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At least 649 records · Page 36
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Future Atmospheric Rivers and Impacts on Precipitation: Overview of the ARTMIP Tier 2 High–Resolution Global Warming Experiment

Atmospheric rivers (ARs) are long, narrow synoptic scale weather features important for Earth’s hydrological cycle typically transporting water vapor poleward, delivering precipitation important for local climates. Understanding ARs in a warming climate is problematic because the AR response to climate change is tied to how the feature is defined. The Atmospheric River Tracking Method Intercomparison Project (ARTMIP) provides insights into this problem by comparing 16 atmospheric river detection tools (ARDTs) to a common data set consisting of high resolution climate change simulations from a global atmospheric general circulation model. ARDTs mostly show increases in frequency and intensity, but the scale of the response is largely dependent on algorithmic criteria. Across ARDTs, bulk characteristics suggest intensity and spatial footprint are inversely correlated, and most focus regions experience increases in precipitation volume coming from extreme ARs. The spread of the AR precipitation response under climate change is large and dependent on ARDT selection.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Co-Occurring Atmospheric Features and Their Contributions to Precipitation Extremes

Object-based identification algorithms for atmospheric features are commonly utilized to attribute global precipitation. This study employs a systematic approach to examine feature co-occurrences and their relationships to mean and extreme precipitation. Four features are identified using existing data sets for atmospheric rivers (ARs), mesoscale convective systems (MCSs), low-pressure systems (LPSs), and fronts (FTs). Often, a single atmospheric phenomenon satisfies the criteria set by multiple feature identification algorithms, yielding an association between precipitation and multiple features. Over the extra-tropics, the number of features attributed to a single event typically increases with precipitation intensity. Over two-thirds of the precipitation is from co-occurring features, with a considerable fraction related to AR-FT co-occurrences. Over the tropics, about one-quarter of precipitation is associated with co-occurring features, with LPS-MCS co-occurrences contributing substantially in monsoon regions. MCSs are the leading single-feature contributors over tropical land and oceans. In the extra-tropics, FTs, ARs, and their co-occurrences account for over half of the total precipitation over oceans. AR-FT-MCS and FT-MCS co-occurrences contribute to extremes (precipitation exceeding the 95th percentile) over both oceans (over 30%) and land (over 20%). Any combination of features involving MCSs shows a larger contribution to high percentiles of precipitation intensity. A case analysis indicates that AR-FT-MCS co-occurrences exhibit convective instability and deep vertical motion, suggesting that the feature trackers and reanalysis are capturing physics relevant to both convective and frontal systems. The results here emphasize the need for simultaneous identifications of multiple features when attributing precipitation to atmospheric phenomena.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

The role of interdecadal climate oscillations in driving Arctic atmospheric river trends

Abstract Atmospheric rivers (ARs), intrusions of warm and moist air, can effectively drive weather extremes over the Arctic and trigger subsequent impact on sea ice and climate. What controls the observed multi-decadal Arctic AR trends remains unclear. Here, using multiple sources of observations and model experiments, we find that, contrary to the uniform positive trend in climate simulations, the observed Arctic AR frequency increases by twice as much over the Atlantic sector compared to the Pacific sector in 1981-2021. This discrepancy can be reconciled by the observed positive-to-negative phase shift of Interdecadal Pacific Oscillation (IPO) and the negative-to-positive phase shift of Atlantic Multidecadal Oscillation (AMO), which increase and reduce Arctic ARs over the Atlantic and Pacific sectors, respectively. Removing the influence of the IPO and AMO can reduce the projection uncertainties in near-future Arctic AR trends by about 24%, which is important for constraining projection of Arctic warming and the timing of an ice-free Arctic.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

More frequent atmospheric rivers slow the seasonal recovery of Arctic sea ice

In recent decades, Arctic sea ice coverage experienced a drastic decline in winter, when sea ice is expected to recover following the melting season. It is unclear to what extent atmospheric processes such as atmospheric rivers (ARs), intense corridors of moisture transport, contribute to this reduced recovery of sea ice. Here, in this study, using observations and climate model simulations we find a robust frequency increase in ARs in early winter over the Barents-Kara Seas and the central Arctic (ABK) between 1979 and 2021. The moisture carried by more frequent ARs has intensified surface downward longwave radiation and rainfall, caused stronger melting of thin, fragile ice cover, and slowed the seasonal recovery of sea ice, accounting for 34% of the sea ice cover decline in ABK. A series of model ensemble experiments suggests that, in addition to a uniform AR increase in response to anthropogenic warming, tropical Pacific variability also contributes to the observed Arctic AR changes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Multiscale drivers of extreme southern California flooding: ENSO, MJO, North Pacific jet, and atmospheric rivers

Extreme rainfall and flooding, driven by a powerful atmospheric river (AR) and a persistent Madden-Julian Oscillation (MJO), hit Southern California in February 2024 during the 2023–2024 El Niño, affecting over 10 million people. ARs are key contributors to extreme rainfall and flooding along the U.S. West Coast. Although the AR-MJO link has been documented, its spatio-temporal variability remains a major forecasting and risk-management challenge. Combining precipitation, stream gauge and demographic data, we quantify the physical drivers and population exposure to this extreme event. Leveraging a Lagrangian MJO precipitation tracking algorithm, we unravel the multiscale interactions responsible for the AR’s development. El Niño favored a large, long-lived MJO that interacted with the North Pacific Jet (NPJ) over more than three weeks. The MJO convective outflow modulated the NPJ by inducing negative potential vorticity advection along the tropopause. The ensuing NPJ extension and acceleration induced explosive cyclogenesis, whose AR-driven moisture transport resulted in extreme rainfall.

Atmospheric dynamics↗
DOE OSTI2025

X-ray absorption spectroscopy measurements of radiatively ionized argon gas

X-ray absorption spectroscopy is a diagnostic tool that can characterize the temperature and ionization state of a plasma. This technique requires experiments to characterize the platform, careful data calibration, and comparison with atomic models to understand the plasma parameters. We performed ionizing radiation wave experiments at the OMEGA Laser Facility that used an ∼ 80 eV X-ray source to heat an Argon (Ar) gas cell at fill pressures of 3 atm. To diagnose the Ar plasma, we used a capsule backlighter offset 10 mm from the gas cell, to produce X-rays that were absorbed by the ionized Ar gas. The absorption analysis was calibrated using the significant line structure in the backlighter spectrum, which served as independent energy fiducials enabling definitive measurements of a 50-eV shift in the energy of the Ar K-edge due to ionization. We compare the measured absorption spectra to two independent atomic models, PrismSPECT and SCRAM, and show that the observed K-edge shift in the heated gas is consistent with ionization up to Ar 4+ and temperatures of 10 eV.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Radio-frequency magnetron sputter deposition of ultrathick boron carbide films

The deposition of thick B 4 C films with low residual stress by conventional direct-current magnetron sputtering is accompanied by the formation of dust particulates contaminating the target, chamber, and substrates and leading to the formation of nodular defects in films. Here, we demonstrate that the formation of particulates is greatly reduced during radio-frequency magnetron sputtering (RFMS). We systematically study properties of B 4 C films deposited by RFMS with a substrate temperature of 330 °C, a target-to-substrate distance of 10 cm, Ar working gas pressure in the range of 4.5–12.0 mTorr (0.6–1.6 Pa), and substrate tilt angles of 0°–80°. All films are x-ray amorphous. A columnar structure develops with increasing either Ar pressure or substrate tilt. For columnar films, the column tilt angle decreases with increasing Ar pressure, which we attribute to a corresponding increase in the width of the distribution of impact angles of deposition flux. In contrast to the Keller–Simmons rule, the deposition rate increases with increasing Ar pressure, which suggests a better coupling of the RF energy to the plasma processes that lead to target sputtering at higher pressures. There is a critical substrate tilt angle above which the total residual stress is close to zero. This critical substrate tilt angle is ~0° for an Ar pressure of 12 mTorr (1.6 Pa). The lower residual stress state, necessary for depositing ultrathick films, is characterized by a larger concentration of nanoscale inhomogeneities and decreased mechanical properties. Based on these results, RFMS deposition of 60-μm-thick B 4 C films is demonstrated.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Intercomparison of Deep Learning Model Architectures for Atmospheric River Prediction

With a rapid surge in the application of machine learning (ML) for a diverse range of tasks in climate science, the present study addresses a challenge for climate scientists when selecting the optimal ML or deep learning (DL) architecture for a given application. In particular, a DL intercomparison study was performed with a focus on forecasting the position of atmospheric rivers (ARs) on short-range time scales (up to 5-day lead times). AR predictions from multiple DL architectures, including various types of convolutional autoencoders and a vision transformer (ViT), were compared against ECMWF ERA5 reanalysis and hindcasts from a global climate model. DL models with similar trainable parameters were trained on ERA5 reanalysis data and AR positions derived from a thresholding algorithm to ensure a fair comparison among the DL models. Each model’s performance and accuracy in forecasting AR location and key input fields within a 5-day window were assessed using metrics of root-mean-square error, anomaly correlation, and mean intersection over union. The ViT architecture outperformed other autoencoder models in most of the metrics. Incorporating additional meteorological fields only yielded slight improvements in forecasting certain fields at longer lead times. The results also suggest that a smaller number of input time steps or smaller number of autoregressive steps can achieve better prediction skills, while also improving the overall computational efficiency. This research offers valuable insights into the strengths and weaknesses of different DL techniques for AR forecasting, hopefully guiding the development of improved models for forecasting this phenomenon.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Alpha Spectrometry Results for Groundwater Samples Collected in Northern Iraq and a Summary of the Environmental Setting of the Adaya Burial Site

The Radiation Protection Center (RPC) of the Iraqi Ministry of Environment continues to evaluate the potential health impacts associated with the Adaya Burial Site, which is located 33 kilometers (20.5 miles) southwest of Mosul. This report documents the radiological analyses of 16 groundwater samples collected from wells located in the vicinity of the Adaya Burial Site and at other sites in northern Iraq. The Adaya Burial Site is a high-risk dump site because a large volume of radioactive material and contaminated soil is located on an unsecure hillside above the village of Tall ar Ragrag. The uranium activities for the 16 water samples in northern Iraq are considered to be naturally occurring and do not indicate artificial (man-made) contamination. With one exception, the alpha spectrometry results for the 16 wells that were sampled in 2019 indicate that the water quality concerning the three uranium isotopes (Uranium-233/234, Uranium-235/236, and Uranium-238) was acceptable for potable purposes (drinking and cooking). However, Well 7 in Mosul had a Uranium-233/234 activity concentration that slightly exceeded the World Health Organization guidance level. Eight of the 16 wells are located in the villages of Tall ar Ragrag and Adaya and had naturally occurring uranium concentrations. Wells in the villages of Tall ar Ragrag and Adaya are located near the Adaya Burial Site and should be sampled on an annual schedule. The list of groundwater analytes should include metals, total uranium, isotopic uranium, gross alpha/beta, gamma spectroscopy, organic compounds, and standard water quality parameters. Our current understanding of the hydrogeologic setting in the vicinity of the Adaya Burial Site is solely based on villager's domestic wells, topographic maps, and satellite imagery. To better understand the hydrogeologic setting, a Groundwater Monitoring Program needs to be developed and should include the installation of twelve groundwater monitoring wells in the vicinity of Tall ar Ragrag and the Adaya Burial Site. Characterization of the limestone aquifer and overlying alluvium is needed. RPC should continue to support health assessments for the villagers in Tall ar Ragrag and Adaya. Collecting samples for surface water (storm water), airborne dust, vegetation, and washway sediment should be conducted on a routine basis. Human access to the Adaya Burial Site needs to be strictly limited. Livestock access on or near the burial site needs to be eliminated. The surface-water exposure pathway is likely a greater threat than the groundwater exposure pathway. Installation of a surface-water diversion or collection system is recommended in order to reduce the potential for humans and livestock to come in contact with contaminated water and sediment. To reduce exposure to villagers, groundwater treatment should be considered if elevated uranium or other contaminants are detected in drinking water. Installing water-treatment systems would likely be quicker to accomplish than remediation and excavation of the Adaya Burial Site. The known potential for human exposure to uranium and metals (such as arsenic, chromium, selenium, and strontium) at the Adaya Burial Site is serious. Additional characterization , mitigation, and remediation efforts should be given a high priority.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Artificial Intelligence/Machine Learning Technologies for Advanced Reactors (Workshop Summary Report)

A workshop on artificial intelligence and machine learning (AI/ML) for advanced reactors (AR) was held October 5-6, 2021. The workshop was to be attended in-person at ANL but COVID restrictions forced the workshop to go virtual. The objectives of the workshop were to identify the most promising AI/ML opportunities for improving advanced reactor design, optimizing plant performance, and enhancing economic competitiveness and to develop an understanding of the scientific, engineering and licensing challenges facing their application. The workshop planning committee included GAIN, EPRI and NEI and members of three national laboratories (ANL, INL, and ORNL). The workshop was attended by more than 200 individuals representing academic and scientific institutions and the nuclear power industry. The definition put forth for an AI/ML system was one that perceives its environment and takes actions that maximize its chance of achieving its goals. In this report AI/ML refers to next generation algorithms that include deep learning, statistical analysis and data analytics and associated scientific computing and their potential application to the design, licensing, operation and maintenance of ARs. These methods typically incorporate models built from process data and may also include data generated by simulations that represent the behavior of a system. The workshop was organized in response to the growing interest in application of AI/ML for improving the economic competitiveness of nuclear energy. Increasingly more resources are being allocated to investigating the benefits of AI/ML methods. The DOE created the Artificial Intelligence & Technology Office to promote their development. And within the Office of Nuclear Energy, resources have been allocated to explore and understand the potential benefits of AI/ML. Additionally, the national laboratories are strategically positioned with DOE computing facilities such as Summit, Perlmutter, Aurora and Frontier that support large-scale simulations, hybrid HPC models with AI surrogates, and the exploration of new types of generative models emerging from multi-model data streams and sources. The workshop was organized with members of the AR community to understand the effort and to identify the level of interest and progress in this emerging technology. The workshop discussions focused on identifying opportunities for AI/ML across diverse areas of the nuclear industry and identifying current scientific and engineering challenges for advanced reactors that might be addressed through transformational uses of AI/ML. Discussion panels focused on four high-interest technical domains for advanced reactors: design, maintenance and operations, energy storage, and materials. The results of those discussions are summarized in this report. This includes opportunities that were identified for exploiting AI techniques and methods to improve the efficacy and efficiency of reactor analysis and to improve the operation and optimization of advanced reactors. Advanced reactor developers expressed an interest in learning more about AI/ML methods and their application. This included understanding whether ML methods can provide an advantage over existing nonlinear data regression methods for collapsing high-fidelity simulation results into faster running models. A consensus emerged that AR advances planned for the next decade will benefit from the use of AI/ML tools. The need exists to understand and model complex systems across length scales and modalities. AI/ML is a tool for discovery that can yield a set of engineering principles for use by nuclear engineers, licensing bodies, and operators to solve problems in plant design, safety analyses, autonomous operation, and predictive maintenance. While AI/ML represents a new set of tools, an awareness by the nuclear community of the full potential is still in the early stages so there is a need to increase awareness. It appears that the wide-spread adoption of AI/ML tools for ARs would be facilitated by future educational workshops that describe foundational methods and capabilities and describe successful applications.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2020

Measurement of Argon-37 Background Concentrations in the Atmosphere

Radioisotopes of noble gases, primarily xenon and argon, are important indicators of underground nuclear explosions. The Comprehensive Nuclear-Test-Ban Treaty (CTBT) includes monitoring capabilities to identify potential nuclear explosions conducted in violation of the CTBT. This monitoring currently focuses on measurement of the xenon isotopes 133 Xe, 135 Xe, 131 mXe and 133 mXe. However, it is predicted that within 100 days of an underground nuclear explosion (UNE) 37 Ar would be released to the atmosphere at higher concentrations than xenon isotopes (Haas et al. 2010). Therefore, inclusion of 37 Ar measurement capabilities at atmospheric International Monitoring System (IMS) stations could improve the capability to detect a nuclear explosion. At an IMS station, it could be difficult to determine what constitutes an elevated concentration as a result of an UNE without first understanding the expected range of background concentrations. This work describes our analysis of atmospheric samples for 37 Ar to evaluate the range of background concentrations. The range of 37 Ar concentrations measured in atmospheric air samples was relatively narrow ranging between less than 1 mBq/m 3 to greater than 2 mBq/m 3 . The average atmospheric concentration of all samples was 0.98 mBq/m 3 , with a standard deviation of 0.5 mBq/m 3 . This is consistent with previous published results. The results presented here demonstrate the capability to detect background concentrations of 37 Ar in atmospheric air.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2025

Characterization of Additively Manufactured Tungsten Heavy Alloy Material (CRADA Final Report)

LLNL and Aerojet Rocketdyne, Inc. (AR), using earlier collaborative work in powder bed printing of other materials, have been collaboratively pursuing the additive manufacture of tungsten heavy alloy (WHA) material since 2014. The goal of the project was to establish the ability to produce the LLNL developed material at the AR production facility. When accomplished, AR would produce an agreed array of test samples to technically characterize the material properties via testing. AR would conduct a portion of that testing and provide those results to LLNL. The goal of this work was to perform a trial transfer of LLNL’s WHA technology to AR

36 MATERIALS SCIENCE↗
DOE OSTI2026

Augmented Reality Technologies for Radiation Safety Training: A Systematic Review of Sensor Integration and Visualization Approaches

This paper presents a comprehensive systematic review examining the application of augmented reality (AR) and sensor technologies for visualizing ionizing radiation in virtual training environments. The review methodology involved systematic identification and analysis of the relevant literature based on predetermined criteria including publication type, year of publication, application domain, and technological approach. The literature search encompassed publications from 2011 to 2021 across four major academic databases: Web of Science, Google Scholar, IEEE Xplore, and Scopus. Through rigorous screening following PRISMA 2020 guidelines, 23 research articles met the inclusion criteria for detailed analysis. From 404 initial database records, 360 were excluded during title/abstract screening (primarily for lacking AR components, radiation focus, or training applications) and 4 during full-text assessment (all for lacking sensor integration). The findings reveal that AR-based ionizing radiation visualization has been successfully implemented across diverse domains, including nuclear facility operations, medical procedures, CERN research activities, and educational and monitoring applications. The analysis identified multiple dimensions of impact, encompassing distinct benefits, emerging opportunities, and implementation challenges associated with AR deployment for ionizing radiation training. Each of these dimensions is comprehensively examined and documented within this review. Additionally, this study identifies critical research gaps that currently limit the full potential of AR technology in supporting ionizing radiation training programs. These gaps are systematically analyzed and discussed to establish clear directions for future research endeavors in this emerging field.

61 - RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2024

Wintertime extreme warming events in the high Arctic: characteristics, drivers, trends, and the role of atmospheric rivers

Abstract. An extreme warming event near the North Pole, with 2 m temperature rising above 0 °C, was observed in late December 2015. This specific event has been attributed to cyclones and their associated moisture intrusions. However, little is known about the characteristics and drivers of similar events in the historical record. Here, using data from European Centre for Medium-Range Weather Forecasts Reanalysis, version 5 (ERA5), we study these winter extreme warming events with 2 m temperature over a grid point above 0 °C over the high Arctic (poleward of 80° N) that occurred during 1980–2021. In ERA5, such wintertime extreme warming events can only be found over the Atlantic sector. They occur rarely over many grid points, with a total absence during some winters. Furthermore, even when occurring, they tend to be short-lived, with the majority of the events lasting for less than a day. By examining their surface energy budget, we found that these events transition with increasing latitude from a regime dominated by turbulent heat flux into the one dominated by downward longwave radiation. Positive sea level pressure anomalies which resemble blocking over northern Eurasia are identified as a key ingredient in driving these events, as they can effectively deflect the eastward propagating cyclones poleward, leading to intense moisture and heat intrusions into the high Arctic. Using an atmospheric river (AR) detection algorithm, the roles of ARs in contributing to the occurrence of these extreme warming events defined at the grid-point scale are explicitly quantified. The importance of ARs in inducing these events increases with latitude. Poleward of about 83° N, 100 % of these events occurred under AR conditions, corroborating that ARs were essential in contributing to the occurrence of these events. Over the past 4 decades, both the frequency, duration, and magnitude of these events have been increasing significantly. As the Arctic continues to warm, these events are likely to increase in both frequency, duration, and magnitude, with great implications for the local sea ice, hydrological cycle, and ecosystem.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Atmospheric river representation in the Energy Exascale Earth System Model (E3SM) version 1.0

Abstract. The Energy Exascale Earth System Model (E3SM) project is an ongoing, state-of-the-science Earth system modeling, simulation, and prediction project developed by the US Department of Energy (DOE). With an emphasis on supporting the DOE's energy mission, understanding and quantifying how well the model simulates water cycle processes is of particular importance. Here, we evaluate E3SM version 1.0 (v1.0) for its ability to represent atmospheric rivers (ARs), which play significant roles in water vapor transport and precipitation. The characteristics and precipitation associated with global ARs in E3SM at standard resolution (1∘ × 1∘) are compared to the Modern-Era Retrospective analysis for Research and Applications, version 2 (MERRA2). Global patterns of AR frequencies in E3SM show high degrees of correlation (≥0.97) with MERRA2 and low mean absolute errors (MAEs; <1 %) annually, seasonally, and across different ensemble members. However, some large-scale condition biases exist, leading to AR biases – most significant of which are the double intertropical convergence zone (ITCZ), a stronger and/or equatorward-shifted subtropical jet during boreal and austral winters, and enhanced Northern Hemisphere westerlies during summer. By comparing atmosphere-only and fully coupled simulations, we attribute the sources of the biases to the atmospheric component or to a coupling response. Using relationships revealed in Dong et al. (2021), we provide evidence showing the stronger North Pacific jet in winter and the enhanced Northern Hemisphere westerlies during summer, associated with E3SM's double ITCZ and related weaker Atlantic meridional overturning circulation (AMOC), respectively, which are significant sources of the AR biases found in the coupled simulations.

58 GEOSCIENCES↗
DOE OSTI2024

Metrics tool for for evaluating atmospheric rivers in climate data

The metric tool code is designed for evaluating atmospheric rivers (AR) in climate models and reanalysis. It is python based, with built in metrics of AR frequency, AR precipitation, AR peak day, AR characteristics (width, length, area, latitude and longitude), and output of diagnostic plots. Is there

Dong, Bo↗
NASA NTRS1972

Radioactivity in returned lunar materials

The H-3, Ar-37, and Ar-39 radioactivities were measured at several depths in the large documented lunar rocks 14321 and 15555. The comparison of the Ar-37 activities from similar locations in rocks 12002, 14321, and 15555 gives direct measures of the amount of Ar-37 produced by the 2 November 1969 and 24 January 1971 solar flares. The tritium contents in the documented rocks decreased with increasing depths. The solar flare intensity averaged over 30 years obtained from the tritium depth dependence was approximately the same as the flare intensity averaged over 1000 years obtained from the Ar-37 measurements. Radioactivities in two Apollo 15 soil samples, H-3 in several Surveyor 3 samples, and tritium and radon weepage were also measured.

Source record↗
NASA NTRS1972

Radioactivities in returned lunar materials.

The difference between the Ar-37 activities from similar locations in the rocks 12002 and 15555 provides direct measures of the Ar-37 activities produced by the 2 November 1969 flare. Differences between the Ar-37 activities in 14321 and 15555 give Ar-37 activities produced by the 24 January 1971 flare. The intensities of the two flares were determined by making use of measured Ar-37 cross sections in simulated lunar material. The depth dependence of tritium in samples and its temperature-release pattern provides information about the sources of the tritium and about the intensity of solar flares integrated over the past 30 years.

Fireman, E. L.↗