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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 379 records · Page 21
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

MOSAiC Radar b1 Processing: Corrections, Calibrations, and Processing Report

The U.S. Department of Energy’s (DOE) Atmospheric Radiation Measurement (ARM) user facility deployed many instruments on board a German ice breaker, the Research Vessel (RV) Polarstern, for one year from October 2019 to October 2020. The purpose of the Multidisciplinary Drifting Observatory for the Study of Arctic Climate (MOSAiC) field campaign was to study the decline in the sea-ice pack around the North Pole, and what factors may be at play. After the campaign ended, efforts were undertaken to provide a calibrated radar data set for future studies. MOSAiC presented new challenges to this process, as existing methodologies often were not applicable for the frozen environment with little to no ground clutter, and concurrent engineering updates or calibrations could not be accomplished once the ship set off. Like the previous Cloud, Aerosol, and Complex Terrain Interactions (CACTI) and Cold-air Outbreaks in the Marine Boundary Layer Experiment (COMBLE) documentation (Hardin et al. 2020, Matthews et al. 2023), the data correction and calibration process is known in ARM as creating a “b1” datastream. This means that the radar datastreams have been well characterized to the best possible quality. This report will detail the status of the raw “a1” level data sets during the MOSAiC campaign, the corrections that were applied to create the “b1” data files, and the details of the applied methods.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Blending Pipeline Analysis Tool for Hydrogen (BlendPATH) Documentation and User Manual

The Blending Pipeline Analysis Tool for Hydrogen (BlendPATH) is a flexible, open-source Python tool designed to provide users with case-by-case analysis capabilities to identify the necessary modifications to repurpose existing natural gas transmission pipeline networks to transport hydrogen as a blend of a user-specified volume fraction of hydrogen or as a pure stream and to estimate the associated capital and operating expenditures resulting from those modifications. This tool is intended to be applied during the initial screening stage of a prospective project when pipeline developers compile transmission pipeline technical documentation and history but prior to performing detailed pipeline inspections. Performing analysis with BlendPATH during this initial screening stage can provide the user with an understanding of promising opportunities and probable economic outcomes of repurposing their pipeline network for hydrogen before proceeding with detailed pipeline materials testing and pipeline inspections. BlendPATH consists of multiple modules to simulate, assess, and modify existing natural gas transmission pipeline network designs to be compatible with hydrogen as a blend or pure stream. The tool employs an open-source gas network hydraulic model to simulate existing, user-specified transmission pipeline networks, and it applies ASME B31.12 to assess the pipe segments within these networks for compatibility with hydrogen and to modify the networks to achieve compatibility where the existing infrastructure is inadequate. Users can specify ASME B31.12 design options for pipeline assessment and can select from multiple methods for pipeline modification. This report details the functionalities of BlendPATH Version 2.0.2 and demonstrates an example of applying BlendPATH to a case study. Potential and intended users of this framework include natural gas pipeline developers and operators and researchers at both public and private institutions. BlendPATH is publicly available at https://github.com/NREL/BlendPATH.

08 HYDROGEN↗
DOE OSTI2020

Frost Table Elevation across a Low-Centered and a High-Centered Polygon, Mapped using Ground Penetrating Radar, Utqiagvik (Barrow), Alaska, 2015

Ground Penetrating Radar, active layer thickness point measurements, and ground elevation surveys were conducted on a low-centered and a high-centered polygon in Utqiagvik (Barrow), AK (on October 2nd, 2015) to map frost table topography and evaluate its influence on hydrological flow. Data were collected to improve our understanding of the topography of the permafrost table across polygons which may have a major influence on hydrology. This dataset includes 2 zip files containing the raw data, 2 csv files of processed data, and a pdf user guide.The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort 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↗
DOE OSTI2023

Soil CO2 and CH4 Chamber Fluxes in Tussock Tundra, Council Road Mile Marker 71, Seward Peninsula, Alaska, 2016-2019

In August-September 2016, June-August 2017, June-August 2018, and June 2019, co-located measurements were made of surface CH4 and CO2 flux, soil temperature, moisture, and thaw depth. Measurements were made at 35 chamber locations at the Council Mile Marker 71 Site (CN_MM71) on the Seward Peninsula, Alaska. Chamber locations include upland moist acidic tussock tundra, thermo-erosional slopes, and periodically inundated lowland water channels. The dataset includes three *.csv data files with measurements using transparent and opaque chambers (CO2 and CH4 fluxes in light and dark) plus one *.pdf user guide.The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort 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↗
DOE OSTI2021

Vegetation Warming Experiment: 15N Uptake Experiment Arctagrostis latifolia Root Traits, Utqiagvik (Barrow), Alaska, 2018

This dataset consists of measured A. latifolia root traits (specific root length (SRL), mass, length, C and N content) from vegetation warming experiment enclosures and paired control plots located on the BEO, Utqiagvik, Alaska. Vegetation warming chambers (Zero Power) were deployed on the Barrow Environmental Observatory (BEO), Utqiagvik, Alaska. These chambers (Figure 1) consistently elevated air temperatures by approximately 4°C using a self-venting system described by Lewin et al (2017). Five chambers were deployed from June 17, 2018 to September 24, 2018 on the BEO within a 1 km2 area centered on 71.275N, -156.641W. Each chamber was co-located with an ambient plot where temperatures were not manipulated on patches of tundra containing the target species Arctagrostis latifolia. An intensive field campaign in late July investigated the impact of warming had on A. latifolia biomass, chemistry, and uptake of 15N labeled ammonia that was injected into the surface soils for one week. Initial measurements were taken on July 21, 2018. Harvest occurred on July 27, 2018. Water-extractable nutrients in soils were measured in July following harvests of A. latifolia plants and underlying soils. Availability of ammonia, nitrate, and phosphate throughout the growing season was measured by extracting nutrients bound to anion and cation binding resins deployed from July through September. Environmental variables (thaw depth, surface soil temperatures, surface soil moisture) were measured. Leaf traits and root traits of A. latifolia were also measured. Files include one *.csv data file and one *.pdf user guide. The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was research effort 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↗
DOE OSTI2024

Shrub Seedling Experiment: Environmental Conditions, Vegetation Composition, and Seedling Recruitment, Seward Peninsula, Alaska, 2018-2019

In June 2018, a manipulative seed-addition experiment was established at the Council Mile Marker 71 (CN_MM71), Quartz Creek Burn Area (Kougarok Mile Marker 86), and Quartz Creek Unburned (Kougarok Mile Marker 80) sites on the Seward Peninsula, Alaska. At each site, 5 replicate blocks were established in disturbed and intact upland tussock tundra (a total of 40 plots). Disturbed plots at Council were located on thermo-erosional (thermokarst) slopes, while disturbed plots in Quartz Creek were in burned areas affected by the Mingvk Lake fire in 2015. Using a split-plot design, an additional manual disturbance treatment (scraping to remove the surface bryophyte layer) was implemented across all blocks in June 2018. Seeds of five erect, deciduous shrubs were broadcast seeded into each plot in June 2018, and seedlings were censused in August 2018 and August 2019. The dataset includes ancillary measurements of plot-level environmental conditions (soil temperature, moisture, chemistry, thaw depth, microtopography, substrate thickness) and vegetation composition (NDVI and estimated percent cover by species and plant functional type). Dataset contains 10 csv data files (five data files with corresponding data dictionaries and one file-level metadata file) and one pdf user guide. The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was research effort 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↗
DOE OSTI2021

HydrogenAR: Interactive Data-Driven Presentation of Dispenser Reliability: Preprint

When delivering presentations to a co-located audience, we typically use slides with text and 2D graphics to complement the spoken narrative. Though presentations have largely been explored on 2D media, augmented reality (AR) allows presentation designers to add data and augmentations to existing physical infrastructure on display. This could provide a more engaging experience to the audience and support comprehension. With HydrogenAR, we present a novel application that leverages the benefits of data-driven storytelling with those of AR to explain the unique challenges of hydrogen dis- penser reliability. Utilizing physical props, situated data, and virtual augmentations and animations, HydrogenAR serves as a unique presentation tool, particularly critical for stakeholders, tour groups, and VIPs. HydrogenAR is a product of multiple collaborative de- sign iterations with a local Hydrogen Fuel research team and is evaluated through interviews with team members and a user study with end-users to evaluate the usability and quality of the interactive AR experience. Through this work, we provide design considera- tions for AR data-driven presentations and discuss how AR could be used for innovative content delivery beyond traditional slide-based presentations.

augmented reality↗
DOE OSTI2022

Industry Level Feasibility of LiDAR Data into Fire Modeling Using Fire Risk Investigation in 3D (FRI3D)

Many evaluation, assessment, and modeling tasks at nuclear power plants require spatial information this often requires physical visits to locations within the facility because the 2D or 3D schematics and current models do not contain enough detail or do not capture as-built and real-world conditions. These visits require extensive manual labor for not only the requesting party, but support groups such as security. LIDAR mapping is trying to solve that problem by providing very detailed 3D models for low costs. However, the use of these models can be very limited because either component reference information is missing and too costly to add, or there is no way to extract specific spatial data needed for other tools. This report presents Idaho National Lab's work with Environmental Intellect (Ei) covering two main efforts. First, to reduce the effort of "tagging" data in large 3D models. By using both existing plant database information, and artificial intelligence (AI) to find and read equipment labels. This research explores the ability of to provide a simple way for the user to tag items and verify plant data, capturing both the speed of AI and human verification. The second part of the work is the development of an interface for importing pieces needed for Modeling & Simulation. Analysis work such as that for fire, flood, or physical security all require spatial or 3D models in various levels of detail. This interface will allow for the retrieval of item location or boundaries, enabling the auto generation of models for varying tools. The application program interface (API) of the fire risk investigation in 3D (FRI3D) was used to test feasibility of exporting the LiDAR tagged spatial information. Outcomes from this work provide preliminary data to determine if the tools and methods could provide substantial industry benefit if fully matured.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Is Knowledge About Running Applications Helping Improve Runtime Prediction of HPC Jobs?

High-performance computing systems rely upon scheduling algorithms to achieve high utilization. These schedulers rely upon user estimates of job resource requirements, such as runtime, to determine optimal scheduling of incoming jobs. These user estimates, however, are prone to error. To mitigate this error, significant research has been directed at providing better estimates of job runtime, usually employing machine learning techniques. These techniques are dependent upon the input features selected. Among the possible features is the primary application used by the job. In a survey of more than 20 papers directed at improving runtime prediction, only four included primary application as an input feature. We focus this investigation specifically on the value of adding primary application as an input feature, and find that it does improve model performance, especially for jobs with longer runtimes, though this improvement varies based on the application used. We recommend further research to determine the cause of this variability as well as an optimal strategy for employing a mixture of models both including and not including primary application as a feature.

feature selection↗
DOE OSTI2024

Centralized Interactive Phenomics Resource: an integrated online phenomics knowledgebase for health data users

Development of clinical phenotypes from electronic health records (EHRs) can be resource intensive. Several phenotype libraries have been created to facilitate reuse of definitions. However, these platforms vary in target audience and utility. Here, we describe the development of the Centralized Interactive Phenomics Resource (CIPHER) knowledgebase, a comprehensive public-facing phenotype library, which aims to facilitate clinical and health services research. The platform was designed to collect and catalog EHR-based computable phenotype algorithms from any healthcare system, scale metadata management, facilitate phenotype discovery, and allow for integration of tools and user workflows. Phenomics experts were engaged in the development and testing of the site. The knowledgebase stores phenotype metadata using the CIPHER standard, and definitions are accessible through complex searching. Phenotypes are contributed to the knowledgebase via webform, allowing metadata validation. Data visualization tools linking to the knowledgebase enhance user interaction with content and accelerate phenotype development. The CIPHER knowledgebase was developed in the largest healthcare system in the United States and piloted with external partners. The design of the CIPHER website supports a variety of front-end tools and features to facilitate phenotype development and reuse. Health data users are encouraged to contribute their algorithms to the knowledgebase for wider dissemination to the research community, and to use the platform as a springboard for phenotyping. CIPHER is a public resource for all health data users available at https://phenomics.va.ornl.gov/ which facilitates phenotype reuse, development, and dissemination of phenotyping knowledge.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Demonstration of a fault impact reduction control module for wind turbines

Abstract. Traditionally, wind turbines in distributed applications make control decisions as isolated systems. They generally provide maximum power output during operation and manage internal faults with little consideration of the rest of the power system. Although fault detection and tolerance schemes are widely researched and implemented, controls to ameliorate such faults are uncommon in research and industry. The rapid shutdown of a wind turbine in a large transmission-connected wind plant will have a minimal impact on a large power system, but in a microgrid or isolated grid context the abrupt loss of a single wind turbine may cause grid instability and high stress on the system. This paper demonstrates a fault impact reduction control (FIRC) module for a wind turbine, which implements wider warning thresholds around fault thresholds. When the turbine crosses a warning threshold, the controller sends its predicted action to the grid controller, which facilitates the grid operator’s response to a potential wind turbine fault and then takes appropriate action to ameliorate the fault. Various test cases demonstrate the controller action under a variety of faults, and various scenarios demonstrate the grid benefit of an FIRC in both microgrid- and grid-connected contexts. The FIRC maximizes wind turbine generation and eases generation transition under a variety of fault scenarios. The FIRC module is easy to integrate with most existing controllers, just requiring derate capability, and can be easily modified to include the various warnings and thresholds that the user desires. This analysis is mainly performed in a MATLAB-Simulink-based research wind turbine model and is also implemented in the existing LabVIEW-based controller of the same research turbine at NREL.

17 WIND ENERGY↗
DOE OSTI2025

Advanced Interactive 3D Visualization Tool for Customizable Analyses of Tomography Datasets in Material Science

Current methods for visualizing and analyzing 3D tomography datasets in materials science often lack the interactivity and depth required for detailed structural insights. This limitation restricts a researchers' ability to accurately interpret complex data, which is critical for advancing material innovations and understanding structural properties. To address this issue, we have developed a novel, web-based interactive 3D visualization and analysis tool from the Trame framework that offers customizable features to enhance data interpretability. The tool allows users to adjust parameters such as visible range, slice planes, data rotation, and layering, providing a more detailed and dynamic view of complex structures. Its user-friendly web interface increases the accessibility and ease of use for both novice and experienced researchers, to visualize large volumetric datasets. The tool supports a diverse range of data formats, making it versatile for various research applications. Unique capabilities include real-time data manipulation, automated feature detection, context-sensitive feedback, and real-time volume calculations and distributions per sliced region or layer, alongside the ability to quickly generate high-quality screenshots and videos for presentations and reports. These advancements offer a comprehensive solution for enhanced 3D data exploration, significantly improving the analysis process and communication of results in materials science.

36 - MATERIALS SCIENCE↗
DOE OSTI2021

National Alliance for Water Innovation (NAWI) Master Technology Roadmap

The National Alliance for Water Innovation (NAWI) is a research consortium formed to accelerate transformative research in desalination and treatment to lower the cost and energy required to produce clean water from nontraditional water sources and realize a circular water economy. Prior to developing this master roadmap, a team of NAWI researchers and water professionals engaged in a detailed process of evaluating water uses, state-of-the-art technologies, emerging technologies and existing uses of desalination and advanced water technologies within five major water user categories in the United States. The resulting Water User Sector Roadmaps were complemented by a Baseline Analysis of a suite of representative treatment systems conducted by NAWI researchers working in collaboration with the hub’s Industrial Partners. This Master Roadmap, which synthesizes the findings from these different efforts as well as feedback from members of NAWI’s Research Advisory Council, serves as the basis for identifying NAWI’s research Areas of Interest (AOIs).

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2023

Testing SOAR tools in use

Investigations within Security Operation Centers (SOCs) are tedious as they rely on manual efforts to query diverse data sources, overlay related logs, correlate the data into information, and then document results in a ticketing system. Security Orchestration, Automation, and Response (SOAR) tools are a relatively new technology that promise, with appropriate configuration, to collect, filter, and display needed diverse information; automate many of the common tasks that unnecessarily require SOC analysts’ time; facilitate SOC collaboration; and, in doing so, improve both efficiency and consistency of SOCs. There has been no prior research to test SOAR tools in practice; hence, understanding and evaluation of their effect is nascent and needed. Here, in this paper, we design and administer the first hands-on user study of SOAR tools, involving 24 participants and six commercial SOAR tools. Our contributions include the experimental design, itemizing six characteristics of SOAR tools, and a methodology for testing them. We describe configuration of a cyber range test environment, including network, user, and threat emulation; a full SOC tool suite; and creation of artifacts allowing multiple representative investigation scenarios to permit testing. We present the first research results on SOAR tools. Concisely, our findings are that: per-SOC SOAR configuration is extremely important; SOAR tools increase efficiency and reduce context switching, although with potentially decreased ticketing accuracy/completeness; user preference is slightly negatively correlated with their performance with the tool; internet dependence varies widely among SOAR tools; and balance of automation with assisting decision making is preferred by senior participants. We deliver a public user- and tool-anonymized and -obfuscated version of the data.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Integrating Intelligent Hydro-informatics into an effective Early Warning System for risk-informed urban flood management

The urban drainage system constantly facing flooding issues in coastal and urban areas. Robust and accurate urban flood management, particularly considering fast-moving compound floods, is crucial to minimize the impact of flood disasters in coastal cities. Till now, Ho Chi Minh City (HCMC) lacks an effective means of urban flood management because of flood risk communication among residents. Existing flood risk communication tools rely on post-disaster flood model outcomes and data. Therefore, this research proposes a real-time Early Urban Flooding Warning System (EUFWS) integrated with a user-friendly web and app interface. The backbone of this system consists of flood models developed using machine learning (ML) algorithms, combined with big data and Web-GIS visualization, with ML serving as the core for constructing the EUFWS. EUFWS offer several key advantages: they are available at all times, accessible from anywhere, and provide a real-time, multi-user working platform. Additionally, the system is flexible, allowing for the easy addition of components and services and scalable, adjusting to workload demands. EUFWS have been successfully deployed in Thu Duc City, Vietnam, as a case study and are operating effectively. EUFWS have been successfully deployed in Thu Duc City, Vietnam, as a case study and are operating effectively. Research results indicate that EUFWS supported decision-makers to be effectively risk informed and make intelligent decisions during urban flood emergencies. Finally, this underscores the significant potential of integrating ML and information technology to enhance the management of smart urban drainage systems in flood-prone cities worldwide.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Cryogenic User Facilities for R&D on Noble Liquid Detectors and Low Temperature Devices

Cryogenic test facilities are critical infrastructure for physics experiments in a variety of fields, perhaps most notably for particle detection with noble liquid detectors, low-temperature device development, and quantum information research. However, considerable investment and technical knowledge are required to construct and operate such facilities. This white paper discusses proposals for user facilities aimed at broadening the availability of testing capabilities for the scientific community.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2023

CasPEDIA Database: a functional classification system for class 2 CRISPR-Cas enzymes

Abstract CRISPR-Cas enzymes enable RNA-guided bacterial immunity and are widely used for biotechnological applications including genome editing. In particular, the Class 2 CRISPR-associated enzymes (Cas9, Cas12 and Cas13 families), have been deployed for numerous research, clinical and agricultural applications. However, the immense genetic and biochemical diversity of these proteins in the public domain poses a barrier for researchers seeking to leverage their activities. We present CasPEDIA (http://caspedia.org), the Cas Protein Effector Database of Information and Assessment, a curated encyclopedia that integrates enzymatic classification for hundreds of different Cas enzymes across 27 phylogenetic groups spanning the Cas9, Cas12 and Cas13 families, as well as evolutionarily related IscB and TnpB proteins. All enzymes in CasPEDIA were annotated with a standard workflow based on their primary nuclease activity, target requirements and guide-RNA design constraints. Our functional classification scheme, CasID, is described alongside current phylogenetic classification, allowing users to search related orthologs by enzymatic function and sequence similarity. CasPEDIA is a comprehensive data portal that summarizes and contextualizes enzymatic properties of widely used Cas enzymes, equipping users with valuable resources to foster biotechnological development. CasPEDIA complements phylogenetic Cas nomenclature and enables researchers to leverage the multi-faceted nucleic-acid targeting rules of diverse Class 2 Cas enzymes.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Enhancing Discoverability and Management of Atmospheric Data at Scale: Solutions from the ARM Data Center

The Atmospheric Radiation Measurement (ARM) is a multi-laboratory and multi-institutional U.S. Department of Energy (DOE) Office of Science National User Facility. The ARM Data Center (ADC), located at Oak Ridge National Laboratory, collects, archives, and shares vast atmospheric data crucial for climate research. The ADC manages over 7 PB of data from 460 instruments worldwide, processing it into more than 11,000 diverse data products using the Network Common Data Form (NetCDF) for machine-independent accessibility. The primary challenge addressed in this paper is the efficient management and distribution of vast and diverse datasets essential for the climate research community, enhancing accessibility through advanced tools like Data Discovery. The ADC has developed advanced infrastructure and software architecture to handle the continuous influx of heterogeneous data to enhance data discoverability, resulting in increased scientific collaboration. In 2023, users from over 34 countries downloaded and utilized ARM data, resulting in 1,455 publications. The ADC’s efforts have significantly improved the discoverability and usability of atmospheric data, fostering extensive scientific research and collaboration. This paper details the solutions implemented by the ADC team for efficient data discovery and distribution, and it demonstrates ARM’s capability of staging processed data for scientific analysis.

Shah, Chirag [ORNL] (ORCID:0000000203145737)↗