with the sensor on board the aircraft, each sensor box hasbeen covered in copper to prevent interference between the aircraft and the sensor.

Winter Weather Numerous studies have highlighted the challenges of forecastingthe type of surface precipitation in winter weather (i.e. rain, freezing rain, ice pellets, snowetc.) [24, 25]. No evidence of UAS measurements of winter storms could be found during aliterature review by undergraduate student Emily Lenhardt. To prepare for measurementsin the next year (2017-2018), Emily has undertaken a literature review to determine whatproperties we should target to future flights.

Vertical profiles of temperature and humidity are particularly important in determin-ing precipitation type, especially characteristics such as the depth and magnitude of thefreezing layer, the depth of the refreezing layer and the moisture amounts [26, 27, 28]. Ad-ditionally, horizontal variability in precipitation type can occur due to heat transport fromwater sources and urban heat islands. As radiosonde measurements of vertical profiles arenot frequent in space or time, and are not necessarily representative of the air that particlesare falling through, it is imperative that we obtain additional measurements of temperatureand moisture locally and at high temporal resolution ahead of winter storms to aid forecast-ing of precipitation type. Interestingly, Carmichael et al. [29] show that if the form of thedroplets/particles is known at approximately 1200m and the vertical profile of temperatureis known, the precipitation type at the surface can be inferred. In preparation for futureflights, two case studies of Oklahoma winter storms (January 2017 and November 2015) arebeing investigated.

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Climate Interactions with the Boundary Layer Collection of temperature,humidity, CO2, and wind measurements from UAS have been ongoing. To increase our un-derstanding of climate and large-scale influences on the local-scale boundary layer, we requirelarge amounts of data collected over different periods and locations before thorough analysis(beyond case studies) can be completed. Future plans include regular (30 minute) profilemeasurements from sunrise to sunset at different times of year and in different environments.Due to the nature of this scientific objective, progress on analysis cannot be made until largeamounts of data are collected and collated.

Workforce Development Three undergraduate students have been actively involved inthis task. Santiago Mazuera (sophomore) has developed the CO2 sensor and plane integrationand in conjunction with Myleigh Neill (senior) has performed several sensor tests. EmilyLenhardt (sophomore) started on the project in January 2017 and has been undertakinga literature review of precipitation type in winter weather. The PI (Martin) is activelyrecruiting a graduate student to begin in Fall 2017.

Leveraged Opportunities and Activities The PI (Martin) and Chilson (OU) are partof a new interdisciplinary trace gas research group at OU aimed at connecting satellite,in-situ, and model simulations of trace gases (including CO2).

2.2.5 Task 2-5: Airborne Sampling Systems

Research Accomplishments Considerable progress has been made in Task 2.5, whichenables us to collect datasets during flight trials in Kentucky and Oklahoma includng theCLOUD-MAP campaigns in Oklahoma. A lightweight device capable of recording relativehumidity, temperature, pressure, and raw signals for methane has been developed for de-ployment during operation of a Skywalker X8 unmanned aerial system (UAS). In addition,under the guidance of the PI (Guzman), co-PI (Bailey), and collaborator (Sama), two Ph.D.graduate students (Schuyler and Pillar-Little) have selected several sensors for quantifyingconcentrations of other trace tropospheric gases.

Customized sensor arrays (CSA) for measurements of atmospheric pressure, temper-ature and relative humidity (RH), and several trace gases have been developed and deployedby the UK, OU and OSU teams. The trace gases we attempted to monitor at the ppmvand ppbv levels were CO2, O3, CH4, and CO2/NH3/NO2. A lightweight wood frame ofdimensions 17.5 cm 7.8 cm 1 cm held the CSA. The total weight (¡ 250 g including bat-tery, microcontroller, and data logger) was minimized to meet the fixed wing Skywalker X8.The CSA was installed on the hatch covering the instrument bed of the Skywalker, andonly separated from the environment by fiberglass screen wire (18 16 mess) that facilitatedthe direct airflow over the sensors while protecting them from debris. A bidirectional logicshifter was incorporated into the CSA to allow for common power, ground, and I2C railsto be shared between all devices. Telemetry and GPS data was collected with a PixhawkAutopilot module.

Several presentations have resulted from this work, including talks at Rice Univer-sity, University of Alabama in Huntsville, and the 2016 IGAC Breckenridge Conference.The theoretical knowledge and experimental results are also part of two manuscripts under

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Figure 30: Illustration of the overall analytical setup for task 2-5 displaying (A) a top profilepicture of the Skywalker X8; (B) the instrument bed; and (C) the customized sensor array(CSA). The labeled components are (1) battery pack; (2) Pixhawk; (3) Arduino board withdata logging shield; (4) CSA; (5) NH3/CO/NO2 multichannel gas sensor; (6) CH4 sensor; (7)CO2 sensor; (8) O3 sensor; (9) pressure sensor; (10) logic shifter; (11) temperature/relativehumidity sensor.

preparation for a Special Issue of the peer-reviewed journal Atmosphere organized by thePI. An example of the kind of data collected is presented below.

The aim of flying robust, lightweight atmospheric sensors on UAS is to monitor airquality, investigate pollution sources, and determine real-world exposures to gases of concernnear or at ground level. Measurements of this type will contribute a detailed inventory forthe profile level of trace gases in the lower troposphere. Data collected onboard UAS duringall flights are paired with GPS data to build up chemical maps.

Workforce Development All the worked described above is performed by graduate stu-dents. Student Pillar is defending her Ph.D. on May 9, 2017, contributing part of her dis-sertation to this task. The interdisciplinary training received by the students is contributingto their professional development from direct interactions with the PI and a co-PIs for thistask.

Leveraged Opportunities and Activities Several proposals are under considerationthat have been developed as a result of the efforts on this project including an NSF NRTproposal with OSU, an internal UK proposal to establish a Center for Autonomous SystemsResearch, and multiple NSF and USDA research proposals involving UAS agricultural andatmospheric sciences.

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Figure 31: Example of data gathered during a flight with a Skywalker X8 and the firstgeneration sensor array during the first CLOUD MAP campaign in Oklahoma. Site name:Marena site. Date: June 29, 2016. Start time: 9:05 am CST.

2.2.6 Task 2-6: Atmospheric Infrasonic Sensing

Research Accomplishments The objective for this task is to develop infrasonic mon-itoring of the atmosphere and assess how infrasonic sensing can be integrated with UASoperations. There are three primary technical goals for Task 2-6 (Atmospheric InfrasonicSensing):

1. Establish a trusted ground-based infrasonic source and receiver

2. Create a database of infrasonic events

3. Integrate the infrasonic sensing with UAS

Technical Goal 1: Establish a trusted ground-based infrasonic source andreceiver This technical goal is critical to the success of Task 2-6 since it establishes groundtruth both for identifying infrasonic signatures of various geophysical processes and compar-ison data for any UAS activities. The following tasks were identified to achieve TG1, andthen progress towards each task is subsequently discussed.

• TG1.1: Build a 3-microphone infrasonic array in Oklahoma

• TG1.2: Develop a portable infrasonic source

• TG1.3: Establish in-house signal processing algorithms

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Figure 32: (upper left) Satellite image (Google) of the location of the infrasonic array with themicrophone denoted with the red Xs. (upper right) Arnesha Threat (MS student) posing withmicrophone 2 after its initial deployment. (lower left) Picture of the infrasonic microphoneas mounted within the white dome. (lower right) Aerial view of the infrasonic microphonemounted on the roof of the Fabrication Laboratory.

Reference Infrasonic Array In the past year, a 3-microphone infrasonic (model24, Chaparral) array has been established on the campus of Oklahoma State University,which is shown in Figure 32. The first microphone (36.1344, -97.0819) was deployed onSeptember 2, 2016 and is located on the roof of the Fabrication Laboratory, which has noHVAC or other equipment on the roof. This microphone has been continuously recordingsince the deployment. Microphones #2 (36.1342, -97.0812) and #3 (36.1347, -97.0814)were simultaneously deployed and began recording data on January 11, 2017. While micro-phones 2 and 3 are deployed on the ground, the mounting structure for all the microphonesare identical and housed within a white painted acrylic dome. All of the microphones arewired to a single data acquisition system (USB-4432, NI) that is controlled via commer-cial software (LabView Sound and Vibration Measurement Suite, NI). The signals are timestamped and recorded in TDMS files.

Infrasonic measurements are plagued by wind noise, which is typically mitigatedwith the use of spatially averaging. For the current array, field and laboratory testing wasperformed to assess the performance of a wide variety of options. This is a critical stepbecause the windscreen configuration determines the measurement range for your array.Based on previous observations, tornadoes produce infrasound in the range of 1-10 Hz.Consequently, the current array was designed to sense signals between 0.1 and 100 Hz range.Example data from a laboratory test used to design the windscreen is provided in Figure33. Here testing was performed with and without a windscreen while a fan was blowingair over the sensor and simultaneously a subwoofer provided a reference signal. The figurecompares the broadband noise reduction (integration over frequency range of the Chaparralmicrophone) to the reduction in the source signal. Here it is apparent that at or below 50Hz there was virtually no reduction in the source signal while the windscreen provided 7.5dB reduction in noise. Then above 50 Hz the signal begins to be attenuated and eventually

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Figure 33: The noise reduction with use of a windscreen plotted versus the frequency of aknown source supplied with a subwoofer.

is suppressed beyond the broadband noise reduction. Note, this is not surprising since thewindscreens act as a low-pass filter and when the signal exceeds the filter range it willbe nearly completely attenuated while the broadband noise still includes performance atlower frequencies. The final windscreen configuration for the array uses four 50-ft soakerhoses attached to each microphone with each hose making a large loop out and back tothe microphone. The final configuration has a cutoff frequency at 120 Hz with microphones2 and 3 having nearly identical performance and microphone 1 exhibiting a slightly lowercutoff frequency.

To test the performance of the infrasonic array as well as processing algorithms, atrusted infrasonic source is required. The primary technical difficulty with infasonic sources isthat any souruce that is of reasonable size will be small compared to the wavelength (e.g. a 1Hz signal has a 340 m wavelength), which makes it an inefficient sound source. Consequently,most studies of infrasound propagation within the atmosphere have used explosives, but thisis not practical in populated areas. Consequently, a literature review of infrasonic sourceswas included in Threatt [30], which included discussion of subwoofers, spherical Helmoltzresonators, and rotary subwoofers. It was decided to pursue a gas-combustion torch, whichwas based on the preliminary work of Smith and Gabrialson [31]. Here the basic concept isderived from the fact that for a simple source the pressure fluctuation amplitude p(r) canbe expressed as

p(r) =πρ

r∀f 2

where ρ is the air density, r is the radial distance from the source, ∀ is the volume displace-ment amplitude, and f is the frequency of the signal. This states that a 1 Hz signal mustmove 400 times more volume of air than a 20 Hz (minimum of human hearing) signal to havean equally load signal. The gas-combustion torch achieves the large displacement volumeamplitude via rapidly expanding and contracting the surrounding air via a controlled pulsingof the flame. The first generation pulsed gas-combustion torch developed for CLOUD-MAP isshown in Figure 34 and was a simple trigger on a propane torch that a user would have toperiodically squeeze to generate the pulses. This provided a successful demonstration of theconcept, but better control was required to generate signals in the 10 Hz range.

Following the success of the initial testing, a senior capstone design team (JacobBertrand, Madison Likins, Jacob Nichols, and Maxwell Niemeyer) was formed to build acustom designed, electromechanical controlled pulsed gas-combustion torch. The system

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Figure 34: Testing with the first generation of the pulsed gas-combustion infrasonic source.

Figure 35: (top left) Schematic of the second generation pulsed gas-combustion infrasonicsystem. (top right) Picture of the pulsed gas-combustion torch in use. (bottom left) Pictureof the system being setup during the 2016 CLOUD-MAP Field Demonstration. (bottomright) Power spectrum generated when the system was pulsed at 5 Hz.

had a piezoelectric ignitor on a pilot line, which supplies a small but continuous supplyof propane to the burner. A separte line has a solenoid valve that is controlled wirelessly(HC-05, Bluetooth) with a microcontroller (Uno, Arduino). The system schematic, picturesof it in use, and the resulting power spectrum are shown Figure 35. It should be noted thatthe power spectrum shown is acquired using low quality infrasonic microphones (Infra20,Infiltec) that could barely detect signals from a subwoofer that was approximately 1 ft away.

Signal Processing Algorithms Several codes have already been developed forhandling and analyzing the received infrasonic signals, but there are still several tools thatneed to be developed to facilitate rapid inspection of signals. The required codes can bebroadly divided between data handling and signal processing. The data is recorded andsaved in TDMS files, a National Instrument data format. A batch processing code has beenwritten to convert these codes to Matlab format. The formatted data is then saved to aserver (efpl-fs.ceat.okstate.edu), which makes it readily available to team members as wellas external collaborators.

Signal processing codes still have significant work to be performed, but the current vi-sion is (1) inspect daily spectrograms to identify significant frequency content during specificperiods of time, (2) bandpass or low-pass signals based spectrogram observations, (3) convert

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filtered data back to time series to identify bearing angle for specific frequency content. Forthe current configuration it is possible to determine the bearing angle of the received signalin both the horizontal and (crudely) vertical planes. Based on discussion with Dr. Shamsat NASA Langley Research Center, it can be determined if the source is nominally cominghorizontally, vertically or 45 from the horizon. Most of the components needed to producethis processing sequence, the code for reversing the FFT and identifying the angle requiresfurther work.

Technical Goal 2 (TG2): Create a database of infrasonic events This is acritical component of Task 2-6 even though it was not explicitly stated in the original pro-posal. Identification of specific geophysical processes via infrasonic monitoring is primarilylimited due to the complete dearth of available infrasound data for such events (especiallystorm systems that produce tornadoes). The following tasks have been identified to achieveTG2:

• TG2.1: Establish a master database of potential infrasound producing events

• TG2.2: Detailed inspection of specific periods/events

• TG2.3: Generate and archive formal infrasonic event reports

Table 3: Overview of the quantity of events that have occurred since the initial microphonedeployment.

Within 50 miles Within 100 miles Within 200 milesTornados 0 5 31

Hail† 18 88 290Wind† 15 63 239

Earthquakes‡ 585 916 974

†Determined from Storm Prediction Center daily reports; ‡Determined from the IRIS earth-quake database.

Master event database This tasks is primarily focused on identifying data fromalternative sources that can be leveraged to determine if a given infrasonic signal correspondswith a specific event. The infrasound data alone is insufficient for any analysis since thereis still so little understood about the infrasonic signatures of various sources. Consequently,an internal database has been created of various potential infrasound producing events.Currently, the code pulls data from IRIS Earthquake Browser [32], OGS Earthquake archive[33], Storm Prediction Center Daily Reports [34], and the NOAA Storm Event Database[35]. We have also been using various websites for tracking airplane flight paths and wildfires (wildfiresnearme.wfmrda.com), but these sights are not currently setup for importingdirectly to the master source database we have created.

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Figure 36: (top) Spectra from a storm on October 4-5, 2016. Times shown are local (CST).(bottom) Radar images of the storm close to the corresponding spectra shown above.

To date, radar data has been only used qualitatively from maps available on variousweather related websites. However, now that the infrasonic array is operational there willbe an increase need in acquiring raw radar based data. This is an excellent opportunityfor collaboration within CLOUD-MAP . In fact, Prof. Matthew Van Den Broeke intends toinspect radar data during hailstorms and compare with infrasonic signatures.

Inspection of specific events Since the initial microphone was deployed, therehas been numerous events that have the potential to produce significant infrasonic signals.This includes 5 tornadoes that were within 100 miles of the infrasonic array. Table 3 providesan overview of the quantity and distance from the infrasonic array that a variety of eventsoccurred. Several examples follow showing infrasonic behavior of storms, wildfires, andearthquakes.

There has been a wide variety of storms that have passed close to the infrasonicarray since its deployment, but we will focus on two examples that illustrate variations instorm behavior. The first two occurred during the night of October 4, 2016. The stormwas heading southeast towards the infrasonic array. While the storm remained intense witha clear coherent structure the infrasound levels were significantly elevated above typicalbackground. It is interesting to note that the “peak storm” spectra shown in Figure 36 wasacquired when there was no rain present at the infrasonic array. As the storm weakened andbegan to break-up the infrasonic signal significantly dropped down. Note that by the timerain began at the array the infrasonic signal was nearly identical to what was observed thefollowing morning after all the storms had cleared. There are two key observations related tothis storm system, (i) rain hitting the dome of the infrasonic sensor has a negligible impacton measurement and (ii) this storm produced only broadband infrasonic data. It is yet tobe proven, but it is hypothesized that storms with straight line winds will not create aninfrasonic tone. A large sample size is required with wind data compared with the infrasonicsignals.

This storm is contrasted with one that occurred a couple days later on October6, 2016. This storm system formed in Oklahoma and then proceeded to head northeastwith the Southern tip of the storm passing over Stillwater. The spectra from this storm is

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Figure 37: (left) Radar data (gis.ncdc.noaa.gov) of a storm that hit on October 6, 2016 and(right) the corresponding spectra it produced.

Figure 38: (left) NASA satellite image of wildfires in Kansas, Oklahoma, and Texas taken onMarch 7, 2017. Red areas indicate heat and the orange star is the infrasonic array location.(right) Spectra from the 3-microphones during the wildfire. Dashed circle highlights peaksthat were present for several days during the wildfire that decreased as the fire got undercontrol.

distinctly different from the storm a few days earlier that had a clear front heading towardsthe infrasonic array. Now there are broad peaks that appear to harmonics of the fundamentalfrequency at approximately 8 to 9 Hz.

Between March 6-22, 2017 a very large wildfire burned on the border between Kansasand Oklahoma. In total 780,000 acres were burned with the fuel source primarily tall grass(2.5 ft) and brush (2 ft). Figure 38 shows a satellite image of the wildfires spread betweenKansas, Oklahoma, and Texas with the largest fires about 150 miles NNW of the infrasonicarray on the Kansas-Oklahoma border. During this period several distinct peaks were presentbetween 15 and 30 Hz that decreased while the fire weakened. This is also consistent withwork using low frequency acoustic waves to suppress fires [36], which reported frequencieson the same order of magnitude can extinguish fires.

Since deploying microphone #1, there has been over 840 earthquakes within 120 mileradius of the infrasonic array. The closest was approximately 1 mile from the array andthe largest was a M5.8 that occurred the morning after the first microphone was deployed(7:02 AM Sept 3, 2016). Figure 39 provides the layout between the earthquake and theinfrasonic array as well as the spectra from the 2 minutes while the earthquake was presentcompared against the spectra from 2 hours before or 1 hour after. This shows how strongthe infrasound was during the earthquake as well as that the infrasound returned to almostthe save value after the earthquake. A major issue with assessing infrasound data during an

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Figure 39: (left) Locations of the earthquake epicenter relative to the infrasonic array. (right)Spectra produced before, during, and after the earthquake occurred.

earthquake is the influence of structural vibrations on the sensor. We have plans to performa shaker test, and to add to the array an accelerometer and geophone to separate groundvibration from acoustic wave propagation.

While it was previously reported in the CLOUD-MAP 2016 Field Demonstration re-port, we will also note that there is some evidence that infrasonic signals appear up to 10minutes before the earthquake occurs as illustrated with one example in Figure 40. Withthe assumption that the vast majority of earthquakes in Oklahoma are from use of injectionwells, the working hypothesis for this observation is that the pulsing from the injection wellpump results in a periodic small amplitude, large diameter flexing of the earth surface. Thiswould make the ground act like a giant infrasonic speaker. A manuscript is currently beingwritten about these observations, and collaboration with the geography team members hopesto relate the surface topology with whether or not the precursor is present.

Generate and archive formal infrasonic event reports To date the infrasonicevents have been inspected in a rather haphazard fashion. While individual events requireunique inspection of available data, a standard method for presenting findings from events isrequired to make them more readily available to the research community. This is a relativelysimple task, but it has to be done with some forethought to make it with enough flexibilityto incorporate any available data source. We are exploring several commonly used methodsfor event recording. The long term vision is to have a website where these reports wouldbe posted so that any researcher could access specific events and request the correspondingdata.

Technical Goal 3 (TG3): Integrate infrasonic sensing with UAS This tech-nical goal is the integration of the infrasonic research with UAS technology. This couldbe achieved in two different manners; (i) an infrasonic sensor could be mounted on a UASand/or (ii) infrasonic measurements could be used to direct/control UAS to acquire high-value-information. The following tasks have been identified to achieve TG3:

• TG 3.1: Integrate an infrasonic sensor with UAS

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0

10

20

30

40

50

60

0 50 100 150 200

SPL

(dB

re 1

0 µP

a)

Frequency (Hz)

Background Before Earthquake (6/27 9:39 PM CDT) Earthquake 24119 (6/27 9:50 PM CDT)

Figure 40: Spectrum acquired before and during a 3.1 magnitude earthquake (OGS ID24119). The epicenter of this earthquake was 203 km from the infrasonic array.

• TG 3.2: Directing UAS to High-Value-Information

Infrasonic microphone integration with UAS Elbing and Gaeta [37] providesa broad discussion about the technical challenges associated with utilizing UAS for infrasonicsensing. The conclusions from this work were that (1) infrasound is an appealing data sourcefor monitoring tornado formation processes since they begin emitting up to 2 hours beforetornadogenesis and (2) integrating an infrasonic sensor with UAS is a daunting task. Thereare key questions and trade-offs that must be considered when designing such a system. Themost significant challenge of using acoustic sensors on aerial platform is overcoming the SNRproblem. Several techniques can be brought to bear on this challenge including designing theaircraft in such a way as to minimize self-noise (from propulsion and structure). Speciallydesigned windscreens for the sensors exposed to the airflow need to be integrated to increaseSNR. Also, active methods such as special signal processing techniques (spatial and adaptivefiltering, coherence techniques) can be used to increase the SNR.

A key note related to the windscreens is that with current commercially availabletechniques, this would force the aircraft to be larger than that currently considered withCLOUD-MAP project. However, Dr. Qamar Shams at NASA Langley Research Center(LaRC) has developed an infrasonic windscreen that is much smaller and more efficient.Funds from the Oklahoma NASA EPSCoR program were secured to send Dr. Elbing andArnesha Threatt to visit LaRC and Dr. Shams. This visit was extremely informative,both on discussions about processing schemes as well as sharing details about how he hasbeen able to develop a windscreen that is approximately 1 ft (Figure 41) instead of 50 ft.Since this meeting Corey Warmack has established a license agreement to begin selling thesewindscreens with the primary focus on using them for clear air turbulence detection, butthe long term goal is also develop capabilities for early detection of tornadoes. Dr. Elbing

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Figure 41: (left) Arnesha Threatt pictured with NASA designed infrasonic windscreen.(right) Close up image of the windscreen (orange ball) next to the portable case used fordata acquisition of the NASA infrasound microphones.

has been in regular communication with Mr. Wormack as he is establishing this startupbusiness. Ultimately, this is extremely beneficial for the CLOUD-MAP project because itprovides an easier path for the purchasing of these windscreens.

Directing UAS to High-Value-Information Locations A primary objectivefor this project is to create and demonstrate UAS capabilities needed to support UAS op-erating in extreme conditions, such as a tornado producing storm system. These stormsystems emit infrasound (acoustic signals below human hearing, <20 Hz) up to 2 hoursbefore tornadogenesis. Tornadogenesis is one of the most difficult processes to study sincecurrent approaches require sending teams to sites to get near-field measurements of such anevent. However, it is not uncommon to have only minutes to get to the desired location.Consequently, there is rarely measurements of the atmosphere immediately before formationof a tornado. However, due to an acoustic ceiling and weak atmospheric absorption, infra-sound can be detected from distances in excess of 300 miles. Thus infrasound could be usedfor long-range, passive monitoring and detection of tornadogenesis as well as directing UASresources to high-decision-value-information.

For the current task we have identify several next steps to advance each of thesetechnical goals over the next year:

• TG1: Establish a trusted ground-based infrasonic source and receiver

– Add an accelerometer and geophone to the array to separate ground/structuralvibrations from acoustic waves

– Have Dr. Qamar Shams visit OSU with his infrasonic array for a comparisonagainst the state-of-the-art system.

– Use the infrasonic source (pulsed gas-combustion torch) to test localization algo-rithms. First must perform a 1/r test with the source to assess the measurementrange and its behavior as a point source.

– Complete the bearing angle and daily inspection codes

• TG2: Create a database of infrasonic events

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– Continue to expand infrasonic source database including expanding to more eventtypes.

– Regular data analysis of specific infrasonic events.

– Develop a standardized event reporting template.

• TG3: Integrate the infrasonic sensing with UAS

– Explore collaboration with NASA and Corey Warmack to obtain the small wind-screens. If obtained, mount infrasonic microphone in the windscreen and attachto a car/pickup and drive the sensor.

Summary of Intellectual Merit The intellectual merit of this effort centers onthe advancement of our fundamental understanding of the connection between infrasonicemissions and the fluid mechanisms responsible for their production. More specifically, thiseffort aims to establish relationship associated with severe storm systems. This will beachieved using data acquired with UAS as well as enabling future direction of UAS to high-decision-value-information once a fundamental understanding of the infrasonic signals isestablished. Findings from this work have been presented in a conference paper and severaltechnical presentations. In addition, the first journal article is being prepared.

Summary of Broader Impacts The broader impact of this task includes studentexposure and involvement in research at all levels. There have been several graduate (MS,PhD) students and undergraduates that have worked on this project. This task has had6 female engineers (4 undergraduate, 1 MS, 1 PhD) and 4 under-represented students (3undergraduate, 1 MS). Undergraduate involvement has included a Wentz scholar, a CapstoneSenior Design Project, a semester long course project in Experimental Fluid Dynamics (MAE4273), and research assistants. The lead student, Arnesha Threatt, was a MS student thatgraduated this past year and was offered a job by Pratt & Whitney to work at the UnitedTechnology Research Center. It should also be noted, that the lead for this task (Dr. Elbing)will have 5 high-school students job-shadowing in his laboratory as part of Oklahoma StateUniversitys Upward Bound program, which is focused on low-income and/or first generationhigh school students. During a 6-week period, these students (junior or senior) interested inengineering will work with Dr. Elbing and his graduate students on this project.

Workforce Development Arnesha Threatt (M.S. Mechanical Engineering, OklahomaState University) was the lead student on this project until she graduated in December of2016. She led a team of primarily undergraduates in the design and deployment of theinfrasonic array. In addition, she processed the initial infrasonic signals acquired. She mademajor advancements in her technical and leadership skills, which is why she was in highdemand and offered an excellent job by Pratt & Whitney to work at the United TechnologyResearch Center.

Shannon Maher (B.S. Mechanical Engineering, Oklahoma State University) onlyworked one semester on the project, but was instrumental in the design and deploymentof the platforms used to mount the infrasonic microphones for the infrasonic array. Shegraduated in December of 2016 and is currently attending Clemson University studyingAutomotive Engineering.

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Madison Likins (B.S. Mechanical Engineering, Oklahoma State University) studiedattenuation of infrasonic signals computationally and experimentally as well as led the Cap-stone Senior Design Project team on the design of the pulsed propane torch. She was aWentz Research Scholar and currently a MS student at Texas A&M.

Jalen Golphin (B.S. Mechanical Engineering, Oklahoma State University) is an un-dergraduate that began working with Arnesha basically pitching in an extra hand. How-ever, with Arnesha and Shannon graduating he has been given more opportunities includ-ing helping Dr. Elbing debug some issues with the infrasonic array and presenting at theAIAA/ASME Oklahoma Symposium on April 15, 2017.

Alexis Vance (B.S. Chemical Engineering, Oklahoma State University) is an AllenScholar who began working on the team as a freshman. She was initially tasked with figuringout how to access data from the IRIS TA database, a national database that has begunrecording infrasound in 2011. She accomplished that and did the work identifying/trackingthe infrasound history of the wildfire on the Oklahoma/Kansas border.

Jared Hartzler (B.S. Mechanical Engineering, Oklahoma State University) joined theteam in Spring 2017 and was immediately tasked with investigating infrasonic signaturesfrom airplanes. To date, he has become familiar with accessing the raw data and processingthe spectra. He was able to show that there is a minor increase in the spectra correspondingto the exact time when a plane flies over, which was confirmed by obtaining exact flightpaths of airplanes.

Chris Petrin (M.S. Mechanical Engineering, Oklahoma State University) to date hehas worked to get familiar with the infrasonic project after the departure of Arnesha Threatt.He will finish his MS degree during Summer 2017, and then will take the lead role on thethe infrasound work. He has also begun exploring possible methods for leveraging the IRISTA infrasound network to identify tornado signatures.

Spring 2016 Senior Capstone Design Team (Jacob Bertrand, Jacob Nichols, MaxwellNiemeyer, and Madison Likins) learned about infrasonics, combustion, flow control, andinstrumentation.

Fall 2015 Experimental Fluid Dynamics Course Project (Madison Likins, Brad Mc-Neely, Kah Hooi Quah, McClain Robinson) designed, executed, and analyzed an experimentfocused on understanding acoustic attenuation from an infrasonic source.

Yasaman Farsiani and Shahrouz Mohagheghian (PhD Mechanical Engineering, Okla-homa State University) both assisted with the preparations for the 2016 CLOUD-MAP fielddemonstration.

This task has broadened participation and diversity in STEM with 5 females takingkey roles (including leadership) on this task, having at least 2 under-represented-minoritiesheavily involved in the research (including leadership), and engaging undergraduate students(11 total) with impactful research opportunities.

Leveraged Opportunities and Activities NIST Disaster Resilience ResearchGrant Proposal Elbing and Frazier have submitted a proposal to quantify tornado strengthvia long-range passive monitoring of infrasound. This is a 3-year proposal that provides 5total summer month of coverage for the PIs. In addition, it includes support for at least 2graduate students and 2 additional undergraduate research assistants.

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Figure 42: (top) Picture from Dr. Shams field testing station at NASA Langley ResearchCenter. (bottom left) Picture of Arnesha Threatt and Dr. Elbing touring LaRC center.(bottom right) Picture of Drs. Shams and Elbing visiting infrasonic measurement sites atLaRC.

NASA Oklahoma EPSCoR Travel Grant A NASA Oklahoma EPSCoR TravelGrant funded Dr. Brian Elbing and M.S. student Arnesha Threatt from Oklahoma StateUniversity to visit NASA Langley Research Center. They met with Dr. Qamar Shamsto discuss potential collaboration on atmospheric sensing of infrasound, acoustic waves atfrequencies below human hearing. In addition, Dr. Shams demonstrated the state-of-the-artin infrasonic sensing, data acquisition, and signal processing. Insights from these visit willdirectly impact the infrasonic array currently being installed at Oklahoma State Universityas well as promote future collaboration. Several concepts for future research efforts have beenidentified and plans are to have Dr. Shams visit OSU for 1-week to acquire measurements inOklahoma from earthquakes, jet engines, and other potential sources. These measurementswill be used as preliminary data in support of future proposals. In addition, this was atremendous opportunity to expose the graduate student to NASA research facilities.

Steve Piltz (Meteorologist in Charge at U.S. National Weather Service) has an interestin infrasound and recorded two tornadoes from a low-cost infrasonic microphone during twomicrophones. He has shared the data files with our team, which we will analyze. If thereis interesting frequency content, we will request data from the IRIS Transportable Array,which had an infrasonic microphone operating at the same time from Leonard, OK.

Ben Hemingway, a PhD student in Geography working with Dr. Frazier, has crossreferenced tornado records and the IRIS TA database found 118,000 combination of oper-ating IRIS TA infrasonic microphones during a tornado outbreak in 2011 (though this isindependent of the distance from the microphone to the tornado). However, the large quan-

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tity of sensor makes it possible to locate signals that are only slightly above the noise floor.Frazer, Elbing, and Hemingway intend to explore these possibilities to broaden the samplesof tornadoes infrasonic signatures.

2.2.7 Task 2-7: Multi-Scale GIS Correlation

Research Accomplishments The technical goals of this task are to (1) determine optimallocations for UAS siting and deployment using geospatial analytics, statistical analyses, andgeographic information systems (GIS), (2) rectify spatial and temporal scale disparities thatoccur due to integration of various types of data collected from multiple platforms, (3)develop methods for predicting fine-scale climate variables from coarse-scale data using UAS-acquired data as an intermediary, and (4) collect, process, and analyze data from post-eventlandscapes to evaluate forecasting decisions.

A statistical approach is used to compute the experimental variogram from sampledata through

γ(h) =1

2n(h)Σ

n(h)i=1 [z(xi)− z(xi + h)]2

where z(xi) is the observed value of z at location xi separated by distance h, and n is thenumber of sample pairs. Values of the semivariance, γ(h), plotted against h result in whatis called the variogram. From the variogram, the distance at which spatial dependence ofthe regionalized variable is no longer present can be determined through analysis of threeproperties: the range, sill, and nugget (Figure 1). The upper bound of semivariance values isreferred to as the sill, which occurs when the measured values between samples are invariantat larger lag distances, and the curve of the variogram levels off. The lag distance at whichthe sill occurs is known as the range, so called because this is the range at which the measuredattributes have spatial dependency. In certain instances, the variogram model may not passthrough the origin but instead intersect the ordinate at (h) greater than zero. While it isreasonable to expect that the semivariance would be zero at a lag distance of zero, there stillis uncertainty in the data, and this phenomenon is known as the nugget effect. Preliminaryresults are shown in Figure 43.

These results will help answer the research questions: At what spatial scales/resolutionsis atmospheric sampling most efficient? How does the diurnal cycle of the ABL effect thisscale? How does optimal sampling scale change between variables sampled? Can fittedmodel be used to accurately predict values at non-sampled locations?

Progress on the first technical goal included submission of three manuscripts to peerreviewed journals.

• The first effort outlines the current progress and ongoing challenges of integratingUAS into Geographic Information Science (GIScience). It is a collaboration of fourCLOUD-MAP personnel (Frazier, Crick, Martin, Weaver) and one graduate student(Hemingway) and has been accepted into the GI Forum journal and conference.

• The second is an invited contribution to an edited volume in the journal Atmosphere(Guest editor: Marcelo Guzman) on optimal sampling scales for atmospheric measure-ments via UAS. The lead author is a graduate student in Geography (Hemingway),

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Figure 43: Sample semivariogram data of temperature and humidity from the CLOUD-MAP 2016 field campaign.

and the manuscript includes three CLOUD-MAP personnel (Frazier, Elbing, Jacob).Manuscript is expected to be submitted by May 1, 2017.

• The third manuscript investigates land surface heterogeneity impacts on tornado for-mation in two tornado-prone regions: Tornado Alley and Dixie Alley. The study usesa combination of GIS analyses, geospatial analytics, and statistics to determine differ-ences of tornado formation likelihoods to help site UAS deployment. Frazier is the leadauthor, and two graduate students trained on the project are co-authors (Hemingwayand Brasher).

Workforce Development Trainee activities are ongoing and include GIS, remote sens-ing, and programming training for two Ph.D. and one undergraduate student in the De-partment of Geography at Oklahoma State University. Manuscript writing and professionaldevelopment activities (e.g., conference presentations) for both graduate students. Outreachactivities included participation at a local elementary schools STEM fair where Grade 2-5students learned about scale (resolution) and different platforms for collecting imagery (e.g.,satellites vs. UAS). A similar activity is being planned for students in grades 8-12 as partof National Lab Day.

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Leveraged Opportunities and Activities Collaborations related to this task (and othertasks) have been leveraged for (1) an NSF National Research Traineeship (NRT) programproposal submitted by UK, (2) a National Institute for Standards and Technology (NIST)grant proposal submitted by PI Elbing, and (3) an NSF Coupled Natural Human Systemsgrant proposal submitted by a non-CLOUD-MAP collaborator (Dr. Jacqueline Vadjunec).

Dr. Frazier, along with a non-CLOUD-MAP collaborator (Dr. Adam Mathews),contributed an invited symposium talk on UAS at the University Consortium for GeographicInformation Science annual symposium. Stemming from this activity, Mathews and Frazierwere invited to contribute an entry on UAS to the online GIS&T Body of Knowledge. Theentry is currently going through peer-review.

Dr. Frazier presented two conference talks related to the technical research progress:(1) the UCGIS annual symposium mentioned above, and (2) Southwest Region of the Amer-ican Association of Geographers (SWAAG) Annual meeting. Frazier served as an invitedpanelist during two sessions at the American Association of Geographers Annual meeting on(1) UAS in the Geography Curriculum and Classroom, and (2) Conceptualizing and Integrat-ing UAS into GIScience and GIS Progress on the second goal also includes ongoing efforts fora related NSF grant (SBE #1561021) to investigate spatial scaling between various types ofareal data (e.g., satellite imagery) using different landscape paradigms (i.e., patch-based vs.continuous). Efforts have been targeted at training a graduate and undergraduate researchassistant in GIS, remote sensing, and statistical techniques.

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2.3 Objective 3

Develop and demonstrate coordinated control and collaboration between au-tonomous air vehicles. The range, endurance and communication capabilities of SUAS isoften less than desired for some of the applications described. By collaborating with mobileground stations, the SUAS, both operating solo and in swarms, can relay communication,offload heavy computation, and potentially land to be refueled by the GCS.

Robust coordinated control of multiple SUAS is needed for routine operations in theNAS. There is a need to optimize control, coordination and communication and examinethe resulting impact that these systems have for characterization of the data. The overarching goal of this objective is to explore a multi-platform approach for observing neededmesoscale atmospheric and meteorological observations with UAS and gain experience indeploying the platforms, collecting atmospheric measurements, and coordinating operationsamong different UAS teams.

2.3.1 Task 3-1: Cooperative Control of Small UAS Formations For DistributedMeasurement

This task focuses on cooperative control of UAS for distributed sensing applications. Forexample, a coordinated group of air vehicles could be used in forest fire scenarios to mea-sure wind velocities, which can then be used to predict how the fire will move. Similarly,coordinated air vehicles could provide distributed measurements for predicting airborne pol-lutant dispersion in a rapidly evolving emergency situation. In the agricultural industry, acoordinated group of air vehicles could conduct crop surveys on large farms.

In all of these applications, it is often desirable to have the vehicles fly in equallyspaced formations. For example, vehicles could travel together in a flock, where vehiclesmaintain desired separation distances, avoid collisions, and match velocities. Swarming isanother valuable control approach for distributed sensing. In this case, a swarm of vehiclescould provide measurement data that is approximately uniform in time and space. Uniformlydistributed data is valuable for a variety of atmospheric measurements, for example, windvelocity measurements for reconstructing an atmospheric flow field.

For some measurement applications, it is valuable to have formations that are ca-pable of reconfiguring based on real-time sensor measurements. For example, considered aformation of vehicles measuring pollutant concentrations. Collectively, these vehicles couldbe used to estimate concentration gradients in real time. Then, the entire formation couldbe manipulated based on the real-time gradient estimates.

This task aims to develop, analyze, and demonstrate new methods of cooperativecontrol for UAS formations—methods for flocking and swarming as well as methods forformations reconfiguration. We will develop these methods through a combination of math-ematical analysis, numerical simulation, and experimentation.

Research Accomplishments Discrete-Time Flocking. We developed a new discrete-time formation (DTF) control method [1–3] for coordinated control of multi-UAV systems.Most existing formation-control approaches are continuous-time formation (CTF) algorithmsand do not account for sample-data effects, which can be significant for applications such as

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formation flying, where communication and sensing constraints limit the speed with relativeposition data can be obtained. In [3], we show that existing CTF methods do not perform wellwith slow-sample-rate feedback data. These algorithms can cause undesirable oscillations inthe inter-vehicle distance and instabilities in the formation.

To demonstrate the advantage of DTF relative to CTF, we consider a numericalsimulation with 2 rotorcraft flying at constant altitude, where the objective is to travel ina formation with a 1-m separation distance. In this example, the feedback data is providedat 20 Hz (which is faster than in the indoor experiments described below). We comparea CTF controller with our DTF controller. Figure 44 shows that CTF causes steady-stateoscillations in the inter-vehicle distance. Thus, the rotorcraft do not achieve the desiredformation. In contrast, DTF forces the vehicles to the desired 1-m separation. AlthoughFigure 44 shows only 15 s of motion, the simulation was conducted for over 1,000 s, andthe inter-vehicle distance with CTF continues to oscillate and does not converge to 1 m.These undesirable oscillations cause persistent corrective control forces, which can expendthe UAV’s energy supplies. In addition, these unpredictable oscillations get worse with morevehicles and could result in collisions with other vehicles or with cattle.

0 5 10 15

0.5

1

1.5

Time (s)

Inter-vehicle

distance

(m) CTF control DTF control

10 150.975

1

1.025

Figure 44: CTF causes the inter-vehicle distanceto oscillate, whereas DTF forces vehicles to a 1-mformation.

To implement DTF, each UAV’sonboard controller requires a measure-ment of the relative positions and veloc-ities of nearby vehicles. DTF uses thisinformation to achieve: i) formation co-hesion, and ii) collision avoidance. For-mation cohesion causes UAVs that aretoo far away from one another to be at-tracted together, while collision avoid-ance causes UAVs that are too close to-gether to be repelled from one another.

We recently implemented DTF ina preliminary indoor experiment with 3rotorcraft. To implement DTF, each ve-hicle requires a measurement of the othervehicles’ relative positions and velocities.We used a motion capture system (withsix 1.3 mega-pixel cameras) to obtain real-time position and velocity estimates, which arethen transmitted to each rotorcraft’s onboard DTF controller. The sample rate for this exper-iment was only 10 Hz. As shown in Figure 45, the 3 rotorcraft form a triangle formation, andthis formation travels in a desired circular trajectory. A video of this indoor flight experimentis available at: https://www.dropbox.com/s/whdzm8ysa7q3zg4/FlockingUAVs.mp4?dl=0.Our DTF research is published in [3], under review in [2], and under development in [1].

Outdoor Flocking With Vision Sensing. We have also made progress towardproof-of-concept outdoor flocking experiments using DTF control. These experiments willuse a fleet of 5 custom rotorcraft to perform flocking maneuvers, where the vehicles arein close formation (i.e., less than 0.5-m separation). Figure 46 shows a prototype of therotorcraft, which has 0.25-m diameter (without propellers). The proposed outdoor experi-ments represent a significant step forward from our preliminary indoor experiments because

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Figure 45: Indoor DTF experiments. The red circles highlight the UAVs. From left to right:a UAV is released to join the formation (left); the UAV autonomously joins the formation(center left and center right); the 3 UAVs maintain a triangle formation and follow a circularleader trajectory (right).

a motion-capture system cannot be used outdoors in an unprepared environment to ob-tain feedback data. Instead, the outdoor experiments will rely on a combination of GPSand vision sensing. GPS alone is not accurate enough for close-formation flocking. Thus,each UAV will be equipped with several inexpensive off-the-shelf IR stereo cameras (i.e.,Intel’s RealSense) to supplement and improve the GPS-based estimates of relative positionsand velocities of the UAVs. Figure 46 (bottom) shows the RealSense camera, which hasan exceptionally small form factor (i.e., 120 mm by 20 mm by 8.5 mm). Although thisproject is a proof-of-concept experiment, the fundamental techniques that we will developand demonstrate apply to wide variety of airborne sensing applications.

Figure 46: Custom 0.25-m diameterrotorcraft (top), and Intel RealSensecamera (bottom).

Flocking and Destination Seeking. Wecompleted work on a new flocking-and-destination-seeking control approach that allows vehicles not onlyto flock but also to leave the flock as they approacha desired destination. Our approach is not a leader-follower method and requires limited real-time infor-mation sharing. This decentralized method is bene-ficial for large formations. This work is published in[4].

Fixed-Wing Flight Experiments. Weare developing a relative-position formation-controlmethod for fixed-wing UAVs. By the fall 2017, weaim to demonstrate distributed measurement using agroup of fixed-wing UAVs flying in formation. Thiswork is a collaboration with S. Bailey. We plan to im-plement a relative-position formation-control methodfor a group of 3 fixed-wing UAVs (specifically, 3BLUECAT V UAVs), and use the formation to ob-tain distributed measurements of atmospheric turbulence.

We also completed our work on inner-loop control of fixed-wing UAVs in turbulentwind conditions. We performed single-vehicle outdoor flight experiments using a new altitudecontrol approach, which will be beneficial for inner-loop control with multiple air vehicles.This work is published in [5].

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Workforce Development During this reporting period, we continued our work with Na-tional Air & Space Education Institute on the Wing Design Competition, which is a first-of-its-kind engineering competition for high-school students. The 2016 Wing Design Competi-tion had approximately 200 high school participants. Publications include 5 conference andjournal publications by graduate students. [38, 39, 40, 41, 3].

2.3.2 Task 3-2: Integration of Spatially Distributed Data from Moving SensorPlatforms

Summary of Objectives Broader Impacts: Obtaining time-dependent spatial distribu-tion of a quantity (x) can be extremely valuable for understanding the turbulent transport,dissipation, and diffusion processes of a quantity (φ, which can be mass, momentum or en-ergy) and the role that the atmospheric properties play in those processes. Predicting thetransport of heat, momentum, water vapor and pollutants due to this turbulence is a crucialpart of many scientific disciplines such as meteorology, climatology, wind engineering andenvironmental science. Thus, increased understanding in this area will lead to improvementsin many diverse and socially important scientific tasks including: modeling weather and cli-mate patterns; prediction of structural loading; energy recovery in wind farms; or trackingpollutants in the atmosphere.

Intellectual Merit: Fixed-wing moving SUAS offer several advantages over smallrotorcraft, including the ability to traverse a larger space during the 30 minute periods ofquasi-statistical-stationarity. Most importantly for turbulence measurements, the sensitivityof pressure-based velocity probes increase with the square of velocity, making fixed-wingaircraft the most desirable option for low-cost wind measurement. However, measurementsmade using fixed-wing UAVs are neither fixed-point measurements, nor measurements of aspatial field, as both the position of the UAV and the flow field are time dependent. Thus,the measured quantity is φ(x(t), t). Hence, the objective of this task is to identify approacheswhich allow the fixed wing UAV to obtain scientifically relevant, spatially distributed data. Itis hoped that by using a unique combination of experimental tools and analysis techniques,the use of SUAS will fill a void in traditional atmospheric boundary layer turbulence re-search capabilities and contribute new understanding atmospheric boundary layer structure,organization and transport processes.

Progress to Date Year 1 has proven successful in integrating 5-hole probe sensors, dataacquisition system, airfame and autopilot with data reduction schemes. These schemes whichsubtract the 6 degree of freedom SUAS position and velocity data acquired by the autopilotand on-board inertial sensors in the inertial frame of reference (ground speed) from themeasured 5-hole probe velocity to obtain a local wind velocity vector. Procedures and initialresults were published in an invited paper (Witte et al. 2016).

Two of these airframes were operated simultaneously in the 2016 CLOUDMAP mea-surement campaign, with each flying different flight profiles in order to test these schemes andobtain a complete data set describing the turbulence evolution during boundary layer tran-sition. 25 flights were flown with only two minor mishaps which could be easily and quicklyaddressed in the field. Additional problems were caused by instrumentation issues early onthe first flight day and on the third flight day, but a wealth of data was obtained. This data

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has been processed and verification checks have so far been successful. Data are currentlybeing examined to verify the need and utility of applying Taylor’s frozen flow approximationto account for the flow evolution during a flight, the first step in producing a spatial fieldfrom the data. So far, successful spatial statistics have been obtained from applying thisassumption and results will be presented this summer at the 10th Turbulence and ShearFlow Phenomena Conference (TSFP-10) and in at least two planned journal submissions.

Work is now ongoing in preparation for the Summer 2017 flight campaign. Projecttasks are looking at:

1. Flying 3 UAVs in formation for obtaining spatial statistics

2. Developing a rotorcraft specifically for obtaining boundary layer profiles, freeing up afixed-wing aircraft for the measurement of spatial statistics

3. Incorporating fast-response temperature probes onto the UAVs for measuring thermaltransport, eddy fluxes

4. Airframe improvements: strengthening airframes, improving radio range, improvingaircraft endurance, and improving internal system layouts

5. Improving instrumentation tower used to provide a reference and comparison point

6. Introducing autonomous takeoffs and landings for each flight, to minimize the risk ofpilot error

7. Developing flight profiles which will improve data quality

Workforce Development One graduate student (male) is being co-advised with Dr. J.Hoagg developing the formation flying technology which is planned for use in Summer 2017which will be applied to Task 3-2.

We also recently graduated 1 graduate student (male) who was actively working onTask 3-2. This student was responsible for planning, executing and analyzing test flightdata and was learning project management, project planning and data analysis skills. Anew student (male) has been recruited from the existing team of undergraduate studentsto replace him and will be transitioning to graduate studies in Fall 2017. This student willalso be responsible for managing by 7 undergraduate students (6 male, 2 female) supportingthe project. These undergraduates are largely responsible for maintaining the hardwareand SUAS involved in this project and are learning skills in problem solving, engineeringdesign, sensing and autonomy. In addition, a high school student (male) is conductinga research placement in which he is learning programming, robotics, and sensing systemsputting together a sensor package for use on the SUAS.

Leverages Opportunities and Activities This project is closely related to NSF CA-REER related research which has resulted in significantly greater productivity and efficiencysolving technical challenges related to both projects. We also participated in University ofKentucky’s Engineering Day (E-Day) in February of 2017 by hosting an open house in theUAV lab. This is an open house in which an estimated 6,000-9,000 kids (of all ages) tour

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Figure 47: Task penalties in manual vs. autonomous assistance modes across 34 test subjects.p < 0.05 in both instances.

the college of engineering. Students working on this NSF-funded research presented theirproject work to visitors, and gave visitors an opportunity to fly small quad-copters in anobstacle course contained within the lab’s CNC enclosure.

In addition, this year we will be presenting modules for the Women in EngineeringSummer Workshop Series targeting the recruitment of female high school students in engi-neering. This is just the second year that Mechanical Engineering will contribute a workshopfor this program, and we will hold a glider design contest which will incorporate instructionin fluid mechanics fundamentals and wind tunnel testing into an optimization problem.

2.3.3 Task 3-3: Heterogenous Robot Control

Research Accomplishments Cognitive capacityIn a scenario where multiple heterogenous aerial systems are deployed for weather

tracking and data collection, it will be useful to integrate human advice into positioning androute planning, but only to the extent that the human operators are reliably able to integratelarge amounts of information and act in the stressful context of a fast-moving and potentiallydangerous weather phenomenon. Research into this question resulted in the publication ofthe manuscript, “Learning to assess the cognitive capacity of human partners” [42].

In this work, a team of robots performed two tasks, one of which they understoodwell enough to compute their own performance metrics, and the other which required humaninput to provide full context into their deployment. During the first task, the robots collectedecologically valid measurements of human behavior as they guided the robots through thetask. Because the task success metric is known to the robot, it was able to learn an associa-tive model between task success and human behavior, using its own success estimations assupervised feedback for the learning algorithm. The robots then used this learned model toestimate the human operator’s cognitive load in a different task for which the robot had noindependent success metric.

Figure 47 shows the error rates (lower is better) for robots engaged in the secondtask which were completely under human control vs. robots which were sensitive to this

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Figure 48: A team of heterogenous UAS with meteorological sensors share a single factorgraph representation of their shared intentions.

cognitive load estimate and acted autonomously in situations where the human controllerwas overwhelmed. This work has obvious safety and performance implications for situationswhere teams of UAS are managed by human operators in stressful conditions such as anactive storm situation.

Distributed control as belief propagation in factor graphs We have been develop-ing a distributed algorithm based on loopy belief propagation within factor graphs that canbe effectively used for coordinating within a team of heterogenous robots, while allowingoccasional human input to affect the robots’ task allocation. In an atmospheric physicsdeployment, a large number of different UAS with different capabilities and sensors may beemployed. For example, the system may incorporate large, fixed-wing UAS with a variety ofsensors, smaller and less expensive rotary-wing UAS, disposable single-use sensors deployedwith parachutes, and many airframe and sensor variations within these categories. In addi-tion, as the project progresses, new varieties of agents will have to be added after the systemis already in place. This work is currently under review for publication [43].

Each robot maintains its beliefs and intentions within a portion of a factor graph,and these portions are connected to form a full graph which encompasses every agent, asillustrated in Figure 48. Edges between robots indicate physical communication channels(implemented using ad hoc wireless networking in the current implementation). A factorgraph is a bipartite graph divided into variables, which represent world state, and factors,which govern the relationships between variables. Beliefs and intentions are communicatedusing the sum-product algorithm in a message passing framework:

µv→a(xv) =1

Z

∏A∈Nv\a

µA→v(xv) (1)

Equation 1 defines the message µ passed from a variable to a factor, which consistsof the normalized product of all of the messages received from the variable’s neighboringfactors, except for the recipient factor.

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Figure 49: Joint distribution of intention by simulated UAVs after factor graph convergence.

µa→v(xa) =∑

x′a:x′

v=xv

φ(x′a)∏

V ∈Na\v

µV→a(x′v) (2)

Equation 2 shows the message µ passed from a factor to a variable, which is the factorfunction applied to the messages from all other connected variable nodes, marginalized overall of the variables except the recipient’s. These messages are passed asynchronously throughlinks that are formed and dropped as the topology of the robot deployment changes, usingloopy belief propagation.

To this point, we have implemented this control architecture in both simulated andreal robots, and conducted experiments on their performance. Figure 49 shows a simplesimulated UAS with three robots, as they communicate using the aforementioned messagepassing in order to agree on joint navigation goals. Each robot incorporates messages fromits own sensors and from its neighboring robots to establish an intentional contour overthe robot’s action space (in this case, simply a three-dimensional navigation task). Aftermessages are propagated, the robots develop a consensus which is acted upon through simplelocal gradient descent.

Workforce Development SM al Mahi (PhD student) is the primary contributor to thiswork. He has been working on the factor graph modeling and algorithm development.

James Kostas (postbaccalaureate student) has been implementing and testing ourapproach on real robots, both ground-based and UAS.

Matthew Atkins (undergraduate student) developed the original cognitive thresholdresearch tasks.

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Figure 50: System architecture.

Leverages Opportunities and Activities This work has led to two other proposals,including a cyberphysical systems project to evaluate development and testing methodologiesin the context of UAS for atmospheric physics, and the establishment of an REU site forrobotics, machine learning and data analysis.

2.3.4 Task 3-4: Multi-agent UAS Simulators

Research Accomplishments Simulated environments have long been vital in roboticsresearch; it is almost always beneficial to perform algorithmic research and proof-of-conceptimplementation in simulation before making use of delicate, expensive, and time-consumingreal-world robotics systems. Such a simulation capability is especially important in thecontext of problems related to multi-agent manned or unmanned aerial systems (UAS):aircraft are expensive to obtain, complicated to deploy, and especially vulnerable to accidentsand faulty control systems. However, none of the general-purpose robot simulation tools incurrent use handle the aviation environment particularly well, where problems of weather,communication, visibility and terrain are critical.

The core elements of the simulator are illustrated in Figure 50. The system integrateswith the Robot Operating System (ROS) environment, accepting the same standard move-ment commands used in any ROS-based simulator or actual robot system. These, in turn,are transformed using flight models for specific airframes into control surface commandswithin an open source flight simulation physics engine. The system also publishes sensorinformation on standard ROS topics for consumption by the robotic controller. In addition,the system supports injecting weather, atmospheric and other phenomena for detection bythe robots’ simulated sensors.

Figure 51 illustrates the current implementation state. The simulated quadcopterrobot operates in the vicinity of an injected atmospheric phenomenon, in this case, a smokeplume. Equipped with an appropriate simulated sensor, the aircraft is able to take measure-ments of the extent, concentration and dispersal behavior of the smoke.

Workforce Development Kyungho Nam (PhD student) is the primary contributor tothis work. He has been working on all aspects of design, implementation and development.

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Figure 51: Simulated UAS investigating injected smoke phenomenon.

2.3.5 Task 3-5: Robust Conformal Antennas for UAS Communication

Research Accomplishments Work on Task 3-5 has focused on providing an antennathat can be conformally attached to plastic, fiberglass, or styrofoam SUAS of varying shapesto enable a robust data link. SUAS need to maintain a constant communication link withthe ground station, as they are remotely controlled. This constant data link is difficult tomaintain as the SUAS maneuvers as antennas do not send a signal perfectly in all directionsat once [44, 45, 46, 47, 48]. Additionally, SUAS are extremely size-constrained form factorsfor antennas placing fundamental limits on the antenna performance [49, 50, 51]. Antennadesigners have worked to approximate a uniform power distribution from an antenna tomaintain signal link regardless of orientation, and have generated several designs. However,the antenna designs for uniform power were not designed to handle arbitrary conformality toan object of arbitrary material. Additionally, the uniform power designs were not miniatur-ized or multibanded to handle the multitude of systems that are needed for data collectionon SUAS for this project.

The overall goal for the antennas being developed under the scope of this project isto maximize the consistency of the data link between a SUAS and the ground station. Thespecific design goals that contribute to improved performance are creating as isotropic of aradiation pattern as possible, minimizing the antennas physical size with minimal degrada-tion so that it may conform under significant form factor constraints, and minimizing theeffect of the SUAS body structure as backing on the performance of a generalized conformalantenna solution.

Many prospective antenna types were evaluated as a part of this research for quasi-isotropic radiation on SUAS. However, two antenna types showed the most promise and arereported here: a circularly-curved folded dipole antenna and an inductor-loaded bent dipoleantenna. The circularly-curved folded dipole antenna was shown to have a more robustradiation pattern when conformed to objects over the inductor-loaded bent dipole antennaand will be the direction of future work. The skew planar antenna and electrically short

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Figure 52: Depiction of angle of curvature at θ = 120 degrees (left) and fully curved antennawith an angle of curvature of θ = curveAng = 180 degrees (right).

rubber duck monopole antenna serve as the baseline design for comparison to the presentwork as they are commonly used in SUAS.

Circularly-Curved Folded Dipole Antenna An antenna that is conformal toup to at least a full half-cylindrical degree of bending in free space without significant degra-dation in antenna performance was developed in simulation and an initial set of prototypeswere constructed. The antenna geometry, depicted in Figure 52, is nominally a half-wavefolded dipole that is comprised of two semi-circular sections connected by a small port gap.The corresponding degree of curvature of 180 degrees for the antenna geometry, is definedas the angle formed by the arc from the edge of the port to the center of the short edge ofthe antenna. The antenna geometry was inspired by the MLA-M [52] antenna which is com-monly seen in amateur radio and other low-frequency applications. The MLA-M antennahas never been analyzed for its method of operation prior to this research. Through ouranalysis, we have adapted the antenna for SUAS application.

The simulated results for the antenna developed at OU for SUAS applications areshown in Figure 53. The radiation patterns shown in Figure 53 show that the antenna pro-duces a power pattern that is quasi-isotropic - a 1.26dBi radiation maximum and nulls thatnever drop below 3dB of its maximum. This is a significant improvement from normal dipoleantennas that commonly have true nulls smaller than -30dB along the radiating element axis.Dipole antennas are what are commonly used for SUAS applications currently. The antennaalso occupies a form factor approximately 3 times smaller than that of traditional lineardipole antenna.

An equivalent linear folded dipole has an impedance of roughly 300Ω and the gen-erated structure has an impedance of almost exactly 50Ω in simulation, so the effects ofprogressive curvature levels towards the final form were investigated to quantify correlationswith curvature and the source of the favorable antenna impedance and radiation pattern.Two key parameters correlated with changing curvature, quantified in Table 4, are the inputimpedance and radiation efficiency at the design frequency in free space and on a 10milRogers 5880 substrate. For both cases, the input impedance and radiation efficiencies pro-gressively decrease with increasing angle of curvature. Examination of S11 versus curvature,depicted in Figure 54, shows that the bandwidth is nominally small and that the full circulargeometry is required to achieve the best impedance match. The radiation pattern transition

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Figure 53: Antenna far-field pattern.

from virtually a linear folded dipole at 1 degree of curvature to the final form at 180 degreesis depicted in Figure 55. The transition from a true null at ±90 degrees in the E-plane to asecond full plane of near isotropic radiation, without significantly compromising the H-plane,emphasizes the vast improvement over traditional dipole geometries.

Table 4: Impedence and radiation efficiency trends.

The favorable radiation pattern is best explained through an investigation of char-acteristic mode theory. In a half wavelength linear folded dipole, the first current modeis almost uniquely engaged, causing the annulus radiation pattern characteristic of basichalf-wave dipole antennas. The folded dipole in the circular configuration, however, has acomposition of at least 3 modes with considerable modal significance which are quantifiedthrough preliminary testing with FEKO in Table 5. The first and second modes are primarilydominant and, due to current maxima occurring on orthogonally directed antenna segments,have near orthogonal maxima and nulls as depicted in Figure 56. The weighted impact ofthese modes based upon their modal significance into the net radiation pattern depicted inFigure 53, which approaches isotropic despite the reduced electrical size of the antenna.

A great deal of consideration must also be given to how the antenna performs when

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Figure 54: Reflection coefficient for discrete curvatures for a 915MHz design where curveAngdenotes the degree of an arc that one half of the antenna covers.

Figure 55: E and H Plane radiation patterns for the 915MHz curved folded dipole antenna.

Table 5: Modal significance.

Figure 56: First three current modes and corresponding normalized radiation patterns.

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Figure 57: Depiction of the conformal angle at φ = 60 degrees (left) and the half-cylindricalbending geometry with a conformal angle φ = bendAng = 90 degrees.

conformed to a shape that does not lie in its own plane. Based on the crafts currently inour test fleet the IRIS, IRIS+, BIX3, etc. we approximated the conformation effects to beapproximately cylindrical. The resulting conformal angle, depicted in Figure 57, is definedas the conformation to a cylinder of a radius such that half of the antenna covers an arcangle equal to the conformal angle. The antenna was tested from a flat (0 degree conformalangle) configuration to the depicted half-cylindrical conformal angle at 90 degrees. As withbending in the plane, the radiation efficiency and real impedance values trend downwardwith increased deviation from the nominal linear folded dipole configuration, quantified inTable 6. The antenna also experiences a moderate shift in the optimal operating frequencyas the antenna is severely conformed and approaches a half-cylindrical bending. With thefrequency shift and impedance reduction comes an increase in the reflection coefficient aswell as depicted in Figure 58. This change incites a reduction in bandwidth as well since thebroadening of the band occurs more slowly than the increase in reflection magnitude. Thenormalized radiation pattern shape is virtually unaffected, but its true magnitude is scaleddown due to the decreased efficiency.

Table 6: Impedence and radiation efficiency trends with confirmation.

The curved folded dipole antenna possesses a pattern that is robust when conformedto an object “ a key design criteria to enable a single-antenna solution for multiple platforms.However, it does demonstrate a small frequency shift when backed by different materials.Future work will focus on either broadening the bandwidth of the antenna to ensure that

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Figure 58: Reflection coefficient for discrete conformal curvatures for a 915MHz design wherebendAng denotes the degree of an arc that half of the antenna covers on a cylinder.

the operating frequency of the communication system has an antenna impedance match nomatter what the backing material. The design will also be multi-banded to enable manyISM band frequencies to be transmitted.

Inductor-Loaded Bent Dipole Antenna The second antenna that was investi-gated was the inductor-loaded bent dipole antenna. The inductor-loaded bent dipole antennais a direct expansion of previous work done with folded dipoles to simply reduce electricalsize while maintaining high antenna performance. End loading a bent dipole with helicalinductors, depicted in Figure 59, shifts the resonant frequency down, allowing for lower fre-quency operation with an antenna substantially reduced in size. Beyond the nominal linearreduction by half incurred by folding the elements orthogonally midway along their length,the inductors allow as much as another factor of 3 reduction. The form factor could thus bereduced by as much as a factor of 6 in simulation, eventually at the cost of significant antennaperformance. The radiation pattern is maximal at broadside and has moderate radiationdeficiency in the plane of the antenna elements. While these nulls, depicted in Figure 60,are an improvement upon the standard linear dipole, the radiation is still deficient beyondthe -3dB in the entire element plane.

The inductor-loaded folded dipole better approximates quasi-isotropic radiation thanthe more traditional choice for SUAS application electrically short monopoles which are oftenreferred to as “rubber duck” antennas. The inductor-loaded folded dipole antenna however,suffers the same problem of a very narrow bandwidth and only a moderate impedance matchresulting from tuning by reactive loading in the antenna elements. These results, quantifiedin Table 7, show that the loaded bent crossed dipole performs worse in beamwidth, nulldepth and impedance match than the curved folded dipole. It does however maintain a highradiation efficiency and a slightly larger average bandwidth than the curved folded dipole.

While theoretical and simulated implementations of the end-loaded bent crosseddipole initially looked promising, several challenges were encountered for realization withinpractical systems. Matching the dipoles to 50 on each of two ports was a straightforward op-timization of the coil parameters. To operate on a single output system, however, the dipoleswould need to be matched to approximately 100 each or a matching network would have tobe implemented between the elements and the feed. Matching a dipole to 100 is challengingwithout operating off-resonance and accepting considerable parasitic reactance. Since the

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Figure 59: Loaded bent crossed dipole geometry.

system also requires a quadrature feed, an integrated phase-shifting mechanism would alsoneed to be included as well, further reducing the ease of implementation or conformability.Lastly, the tuning of the coils needs to be precise for moderate tuning and could easily beaffected by outside influences such as multi-platform variety of SUAS materials as backing,causing unintended frequency shifts and volatility in performance between different imple-mentations. The antenna can and would have been translated to a planar and conformalequivalent if it was deemed an effective choice, but the additional considerations discussedwould still have to be designed for to an unnecessary degree of complexity compared to thecurved folded dipole that is now under investigation in its place.

Skew Planar Antennas The skew planar antenna has been widely used, but neverfully characterized. Therefore, we simulated the antenna to investigate performance metrics.For non-conformal applications, the skew planar – also referred to as a wheel or cloverleaf –antenna is a common choice for hobbyists and professionals alike. The antenna is comprisedof three one-wavelength long wedge-shaped elements depicted in Figure 61. This geometry isnot remotely conformal but its simple construction and above average general performancemakes it practically effective for many applications. It boasts a quasi-isotropic pattern atapproximately 1.5dBi gain with circular polarization and a good 50 impedance match fora simple design. As one of the standard antennas for high-performance and quasi-isotropicapplications, it was briefly investigated to generate a baseline for comparison of the conformalantennas investigated thus far. The major shortfall of this design is the very deep nulls ofapproximately -48dB, depicted in Figure 62, along the feed axis of the antenna. This rendersthe antenna virtually useless at range when trying to communicate either directly above orbelow the central axis antenna, with substantially degraded performance at any angle nearthe central axis as well. The skew planar antenna has a better impedance match, radiationefficiency, and impedance match than the two dipole structures quantified in Table 8, but

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Figure 60: LBCD radiation pattern.

it ultimately has no comparable conformal form, is electrically large on the order of a half-wavelength cube, and is ineffective for communication in one entire dimension.

Table 7: Inductor loaded crossed dipole antenna parameters.

The circularly-curved folded dipole antenna, shown in Figure 63, was fabricatedthrough photolithography on a Rogers 5880 substrate and tested for impedance match incomparison to simulated performance. An impedance match of approximately -15dB wasachieved in the prototype antennas with a -10dB bandwidth of approximately 20MHz. Therewas however a moderate frequency shift, as high as 80MHz, incurred when backing the an-tenna with various plastics. This does shift the antenna out of the ideal operating range,so broadbanding will need to be investigated. The magnitude of the shift, however, did notvary greatly regarding various thicknesses or types of plastics it was adhered to. It may thusbe possible to design the frequency expecting such a down shift and proceed with much lesshindrance, but further research and prototyping will be needed to confirm this.

The circularly-curved folded dipole antenna has been shown to be comparable to orbetter than conventionally popular antennas and boasts an ability for conformity despiteits small size. Once optimized and characterized for specific use, the antenna could become

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Table 8: Parameter comparison for antennas evaluated.

Figure 61: Skew Planar Geometry.

an industry standard as it requires less space, has a more isotropic pattern, and is moreapplication flexible than most antennas commercially available. To achieve this status how-ever, some specific future work must be accomplished. The antenna must be broad-bandedfor flexibility of single-design use on comparable packages. The curved folded dipole struc-ture has been shown [53] to have a potential for multi-band integration and this should beleveraged for further functionality for multi-frequency operation and system efficiency. Theentire implementation also needs to be tested in a fully-functional flight setting to assess thetangible improvements in communication. Lastly, the dipole structure will inevitably fail tosome degree on airframes that have moderate to high conductive properties, so a second classof antennas will need to be developed for conductive surfaces such as aluminum or carbonfiber.

Workforce Development In the first year of the project, this funding supported anundergraduate student, Taylor Poydence, to complete his undergraduate thesis in the min-imization of dipole antenna structures for SUAS. Taylor was drawn into graduate studiesthrough the support of this funding and an available graduate research assistantship. Taylortransitioned into a graduate research assistantship supported by this funding in August of

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Figure 62: Skew Planar Radiation Pattern.

Figure 63: Fabricated circularly-curved folded dipole antenna on Rogers 5880 substrate.

2016 after a summer internship in the field of antenna design. A new undergraduate student,Hope Schneider, joined the project in August of 2016 as well. Hope is working on a collab-orative project that is the result of conversations amongst investigators. She is pursuing amethodology to relate RF signal strength between two SUAS to water content in the atmo-sphere. With this research, we hope to be able to extract more than the point measurementsfor humidity or rain rate that can currently be captured with sensors deployed on an SUAS.

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2.4 Objective 4

Develop and conduct UAS themed outreach in support of NSF’s technologyeducation and workforce development. We will build on current STEM activitiesin Oklahoma, Nebraska and Kentucky to develop national K-12 activities. This will alsoinclude community efforts to obtain a better understanding of public perceptions of UASapplications to assist policy development concerning the potential widespread application ofUAS for atmospheric science.

Primary outcomes of this objective include in the wider sense education of the publicon the use of UAS and in an academic emphasis to facilitate the broader application of UASfor atmospheric science. This will include seminars for faculty from EPSCoR states who areinterested in learning how to integrate UAS into research in the atmospheric sciences. Wealso wish to facilitate the application of UAS in secondary education pedagogy, specificallyworking with experts in K-12 education (PLTW) to develop examples of how UAS canbe used in the classroom to illustrate basic atmospheric science and engineering principles.For example, atmospheric profiling using UAS can illustrate the dependence of temperatureand pressure on height and how this evolves throughout the day; the aircraft itself can bethe focus of discussions concerning remote command and control and basic aeronautics; byincorporating simple onboard autopilots, students could use basic computer coding principlesto design flight paths.

2.4.1 Task 4-1: Public Perception

Research Accomplishments The CLOUD-MAP project will lead to revolutions in thestudy of the atmosphere by advancing the capabilities of UASs, their sensors, and the associ-ated data analysis and scientific modeling. Broad use of UASs, however, will require carefulconsideration of the public response to the use of UASs by scientists in the national airspace.The overall goal of this work is to assess public perception of UASs and identify key issuesthat may arise. In addition, this work will investigate how methods, such as responsibleresearch and innovation (RRI) as applied to technology, impact the perception and adoptionof such technology. The specific goals of this task are to: 1) Determine key issues that arelikely to arise among the public related to the use of UASs for atmospheric measurementand other applications; and 2) Determine the impact of different forms of RRI and respon-sivity on public trust. The approach used in this task merges social science methods withtechnology development to better close the loop between public perception and technologydevelopment. This will impact not only the other tasks in this project, but will result infindings that will inform best practices for technology development in a wide range of fields.

We have made significant progress in assessing the public perception of UASs operatedin a variety of contexts.

Since last year, our primary activities have included:

1. Completing the analyses and presenting results of the mixed-method study that wasdeveloped and deployed regionally and nationally in 2015;

2. Conducting additional analyses of and extending a longitudinal national survey ex-periment (now having been conducted in fall of 2014, 2015 and 2016) exploring how

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the views of various publics are impacted by factors such as the terminology used todescribe UASs, who is using the UAS, the purpose of the UAS, and level of UASautonomy;

3. Building upon these prior studies to design and execute a nationwide survey experimentspecifically focused on comparing the impacts of identified key variables, includingrural/non-rural use of UASs, use for noble versus non-noble purposes, and use bypublic versus private actors; and

4. Reaching out to and beginning NWS focus groups and interviews (i.e., with employeesfrom the National Weather Service) concerning the data needs that might be met bythe CLOUD-MAP UAS.

We will discuss each of these efforts in more detail, including describing major findings todate.

Mixed-Method Study Our study used a convergent design mixed methods ap-proach where we merged the results of quantitative (survey) and qualitative (focus group)data to provide a more complete understanding of various perspectives on UAS technologyand how people formed their perspectives.

Stage 1 of the study employed a 30-minute quantitative recruitment survey involving159 participants recruited through Amazons Mechanical Turk (for a nationwide sample)and via Craigs List ads targeted to oversample persons from the three states involved inthe UASs development tasks. The survey sample came from 36 different states and wasapproximately 64% female, with a mean age of 41 (SD=12 years), and 70% reporting white,7% black/African American, 2% Asian, 2% American or Alaskan native, and 6% Spanish,Hispanic or Latino/a.

Participants in the recruitment survey reported their general attitudes and affectstoward UASs and support for UASs under different specific conditions (scenarios). Specifi-cally, participants were randomly assigned to read about use of UASs for weather researchor tornado forecasting followed by another other (non-weather) scenario. Because we wereinterested in how perceptions of use of UASs for weather purposes compared to perceptionsof UASs for other purposes, for the second scenario, participants were randomly assignedto read about use of UASs for infrastructure inspection, prescribed fires, video and moviemaking, package delivery, agriculture, and water sampling. Specific to each scenario, partic-ipants were asked to rate their perceptions of the trustworthiness and untrustworthiness ofUAS users, UAS regulators; and of the technology.

In Stage 2, to gain insight into the survey responses, we next conducted eight 90-minute focus groups with a sub-sample of the 30 of the respondents to the survey. Focusgroup participants were given a $50 Amazon gift card for their participation. CLOUD-MAP members with expertise in UAS development and use of UASs for weather researchand prediction were present at the focus groups to answer questions that arose and to hear forthemselves public reactions and recommendations. We varied the scenarios that we askedparticipants in the groups to read and respond to in the focus groups as shown in Table1. All groups discussed a weather-related and non-weather related scenario, with order ofdiscussion counterbalanced. If there was time for discussion of a third scenario, we used

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the commercial delivery scenario as the 3rd because it appeared to result in the greatestdiversity of responses and to have the greatest contrast with the weather scenarios. Duringthe focus groups, we used a semi-structured protocol and specifically asked participants toreport their reactions to the scenarios; to discuss their hopes, concerns, and recommendationsrelated to use of UASs in different situations; and to share their thoughts, if any, regardingUAS autonomy in different situations.

Table 9: Scenarios by focus group.

Some of the key findings from our mixed-method study included the following (thesefindings have also been discussed in our presentations and publications [54, 55, 56].

• People support weather UASs more than other uses (especially more thancommercial delivery uses). A particularly promising finding from the focus groupsis that most participants expressed more trust and acceptance of UASs operated byscientists for tasks that could improve their lives. Specifically, using UASs in relativelyrural locations to measure atmospheric conditions to improve weather forecasting andmodeling was viewed positively by most participants. This is in contrast to commercialapplications, such as UAS package delivery, that were viewed largely negatively due tosignificant safety and privacy concerns. As two participants noted,

I see a lot of [problems] with like the delivery though, cuz um like my husbandand I were talking you know what if the package gets dropped off at somebodyelses house, like the privacy issues. I know it says like 2500 feet, but thatstill concerns me. I dont really like that part on that. But like the tornadoesand stuff like that, I like that. ...if they are everywhere I would feel like Iwas being constantly watched. The use situations we have talked about dontmake me feel like that at all. Tornadoes, send them out there. Im not by atornado, god-willing. And so, same thing with farms, Im not out there, soits ok and its self-limiting.

In the quantitative data, support for use of UASs for general weather research andstudy or prediction of tornados was significantly more supported than use of UASsfor other purposes according to within group pair t-tests. As shown in Figure 1,

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when results were broken out by specific scenario, the greatest difference was betweenweather UASs and UASs used for commercial package delivery. In general, ratings oftrustworthiness and distrustworthiness of various actors followed ratings of support asone might expect. That is, people tended to trust those using and regulating UASsand the UASs themselves, more when they also said they were supportive of a givenuse of the UAS (|r|s = .4 to .7).

• Trust appears to be compensatory rather than compulsory. Our team hadprior data from a study conducted just before the beginning of this grant indicatingsurprisingly small mediating effects of trust in the users on support for the use of UASsunder different scenarios (see activity 2 below). In the present study we assessed andexamined the impacts of perceived trustworthiness of users, regulators, and the UASsthemselves. Not only did each form of trust appear to account for independent variancein support for UASs, but interactions between the trust in different targets indicatedthat trust in one target mattered most if trust in another target was low; but matteredto a significantly smaller extent if trust in another target was high. This suggests thattrust in one target (e.g., UASs) may compensate for trust in other targets (e.g., usersand/or regulators).

• Terminology seems not to matter. Many people refer to UASs as “drones,” andwe expected that people may have a more negative view of the term drone over UASs;however, our results from this and other studies (see 2 below) indicate that this isnot the case. The UAS community is typically resistant to using the term drone, butthese results may indicate that insisting on using the term UAS may not affect publicperception – except to raise questions in the publics mind. Indeed, we have found thatpeople express significantly less familiarity with the technology when our surveys haveused terms like UAS or UAV, with certain respondents asking “are you talking aboutdrones?” or commenting that the survey would be clearer if we simply used the term“drones.”

• Major Hopes: Advance research, improve safety, health and efficiency.Emergent coding of transcripts from the focus groups revealed salient hopes pertainingto use of drones to advance research (e.g., on the weather), while improving safety andhealth as well as increasing efficiency. As one participant noted:

... it [drone use] gets you much more realistic real time information otherthan having people driving along watching the tornadoes and the storms Youreeliminating the risk of the storm watch, and youre getting much more devel-oped information.

• Major Concerns: privacy, safety, nuisance, potential misuse, technologicalfailures or susceptibilities, and over use. Privacy, safety, and potential misuse ofUASs continue to come up as primary concerns by the public. Focus group partici-pants also often observed that technological advances which enable identification of theuse/user of a drone might allay some privacy concerns. As one focus group participantnoted,

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Figure 64: Ratings of support and trustworthiness (TW) and distrustworthiness (dTW)across scenarios..

...how does this affect my privacy, again, identification, ya know, how do Iknow that this is a trusted resource as compared to something that has maliceor other intentions...?

• Recommendations: Regulation, monitoring, identification, coordination.Many of the recommendations and suggestions offered by participants stemmed fromtheir concerns. In discussions in the focus groups when people learned that not allUASs had cameras, the participants felt much more comfortable with the systems op-erating near their houses and property. This could directly impact technology designsince developing systems without cameras could significantly reduce public concerns ifthe public is also informed of these changes.

Participants expressed concerns about how drones would be regulated in a way thatwas safe and effective, but not overly restrictive. Individuals saw a need for creatingregulations, whether through the existing FAA or a new agency: I can see a clear needfor some kind of a new agency that looks at airspace in three dimensions at all times.

Obviously the FAA ... they are the agency that we rely on for safety in flightand they should be the ones who create the regulations and especially whenits, you know we have drones being used for this kind of commercial aspectthere should be certain regulations and other things like that. Thats only tobe regulated by the FAA.

In order to manage regulations, participants suggested collaborative efforts betweengovernment, coalitions, and companies using drones. Most participants discussed theneed for identification of drones, whether on the drone itself, or through public notifi-cation on a website or media release.

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I dont Ive, Ive never actually seen a drone in real life so I dont know quitehow big they are but if maybe there was some way to put like a giant QRcode on the bottom of the drone so if you see one flying around you can takea picture, figure out who owns that drone or what the purpose of the doneis, if its like a personal drone or commercial drone from Amazon or fromWalmart.

...it would almost be nice to be able to design drones in such a manner, Imean, we can look at a vehicle, and tell what that vehicle is used for. Itwould be interesting if they could design drones in such a manner that theywere known for emergency response, or for data gathering.

Participants also suggested that essential regulations about drones should be centeredon privacy protection, air traffic management, and user management. Operator train-ing or licensure was a recommendation for many.

• Mixed views on autonomy. Participants in our focus groups expressed mixedviews on the amount of autonomy available to the vehicle. One person noted,

Well I dont think fully autonomous is the direction but I also think its usefulto have some controls on it... I think some limits on the device as far aselevation and height would maybe be a first step towards something like that.But I dont think that throwing something up in the air and letting it do itsthing without some manual control [is a good idea].

Some participants felt more comfortable if the UAS had high levels of autonomy sincethis reduced privacy concerns (since a person is not watching) and also alleviatedquestions about how they could be operated in remote locations potentially far fromoperators. Other participants, however, expressed concern about the capabilities ofautonomous systems to make the right decisions, especially when faced with challengingflight conditions or other aircraft.

These findings provide information that can be incorporated into system design andalso informed the design of our subsequent studies.

Longitudinal National Survey Experiment We began a longitudinal nationalsurvey experiment just prior to receiving this award and, given the relevance of that data toproject goals, in the past year we conducted additional analyses and extended the projectby collecting an additional wave of data from two separate samples. The study was designedto examine five experimentally varied factors which were manipulated in extremely shortscenarios read by participants.

Participants were recruited through Amazons Mechanical Turk, required to be Amer-icans, and paid a small amount for participation. An identical survey was administered in2014 (n = 576) and repeated in 2015 (n = 315).

Findings from analyses of these data and of the main effects of varied factors includedthe following (many of these results have been presented in, e.g., PytlikZillig et al., 2016;PytlikZillig et al., 2017; [58, 59] additional results are in preparation for publication) :

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• Little or no effect of terminology on support. Terminology used to describe UASswas related to familiarity of the technology. Respondents indicated they were mostfamiliar with the terms drones and unmanned aerial systems, but substantially lessfamiliar with the terms unmanned aerial vehicles and aerial robots. Consistent withprior studies (e.g., see 1 above), terminology was not related to support for the UASs.In addition, it was not related to trust in the actors using the technology.

• Mixed and changing effects of autonomy. Autonomy of the UAS was related to theestimated time horizon for use of the UASs, with fully controlled UASs estimatedas having the longest time horizon until use. Autonomy was also related to changesover time in judgements of user trustworthiness. Specifically, there was a trend foractors using fully autonomous UASs to be rated as more trustworthy than those usingpartially autonomous UASs in 2015, but the reverse was true in 2014.

• Large effects of UAS purposes. Use of the UASs for different purposes was relatedto different estimated time horizons for UAS use, with use for economic purposesestimated to be furthest in the future, use for environmental purposes estimated to benearest in the future, and the time horizon for security purposes estimated as fallingbetween the other two. UAS purpose was also related to support, with environmentalpurposes receiving more support than other purposes, security purposes receiving theleast support, and economic purposes receiving mid-level support. Also, perceptionsof actors trustworthiness and distrustworthiness related to purpose, with those usingUASs for security purposes perceived as least trustworthy and most distrustworthy.

• Actors somewhat impact support, with trust being a surprisingly small mediator ofthe actor effects. Relating to actors using the UASs, use of UASs by the governmentwas supported to a greater extent than use by private companies. While some of thedifferences in support for use by different actors was mediated by trust in the actors,the mediated effect was surprisingly small.

• Prevention-focused framing increases support. Finally, overall, framing of the UATpurposes also affected support. Specifically, prevention-focused descriptions UAT pur-poses resulted in more support than promotion-focused descriptions. However, framingdid not impact perceptions of the trustworthiness of actors using the UATs.

• Some political polarization observed, but primarily relating to issues rather than toUASs per se. We investigated the extent to which the public appeared to be politicallypolarized regarding drones use and found only slight evidence that political partyand/or ideology was related to support for drones. Furthermore, the polarizationappeared to be more related to the purpose of the UASs rather than the UASs per se.For example, liberals supported drone use for environmental causes to a significantlygreater extent than conservatives.

In 2016 we extended the longitudinal survey by (a) administering it again at a largerscale (two samples instead of one, ns = approximately 2000 each), (b) including a nationallyrepresentative sample in addition to the Mturk sample for comparison of findings acrosssamples, and (c) including assessments of perceptions of trustworthiness/ distrustworthiness

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of a larger set of targets (users, regulators, and the UASs themselves). Over the next yearwe will be examining those data to determine if trends over time can be observed and ifresults from the Mturk sample generalize to the representative national sample, or how theresults from the two samples differ.

Nationwide survey experiment Having identified some potentially key issues asa result of activities 1 and 2, we sought to conduct a more controlled experimental studyof those factors in a nationally representative sample. Our goals in this study are to betterunderstand and quantify the individual and combined effects of three key factors whileholding other factors constant. From the focus groups (see activity 1), it appeared thatuse of UASs (1) in rural versus non-rural places, (2) for noble of non-noble causes, and(3) by different actors, played important roles in peoples opinions and support for UASuse. Our end goal is to determine if there adjustments to technology design that mightoff-set observed effects (e.g., eliminate decreased support for non-noble purposes). As apreliminary step toward that goal, we created a survey experiment to quantify the effects ofthese three factors. We piloted (n=300, Nov 2016) and administered the survey experimentto a nationally representative sample (n = 2100, Feb 2017) and are currently analyzing thedata. After those analyses are complete we intend to conduct another study or set of studiesto examine how and if UAS design features and regulations might moderate the impact ofthe known effects.

NWS employee interviews and focus groups In last years report we notedthat we were seeking the views of a unique group of stakeholders (National Weather Serviceemployees) to obtain their views of weather UASs and to better understand their data anddata presentation needs, with the goal of responsively developing UASs that might meet thoseneeds. Despite numerous obstacles to recruitment we have conducted two focus groups andhave follow up interviews planned with additional NWS employees. We expect to reportresults of this qualitative study next year.

Workforce Development This task has involved workforce developments at a numberof levels. First, the team on this task involves an Associate Professor (Dr. Detweiler), aResearch Associate Professor (Dr. PytlikZillig), and an Associate Professor (Dr. Houston).They have all been actively involved in the development and deployment of the surveys andfocus groups, which has also led to a number of mentoring opportunities.

Second, there are two students involved in this work. Janell Walther is a PhD studentpursuing a degree in communication and has been working closely with Dr. PytlikZillig andthe rest of the team in conducting and analyzing the focus groups and interviews. AjayShankar is a PhD student working on this project under the supervision Dr. Detweiler. Hehas had the opportunity to interact with other members of the team and is learning aboutUASs and the particular challenges associated with collecting atmospheric data. In 2015, twoundergraduates pursuing social science degrees (Alexandria PytlikZillig, psychology major,and Addison Fairchild, political science major), and in 2016 an additional undergraduate(Jake Kawamoto, political science) have also gained experience conducting research andanalyzing and presenting social science qualitative and quantitative data as a result of this

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project. Finally, Ashraful Islam is a new PhD student working with Dr. Detweiler that willbe starting on this project leading up to the summer field trials.

Third, this work has been disseminated in talks both locally and nationally, includingpresentations at the 4th Conference of the International Society for Atmospheric Researchusing Remotely-piloted Aircraft (ISARRA), Society for the Psychological Study of SocialIssues Conference (SPSSI), and 97th annual meeting of the American Meteorological Society(AMS).

Finally, to date the surveys have involved more than 6000 participants and the focusgroups and interviews have involved more than 30 participants with diverse backgrounds. Atthe end of each focus group we give the participants the opportunity to ask further questionsto the UAS experts involved in the focus group. This has been remarked as one of thehighlights of the focus group. Because both the surveys and focus groups provide links torelevant information about UASs, including links to work from the present project, they areenabling additional dissemination of the work in this project beyond traditional means.

Leveraged Opportunities and Activities As mentioned above, this task has leverageda prior longitudinal survey of attitudes toward UASs. It also has leveraged many of theother tasks to define atmospheric science scenarios, such as convection initiation (Task 2-1), and also potential technological advances, such as the ability to use swarms (Task 3-1).These scenarios are used as part of the surveys and focus groups to assess public perceptionon different types of applications and technologies. We are also collaborating with anotherfaculty member at UNL, Dr. Brittany Duncan, to examine factors that impact how usersand bystanders interact with UASs. We expect to have results from experiments with Dr.Duncan over the summer of 2017. This task will likely have a significant impact on all othertasks as the results will help inform how the technology should be developed and deployedto alleviate concerns by the public and policy makers.

2.4.2 Task 4-2: UAS Workshops

Research Accomplishments As part of the community effort to foster better under-standing of public perception to UAS applications, this task is developing outreach edu-cational workshops for the public on locally specific applications of UAS. By highlightingthe potential benefits of UAS technology and determining the best ways to engage withthe public from Task 4-1, such as immediate flight capabilities and finer spatial resolutionsthat allow individual plant-level identification, to local communities, this task will work toimprove public perceptions of UAS technologies and provide the public with real-world ex-amples of how UAS technology can work for them. These workshops will then be scaledto K-12 educational activities to promote interest in STEM fields and teach primary schoolstudents how multiple fields (e.g., mechanical and aerospace engineering, remote sensing, ge-ography, agriculture) are being combined to overcome real-world challenges. The workshopswill take several forms, including traditional in-class and hands-on workshops, seminars, on-line courses, and summits. CLOUD-MAP will actively participate as sponsor and organizerin UAS in Weather focused summits, in particular those involving stakeholder partners.The first video in a planned series on Introduction to Unmanned Aircraft Systems has beencompleted and will be published soon. The CLOUD-MAP web-site, initially a placeholder

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Figure 65: CLOUD-MAP web-site.

to inform the broader community about the program, is undergoing beta testing for V 2.0.This site will include sections where participants can upload vehicle and sensor informationto share with the community and non-participants can download vetted data for comparisonand analysis. This is shown in Figure 65.

In collaboration with NCAR’s Earth Observing Laboratory (EOL), CLOUD-MAP PIsand students participated in a workshop focused on the role of UAS in atmospheric research(Figure 66). The aim of the workshop was to gather input from active UAS researchers,including key national and international experts as well as the scientific community at large,on the highest priority needs in atmospheric and atmosphere interface research that may bemet by UAS. The ultimate goal of the workshop is to gather community input on the neededsupport to the NSF-funded atmospheric and geosciences research communities in developinginstrumentation specific to and in utilizing capabilities of UAS within the observationalscience support mission of the NCAR Earth Observing Laboratory. Topics included:

• Key areas of UAS research, such as boundary layer and atmospheric interfaces

• Key UAS measurements including fluxes, pollution, constituents, aerosols, and radia-tion

• Instrumentation, from standard to novel, including adaptation of existing in-situ sound-ing instrumentation

• Wind measurements from UAS

• Calibration of UAS instruments and verification of corresponding UAS-based measure-ments

• UAS operations and flights in hazardous weather conditions such as strong winds,icing, rain, and low visibility

• Working with the FAA in a complex regulatory environment

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Figure 66: NCAR/EOL Workshop: Unmanned Aircrat Systems for Atmospheric Research.

The information gathered at the workshop will be summarized in a report, which will includecommunity recommendations, potential collaborations, instrumentation and airframe gaps,and possible next steps on how best to facilitate NSF funded atmospheric and geosciencesresearch using UAS. Dr. Chilson served on the organizing board and both Drs. Chilson andJacob organized two of the four focus presentations discussing topics of interest.

Leveraged Opportunities and Activities The 4 university PIs have embarked on acampaign of outreach to the meteorological and atmospheric science communities by givingpresentations on CLOUD-MAP at the usual suspects, viz. events hosted by the AmericanMeteorological Society including the annual meeting and the Severe Local Storms Conferenceas well as other aligned organizations, such as ISARRA International Society for AtmosphericResearch using Remotely piloted Aircraft and the National Weather Service. We have alsoworked to present the research to a much broader community that will also be interested inthe results. This includes, but is not limited to,

• Friends and Partners in Weather

• FAA Air Traffic Control Workshop

• AUVSI

• Unmanned Traffic Management Symposium

• Earth Science Information Partners

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Figure 67: Mesonet seminar flyer - one of a set of seminars presented to the weather andmesonet community.

Public seminars have focused on the benefit to the public-at-large, particularly improvedweather forecasting through the development of the 3D Mesonet (Figure 67).

2.4.3 Task 4-3: Rapid Dissemination of Risk Information

Research Accomplishments The technical goals of this task are to (1) determine in-formation needs and data gaps by working with stakeholder groups such as local/regionalfirst responders, NWS, and emergency managers, (2) integrate UAS-acquired data and newdata streams into risk modeling processes and validate the contributions of these new streamsthrough post-event assessment, and (3) determine best practices for geovisualization of UAS-acquired data for risk mapping through public focus groups, surveys, and collaboration withstakeholder groups.

Progress on these technical goals has included collaboration with CLOUD-MAP personnelElbing to begin processing of the IRIS USArray Transportable infrasound station data. ACLOUD-MAP graduate student (Hemingway) developed a distance matrix of all stations toeach of the tornadoes that occurred during the 2011 super outbreak in the southwesternUS. The next goal of this work is to triangulate signals from multiple stations for a knownevent with a known location, which can then be used to isolate the infrasound signature oftornadoes.

One manuscript related to this task has been submitted to a peer review journal. Thestudy investigates land surface heterogeneity impacts on tornado formation in two tornado-prone regions: Tornado Alley and Dixie Alley. The study uses a combination of GIS analyses,geospatial analytics, and statistics to determine differences of tornado formation likelihoodsto aid in better risk assessment. Frazier is the lead author, and two graduate students trainedon the project are co-authors (Hemingway and Brasher).

Frazier has initiated discussions with a potential collaborator from OSU (Dr. TristanWu, Political Science) whose research focuses on disaster information use, household disasterresponse, and perceptions of environmental threats.

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Figure 68: Severe storm risk regions.

Workforce Development Trainee activities are ongoing and include training in geospa-tial and geovisualization techniques for one PhD student in the Department of Geographyat Oklahoma State University. Additionally, that student is being trained in manuscriptpreparation and professional development (e.g., conference presentations)

Leveraged Opportunities and Activities Collaborations related to this task have beenleveraged for (1) a National Institute for Standards and Technology (NIST) grant proposalsubmitted by PI Elbing, and (2) an NSF National Research Traineeship (NRT) programproposal submitted by CLOUD-MAP UK partners.

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3 Additional Major Project Elements

3.1 Interjurisdictional Collaborations

Collaborative proposals were highlighted earlier in the report indicating an increasing andhigh amount of research collaboration. Research collaboration, particularly interjurisdic-tional collaboration, among the project participants has been high with second year ex-ceeding expectations. Figure 69a and 69b shows research number of researcher publicationsby award year: total, collaborative between award researchers, and non-collaborative andnormalized number of researcher publications per researcher for all researchers, early-careerresearchers, and senior researchers by award year, respectively. (Note that these numbers areresults of the self-survey of the PIs that were used to create the biographs presented earlier.)Examining these numbers in more detail, before CLOUD-MAP , approximately 70% of thetotal faculty collaborations were intra-university. Even so, many of the CLOUD-MAP teamhad not worked together previously so there was capacity to grow internal collaborations aswell as external ones.

(a) Researcher publications. (b) Normalized publications per researcher.

Figure 69: Research publications for early-career researchers and senior researchers in years1 and 2 of program.

Table 10: Intra- and inter-jurisdictional collaborations among CLOUD-MAP researchers.

Annual SummaryYear 0 Year 1 Year 2

Intra- Inter- Intra- Inter- Intra- Inter-Research 27 10 43 30 43 34

Journal Papers 3 1 5 9 8 11Conference Papers 8 1 11 6 9 21

Proposals 28 12 28 12 20 10Total 66 24 87 57 80 85

With 17 faculty (which includes the loss of 1 faculty in year 2), the total number ofcollaborations possible in any one category (research, journal papers, conferences, proposals)

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if all worked with all is 17 · 16 = 272, so for four categories the maximum possible is1,088. Each collaboration between a pair of faculty is represented twice here with each onecounting the other. The survey reflects this as well, and occasionally one faculty member willnote working with another that is not reciprocated. Thus, the survey represents perceivedcollaboration at that moment in time when taken, not the actual collaboration that wouldbe deduced by counting paper co-author pairs or proposal co-PI pairs.

Considering research collaborations in general or proposal collaborations specifically,before CLOUD-MAP the number of intra-university collaborations was more than doublethat of inter-university collaborations (research, 27/10; proposals, 28/12), starting at about15% of the total collaboration possible in each of these two areas. Papers and conferenceswere much lower, so overall, collaborations started at 8% of the maximum capacity.

During the first year of CLOUD-MAP, the number of collaborations on proposalsremained the same internally and externally, while intra-university research collaborationsincreased 60%. However, inter-university research collaborations increased 200%. Overall,intra-university collaborations increased just over 30%, while inter-university collaborationsincreased by over 130%.

In Year-2, differences between intra- and inter-university collaborations were evenmore pronounced, with overall internal collaborations falling slightly (8%) and external col-laborations increasing by an additional 49% to more than 250% with respect to the before-CLOUD-MAP baseline.

The project has engaged undergraduate students at a substantial level with bothintegrated involvement in research projects (under direct mentorship of a graduate student)and with class-related and outreach projects. The number of undergraduate students activelyinvolved in the project was so great that it exceeded the ability of the DOP to properly trackthe students working on the effort.

3.2 Early Career Faculty Advancement

Collaborative proposals and publications were highlighted earlier in the report under re-search productivity in §1.3, with detailed numbers on proposal development and collabora-tion amongst the team. This is indicative of a highly successful faculty team at both thesenior and early career levels. Early career faculty advancement has proceeded well, withECE faculty participating at all levels within and without the project as indicated by theDOP results (shown elsewhere and in appendix). Promotions included 2 faculty obtainingtenure at the University of Kentucky and 2 at the University of Nebraska-Lincoln.

Table 11: Early career faculty promotions.

Year Early Career Faculty Institution2016 Jesse Hoagg University of Kentucky

Carrick Detweiler University of Nebraska-Lincoln2017 Marcelo Guzman University of Kentucky

Matthew Van Den Broeke University of Nebraska-Lincoln

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To date, 20 publications have been published or are pending publication, including3 journal articles, 15 conference proceedings, and 1 each book and book chapter. A spe-cial submission for the journal Atmosphere is being organized and edited by Prof. MarceloGuzman of UK to highlight many aspects of this project, among others.

3.3 Public Outreach

The PIs have given both general and technical presentations and seminars discussing thedetails and broad benefits of the NSF sponsored program. These include but are not limitedto the American Institute of Aeronautics and Astronautics, the American MeteorologicalSociety, the Association of Unmanned Vehicle Systems International, the National Centerfor Atmospheric Research, the National Weather Service and the National Severe StormsLaboratory, Friends and Partners In Aviation Weather, NASA, and the FAA. There hasbeen a growing interest in UAS related atmospheric monitoring by various news outlets.We have provided interviews with several of the local newspapers, magazines, and newsstations. There has also been attention from more widely recognized news outlets such asPopular Science, PBS News Hour, Weather Channel, CBS News, and others.

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4 Appendix - Data Outcomes Portal Information

The following pages provide data prepared and encapsulated by Integrated Learning In-novations through the NSF EPSCoR RII Track-2 Data Outcomes Portal (DOP). Datasummarized in the report are provided by individual NSF EPSCoR RII Track-2 CLOUD-MAP participants. As such, the data are presented with the caveat that accuracy and com-pleteness of results presented herein are not guaranteed, but the project principal inves-tigators have done their best to ensure that the data reported in the DOP are thorough.Regardless, the data included in the DOP are representative of the project as a whole.

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