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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 973 records · Page 54
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Development of an Enhanced Radiation Physics Toolset for Modeling Spectral and Imaging Signatures in the Warm Dense Matter Experiments

Radiative and atomic processes in plasmas play a critical role in a wide variety of high energy density laboratory plasma (HEDLP) experiments. The emission, absorption, and transport of radiation can strongly affect the overall energetics and evolution of such plasmas. In addition, radiation-based diagnostics – including imaging, spectroscopy, and absolute flux measurements – are widely used to determine key features of HEDLPs. To advance our understanding of HEDLP science, it is vital to have high-fidelity computational physics tools that have well-tested radiation physics modeling, and that are readily accessible to researchers in the HEDLP community. Simulations play an extremely important role for planning and designing the experiments, as well as for post-experiment data analysis. Prism Computational Sciences develops software that is used by National Laboratories and universities (including five members of LaserNetUS network). Prominent examples of such research efforts include z-pinch and short-pulse laser experiments designed to study the basic physics of photoionized plasmas and photoionization fronts, as well as their application to astrophysical plasmas. The main effort was dedicated to the development of non-equilibrium equation-of-state (EOS) models within the HELIOS-CR code, a hydrodynamics code with inline collisional-radiative atomic kinetics. Gas cell experiments on Z and Omega demonstrated the importance of non-equilibrium effects on atomic kinetics in photoionized plasmas. Recent proof-of-principle experiments on Omega EP confirmed the advantages of using a short-pulse laser to create an intense radiation drive, leading to additional experiments being proposed. Photoionization front experiments at LLE also emphasizes the importance of radiation and atomic physics. In both studies, HELIOS-CR simulations played a crucial role in computing non-equilibrium opacities and ionization distributions. A newly developed non-LTE EOS model will help addressing possible non-equilibrium effects, on for example specific heat, and their influence in plasma evolution. Prism also implemented support for open-source atomic data generated by the Flexible Atomic Code. This allows researchers to generate custom atomic tables and use them within the complex framework of simulation tools developed by Prism. The ability to use open-source atomic data would be extremely valuable for hydrodynamics and spectroscopic simulations that include high-Z materials, e.g., picosecond x-ray pulse generation experiments. Support for new atomic structures was fully implemented, and the data can be used by all simulation tools developed at Prism: radiation-hydrodynamics, imaging and spectroscopy, EOS and opacity. The development resulted in a significant fidelity enhancement to the simulations tools developed by Prism that are currently used in other cutting-edge experiments including: opacity measurement experiments performed to both understand the basic radiative and atomic properties of plasmas as well as provide data for more accurately modeling the internal structure of the Sun and other stars, high-intensity short-pulse laser experiments performed to develop short-wavelength light sources for use as backlighters and to investigate fast ignition concepts for inertial fusion energy; capsule implosion experiments designed to develop inertial fusion as an energy source, etc.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

2025 Peregrine in-situ monitoring and training dataset for laser powder bed fusion and binder jet printers

Peregrine, a software tool developed at Oak Ridge National Laboratory (ORNL), was used to collect and analyze in-situ monitoring (ISM) data from a Concept Laser M2 (Colibrium Additive) laser powder bed fusion (L-PBF) printer and an ExOne Innovent (Desktop Metal) binder jet printer. Data for four builds (print jobs) were saved to HDF5 (high performance data) files for release. Additionally, process anomalies were annotated by the authors across 37 image stacks (i.e., print layers) and are also provided as HDF5 files.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Image Deconvolution and Point-spread Function Reconstruction with STARRED: A Wavelet-based Two-channel Method Optimized for Light-curve Extraction

We present starred, a point-spread function (PSF) reconstruction, two-channel deconvolution, and light-curve extraction method designed for high-precision photometric measurements in imaging time series. An improved resolution of the data is targeted rather than an infinite one, thereby minimizing deconvolution artifacts. In addition, starred performs a joint deconvolution of all available data, accounting for epoch-to-epoch variations of the PSF and decomposing the resulting deconvolved image into a point source and an extended source channel. The output is a high-signal-to-noise-ratio, high-resolution frame combining all data and the photometry of all point sources in the field of view as a function of time. Of note, starred also provides exquisite PSF models for each data frame. We showcase three applications of starred in the context of the imminent LSST survey and of JWST imaging: (i) the extraction of supernovae light curves and the scene representation of their host galaxy; (ii) the extraction of lensed quasar light curves for time-delay cosmography; and (iii) the measurement of the spectral energy distribution of globular clusters in the "Sparkler," a galaxy at redshift z = 1.378 strongly lensed by the galaxy cluster SMACS J0723.3-7327. starred is implemented in jax, leveraging automatic differentiation and graphics processing unit acceleration. This enables the rapid processing of large time-domain data sets, positioning the method as a powerful tool for extracting light curves from the multitude of lensed or unlensed variable and transient objects in the Rubin-LSST data, even when blended with intervening objects.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

An instrument for in situ characterization of powder spreading dynamics in powder-bed-based additive manufacturing processes

In powder-bed-based metal additive manufacturing (AM), the visualization and analysis of the powder spreading process are critical for understanding the powder spreading dynamics and mechanisms. Unfortunately, the high spreading speeds, the small size of the powder, and the opacity of the materials present a great challenge for directly observing the powder spreading behavior. In this study, we report a compact and flexible powder spreading system for in situ characterization of the dynamics of the powders during the spreading process by high-speed x-ray imaging. The system enables the tracing of individual powder movement within the narrow gap between the recoater and the substrate at variable spreading speeds from 17 to 322 mm/s. The instrument and method reported here provide a powerful tool for studying powder spreading physics in AM processes and for investigating the physics of granular material flow behavior in a confined environment.

47 OTHER INSTRUMENTATION↗
DOE OSTI2021

Measurement of Deformation Heterogeneity During Shale Swelling Using Digital Image Correlation

Abstract Rock-fluid interactions in shale formations are one of the main sources of wellbore instability issues and inadequate stimulation performance. For better planning of fracturing operations and optimizing production from these reservoirs, it is important to understand the mechanisms behind these interactions. These issues are especially prevalent in clay-rich shales, such as Tuscaloosa Marine Shale, which is the subject of this study. Conventional techniques to quantify these shale-fluid interactions comprise of measuring swelling in powdered rock grains or measurement of deformation in the whole pieces of the core using a linear variable differential transformer and strain gages. However, the contribution from individual laminae to overall deformation cannot be evaluated using these methods. In this study, we developed an experimental setup to evaluate the spatial deformation in shale during interaction with water using digital image correlation (DIC). Deformation of two shale samples, with 34 wt% to 51 wt% clay content, was studied. White paint was used to generate a random speckle pattern on the specimen and then immersed in deionized water. The deformation process was captured using a digital camera and images were analyzed using DIC to quantify the deformation. The implementation of the DIC technique enables the visualization and quantification of spatial deformation in the specimen during interacting with water. The results show the localization of large strains in select laminations. The results provide a better understanding of shale deformation when interacting with water in comparison to traditional measurements that can provide only an average strain value.

Energy & Fuels↗
DOE OSTI2020

X-ray Computed Tomography on UNESE Core: FY2020 Data Report to Support Fracture and Multiphase Fluid Flow Studies

Natural and induced fractures are potential preferential pathways for migration of radioactive gases to earths surface from underground nuclear explosions (UNEs). This report documents X-ray computed tomography (XRCT) imaging on 26 samples of rock core that was collected to support the Underground Nuclear Explosion Signatures Experiment (UNESE) program. The XRCT datasets are intended to help fill a data gap on the three-dimensional (3D) characteristics of natural and/or induced fractures at the centimeter and smaller scale, which may strongly influence multiphase fluid flow and transport properties of preferential flow paths and interaction with the matrix of the surrounding host rock. Pre- and post-UNE rock samples were carefully chosen to enable comparison of fractures as a function of lithologic and petrophysical properties, as well as distance to the past UNEs. This report serves as documentation for the data, including an introduction with the research motivation, a methods and materials section, descriptions of the XRCT datasets without post-processing, and recommendations for 3D quantification via image analysis and digital rock physics.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2020

Evaluation of Techniques for Vegetation Removal from UAV-Based Photogrammetric DSM near WIPP Land Withdrawal Act Boundary, NM

The Waste Isolation Pilot Plant is located within New Mexico's karst landscape and is the United States' only deep geologic radioactive waste repository which isolates transuranic waste from defense activities underground in a bedded salt formation. Characteristic karst topography in this region is made up of material (such as limestone) that can be dissolved by water over a long period of time, and includes features such as springs, surface streams, sinking streams, caves, and sinkholes. Karst features can potentially impact the integrity of the waste repository in the future and compromise WIPP's performance, particularly when coupled with incompatible land-use activities within the Land Withdrawal Act (LWA) boundary as well as increased water withdrawals outside the LWA. In order to better capture the ground surface topography to support hydrology modeling efforts, the U.S. DOE has enlisted FIU to develop a high-resolution digital elevation model (DEM). In a previous field study, a high-resolution digital surface model (DSM) of an adjacent representative site, Basin 6, was developed by photogrammetry using aerial images captured with unmanned aerial vehicles. The DSM generated was based on point clouds representing the entire landscapes, including points of terrain, vegetation and infrastructure. These above-ground features need to be removed to create a DSM/DEM with good representation of the bare ground. In recent years, many techniques including both pre- and post-processing of DSMs, were developed for vegetation removal with varied success. This research focuses on applying and comparing various vegetation removal methods using point clouds generated of the Basin 6 pilot study area. The 3D RGB-based point cloud classification using Python scripts was a modification of the procedure by Themistocleous (2019). The method using Liblas classifies the point cloud three-dimensionally, whereas the method by Themistocleous (2019) is two-dimensional. The preliminary result of the method using Liblas, however, still had a large amount of vegetation remaining compared to the Pix4D Machine Learning method. The vegetation layer remains highly visible in the DEMs processed using the vegetation indices as opposed to the Pix4D Machine Learning method, which produces a DEM where mostly bare ground is seen. Further modifications to the input parameters are required to improve the vegetation vs. bare ground classification using this approach. The next step will be to improve the method using Liblas and to also test other vegetation removal methods found in the literature review to compare them against the two described here.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2024

Informing solar blind radioluminescence imaging through a calibrated spectrum

While direct radiation detection methods offer great insight into the origin of ionizing particles and photons, their use to locate contaminated areas or concealed radioactive sources can lead to undue exposure of personnel and equipment to ionizing radiation or the potential for contamination. These same sources induce ultraviolet (UV) optical photon fluorescence in air – a process referred to as radioluminescence – that may be imaged from low dose regions over larger attenuation lengths than ionizing radiation. However, most optical detection methods are limited to low lighting conditions to image the more abundant ultraviolet-A (UV-A) photons. To extend this capability to room light or daytime conditions, the solar blind region (Ultraviolet-C (UV-C), <280 nm) can be tapped. Though the emission yield of UV-C photons is roughly two orders of magnitude lower than that in the UV-A regime, the UV-C offers dramatic improvements in signal-to-noise ratios under bright lighting conditions due to decreased background interferences. The yield of specific UV-C lines, if present in the literature at all, varies widely, which has a large impact in modeling and analyzing standoff UV-C measurement scenarios. Thus, we have captured improved radioluminescence spectra over 250–400 nm and identified observed emission peaks in ambient air. Many of the UV-C photons produced by ionizing radiation excitation result from high-energy, molecular nitrogen Gaydon-Herman transitions which have had limited study to date for this application. Relating these findings to published UV-A yields, we estimate emissions between 0.15–0.19 photons/MeV over 250–280 nm. Additionally, we also use a commercial corona-discharge imaging camera to demonstrate outdoor UV-C radiation mapping of alpha and gamma emitters from 50 and 75 m standoffs, respectively. The imaged “counts” are compared to optically modelled values and show the same trend over distance.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

Bioimaging Science Program: 2022 Principal Investigator Meetings Proceedings

The mission of the U.S. Department of Energy’s (DOE) Biological and Environmental Research (BER) program’s Bioimaging Science Program (BSP) is to understand the translation of genomic information into the mechanisms that power living cells, communities of cells, and whole organisms. The goal of BSP is to develop new imaging and measurement technologies to visualize the spatial and temporal relationships of key metabolic processes governing phenotypic expression in plants and microbes. The extended goal of dynamic imaging is to functionally connect cellular components and interdependent organisms. Information about the time and place of chemical reactions in situ can identify causal relationships between biological activators and downstream effectors. BSP held its annual PI meeting virtually February 28–March 1. Contributing investigators are convened to review progress and current state-of-the-art bioimaging research. Holding the 2022 BSP meeting as part of the broader Genomic Science Program (GSP) PI meeting allowed researchers to interact with the extended GSP community. This convergence provided a platform for networking and exchange of ideas with experts in other technologies and in target BSP application areas, helping to forge new multidisciplinary collaborations among investigators from the sister programmatic areas within BER’s Biological Systems Science Division. An important highlight of the BSP meeting was the keynote presentation by Nobel Laureate Dr. Joachim Frank on Time-Resolved Macromolecular Imaging using Cryo-EM. He discussed microfluidic mixing and fast freezing to capture nonequilibrium intermediate states during molecular binding and conformational changes. The action of molecular machines can be captured at nanometer resolution and millisecond discrimination. BSP PIs made presentations describing their research focus and progress in plenary sessions on bioimaging science and on quantum-enabled bioimaging science research projects. BSP research at universities and DOE laboratories is presented in this report. A final discussion of the BSP was organized by meeting plenary session chairs, who prepared the following Executive Summary of current BSP research, research challenges, future opportunities, and potential ideas for expanding the BSP’s impact and interactions.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Deep Neural Network Algorithm for CMC Microstructure Characterization and Variability Quantification

Microstructure characterization and variability quantification are crucial for understanding ceramic matrix composites (CMCs) mechanical behavior and deformation mechanisms across length scales. Traditionally, analyses of the micrographs obtained from microscopy are labor-intensive. However, with the vast improvement in computer vision (CV) and deep learning (DL), an automated algorithm can be designed to extract essential microstructure variability from micrographs which can then be used to construct a statistically representative volume element (SRVE). The DL-based algorithm spans the taxonomy of microstructure analyses, including semantic segmentation of microstructure constituents, secondary phases, matrix/fiber interface, and defects, and quantifying the microstructure variability in terms of probability distributions. In this work, C/SiNC and SiC/SiNC CMCs microstructures are semantically segmented through a deep convolutional neural network, followed by variability quantification through the implementation of a fully connected regression layer, hence forming a deep regression network. The deep regression network operates in a feedforward regime, in which the neuron output signal traverses through the network in a unidirectional manner. The weight tensor associated with each layer is updated through a backpropagation stochastic gradient descent approach. The input gray-scale image obtained through in-house scanning electron microscope and confocal microscope micrographs is augmented through affine transformations to increase the training set size, which is then processed through four strided convolutional layers. This compresses the image resolution by half at each layer while increasing the image depth by applying different filters (image encoding). The class activation maps (CAMs) corresponding to the applied filters highlight the key architectural features and assist with the semantic segmentation of the microstructure.

Hamza, Mohamed H.↗
DOE OSTI2020

In-situ Observations of Directed Energy Deposition Additive Manufacturing Using High-Speed X-ray Imaging

In laser-based directed energy deposition (DED) additive manufacturing, interactions among the laser beam, particle flow, and melt pool influence the properties of the solidified final part. Two separate DED systems, one with high powder flow rates to represent industrial-scale DED processing and the other with low powder flow rates for individual particle tracking, were synchronized with the high-speed imaging setup at the Advanced Photon Source in Argonne National Laboratory. In-situ x-ray imaging of the DED process using both systems highlighted the influence of powder flow rates. Increased powder flow rates resulted in less laser absorption into the melt pool, leading to a transition from a keyhole mode to a melt pool without a keyhole but with surface fluctuations due to powder flow. Increased velocities of particles during powder flow resulted in a decrease in particle melting times and a greater propensity for porosity formation. Altogether, better understanding of the interactions that occur during various scales of the DED process will enable flexibility, control, and new materials development in DED-based additive manufacturing.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Probe the Localized Electrochemical Environment Effects and Electrode Reaction Dynamics for Metal Batteries using In Situ 3D Microscopy

Uncontrollable dendrite growth is closely related to non-uniform reaction environments. However, there is a lack of understanding and analysis methods to probe the localized electrochemical environment (LEE). Here the effects of the LEE are investigated, including localized ion concentrations, current density, and electric potential, on metal plating/stripping dynamics and dendrite minimization. A novel in situ 3D microscopy technique is developed to image the morphology dynamics and deposition rate of Zn plating/stripping processes on 3D Zn–Mn anodes. Using the in situ 3D microscope, the electrode morphology changes during the reactions are directly imaged and Zn deposition rate maps at different time points are obtained. It is found that reaction kinetics are highly correlated to LEE and electrode morphology. To further quantify the LEE effects, the digital twin technique is employed that allows the accurate calculation of the electrochemical environments, such as localized ion concentrations, current density, and electric potential, which cannot be directly measured from experiments. We found that the curvature of the 3D electrode surface determines the LEE and significantly influences reaction kinetics. This provides a new strategy to minimize the dendrite formation by designing and optimizing the 3D geometry of the electrode to control the LEE.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Remote sensing images, DEM, and point clouds associated with “Accuracy evaluation of cost-effective 3D reconstruction approaches for hydrobiogeochemical processes in non-perennial stream riverbeds”

This data package is associated with the publication “Accuracy evaluation of cost-effective 3D reconstruction approaches for hydrobiogeochemical processes in non-perennial stream riverbeds” published in Frontiers in Environmental Science, Environmental Informatics and Remote Sensing (Bao et al., 2026; doi: 10.3389/fenvs.2026.1725258). This data package includes the drone photos for a section of Umtanum Creek in Washington, Unted States. The photos were used to reconstruct the 3-dimensional (3D) digital elevation model (DEM) of the riverbed for the investigated stream section. The reconstruction results from four approaches are provided: (1) unoccupied aerial vehicle (UAV, colloquially known as drone) imagery-based Structure-from-Motion (SfM), (2) a machine learning-based 3D reconstruction model, Visual Geometry Grounded Deep Structure from Motion (VGGSfM), (3) Visual Geometry Grounded Transformer for long sequence of images (VGGT-Long), and (4) handheld smartphone LiDAR scanning. The ground truth measurements by tripod-mounted optical level kit and ground control points GPS locations for evaluating the accuracy of the four reconstruction approaches are also provided in this data package. A preliminary version of this data package was published in October 2025 at the time of manuscript submission. It was updated in March 2026, at the time of manuscript acceptance, to include additional metadata (this readme, data dictionary, and file level metadata). The data did not change. For details on how to navigate data packages generated by this project, see https://data.ess-dive.lbl.gov/portals/PNNLRiverCorridorSFA/About. In addition to a readme, this data package also includes a file-level metadata (FLMD) file that describes each file and a data dictionary (DD) that describes all column/row headers and variable definitions. This dataset is comprised of (1) 8 folders; (2) the detailed flight configuration html files; (3) field metadata; (4) a readme; (5) a data dictionary; and (6) file-level metadata. The folders “2024_10_18_d01” and “2024_10_18_d02” contain the original drone photos for the two drone flights (d01 and d02) on October 18, 2024. The reconstruction results from each of the approaches are in the folders called “ODM_SfM”, “VGGSfM”, “VGGTLong”, and “LiDAR”. The ground truth measurements are in the folder called “optical_level_kit”. Lastly, results comparing the different approaches are in the folder called “comparisons”. All files are .csv, .html, .jpg, .obj, .txt, and .npy. For information on using the .obj and .npy files, see the readme files within the same folder as the files.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

High-speed X-ray imaging of droplet-powder interaction in binder jet additive manufacturing

Binder jetting (BJ) is an additive manufacturing process that uses a powder feedstock in a layer wise process to print parts by selectively depositing a liquid binder into the powder bed using inkjet technology. This study presents findings from high-speed synchrotron imaging of binder droplet-interaction during the BJ printing process. A custom laboratory-scale BJ test platform was used for testing which enabled control of relevant process parameters including powder material, print geometry, spacing between droplets, powder bed density, and powder moisture content. Powder ejection was observed above the powder bed surface and powder relocation due to droplet impact was observed below the powder bed surface. Powder relocation was observed to be sensitive to powder material, powder bed density, powder bed moisture, droplet spacing, and print geometry. Increasing powder bed density was found to increase particle ejection velocity but reduce the total number of particles ejected. Process parameters that increase binder / moisture content in the powder bed were found to reduce powder ejection. The number of ejected powder particles was reduced for lower droplet spacings. Both powder ejection and powder relocation below the powder bed were reduced by treating the surface of the powder bed with a water/triethylene glycol (TEG) mixture before printing. In conclusion, results from this study help to build understanding of the physical mechanisms in the BJ printing process that may contribute to formation of defects observed in final parts.

36 MATERIALS SCIENCE↗
DOE OSTI2020

A combined SECM and electrochemical AFM approach to probe interfacial processes affecting molecular reactivity at redox flow battery electrodes

Redox flow batteries are attractive technologies for grid energy storage since they use solutions of redox-active molecules that enable a superior scalability and the decoupling of power and energy density. However, the reaction mechanisms of the redox active components at RFB electrodes are complex, and there is currently a pressing need to understand how interfacial processes impact the kinetics and operational reversibility of RFB systems. Here, we developed a combined electrochemical imaging methodology rooted in scanning electrochemical microscopy (SECM) and atomic force microscopy (AFM) for exploring the impact of electrode structure and conditioning on the electron transfer properties of model redox-active dialkoxybenzene derivatives, 2,5-di-tert-butyl-1,4-bis(2-methoxyethoxy)benzene (C1) and 2,3-dimethyl-1,4-dialkoxybenzene (C7). Using AFM and secondary-ion mass spectrometry (SIMS), we observed the formation of interfacial films with distinct mechanical properties compared to those of cleaved graphitic surfaces, and exclusively during reduction of electrogenerated radical cations. These films had an impact on the median rate and distribution of the electron transfer rate constant at the basal plane of multilayer and single layer graphene electrodes, displaying kinetically-limited values that did not yield the activation expected per the Butler–Volmer model with a transfer coefficient ~0.5. These changes were dependent on redoxmer structure: furthermore, SECM showed strong attenuation of C7 kinetics by a surface layer on MLG and SLG, while C1 kinetics were only affected by SLG. SECM and AFM results together show that these limiting films operate exclusively on the basal plane of graphite, with the edge plane showing a relative insensitivity to cycling and operation potential. Ultimately, this integrated electrochemical imaging methodology creates new opportunities to understand the unique role of interfacial processes on the heterogeneous reactivity of redoxmers at electrodes for RFBs, with a future role in elucidating phenomena at high active concentrations and spatiotemporal variations in electrode dynamics.

25 ENERGY STORAGE↗
DOE OSTI2024

Surface elevation, snow depth, vegetation height, and color imagery from multiple UAV surveys from 2018 to 2023 across a watershed near Teller road mile marker 27, Seward Peninsula, Alaska

Multiple photogrammetric surveys using an unoccupied aerial system (UAS) were performed across a watershed along Teller road on the Seward Peninsula (Alaska) to investigate terrain, vegetation and snowpack properties. This work was led by the environmental geophysics team from Lawrence Berkeley National Laboratory as part of the Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic). The multiple photogrammetric surveys used mostly a DJI Matrice 210 UAS with a DJI X5S camera, Ground Control Points (GCPs) surveyed with a Real Time Kinematic (RTK)-GPS, and a structure from motion (SfM) technique for photogrammetric reconstruction. Digital Surface elevation Models (DSMs) were inferred at various times of year, including near peak in plant and leaf density (July 19 2017), close to peak in snow depth (April 1 2019 and April 11 2022), and at low plant and leaf density of tall after the first bare-ground date (June 9 2019). Snow and canopy height were obtained by subtracting DSMs from a Digital Terrain Model (DTM) proxy inferred from the June 9 2019 survey. The derived products include ortho-mosaiced RGB map, DSMs, a DTM proxy, snow depth (April 2019 and 2022) and canopy height (July 2017). Unprocessed and processed data products are included in this package (processing levels 0-2). Data and metadata are provided as text (*.csv), image (*.jpg), and GeoTiff (*.tif) formats. This metadata document contains a description of the survey and processing steps and the inferred products.The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic) was a 15-year research effort (2012-2027) to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy’s Office of Biological and Environmental Research. The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska. Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy’s Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗