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At least 37 records · Page 2

Measurement report: Vertically resolved atmospheric properties observed over the Southern Great Plains with the ArcticShark uncrewed aerial system

Abstract. This study presents the unique capability of the Department of Energy (DOE) ArcticShark – a mid-size fixed-wing uncrewed aerial system (UAS) – for measuring vertically resolved atmospheric properties over the Southern Great Plains (SGP) of the United States. Focusing on atmospheric states, such as ambient temperature, wind, and aerosol properties, we overview measurements from 32 research flights (∼ 97 flight hours) in 2023. The August operations, aided by a visual observer on a chase plane, allowed for extensive UAS coverage, surpassing typical UAS operation envelopes. Our data from March, June, and August 2023 reveal distinctive seasonal patterns within the atmospheric column through unique chemical composition measurements. In situ measurements combined with remote sensing retrievals and radiosonde measurements provided valuable insights into their consistency and complementarity. Furthermore, we demonstrate the capabilities of the ArcticShark through several case studies, including the analyses of correlations between UAS-derived atmospheric profiles and conventional radiosonde measurements, as well as the derivation of vertically resolved profiles of aerosol chemical, optical, and microphysical properties. These case studies highlight the versatility of the ArcticShark UAS as a powerful tool for comprehensive atmospheric research, effectively bridging data gaps and enhancing our understanding of vertical atmospheric structures in the region.

54 ENVIRONMENTAL SCIENCES↗

A new aerial approach for quantifying and attributing methane emissions: implementation and validation

Methane (CH 4 ) is a powerful greenhouse gas that is produced by a diverse set of natural and anthropogenic emission sources. Biogenic methane sources generally involve anaerobic decay processes such as those occurring in wetlands, melting permafrost, or the digestion of organic matter in the guts of ruminant animals. Thermogenic CH 4 sources originate from the breakdown of organic material at high temperatures and pressure within the Earth's crust, a process which also produces more complex trace hydrocarbons such as ethane (C 2 H 6 ). Here, we present the development and deployment of an uncrewed aerial system (UAS) that employs a fast (1 Hz) and sensitive (1–0.5 ppb s -1 ) CH 4 and C 2 H 6 sensor and ultrasonic anemometer. The UAS platform is a vertical-takeoff, hexarotor drone (DJI Matrice 600 Pro, M600P) capable of vertical profiling to 120 m altitude and plume sampling across scales up to 1 km. Simultaneous measurements of CH 4 and C 2 H 6 concentrations, vector winds, and positional data allow for source classification (biogenic versus thermogenic), differentiation, and emission rates without the need for modeling or a priori assumptions about winds, vertical mixing, or other environmental conditions. The system has been used for direct quantification of methane point sources, such as orphan wells, and distributed emitters, such as landfills and wastewater treatment facilities. With detectable source rates as low as 0.04 and up to ~1500 kg h -1 , this UAS offers a direct and repeatable method of horizontal and vertical profiling of emission plumes at scales that are complementary to regional aerial surveys and localized ground-based monitoring.

54 ENVIRONMENTAL SCIENCES↗

Orphaned oil and gas well methane emission rates quantified using Gaussian plume inversions of ambient observations

Abstract. Annually, ∼ 3.6 million abandoned oil and gas wells in the US emit a combined ∼ 2.6 Tg methane (CH4), adversely affecting climate and regional air quality. However, these estimates depend on emission factors derived from measuring subpopulations of wells that vary by orders of magnitude due to very limited field sampling and poorly characterized distributions. Currently, US protocols to remediate orphaned wells lacks standardized quantification methods needed to both prioritize plugging and account for emission reductions. Therefore, sensitive, reliable, affordable, and scalable CH4 flux quantification methods are needed. We report the use of a simple Gaussian plume method where the dispersion parameters are constrained by in situ ground measurements of CH4 concentration at four locations 7.5–49 m downwind of the orphan well as well as local winds to estimate the leak rate from an orphan well. We derive a flux of 10.53 ± 1.16 kg CH4 h−1 during a venting procedure in April 2023 that agrees with the directly measured volumetric flow rate of 9.00 ± 0.25 kg CH4 h−1. This is 71 % greater than the 5.3 kg CH4 h−1 flux measured 7 months prior. Additionally, we discovered a secondary leak through the surface casing inferred as 0.43–0.67 kg CH4 h−1 both by our ground Gaussian analysis and by transecting the plume with an uncrewed aerial system (UAS). We show that in situ determination of the dispersion parameters used in our Gaussian inversions allows us to measure methane emissions to 15 % accuracy, significantly reducing errors when compared to the standard practice of assuming stability class. Our results help develop simpler methods and protocols for robust orphan well emission quantification that can be used for reporting.

Follansbee, Emily↗

Observational data from uncrewed systems over Southern Great Plains

Uncrewed Systems (UxS), including uncrewed aerial systems (UAS) and tethered balloon/kite systems (TBS), are significantly expanding observational capabilities in atmospheric science. Rapid adaptation of these platforms and the advancement of miniaturized instruments have resulted in an expanding number of data sets captured under various environmental conditions by the Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) user facility. In 2021, observational data collected using ARM UxS platforms, including seven TigerShark UAS flights and 133 tethered balloon system (TBS) flights, were archived by the ARM Data Center and made publicly available to the user community https://adc.arm.gov/discovery/#/results/s::sgp*U3 and https://adc.arm.gov/discovery/#/results/s::sgp*tbs. These data streams provide new perspectives on spatial variability of atmospheric and surface parameters, helping to address critical science questions in Earth system science research. This manuscript describes the DOE UAS/TBS datasets, including information on the acquisition, collection, and quality control processes, and highlights the potential scientific contributions using UAS and TBS platforms.

54 ENVIRONMENTAL SCIENCES↗

NPFTURBULENCE: Best Estimate Aerosol Size Distribution by airborne measurements

The original data were collected during the field campaign of “Turbulent layers promoting New Particle Formation” experiment (NPFTURBULENCE; https://www.arm.gov/research/campaigns/aaf2024npfturbulence) over the Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) Atmospheric Observatory (https://www.arm.gov/capabilities/observatories/sgp ) in north-central Oklahoma. The ARM Aerial Facility ArcticShark uncrewed aerial system (UAS) was based at Blackwell–Tonkawa Municipal Airport (IATA: BWL, ICAO: KBKN, FAA LID: BKN, 36.74475° N, 97.34918° W, 313.9 m MSL), for the field campaign from May 5 through May 29, 2024. The ArcticShark UAS performed 11 flights, including 10 research flights over the Central Facility of the ARM SGP to measure atmospheric state, turbulence, surface IT temperature and imagery, aerosol number concentration and size distribution. The current data set presents Best Estimate Aerosol Size Distribution: a merged aerosol size distribution composed of the data from 2 sensors: miniaturized Scanning Electrical Mobility Sizer (mSEMS) and Portable Optical Particle Spectrometer (POPS). The mSEMS data were interpolated to 1 second from “native” time resolution of about 15 second to match the other probe. The POPS data were converted from equivalent optical size into geometric size using value of aerosol refractive index of 1.477 from the HISCALE field campaign (same geographical area, altitudes, and time of year; http://www.arm.gov/campaigns/aaf2016hiscale ).

54 ENVIRONMENTAL SCIENCES↗

NPFTURBULENCE: Turbulent Parameters by airborne measurements

The original data were collected during the field campaign of “Turbulent layers promoting New Particle Formation” experiment (NPFTURBULENCE; https://www.arm.gov/research/campaigns/aaf2024npfturbulence) over the Atmospheric Radiation Measurement (ARM) user facility's Southern Great Plains (SGP) atmospheric observatory (https://www.arm.gov/capabilities/observatories/sgp ) in north-central Oklahoma. The ARM Aerial Facility ArcticShark uncrewed aerial system (UAS, https://www.arm.gov/capabilities/observatories/aaf/uas) was based at Blackwell–Tonkawa Municipal Airport (IATA: BWL, ICAO: KBKN, FAA LID: BKN, 36.74475° N, 97.34918° W, 313.9 m MSL), for the field campaign from May 5 through May 29, 2024. The ArcticShark UAS performed 11 flights, including 10 research flights over the Central Facility of the ARM SGP to measure atmospheric state, turbulence, surface IR temperature and imagery, and aerosol number concentration and size distribution. The current data set presents a comprehensive collection of turbulent parameters in the atmospheric boundary layer or lower free troposphere based on airborne measurement throughout the field campaign. The primary instruments used to create the current data set were the Aircraft Integrated Meteorological Measurement System (AIMMS-30) and the fine-wire thermocouple probe. For user convenience, the current data set includes several parameters commonly used in turbulent research for normalization and/or scaling: atmospheric boundary-layer height, surface conditions, convective scales for temperature, and velocity, etc.

54 ENVIRONMENTAL SCIENCES↗

NPFTURBULENCE: Fast Response Temperature by Airborne Measurements

The original data were collected during the field campaign of the “Turbulent layers promoting New Particle Formation” experiment (NPFTURBULENCE; https://www.arm.gov/research/campaigns/aaf2024npfturbulence; https://www.arm.gov/publications/programdocs/doe-sc-arm-25-004.pdf ) over the Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) Atmospheric Observatory (https://www.arm.gov/capabilities/observatories/sgp ) in north-central Oklahoma. The ARM Aerial Facility ArcticShark uncrewed aerial system (UAS, https://www.arm.gov/capabilities/observatories/aaf/uas) was based at Blackwell–Tonkawa Municipal Airport (IATA: BWL, ICAO: KBKN, FAA LID: BKN, 36.74475° N, 97.34918° W, 313.9 m MSL), for the field campaign from May 5 through May 29, 2024. The ArcticShark UAS performed 11 flights, including 10 research flights over the Central Facility of the ARM SGP to measure atmospheric state, turbulence, surface IR temperature and imagery, aerosol number concentration and size distribution. The current dataset presents fast response temperature in the atmospheric boundary layer and lower free troposphere measured on the airborne platform throughout the field campaign. The primary instruments used to create the current dataset were the fine wire thermocouple probe, Pitot-static system (part of the UAS control), and infrared gas analyzer sensor for H2O and CO2 (LI-840A). All parameters used in the temperature calculations (static and dynamic pressure, absolute humidity in form of dew point temperature) were included in the data set. For user convenience, one additional parameter was also included: the type of flight flag (level, up, down, turn, and combination of thereof).

Air temperature↗

Fast Response Temperature by airborne measurements over BNF

The original data were collected during the AAF Engineering Flights (AEF2025) in the vicinity of the ARM Bankhead National Forest (BNF) Atmospheric Observatory (https://www.arm.gov/capabilities/observatories/bnf ) in northwestern Alabama in March 2025. The ARM Aerial Facility ArcticShark uncrewed aerial system (UAS, https://www.arm.gov/capabilities/observatories/aaf/uas) was based at the public-use airport of Posey Field, Alabama (FAA LID: 1M4, 34.28027778° N, 87.60055556° W, 283m MSL) from March 9 through March 24, 2025. The ArcticShark UAS performed nine flights, including eight research flights over the AMF3 (BNF Main Site) and Supplemental Facilities to measure atmospheric state, turbulence, surface IR temperature and imagery, and aerosol number concentration and size distribution. The current data set presents fast response temperature in the atmospheric boundary layer and lower free troposphere measured on the airborne platform throughout the field campaign. The primary instruments used to create the current data set were the fine wire thermocouple probe, the Aircraft Integrated Meteorological Measurement System (AIMMS-30), the Pitot-static system (part of UAS flight control), and the infrared gas analyzer sensor for H2O and CO2 (LI-840). All parameters used in temperature calculations (static pressure, True Air Speed, and absolute humidity in form of dew point temperature) were included in the data set. For user convenience, one additional parameter was also included: the type of flight flag (level, up, down, turn, and combination of thereof).

Air temperature, fast response↗

CHELAX-BNF: Fast Response Temperature by airborne measurements

The original data were collected on board the ARM Aerial Facility ArcticShark uncrewed aerial system (UAS; https://www.arm.gov/capabilities/observatories/aaf/uas ) during the “Characterizing HEterogeneous Land-Atmosphere eXchanges at BNF” field campaign (CHEAX-BNF; https://arm.gov/research/campaigns/aaf2025CHELAX-BNF ). The ARM Aerial Facility ArcticShark UAS was based at the public-use airport of Posey Field, AL (FAA LID: 1M4, 34.28027778° N, 87.60055556° W, 283m MSL) from May 28 to June 23, 2025. The ArcticShark UAS performed 5 flights, including 4 research flights over the BNF Main Site (ARM Mobile Facility 3, https://arm.gov/capabilities/observatories/amf ) and Supplemental Facilities to measure atmospheric state, turbulence, surface IR temperature and imagery, aerosol number concentration, and aerosol size distribution. The current data set presents fast response temperature in the atmospheric boundary layer and lower free troposphere measured on the airborne platform throughout the field campaign. The primary instruments used to create the current data set were the fine wire thermocouple probe, the Aircraft Integrated Meteorological Measurement System (AIMMS-30), the Pitot-static system (part of UAS flight control), and the infrared gas analyzer sensor for H2O and CO2 (LI-840). All parameters used in temperature calculations (static pressure, True Air Speed, and absolute humidity in the form of dew point temperature) were included in the data set. For user convenience, one additional parameter was also included: the type of flight flag (level, up, down, turn, and combination of thereof).

Air temperature, fast response↗

Turbulent Parameters by airborne measurements over BNF in March 2025

The original data were collected during the AAF Engineering Flights (AEF2025) in the vicinity of the ARM Bankhead National Forest (BNF) Atmospheric Observatory (https://www.arm.gov/capabilities/observatories/bnf ) in northwestern Alabama in March 2025. The ARM Aerial Facility ArcticShark uncrewed aerial system (UAS, https://www.arm.gov/capabilities/observatories/aaf/uas) was based at the public-use airport of Posey Field, Alabama (FAA LID: 1M4, 34.28027778° N, 87.60055556° W, 283m MSL) from March 10 through March 24, 2025. The ArcticShark UAS performed nine flights, including eight research flights over the AMF3 (BNF Main Site) and Supplemental Facilities to measure atmospheric state, turbulence, surface IR temperature and imagery, and aerosol number concentration and size distribution. The current data set presents a collection of turbulent parameters in the atmospheric boundary layer or lower free troposphere based on airborne measurement throughout the field campaign. The primary instruments used to create the current data set were the Aircraft Integrated Meteorological Measurement System (AIMMS-30) and the fine-wire thermocouple probe.

Atmosphere↗

CHELAX-BNF: Turbulent Parameters by airborne measurements

The original data were collected on board the ARM Aerial Facility ArcticShark uncrewed aerial system (UAS; https://www.arm.gov/capabilities/observatories/aaf/uas ) during the “Characterizing HEterogeneous Land-Atmosphere eXchanges at BNF” field campaign (CHEAX-BNF; https://arm.gov/research/campaigns/aaf2025CHELAX-BNF ). The ARM Aerial Facility ArcticShark UAS was based at the public-use airport of Posey Field, AL (FAA LID: 1M4, 34.28027778° N, 87.60055556° W, 283m MSL) from May 28 through June 23, 2025. The ArcticShark UAS performed 5 flights, including 4 research flights over the BNF Main Site (ARM Mobile Facility 3, https://arm.gov/capabilities/observatories/amf ) and Supplemental Facilities to measure atmospheric state, turbulence, surface IR temperature and imagery, aerosol number concentration, and aerosol size distribution. The current data set presents a collection of turbulent parameters in the atmospheric boundary layer or lower free troposphere based on airborne measurement throughout the field campaign. The primary instruments used to create the current data set were the Aircraft Integrated Meteorological Measurement System (AIMMS-30) and the fine-wire thermocouple probe.

Aircraft Integrated Meteorological Measurement Sys↗

Hot on the Trail: Mapping Wildland Fires With Citizen Science and Live Image Processing

Heightened fire activity is straining existing firefighting resources and budgets across the nation and worldwide. For this reason, the 2021 NASA Academy at Langley Research Center has the challenge of applying NASA technology to the challenges faced by wild-land firefighting professionals. The approach is three-fold: Data Acquisition, Communications, and Equipment Development. The Communications Team focused on improving data latency for fire mapping and advancing communications between civilians and incident dispatch. The team developed two main concepts to address these areas: First, they tried to identify, map, and transmit fire perimeter data via an Uncrewed Aerial System (UAS), and second, they suggest a fully functioning web-app for early fire detection and fire monitoring using citizen science. This report outlines the materials and methods of these concepts, as well as future work necessary to bring the proposed technology into the field.

UAS data transfer ground stations↗

A Field-Deployable Wireless Data Acquisition System for Ground-Test Arrays

This paper describes the development, characterization, and deployment of a field-deployable wireless data acquisition system for ground-test arrays, applicable in noise-source localization or beamforming measurements such as those encountered during airframe noise flyover measurement tests. The system design is enabled by commercially available, low-power Internet of Things (IoT) processors and Wi-Fi communication components. Time is synchronized across the array wirelessly by leveraging the Coordinated Universal Time (UTC) time provided by the Global Positioning System (GPS). Initial laboratory characterization of the system demonstrated its ability to meet acoustic bandwidth, dynamic range, time synchronization, environmental, and battery life requirements for typical ground-test array deployments. A successful system deployment at NASA Langley was performed that included measurement of a suspended elevated static noise source, and Uncrewed Aerial System (UAS) vehicle measurements using a quadcopter operated both in hover mode and forward flight. Beamform analysis of the acquired data showed an excellent ability of the array to extract accurate sound pressure levels from the suspended source. Synchronization of the array and vehicle GPS timecodes allowed the ability to extract acoustic signatures from the UAS vehicle during hover and forward flight maneuvers over the array. These results validate that the use of a high channel count wireless array is advantageous in airframe and propulsion noise flyover test campaigns.

wireless↗

Design of Safe Separation Bounds for Temporally Deconflicted Trajectories Under Bounded Uncertainties

This paper explores the derivation of safe separation bounds for a heterogeneous group of~$n$ Uncrewed Aerial Systems (UAS) that are assigned temporally deconflicted trajectories. Compared to spatially deconflicted trajectories, temporal deconfliction can lead to higher traffic capacities and a more efficient use of the available airspace. One challenge with this type of deconfliction is that collisions can occur if some cooperating UAS are behind or ahead of schedule. To overcome this risk, this paper derives a lower bound on the safety distance between two heterogeneous UAS in the presence of bounded uncertainties. This safety distance can be leveraged to inform trajectory generation algorithms. The proposed bound establishes a rigorous safety margin when the fleet deviates from the planned trajectories, both temporally and spatially. For its derivation the paper assumes the UAS implement a distributed coordination algorithm that allows the fleet to maintain their schedules synchronized within a bounded temporal error, and a path-following algorithm that lets the vehicles track a target that moves along the planned trajectory with a bounded spatial error.

autonomy↗

Uncrewed Aerial Systems for Emergency Medical First Response: A Market Research Report

This report presents the findings from market research conducted for NASA’s Aerial Aid Convergent Aeronautics Solutions (CAS) exploration project, which aims to assess the current state of the market and technological readiness for Uncrewed Aerial Systems (UAS) for medical emergency first response. The research reveals a robust and rapidly growing market for UAS, with a notable emerging sector for Drones as First Responders (DFR). Despite this growth, DFR applications are currently limited by regulatory, technical, and other challenges, which restrict their use primarily to manned remote video surveillance, and therefore are primarily employed by police units. To our knowledge, there is no evidence of UAS being utilized by medical first responders for scene assessment. Limited evidence exists for closely related applications; however, these are mostly confined to pilot programs for the delivery of medical supplies or equipment. Although there has been discussion around fully autonomous DFR applications for medical purposes such as UAS ambulances or patient transport drones, these applications are generally not yet operational in practice. The technology for full autonomy, especially in guidance and control, has seen significant advancements, and recent Federal Aviation Administration (FAA)regulations are likely to accelerate adoption. Computer vision algorithms for fully autonomous medical emergency response scene surveillance are primed for advancement and deployment. A notable gap likely exists between advancements in computer vision research and what is being integrated in the commercial DFR sector. This gap is primarily due to challenges such as quality assurance for autonomous systems, the availability of application-specific training datasets for computer vision algorithms, regulatory constraints, and public perception and privacy concerns.

Joshua M Fody↗

Developing a Cybersecurity Architecture for Extensible Traffic Management (xTM)

This paper explores the development of a cybersecurity architecture tailored for Extensible Traffic Management (xTM) to address emerging challenges in managing diverse aerial vehicles within the National Airspace System (NAS). Driven by technological advances and the rise of uncrewed aerial systems (UAS), urban air mobility (UAM), and high-altitude traffic (ETM), the NAS is undergoing a paradigm shift. Traditional air traffic management, reliant on traditional Federal Aviation Administration (FAA) control, will give way to decentralized coordination among autonomous and semi-autonomous systems. The proposed xTM Security Architecture, designed as a high-level framework, focuses on ensuring the confidentiality, integrity, and availability of data and operations in this evolving ecosystem. Utilizing threat modeling, the research identifies potential risks across key flight phases, operations and use cases to offer security control recommendations. Key objectives include analyzing interactions between novel airspace entrants and existing NAS traffic, cataloging vulnerabilities, and developing mitigative strategies to ensure safety, operational stability, and secure data exchanges. This research lays the groundwork for regulatory and industry adaptation, providing critical insights into managing cybersecurity risks in this complex, multi-domain environment.

UAM↗

Advancing Wildland Fire Response with NASA’s Second Shift Capabilities

Novel “Second-Shift” capabilities—leveraging Uncrewed Aerial Systems and Optionally Piloted Vehicles—are identified that could extend wildfire aerial logistics support into night or low-visibility conditions. Expert elicitations with subject-matter experts informed the development of these conceptual capabilities and identified key challenges in wildfire logistics operations. Furthermore, the potential of these capabilities could extend beyond aerial logistics support, encompassing aerial suppression, observation, and emergency extraction support for wildland firefighting. An approach to development, implementation and validation of the novel capabilities is described.

Wildfire logistics↗

Advancing Wildland Fire Response with NASA’s Second Shift Capabilities

Novel “Second-Shift” capabilities—leveraging Uncrewed Aerial Systems and Optionally Piloted Vehicles—are identified that could extend wildfire aerial logistics support into night or low-visibility conditions. Expert elicitations with subject-matter experts informed the development of these conceptual capabilities and identified key challenges in wildfire logistics operations. Furthermore, the potential of these capabilities could extend beyond aerial logistics support, encompassing aerial suppression, observation, and emergency extraction support for wildland firefighting. An approach to development, implementation and validation of the novel capabilities is described.

Wildfire logistics↗