About

Brian Argrow is a Distinguished Professor in the Aerospace Engineering Sciences department at the University of Colorado Boulder, where he also holds the Glenn Murphy Endowed Chair and serves as the Director of IRISS. His research focuses on unmanned aerospace vehicles (UAVs), high-speed and hypersonic aerodynamics, dense gas dynamics, and rarefied gas dynamics. He has been a faculty member at the University of Colorado since 1999, progressing from Assistant Professor to his current distinguished role, and has held leadership positions including Chair of Aerospace Engineering Sciences and Director of the Research and Engineering Center for Unmanned Vehicles. Argrow earned his PhD in Aerospace Engineering from the University of Oklahoma in 1989, along with a Master’s in Mechanical Engineering and a Bachelor’s with distinction in Aerospace Engineering from the same institution. His professional experience includes teaching and research roles at the University of Oklahoma and the University of Colorado, where he has contributed significantly to aerospace engineering education and research. He is a member of the National Academy of Engineering (2022) and a Fellow of the American Institute of Aeronautics and Astronautics (AIAA, 2016). His awards include the UCB Gold Best Should Teach Award, the Marinus Smith Award, and the President's Teaching Scholar, among others.

Research topics

• Computer Science
• Engineering
• Meteorology
• Environmental science
• Political Science
• Geography
• Aeronautics
• Computer Security
• World Wide Web
• Operating system
• Business
• Physics
• Aerospace engineering
• Risk analysis (engineering)

Selected publications

• CFD-Enhanced Calibration of a Multihole Probe for Small Uncrewed Aircraft Systems

  Journal of Atmospheric and Oceanic Technology · 2026-01-09

  articleOpen access

  Abstract A robust calibration method with a 9-hole probe (9HP) for inertial wind vector measurements from a small uncrewed aircraft system (sUAS) is presented. Calibration accuracy is improved by using computational fluid dynamics (CFD) to estimate corrections, such as those for test section blockage, that are generally applied in wind tunnel testing. A method for estimating experimental bias is presented to account for flow variations over the pressure taps as the 9HP is repositioned in the test section. Installation effects from flow over the airframe and upwash produced by the wings are estimated from CFD simulations of the entire airframe at the expected cruise speed. An initial CFD analysis has demonstrated a quantifiable linear relationship between the measured and actual angle of attack as measured from the relative wind frame. The objective of these additional steps is to increase the accuracy of 9HP calibration. Significance Statement Multihole probes (MHPs) are flown on small uncrewed aircraft systems (sUAS) to gather precise atmospheric measurements deriving inertial wind. This study demonstrates that MHP calibration accuracy can be enhanced by using computational fluid dynamics (CFD) to characterize the flow field around a probe. CFD analysis can offer corrections to address wind tunnel blockage effects observed during calibration and upwash effects observed during flight.

  PublisherOA PDFDOI

• RAAVEN data processing for TORUS and TORUS-LItE

  2026-04-20

  articleOpen accessSenior author

  Abstract. RAAVEN (Robust Autonomous Airborne Vehicle - Endurant and Nimble) uncrewed aircraft systems (UAS) were deployed in and around supercell thunderstorms during the Targeted Observation by Radars and UAS of Supercells (TORUS) and TORUS Left Flank Intensive Experiment (TORUS-LItE) field campaigns. On-board sensors measured temperature, humidity, pressure, and wind. Despite extensive predeployment testing, the demanding environments where data collection occurred presented numerous challenges to data quality. In this article, extensive quality control procedures adopted for these data are described. Many of these procedures aim to quantify data-quality uncertainty, in lieu of correcting questionable data. Procedures address the dependency of estimated wind on aircraft manoeuvring, periodically faulty sensors, questionable data induced by sensor wetting in rain, and sensor hysteresis and bias. Bulk data statistics are also presented, in part to assert data quality but also to highlight unique qualities of UAS data collected during TORUS and TORUS-LItE.

  PublisherOA PDFDOI

• Comment on essd-2026-134

  2026-04-28

  peer-reviewOpen accessSenior author

  <strong class="journal-contentHeaderColor">Abstract.</strong> RAAVEN (Robust Autonomous Airborne Vehicle - Endurant and Nimble) uncrewed aircraft systems (UAS) were deployed in and around supercell thunderstorms during the Targeted Observation by Radars and UAS of Supercells (TORUS) and TORUS Left Flank Intensive Experiment (TORUS-LItE) field campaigns. On-board sensors measured temperature, humidity, pressure, and wind. Despite extensive predeployment testing, the demanding environments where data collection occurred presented numerous challenges to data quality. In this article, extensive quality control procedures adopted for these data are described.&nbsp; Many of these procedures aim to quantify data-quality uncertainty, in lieu of correcting questionable data.&nbsp; Procedures address the dependency of estimated wind on aircraft manoeuvring, periodically faulty sensors, questionable data induced by sensor wetting in rain, and sensor hysteresis and bias. Bulk data statistics are also presented, in part to assert data quality but also to highlight unique qualities of UAS data collected during TORUS and TORUS-LItE.

  PublisherDOI

• TORUS-LItE: RAAVEN UAS Data. Version 1.0

  Open MIND · 2026-01-01

  datasetOpen access1st authorCorresponding

  Flight level meteorological and aircraft state data from the University of Colorado RAAVEN (Robust Autonomous Aerial Vehicle - Endurant and Nimble) sUAS (small Unmanned Aircraft System) platform that flew during the TORUS-LItE (Targeted Observations using Radars and UAS in Supercells Left Flank Intensive Experiment ) campaign over the plains region of the United States. The UAS flew near inflow and left flank missions in and around supercell thunderstorms.

  PublisherOA PDFDOI

• Autonomous Uncrewed Aircraft for Mobile Operations in Severe Weather

  Lecture notes in computer science · 2025-08-25

  book-chapterSenior author
  PublisherDOI

• Targeted Observation by Radars and UAS of Supercells: TORUS

  Bulletin of the American Meteorological Society · 2025-10-31

  article

  Abstract Targeted Observation by Radars and Uncrewed Aircraft Aystems (UAS) of Supercells (TORUS) aimed to improve the conceptual model of supercell thunderstorms through advancing the understanding of the role of storm-generated airmass boundaries and coherent structures in the development of near-surface rotation. Research questions guiding the field phase of TORUS focused on left-flank vertical vorticity sheets, streamwise vorticity currents, left-flank convergence boundaries, and rear-flank internal surges. Research questions also aimed to address the relationship between inflow modification and supercell characteristics. Across three field seasons (2019, 2022, and 2023), data on 46 supercell thunderstorms were collected through coordinated deployments of radars, lidars, mobile mesonets, UAS, manned aircraft, radiosondes, and swarmsondes. More than 200 scientists and engineers (many of whom were students) participated in the TORUS field deployments. The scientific motivation for TORUS, experiment design, and examples of data/analysis are presented in this article. Significance Statement Targeted Observation by Radars and Uncrewed Aircraft Systems (UAS) of Supercells (TORUS) was a collaborative research project funded by the National Science Foundation and the National Oceanic and Atmospheric Administration to advance understanding of supercells. TORUS involved more than 200 scientists and engineers (many of whom were students) who led data collection on 46 supercell thunderstorms across three field seasons. This effort constituted the most deployments of UAS within supercells and, on 17 May 2019, likely yielded the longest continuous airborne multi-Doppler radar sampling of a Great Plains supercell ever conducted.

  PublisherDOI

• Deployment of a Fixed-Wing Small Uncrewed Aircraft System for Urban Traffic–Related Air Pollution Assessment

  Journal of Atmospheric and Oceanic Technology · 2025-03-17

  articleOpen accessSenior author

  Abstract Existing motor vehicle pollutant measurement techniques, including those that employ ground-based and multirotor small uncrewed aircraft system (sUAS) methods, can accurately measure traffic-related air pollution (TRAP) concentrations at a single location. However, these techniques often lack the mobility to assess pollutant trends across a large horizontal area. Fixed-wing sUAS represents an alternative instrument platform compared to ground-based systems and multirotor sUAS, as fixed-wing sUASs are able to carry air pollutant monitor payloads, have extended endurance, and offer expansive three-dimensional ranges across a field site. To demonstrate the utility of fixed-wing sUAS for urban TRAP assessment, we conducted two flights using a Super Robust Autonomous Aerial Vehicle–Endurant Nimble (RAAVEN) sUAS [University of Colorado (CU) Boulder] at a large field site adjacent to a major highway in Erie, Colorado. Concentrations of solid particulate matter (PM 10 ) and gas-phase (carbon monoxide) pollutants displayed decay as a function of altitude. During the morning flight, PM 10 concentrations decreased from 19.0 μ g m −3 at ground level to a minimum concentration of 14.3 μ g m −3 at 90 m above ground level. During the afternoon flight, concentrations of PM 10 displayed minimal vertical stratification, ranging from 8.9 at ground level to 10.0 μ g m −3 at 45 m above ground level. Similarly, pollutants displayed decreasing concentrations as the horizontal distance from the roadway increased. Concentrations of TRAP may be significantly elevated in the area both above and beyond roadways, which contribute to additional pollutant exposure from on-road pollution sources. This study demonstrated that the general behavior of TRAP in near-road environments and that the use of fixed-wing sUAS are viable option for urban air quality measurements. Significance Statement This study represents one of the first uses of a fixed-wing small uncrewed aircraft system (sUAS) to assess near-roadside concentrations of traffic-related air pollution (TRAP) in urbanized areas. We found that local meteorology, including local wind and solar radiation, had a substantial influence on the concentrations of common air pollutants, including particulate matter, black carbon, carbon monoxide, and carbon dioxide. Furthermore, we found large-scale spatiotemporal variation in pollutant concentrations as a function of the vertical and horizontal distance from the highway, indicating that diminished spatial variation employed in multirotor sUAS studies may not be sufficient to fully assess TRAP in roadside environments.

  PublisherOA PDFDOI

• “A Good Bot Always Knows Its Limitations”: Assessing Autonomous System Decision-Making Competencies through Factorized Machine Self-Confidence

  ACM Transactions on Human-Robot Interaction · 2025-04-28 · 4 citations

  articleSenior author

  How can intelligent machines assess their competency to complete a task? This question has come into focus for autonomous systems that algorithmically make decisions under uncertainty. We argue that machine self-confidence—a form of meta-reasoning based on self-assessments of system knowledge about the state of the world, itself, and ability to reason about and execute tasks—leads to many computable and useful competency indicators for such agents. This article presents our body of work, so far, on this concept in the form of the Factorized Machine Self-Confidence (FaMSeC) framework, which holistically considers several major factors driving competency in algorithmic decision-making: outcome assessment, solver quality, model quality, alignment quality, and past experience. In FaMSeC, self-confidence indicators are derived via “problem-solving statistics” embedded in Markov Decision Process solvers and related approaches. These statistics come from evaluating probabilistic exceedance margins in relation to certain outcomes and associated competency standards specified by an evaluator. Once designed, and evaluated, the statistics can be easily incorporated into autonomous agents and serve as indicators of competency. We include detailed descriptions and examples for Markov Decision Process agents and show how outcome assessment and solver quality factors can be found for a range of tasking contexts through novel use of meta-utility functions, behavior simulations, and surrogate prediction models. Numerical evaluations are performed to demonstrate that FaMSeC indicators perform as desired (references to human subject studies beyond the scope of this article are provided).

  PublisherDOI

• Studying Aerosol, Clouds, and Air Quality in the Coastal Urban Environment of Southeastern Texas

  Bulletin of the American Meteorological Society · 2025-08-04 · 3 citations

  article

  Abstract A multi-agency succession of field campaigns was conducted in southeastern Texas during July 2021 through October 2022 to study the complex interactions of aerosols, clouds and air pollution in the coastal urban environment. As part of the Tracking Aerosol Convection interactions Experiment (TRACER), the TRACER- Air Quality (TAQ) campaign the Experiment of Sea Breeze Convection, Aerosols, Precipitation and Environment (ESCAPE) and the Convective Cloud Urban Boundary Layer Experiment (CUBE), a combination of ground-based supersites and mobile laboratories, shipborne measurements and aircraft-based instrumentation were deployed. These diverse platforms collected high-resolution data to characterize the aerosol microphysics and chemistry, cloud and precipitation micro- and macro-physical properties, environmental thermodynamics and air quality-relevant constituents that are being used in follow-on analysis and modeling activities. We present the overall deployment setups, a summary of the campaign conditions and a sampling of early research results related to: (a) aerosol precursors in the urban environment, (b) influences of local meteorology on air pollution, (c) detailed observations of the sea breeze circulation, (d) retrieved supersaturation in convective updrafts, (e) characterizing the convective updraft lifecycle, (f) variability in lightning characteristics of convective storms and (g) urban influences on surface energy fluxes. The work concludes with discussion of future research activities highlighted by the TRACER model-intercomparison project to explore the representation of aerosol-convective interactions in high-resolution simulations.

  PublisherDOI

• In-Situ Turbulence and Particulate Measurements in Support of the BOLT II Flight Experiment

  Journal of Spacecraft and Rockets · 2024-09-12 · 5 citations

  article

  The results of a measurement campaign to characterize the freestream atmospheric turbulence intensity and particulate concentrations experienced by the Boundary Layer Transition (BOLT) II flight experiment are described. These measurements were obtained using custom high-altitude balloon-borne payloads using a modified commercial optical particle counter and a high-bandwidth coldwire thermometer and hotwire anemometer, designed for low cost and light weight, with telemetered data so that physical payload recovery is not necessary. Sixteen flights were carried out over a 10-day campaign, with viable data returned from 14 flights, including four centered on the BOLT II launch with 1-h cadence. Turbulence and particulate measurements are shown vs altitude for all 14 data sets, along with statistics derived from the measurements, quantifying the BOLT II atmospheric environment for use in assessing the impact of these freestream disturbances on the hypersonic-vehicle boundary layer.

  PublisherDOI

Recent grants

• Collaborative Research: Targeted Observation by Radars and UAS (Unmanned Aircraft Systems) of Supercells (TORUS)

  NSF · $644k · 2018–2023

• Analysis to evaluate and improve model performance in the Central Arctic: Unique perspectives from autonomous platforms during MOSAiC

  NSF · $1.7M · 2018–2025

Frequent coauthors

• Gijs de Boer

  University of Colorado Boulder

  115 shared

• Eric W. Frew

  63 shared

• Jack Elston

  University of Illinois Urbana-Champaign

  52 shared

• Dale Lawrence

  University of Colorado Boulder

  51 shared

• Steven Borenstein

  University of Colorado Boulder

  49 shared

• Jonathan Hamilton

  University of Colorado Boulder

  43 shared

• Radiance Calmer

  Cooperative Institute for Research in Environmental Sciences

  42 shared

• Cory Dixon

  University of Colorado Boulder

  38 shared

Education

• Ph.D., Aerospace Engineering

  University of Oklahoma

  1989

• M.S., Mechanical Engineering

  University of Oklahoma

  1986

• B.S., Aerospace Engineering

  University of Oklahoma

  1983

Awards & honors

• Member, National Academy of Engineering (2022)
• Fellow, American Institute of Aeronautics and Astronautics (…
• UCB Gold Best Should Teach Award (2007)
• Marinus Smith Award (2003)
• President's Teaching Scholar (2000)