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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Pycheron: A Python-Based Seismic Waveform Data Quality Control Software Package

Supplementing an existing high-quality seismic monitoring network with openly available station data could improve coverage and decrease magnitudes of completeness; however, this can present challenges when varying levels of data quality exist. Without discerning the quality of openly available data, using it poses significant data management, analysis, and interpretation issues. Incorporating additional stations without properly identifying and mitigating data quality problems can degrade overall monitoring capability. If openly available stations are to be used routinely, a robust, automated data quality assessment for a wide range of quality control (QC) issues is essential. To meet this need, we developed Pycheron, a Python-based library for QC of seismic waveform data. Pycheron was initially based on the Incorporated Research Institutions for Seismology’s Modular Utility for STAtistical kNowledge Gathering but has been expanded to include more functionality. Pycheron can be implemented at the beginning of a data processing pipeline or can process stand-alone data sets. Its objectives are to (1) identify specific QC issues; (2) automatically assess data quality and instrumentation health; (3) serve as a basic service that all data processing builds on by alerting downstream processing algorithms to any quality degradation; and (4) improve our ability to process orders of magnitudes more data through performance optimizations. This article provides an overview of Pycheron, its features, basic workflow, and an example application using a synthetic QC data set.

58 GEOSCIENCES↗
DOE OSTI2024

Diaspora: Resilience-enabling services for science from HPC to edge

Scientific applications of interest to DOE must increasingly engage distributed resources (e.g., instruments, remote computers, data stores, edge devices) and deliver more stringent levels of service (e.g., uninterrupted processing of experiment data streams). In such systems, state is distributed and components can fail in many ways, often silently, making application resilience a major concern. Addressing the resilience needs of such applications requires methods for gaining knowledge of resources and applications and for translating that knowledge into action. We are working on addressing these needs in the context of multi-messenger astronomy, where detecting and responding to unusual transient events in multiple cosmic messengers (gravitational wave, electromagnetic, high- energy particles) from different instruments leads to a federated learning problem.

47 OTHER INSTRUMENTATION↗
DOE OSTI2026

LuSEE-night power distribution system design

The Lunar Surface Electromagnetic Experiment at Night (LuSEE-Night) is a low-frequency, 0.5 to 50 MHz, radio experiment on the radio-quiet far side of the Moon. The instrument will be launched by NASA Commercial Lunar Payload Services in 2026. The LuSEE-Night instrument core is composed of a radio frequency spectrometer (SPT) processing signals from four antennas, the Data Controller Board (DCB), low electromagnetic interference (EMI) Picket Fence Power Supply (PFPS), and Power Distribution Unit (PDU). The battery powers the instrument during the lunar night and stores energy harvested by the solar panel array during the lunar day. The battery charging is controlled by the Power Conditioning and Distribution Unit (PCDU). The unregulated power is supplied either by the SpaceCraft (S/C) or the battery and gets distributed to the PFPS, communication radio, heaters, and deployables through the PDU. The PFPS generates all regulated low-voltage rails using switching regulation synchronized to the LuSEE-Night clock, which ensures self-generated EMI will be confined to well-defined frequency bins. Here, we discussed the unregulated power distribution system architecture and functionality. The PDU engineering and flight modules are developed and characterized to confirm compliance with LuSEE-Night requirements. At the time of writing, all power subsystem components have been integrated into the payload.

47 OTHER INSTRUMENTATION↗
DOE OSTI2023

Workflows Community Summit 2022: A Roadmap Revolution

Scientific workflows have become integral tools in broad scientific computing use cases. Science discovery is increasingly dependent on workflows to orchestrate large and complex scientific experiments that range from the execution of a cloud-based data preprocessing pipeline to multi-facility instrument-to-edge-to-HPC computational workflows. Given the changing landscape of scientific computing (often referred to as a computing continuum) and the evolving needs of emerging scientific applications, it is paramount that the development of novel scientific workflows and system functionalities seek to increase the efficiency, resilience, and pervasiveness of existing systems and applications. Specifically, the proliferation of machine learning/artificial intelligence (ML/AI) workflows, need for processing large-scale datasets produced by instruments at the edge, intensification of near real-time data processing, support for long-term experiment campaigns, and emergence of quantum computing as an adjunct to HPC, have significantly changed the functional and operational requirements of workflow systems. Workflow systems now need to, for example, support data streams from the edge-to-cloud-to-HPC, enable the management of many small-sized files, allow data reduction while ensuring high accuracy, orchestrate distributed services (workflows, instruments, data movement, provenance, publication, etc.) across computing and user facilities, among others. Further, to accelerate science, it is also necessary that these systems implement specifications/standards and APIs for seamless (horizontal and vertical) integration between systems and applications, as well as enable the publication of workflows and their associated products according to the FAIR principles.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

COMPASS-FME Terrestrial Ecosystem Manipulation to Probe the Effects of Storm Treatments (TEMPEST) Experiment Level 1 Sensor Data v2-1

This is the version 2-1 Level 1 (L1) data release for COMPASS-FME environmental sensors located at our Terrestrial Ecosystem Manipulation to Probe the Effects of Storm Treatments (TEMPEST) experimental site. This manipulative, ecosystem-scale TEMPEST experiment addresses the potential for freshwater and estuarine-water disturbance events to alter tree function, species composition, and ecosystem processes in a deciduous coastal forest in MD, USA. The experiment uses a large-unit (2000 m2), un-replicated experimental design, with three 50 m × 40 m plots serving as control, freshwater, and estuarine-water treatments. L1 data are close to raw, but are units-transformed and have out-of-instrument-bounds, out-of-service, and outlier flags added. Duplicates and missing data are removed but otherwise these data are not filtered, and have not been subject to any additional algorithmic or human QA/QC. Any scientific analyses of L1 data should be performed with care. **This dataset will be updated quarterly with new data for the duration of the project** This dataset includes: - An overall dataset README file that describes the current version, gives citation and contact information, etc. - Site- and year-specific folders, each holding variable-specific CSV (comma separated value) data files for each site and plot in that year. - Metadata files within each site-year folder provide full information on data units, expected ranges, contact information, detailed flood times, as well as a general description of the site. - Environmental sensor types that appear in the data files include weather (ClimaVUE50, CS, RM Young, and LI instruments in the graphs below); soil conditions (TEROS12); soil redox state (Redox); groundwater variables (AquaTROLL200 and AquaTROLL600); open water sondes (Exo); tree sap velocity (Sapflow); and system voltage and state (Datalogger). Data are normally logged every 15 minutes. Please see v2-1 TEMPEST L1 Sensor Package Quick Start.pdf for detailed information on data package structure, temporal coverage, and versioning. The TEMPEST flood events occurred on the following dates. They lasted for ~10 hours each day and delivered ~80,000 gallons to each plot; many data streams are available at 1 or 5 minute frequency during these periods. * Tests: Aug 25 (fresh plot) and Sep 9 (salt plot), 2021 * TEMPEST 1: June 22, 2022 * TEMPEST 2: June 6-7, 2023 * TEMPEST 3: June 11-13, 2024 This dataset was updated 2026-03-12: (i) data now go through 2025-12-31 (previous end was 2025-06-30) and (ii) dataset and file names updated to “…v2-1” (previously was “v2-0”).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

COMPASS-FME Terrestrial Ecosystem Manipulation to Probe the Effects of Storm Treatments (TEMPEST) Experiment Level 1 Sensor Data v1-2

This is the version 1-2 Level 1 (L1) data release for COMPASS-FME environmental sensors located at our Terrestrial Ecosystem Manipulation to Probe the Effects of Storm Treatments (TEMPEST) experimental site. This manipulative, ecosystem-scale TEMPEST experiment addresses the potential for freshwater and estuarine-water disturbance events to alter tree function, species composition, and ecosystem processes in a deciduous coastal forest in MD, USA. The experiment uses a large-unit (2000 m2), un-replicated experimental design, with three 50 m × 40 m plots serving as control, freshwater, and estuarine-water treatments.L1 data are close to raw, but are units-transformed and have out-of-instrument-bounds and out-of-service flags added. Duplicates and missing data are removed but otherwise these data are not filtered, and have not been subject to any additional algorithmic or human QA/QC. Any scientific analyses of L1 data should be performed with care. **This dataset will be updated quarterly with new data for the duration of the project**This dataset includes:- An overall dataset README file that describes the current version, gives citation and contact information, etc.- Site- and year-specific folders, each holding up to 12 CSV (comma separated value) data files for each site and plot in that year.- Metadata files within each site-year folder provide full information on data units, expected ranges, contact information, detailed flood times, as well as a general description of the site.- Environmental sensor types that appear in the data files include weather (ClimaVUE50, CS, RM Young, and LI instruments in the graphs below); soil conditions (TEROS12); soil redox state (Redox); groundwater variables (AquaTROLL200 and AquaTROLL600); open water sondes (Exo); tree sap velocity (Sapflow); and system voltage and state (Datalogger). Data are normally logged every 15 minutes.Please see v1-2 TEMPEST L1 Sensor Package Quick Start.pdf for detailed information on data package structure, temporal coverage, and versioning.The TEMPEST flood events occurred on the following dates. They lasted for ~10 hours each day and delivered ~80,000 gallons to each plot; many data streams are available at 1 or 5 minute frequency during these periods.* Tests: Aug 25 (fresh plot) and Sep 9 (salt plot), 2021* TEMPEST 1: June 22, 2022* TEMPEST 2: June 6-7, 2023* TEMPEST 3: June 11-13, 2024

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Colorado State University Extension Industrial Assessment Center

Since its inception in 1984, the Colorado State University Industrial Assessment Center has performed industrial assessments at more than 720 manufacturing facilities in Colorado, Montana, Nebraska, Nevada, New Mexico, North Dakota, South Dakota, Utah, and Wyoming. From 2017 to 2019 there was a funding gap and the IAC shutdown. In 2020 through DOE extension funding the University relaunched the IAC as an extension center in order to provide assessments to underserved areas. Under this award, the CSU Industrial Assessment Center (IAC) was rebuilt with the help of student employees and the director. The CSU team experienced difficulty as the program was in the process of being restarted right as the 2020 pandemic hit. Nonetheless, the CSU IAC was instrumental in providing energy assessments to manufacturers in Colorado and Wyoming during the period of performance of 09/2019 – 12/2022. The Department of Energy's Industrial Assessment Centers (IACs) provide a valuable service to small and medium-sized manufacturers seeking to optimize their operations. These university-based centers offer no-cost, on-site assessments conducted by engineering faculty and students, analyzing energy consumption, production processes, and waste streams. They utilize advanced data acquisition systems to record operational data, and then use the data to create assessment recommendations. These recommendations form the foundation of the comprehensive energy report. The comprehensive report delivers actionable recommendations for enhancing energy efficiency, reducing waste, and reducing greenhouse gas emissions and improving productivity, often identifying significant cost savings. Furthermore, IACs facilitate access to implementation grants, enabling the businesses to readily adopt these improvements. This program not only strengthens individual businesses but also contributes to national goals of training the next generation of energy experts, as well as increasing industrial competitiveness and reduced environmental impact.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Development of an end state vision to implement digital monitoring in nuclear plants

Transitioning from an onsite Maintenance & Diagnostics Center to cloud-based services offers many new opportunities with computing power and storage, but also new challenges in terms of networking and security. This report will cover everything required for that transition including data processing and uploading to cloud services, feature selection, model creation, and result visualization for decision making. Although there are several other cloud-based services (e.g. Amazon Web Services and Google Cloud), this report explores Microsoft Azure to simplify nomenclature and maintain a consistent focus. Many of the services offered by Microsoft Azure are also available in the other cloud-based services, and their differences have been recorded in other literature. The Azure services most important to a nuclear power plant including networking & security, storage & databases, and Artificial Intelligence (AI) are reviewed here. Networking covers all aspects related to communication to Azure resources including security, privacy, and redundancy. Storage & databases includes data storage, upgrading, patching, backups, and monitoring. The AI services allows the user access to the machine learning (ML) techniques developed with Azure including automated ML, anomaly detection, computer vision, and natural language processing. This report summaries the features, capabilities, and challenges when using cloud-based services in a user-friendly manner.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2020

AI-Science for Performance Optimization and Diagnosis of Science Instrument Federations

Next generation of science workflows are expected to be executed over complex federations composed of supercomputers, science instruments, storage systems and networks, with new additions of the edge and cloud systems and services. The sheer complexity of these multi-domain federations makes it hard to manage them and optimize their performance, as small impedance mismatches (that can dynamically develop between systems) could drastically degrade the entire federation performance. Recent proliferation of Software Defined Everything (SDX) technologies combined with containerization frameworks provide custom instruments that can monitor and collect critical measurements at various levels to support diagnoses and performance optimization; but their data too enormous for human operators and analysts to process and generate decisions. Machine Learning (ML) methods that extract critical parameters, relationships and trends from the data offer general solutions. Artificial Intelligence (AI) and ML methods must be custom-developed for these problems based on solid, rigorous foundations, since black-box approaches are often ineffective and unsound.We propose to develop comprehensive AI-Science for the performance of science federations to (i) monitor and control storage, networks, experiments, and computing systems across multiple domains via softwarization layers, at speeds and scales orders of magnitude superior to current practice, (ii) optimally realize and orchestrate complex workflows with high performance by using dynamic state and performance estimation methods, and (iii) aggregate measurements across sites and time to develop infrastructure-level profiles, optimizations and diagnoses using AI-Science based on foundational principles from ML, game theory, and information fusion areas.

Rao, Nageswara↗
DOE OSTI2025

Modeling of Vertical Motor-driven Pump for Simulation of a Fault Signature \\ for Condition Monitoring

As part of the ongoing effort to transition from preventive maintenance strategies to condition-based maintenance strategies in nuclear power plants, there is significant reliance on using machine learning techniques. To develop a robust machine learning model that can diagnose all the fault modes of a vertical motor-driven pump, data capturing the unique signature of each fault mode is required. In practice, it is difficult to collect or capture data that captures all the fault modes from a single plant site. So to address this situation, a computational model of a vertical motor-driven pump is developed using the multipurpose finite element software COMSOL Multiphysics. The developed model is used to generate simulated data under normal operation and is compared with the vibration data collected using vibration sensors. Once the simulation model is verified under normal operating condition, simulated data for the fault mode for which minimal or no evidence is available in historical plant process data is developed. This simulated data is used to develop fault signatures to achieve robust predictive models. This paper presents modeling details and verification of the model that can used to generate data for fault modes that are not available at a plant site for condition monitoring purpose.

46 - INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AN↗
DOE OSTI2020

Challenges and Opportunities in Turning Large U.S. Archives of Analog Seismograms into a Modern Usable Resource

Abstract Quantitative seismology is based firmly on the analysis of actual ground motions, and the transition to digital recording in the 1980s enabled sophisticated new capabilities to extract useful results from waveforms. With some effort, these tools can also be applied to analog records. Focusing on assets available within U.S. institutions, we review the necessary steps and the challenges in enabling “data rescue”—that is, preserving the scientific information latent in large analog seismogram archives and making it usable. They include: determining what assets are available (the analog seismogram archives held by various institutions, with associated metadata on instrument responses, station locations, and timing information); developing a consensus on the top level of a triage process (which analog records most definitely should be rescued?); deciding the level of quality needed in copying original seismograms to media suitable for digitizing; assessing the relative merits of scanning and digitizing; and, the need for a community service in distributing scans and digital records, as they accumulate. The necessary level of effort can benefit from practical experience. For example, specific studies have used digitized versions of analog recordings to model earthquake sources and assess seismic hazard. Other studies have used them to gain experience with nuclear explosion signals recorded at regional distances, noting that regional signals enable explosions to be monitored down to levels much lower than those attainable teleseismically. The opportunities presented by large archives of analog seismograms include the insights they present to current and future seismologists studying earthquakes and explosions, into the practical areas of assessing seismic hazard, monitoring for test ban compliance down to low explosion yields—and prompt characterization of actual explosions should they occur, as well the traditional academic pursuit of a better understanding of earthquake physics.

Geochemistry & Geophysics↗
DOE OSTI2025

Advancing ocean monitoring and knowledge for societal benefit: the urgency to expand Argo to OneArgo by 2030

The ocean plays an essential role in regulating Earth’s climate, influencing weather conditions, providing sustenance for large populations, moderating anthropogenic climate change, encompassing massive biodiversity, and sustaining the global economy. Human activities are changing the oceans, stressing ocean health, threatening the critical services the ocean provides to society, with significant consequences for human well-being and safety, and economic prosperity. Effective and sustainable monitoring of the physical, biogeochemical state and ecosystem structure of the ocean, to enable climate adaptation, carbon management and sustainable marine resource management is urgently needed. The Argo program, a cornerstone of the Global Ocean Observing System (GOOS), has revolutionized ocean observation by providing real-time, freely accessible global temperature and salinity data of the upper 2,000m of the ocean (Core Argo) using cost-effective simple robotics. For the past 25 years, Argo data have underpinned many ocean, climate and weather forecasting services, playing a fundamental role in safeguarding goods and lives. Argo data have enabled clearer assessments of ocean warming, sea level change and underlying driving processes, as well as scientific breakthroughs while supporting public awareness and education. Building on Argo’s success, OneArgo aims to greatly expand Argo’s capabilities by 2030, expanding to full-ocean depth, collecting biogeochemical parameters, and observing the rapidly changing polar regions. Providing a synergistic subsurface and global extension to several key space-based Earth Observation missions and GOOS components, OneArgo will enable biogeochemical and ecosystem forecasting and new long-term climate predictions for which the deep ocean is a key component. Driving forward a revolution in our understanding of marine ecosystems and the poorly-measured polar and deep oceans, OneArgo will be instrumental to assess sea level change, ocean carbon fluxes, acidification and deoxygenation. Emerging OneArgo applications include new views of ocean mixing, ocean bathymetry and sediment transport, and ecosystem resilience assessment. Implementing OneArgo requires about $100 million annually, a significant increase compared to present Argo funding. OneArgo is a strategic and cost-effective investment which will provide decision-makers, in both government and industry, with the critical knowledge needed to navigate the present and future environmental challenges, and safeguard both the ocean and human wellbeing for generations to come.

ARGO↗
DOE OSTI2022

Hardware-accelerated inference for real-time gravitational-wave astronomy

The field of transient astronomy has seen a revolution with the first gravitational-wave detections and the arrival of multi-messenger observations they enabled. Transformed by the first detection of binary black hole and binary neutron star mergers, computational demands in gravitational-wave astronomy are expected to grow by at least a factor of two over the next five years as the global network of kilometer-scale interferometers are brought to design sensitivity. With the increase in detector sensitivity, real-time delivery of gravitational-wave alerts will become increasingly important as an enabler of multi-messenger followup. In this work, we report a novel implementation and deployment of deep learning inference for real-time gravitational-wave data denoising and astrophysical source identification. This is accomplished using a generic Inference-as-a-Service model that is capable of adapting to the future needs of gravitational-wave data analysis. Overall, our implementation allows seamless incorporation of hardware accelerators and also enables the use of commercial or private (dedicated) as-a-service computing. Based on our results, we propose a paradigm shift in low-latency and offline computing in gravitational-wave astronomy. Such a shift can address key challenges in peak-usage, scalability and reliability, and provide a data analysis platform particularly optimized for deep learning applications. The achieved sub-millisecond scale latency will also be relevant for any machine learning-based real-time control systems that may be invoked in the operation of near-future and next generation ground-based laser interferometers, as well as the front-end collection, distribution and processing of data from such instruments.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2023

Development of a Scalable Risk-informed Predictive Maintenance Cloud-based Strategy at Nuclear Power Plants

The fact that light-water reactor operation and maintenance costs are prohibitively expensive and contribute to the premature decommissioning of nuclear power plants is partly due to how the equipment is monitored. In recent years, cloud computing has emerged as a dominant technology, as its low cost, computing and storage adaptability, and ability to host applications across numerous virtual infrastructures potentially make it a cost-effective alternative to onsite storage and diagnostics. In this paper, a technological assessment is carried out on a provisional cloud deployment architecture for a nuclear power plant predictive monitoring system. This cloud-based monitoring system would enable maintenance and diagnostic analysts and other authorized plant users to remotely monitor equipment functionality, thus enabling early fault detection and effective predictive maintenance practices. To provide data processing and storage, sensor device networking, and database management, the Microsoft Azure cloud platform is utilized as part of the proposed cloud architecture; however, this analysis could be extended to other cloud computing service providers as well. The focus of this paper is on application of cloud resources for enabling predictive maintenance, identification of technological hurdles associated with moving to a cloud-computing-based architecture, and potential benefits from moving to a centralized cloud system.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2023

From remotely-sensed solar-induced chlorophyll fluorescence to ecosystem structure, function, and service: Part II—Harnessing data

Although our observing capabilities of solar-induced chlorophyll fluorescence (SIF) have been growing rapidly, the quality and consistency of SIF datasets are still in an active stage of research and development. As a result, there are considerable inconsistencies among diverse SIF datasets at all scales and the widespread applications of them have led to contradictory findings. The present review is the second of the two companion reviews, and data oriented. It aims to (1) synthesize the variety, scale, and uncertainty of existing SIF datasets, (2) synthesize the diverse applications in the sector of ecology, agriculture, hydrology, climate, and socioeconomics, and (3) clarify how such data inconsistency superimposed with the theoretical complexities laid out in may impact process interpretation of various applications and contribute to inconsistent findings. We emphasize that accurate interpretation of the functional relationships between SIF and other ecological indicators is contingent upon complete understanding of SIF data quality and uncertainty. Additionally, biases and uncertainties in SIF observations can significantly confound interpretation of their relationships and how such relationships respond to environmental variations. Built upon our syntheses, we summarize existing gaps and uncertainties in current SIF observations. Further, we offer our perspectives on innovations needed to help improve informing ecosystem structure, function, and service under climate change, including enhancing in-situ SIF observing capability especially in “data desert” regions, improving cross-instrument data standardization and network coordination, and advancing applications by fully harnessing theory and data.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

AmeriFlux US-GL1 Stannard Rock

This is the AmeriFlux version of the carbon flux data for the site US-GL1 Stannard Rock. Site Description - Stannard Rock is located 39 km from the nearest shore (Keweenaw Peninsula) in Lake Superior, 44 miles NNE of Marquette, Michigan, and 24 miles ESE of Manitou Island. The site is located on the historic Stannard Rock Lighthouse, which was completed in 1882. Eddy covariance instrumentation was installed in 2008 by a network of scientists from both US and Canada, eventually to be called the Great Lakes Evaporation Network (GLEN). The intent of GLEN has been to provide observations of over-lake meteorology and evaporation, improve forecasting of Great Lakes water levels, and support a wide variety of stakeholders, including the National Weather Service (NWS), Environment and Climate Change Canada, National Oceanic and Atmospheric Administration, U.S. Coast Guard, recreational boaters and commercial shipping, emergency management officials, and the Great Lakes research community. The eddy covariance station along with other ancillary meteorological instrumentation is located at an approximate elevation of 39.2 meters above mean lake water level. Meteorological data from the lighthouse are sent to the National Data Buoy Center, where they can be viewed in real-time at http://www.ndbc.noaa.gov/station_page.php?station=stdm4 Uncorrected half-hour fluxes were computed directly on the logger using a 30-minute block averaging period as high-frequency data were not available. The half hour flux measurements downloaded from the datalogger were post-processed with the following filters and corrections. Latent and sensible heat and carbon dioxide fluxes were corrected with 2-D coordinate rotation. Although the primary objective of data collection at Stannard Rock was to quantify the evaporative flux, additional preliminary measurements of the carbon dioxide concentration and flux from the LI-7500 are also included in this dataset but it is advised to use the carbon data with caution. Carbon data reported in this dataset includes turbulent fluxes of CO2 with no storage correction (FC, µmol m-2 s-1) and CO2 density in mole fraction of wet air (CO2), which was originally output on the datalogger as average CO2 density (mg m-2 s-1) and converted into µmol mol-1 using air temperature and pressure in post-processing. Webb, Pearman, and Leuning terms were applied to account for density fluctuations for water vapor and CO2. Sonic path length, high-frequency attenuation and sensor separation were accounted for according to Horst and Massman, and the oxygen absorption correction for the KH2O hygrometer was also applied. Latent and sensible heat fluxes were assumed to be unrealistic above an absolute value of 1000 W m-2 and were removed. Both carbon flux (FC) and carbon dioxide mole fraction in wet air (CO2) and were assumed to be unrealistic above 1000 µmol m-2 s-1 and 1000 µmol mol-1 respectively. Spikes in latent and sensible heat and carbon fluxes and densities (often due to periods of precipitation) were identified by computing the mean and standard deviation over a moving, overlapping window of 336 half-hours (7 days), similar to Shao et al., and were removed when the flux was more than 1.5 standard deviations from the moving window’s mean. While Vickers and Mahrt use a threshold of 3.5 standard deviations from the mean, a conservative value of 1.5 was chosen due to the noisy nature of over-lake data at this particular site. This process was repeated twice for latent and sensible heat, and carbon dioxide fluxes and densities and therefore it is possible that some real, realistic data was filtered out in this process. No detrending was performed. As per AmeriFlux standards, no friction velocity (USTAR, m s-1) filtering was performed.

Spence, Chris↗
DOE OSTI2025

AmeriFlux FLUXNET-1F US-GL1 Stannard Rock

This is the AmeriFlux Management Project (AMP) created FLUXNET-1F version of the carbon flux data for the site US-GL1 Stannard Rock. This is the FLUXNET version of the carbon flux data for the site US-GL1 Stannard Rock produced by applying the standard ONEFlux (1F) software. Site Description - Stannard Rock is located 39 km from the nearest shore (Keweenaw Peninsula) in Lake Superior, 44 miles NNE of Marquette, Michigan, and 24 miles ESE of Manitou Island. The site is located on the historic Stannard Rock Lighthouse, which was completed in 1882. Eddy covariance instrumentation was installed in 2008 by a network of scientists from both US and Canada, eventually to be called the Great Lakes Evaporation Network (GLEN). The intent of GLEN has been to provide observations of over-lake meteorology and evaporation, improve forecasting of Great Lakes water levels, and support a wide variety of stakeholders, including the National Weather Service (NWS), Environment and Climate Change Canada, National Oceanic and Atmospheric Administration, U.S. Coast Guard, recreational boaters and commercial shipping, emergency management officials, and the Great Lakes research community. The eddy covariance station along with other ancillary meteorological instrumentation is located at an approximate elevation of 39.2 meters above mean lake water level. Meteorological data from the lighthouse are sent to the National Data Buoy Center, where they can be viewed in real-time at http://www.ndbc.noaa.gov/station_page.php?station=stdm4 Uncorrected half-hour fluxes were computed directly on the logger using a 30-minute block averaging period as high-frequency data were not available. The half hour flux measurements downloaded from the datalogger were post-processed with the following filters and corrections. Latent and sensible heat and carbon dioxide fluxes were corrected with 2-D coordinate rotation. Although the primary objective of data collection at Stannard Rock was to quantify the evaporative flux, additional preliminary measurements of the carbon dioxide concentration and flux from the LI-7500 are also included in this dataset but it is advised to use the carbon data with caution. Carbon data reported in this dataset includes turbulent fluxes of CO2 with no storage correction (FC, µmol m-2 s-1) and CO2 density in mole fraction of wet air (CO2), which was originally output on the datalogger as average CO2 density (mg m-2 s-1) and converted into µmol mol-1 using air temperature and pressure in post-processing. Webb, Pearman, and Leuning terms were applied to account for density fluctuations for water vapor and CO2. Sonic path length, high-frequency attenuation and sensor separation were accounted for according to Horst and Massman, and the oxygen absorption correction for the KH2O hygrometer was also applied. Latent and sensible heat fluxes were assumed to be unrealistic above an absolute value of 1000 W m-2 and were removed. Both carbon flux (FC) and carbon dioxide mole fraction in wet air (CO2) and were assumed to be unrealistic above 1000 µmol m-2 s-1 and 1000 µmol mol-1 respectively. Spikes in latent and sensible heat and carbon fluxes and densities (often due to periods of precipitation) were identified by computing the mean and standard deviation over a moving, overlapping window of 336 half-hours (7 days), similar to Shao et al., and were removed when the flux was more than 1.5 standard deviations from the moving window’s mean. While Vickers and Mahrt use a threshold of 3.5 standard deviations from the mean, a conservative value of 1.5 was chosen due to the noisy nature of over-lake data at this particular site. This process was repeated twice for latent and sensible heat, and carbon dioxide fluxes and densities and therefore it is possible that some real, realistic data was filtered out in this process. No detrending was performed. As per AmeriFlux standards, no friction velocity (USTAR, m s-1) filtering was performed.

Spence, Chris [Environment and Climate Change Cana↗
DOE OSTI2021

Data Fusion to Enhance Quality Control and Analysis with Instruments at the Marine and Coastal Research Laboratory

Deploying environmental monitoring instruments in the marine environment can be challenging, facing challenges around device survivability, biofouling and corrosion, and consistent data collection. This project explores the use of data fusion – the process of integrating multiple data sources to produce more consistent, accurate, and useful information – to build a consistent long-term monitoring system at the Marine and Coastal Research Laboratory (MCRL) in Sequim, Washington. Unused instruments that had been acquired from past projects were inventoried and deployments planned on the MCRL pier and floating dock. A total of 8 instruments were deployed including a tide gauge, hydrophone, acoustic Doppler current profiler (ADCP), photosynthetically active radiation (PAR) sensors, meteorological station, and three water quality sensors. Deployments were planned to be well-protected around the pier structure and a maintenance schedule was created for cleaning and recalibration. An automated data pipeline was created to aggregate data on edge computers that push data to Amazon Web Services (AWS) cloud storage every 15 minutes, performing automated quality control and data transformations using the Time Series Data Analytical Toolkit (TSDAT). Continued efforts are underway to maintain this system into the future, take a data-driven approach to maintenance scheduling, improve the reliability of the system, and share the data with a variety of end-users.

54 ENVIRONMENTAL SCIENCES↗