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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 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

A stochastic model of future extreme temperature events for infrastructure analysis

Applying extreme temperature events for future conditions is not straightforward for infrastructure resilience analyses. This work introduces a stochastic model that fills this gap. The model uses at least 50 years of daily extreme temperature records, climate normals with 10%-90% confidence intervals, and shifts/offsets for increased frequency and intensity of heat wave events. Intensity and frequency are shifted based on surface temperature anomaly from 1850-1900 for 32 models from CMIP6. A case study for Worcester, Massachusetts passed 85% of cases using the two-sided Kolmogorov-Smirnov -value test with 95% confidence for both temperature and duration. Future shifts for several climate scenarios to 2020, 2040, 2060, and 2080 had acceptable errors between the shifted model and 10- and 50-year extreme temperature event thresholds with the largest error being 2.67 degrees C. The model is likely to be flexible enough for other patterns of extreme weather such as extreme precipitation and hurricanes.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2015

The violent hard X-ray variability of MRK 421 observed by NuSTAR in 2013 April

The well-studied blazar Markarian 421 (Mrk 421, z = 0.031) was the subject of an intensive multi-wavelength campaign when it flared in 2013 April. The recorded X-ray and very high-energy (E > 100 GeV) γ-ray fluxes are the highest ever measured from this object. At the peak of the activity, it was monitored by the hard X-ray focusing telescope Nuclear Spectroscopic Telescope Array (NuSTAR) and the Swift X-Ray Telescope (XRT). In this work, we present a detailed variability analysis of NuSTAR and Swift-XRT observations of Mrk 421 during this flaring episode. We obtained the shortest flux doubling time of 14.01 ± 5.03 minutes, which is the shortest hard X-ray (3–79 keV) variability ever recorded from Mrk 421, and is on the order of the light-crossing time of the black hole's event horizon. A pattern of extremely fast variability events superposed on slowly varying flares is found in most of the NuSTAR observations. We suggest that these peculiar variability patterns may be explained by magnetic energy dissipation and reconnection in a fast-moving compact emission region within the jet. Based on the fast variability, we derive a lower limit on the magnetic field strength of B⩾0.73δ{sub 1}{sup −2/3} ν{sub 19}{sup 1/3} G, where δ{sub 1} is the Doppler factor in units of 10, and ν{sub 19} is the characteristic X-ray synchrotron frequency in units of 10{sup 19} Hz.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2018

Prognostic Power of Extreme Rainfall Scaling Formulas Across Space and Time Scales

Some studies documenting changes in extreme precipitation use scaling formulas to approximate the large percentiles of the rainfall distribution from average dynamical and thermodynamical variables, called predictors. Here we instead assess the performance of these formulas as approximations to the rain rates in individual events. We evaluate the accuracies of the scaling relationships as functions of spatial and temporal scales by analyzing tropical rainfall in a superparameterized model. Relationships using full vertical profiles of the predictors are more accurate than those using their values at specific vertical levels because they better characterize the specific dynamics of each event. Both types of scaling relationships perform well over a range of length scales from 200 to 2,000 km and time scales from an hour to a week, and their precision is higher in the case of simulations with superparameterization than with parameterized convection. Uncertainties emerging from the local use of the scaling relationships suggest that they may only characterize the intensification of individual extremes for a warming of 4–5 K or larger. Finally, we argue that these formulas can be used to reconstruct the tail of the rainfall distribution directly from its predictors without prior information on P. While scalings have been used as diagnostic equations conditioned on the occurrence of extreme rainfall, they are actually able to mimic the prognostic behavior of climate model parameterizations on a variety of scales when estimating the intensity, frequency, and spatial patterns of extremes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Variational AutoEncoders Reveal Intensifying GPP Extremes in Continental United States based on CESM2 Simulations

Climate extremes significantly impact terrestrial carbon cycle dynamics, necessitating robust methods for detecting and analyzing anomalous behavior in plant productivity. This study presents a novel application of variational autoencoders (VAE) for identifying extreme events in gross primary productivity (GPP) from Community Earth System Model version 2 simulations across four AR6 regions in the Continental United States. We compare VAE-based anomaly detection with traditional singular spectral analysis (SSA) methods across three time periods: 1850-80, 1950-80, and 2050-80 under SSP5-8.5 scenario. The VAE architecture employs three dense layers and a latent space with input sequence length of 12 months, training on normalized GPP time series to reconstruct the GPP and identify anomalies based on reconstruction errors. Extreme events are defined using 5th percentile thresholds applied to both VAE and SSA anomalies. Results demonstrate strong regional agreement between VAE and SSA methods in spatial patterns of extreme event frequencies, despite VAE consistently producing higher threshold values (179-756 GgC for VAE vs. 100-784 GgC for SSA across regions and periods). Both methods reveal increasing magnitudes and frequencies of negative carbon cycle extremes toward 2050-80, particularly in Western and Central North America. The VAE approach shows comparable performance to established SSA techniques while offering computational advantages and enhanced capability for capturing non-linear temporal dependencies in carbon cycle variability. This research demonstrates the potential of deep learning approaches for extremes detection and provides a foundation for improved understanding of future carbon cycle risks under future conditions.

Sharma, Bharat [ORNL] (ORCID:0000000266982487)↗
DOE OSTI2015

TECA: Petascale pattern recognition for climate science

Climate Change is one of the most pressing challenges facing humanity in the 21st century. Climate simulations provide us with a unique opportunity to examine effects of anthropogenic emissions. Highresolution climate simulations produce “Big Data”: contemporary climate archives are ≈ 5PB in size and we expect future archives to measure on the order of Exa-Bytes. In this work, we present the successful application of TECA (Toolkit for Extreme Climate Analysis) framework, for extracting extreme weather patterns such as Tropical Cyclones, Atmospheric Rivers and Extra-Tropical Cyclones from TB-sized simulation datasets. TECA has been run at full-scale on Cray XE6 and IBM BG/Q systems, and has reduced the runtime for pattern detection tasks from years to hours. TECA has been utilized to evaluate the performance of various computational models in reproducing the statistics of extreme weather events, and for characterizing the change in frequency of storm systems in the future.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2014

Abundance profiling of extremely metal-poor stars and supernova properties in the early universe

After the big bang nucleosynthesis, the first heavy element enrichment in the universe was made by a supernova (SN) explosion of a population (Pop) III star (Pop III SN). The abundance ratios of elements produced from Pop III SNe are recorded in abundance patterns of extremely metal-poor (EMP) stars. The observations of the increasing number of EMP stars have made it possible to statistically constrain the explosion properties of Pop III SNe. We present Pop III SN models whose nucleosynthesis yields well reproduce, individually, the abundance patterns of 48 such metal-poor stars as [Fe/H] ≲ – 3.5. We then derive relations between the abundance ratios of EMP stars and certain explosion properties of Pop III SNe: the higher [(C + N)/Fe] and [(C + N)/Mg] ratios correspond to the smaller ejected Fe mass and the larger compact remnant mass, respectively. Using these relations, the distributions of the abundance ratios of EMP stars are converted to those of the explosion properties of Pop III SNe. Such distributions are compared with those of the explosion properties of present day SNe: the distribution of the ejected Fe mass of Pop III SNe has the same peak as that of the present day SNe but shows an extended tail down to ∼10{sup –2}-10{sup –5} M {sub ☉}, and the distribution of the mass of the compact remnant of Pop III SNe is as wide as that of the present-day, stellar-mass black holes. Our results demonstrate the importance of large samples of EMP stars obtained by ongoing and future EMP star surveys and subsequent high-dispersion spectroscopic observations in clarifying the nature of Pop III SNe in the early universe.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Projected evolution of droughts and human exposure in the contiguous United States under SSP5-8.5: a regional downscaling perspective

The increasingly unpredictable and extreme weather patterns under a warming climate underscore the urgency of accurate regional assessments of future drought risk. This study evaluates the projected drought evolution in the contiguous United States under the high-emission shared socioeconomic pathway 5–8.5 climate scenario for the coming decades. Using a multi-model ensemble of six Coupled Model Intercomparison Project Phase 6 global climate models combined with dynamical downscaling techniques, we analyzed near-term (2020–2039) and mid-term (2040–2059) drought patterns using the self-calibrating palmer drought severity index (ScPDSI), the standardized precipitation index (SPI-12), and the Standardized Precipitation-Evapotranspiration Index (SPEI-12). Results reveal a widespread increase in abnormally dry (D0) and moderate drought (D1) conditions, particularly in urban areas, while severe (D2), extreme (D3), and exceptional (D4) droughts are expected to become less common in many regions. Meanwhile, persistent and intensifying droughts are projected in the western and southwestern U.S., driven by long-term soil moisture deficits. The ScPDSI projects that 1.1 million urban residents will be affected by D0 conditions in 2050, while SPI-12 suggests a decrease in the total affected populations after 2040. ScPDSI indicates prolonged droughts in the West, and SPI-12 captures transient variability. Although the total drought-exposed population is expected to decrease, urban areas will continue to bear a greater burden, particularly for mild droughts (D0, D1). These findings highlight a shift toward more frequent mild droughts, fewer severe droughts, and persistent drying in the Southwest, emphasizing the need for region-specific adaptation strategies.

CMIP6↗
DOE OSTI2016

Trends in atmospheric patterns conducive to seasonal precipitation and temperature extremes in California

Recent evidence suggests that changes in atmospheric circulation have altered the probability of extreme climate events in the Northern Hemisphere. We investigate northeastern Pacific atmospheric circulation patterns that have historically (1949–2015) been associated with cool-season (October-May) precipitation and temperature extremes in California. We identify changes in occurrence of atmospheric circulation patterns by measuring the similarity of the cool-season atmospheric configuration that occurred in each year of the 1949–2015 period with the configuration that occurred during each of the five driest, wettest, warmest, and coolest years. Our analysis detects statistically significant changes in the occurrence of atmospheric patterns associated with seasonal precipitation and temperature extremes. We also find a robust increase in the magnitude and subseasonal persistence of the cool-season West Coast ridge, resulting in an amplification of the background state. Changes in both seasonal mean and extreme event configurations appear to be caused by a combination of spatially nonuniform thermal expansion of the atmosphere and reinforcing trends in the pattern of sea level pressure. In particular, both thermal expansion and sea level pressure trends contribute to a notable increase in anomalous northeastern Pacific ridging patterns similar to that observed during the 2012–2015 California drought. Collectively, our empirical findings suggest that the frequency of atmospheric conditions like those during California’s most severely dry and hot years has increased in recent decades, but not necessarily at the expense of patterns associated with extremely wet years.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Compound Continental Risk of Multiple Extreme Floods in the United States

Abstract Understanding spatially correlated floods and modeling joint hazard associated with threshold exceedances across multiple locations is crucial for accurate estimation of continental‐scale portfolio risk. This work uses a non‐parametric copula‐based spatial simulator to analyze peak floods across the United States to derive the first‐of‐its‐kind continental portfolio risk estimates at the 10‐ and 100‐year return levels. We find significant interdependence in floods across the nation, revealing the recurring pattern of extreme events affecting the Northeast, Central, West, and Northwest United States in the same year. The stochastic simulator effectively manages high‐dimensional data and offers reliable uncertainty estimates for both spatially dependent floods and the aggregated flood losses at the continental level. El Niño‐Southern Oscillation and Atlantic Multidecadal Oscillation are identified as statistically significant tele‐connectors of aggregate loss. This research aims to advance the understanding of compound continental flood hazard and the potential large‐scale climate teleconnections that lead to such compound floods.

Geology↗
DOE OSTI2016

HiSPoD : a program for high-speed polychromatic X-ray diffraction experiments and data analysis on polycrystalline samples

Here, a high-speed X-ray diffraction technique was recently developed at the 32-ID-B beamline of the Advanced Photon Source for studying highly dynamic, yet non-repeatable and irreversible, materials processes. In experiments, the microstructure evolution in a single material event is probed by recording a series of diffraction patterns with extremely short exposure time and high frame rate. Owing to the limited flux in a short pulse and the polychromatic nature of the incident X-rays, analysis of the diffraction data is challenging. Here, HiSPoD , a stand-alone Matlab-based software for analyzing the polychromatic X-ray diffraction data from polycrystalline samples, is described. With HiSPoD , researchers are able to perform diffraction peak indexing, extraction of one-dimensional intensity profiles by integrating a two-dimensional diffraction pattern, and, more importantly, quantitative numerical simulations to obtain precise sample structure information.

36 MATERIALS SCIENCE↗
DOE OSTI2019

The east–west division of changing precipitation in Nepal

Abstract Changes to precipitation patterns and extremes over the Nepalese Himalayas were examined using a high‐resolution, station‐based daily dataset, Asian Precipitation‐Highly Resolved Observational Data Integration Towards Evaluation (0.05° × 0.05° APHRODITE) from 1951 to 2007. The annual statistics of extreme precipitation across Nepal show a significant increase since the end of the 20th century. However, seasonal mean precipitation shows a remarkable decrease in western Nepal, particularly since 1980, forming an east–west division in the precipitation change. This decreasing trend of precipitation led to a reduction to the dry‐season stream flow of Karnali River, the major river in western Nepal. At the same time, the increasing extreme precipitation produced greater threat of flash flood in Nepal. This east–west division of the precipitation trend agrees with the second leading mode of the mean precipitation variability, which was traced to the interannual variability of the Indian Ocean sea surface temperature that showed a slowdown of warming. Similar to the APHRODITE trends, precipitation simulated by the fifth phase of the Coupled Model Intercomparison Project (CMIP5) models depicted the decreasing historical trend in western Nepal, but future projections reverse that trend towards an all‐Nepal increase. CMIP5 future climate projections depict continual warming in the Indian Ocean, potentially reversing the historical decreasing trends of precipitation in western Nepal.

Pokharel, Binod↗
DOE OSTI2026

Subseasonal Tropical Convection Characteristics in the Energy Exascale Earth System Model Version 2

Accurate simulation of subseasonal tropical moist convection remains a key challenge for Earth system models. The difficulties stem from the reliance of cumulus cloud processes on model parameterizations and the need to represent the multiscale nature of interactions among clouds, radiation, moisture, circulation, and surface energy fluxes. Equatorial convection drives circulation anomalies that can affect weather patterns and extremes globally, motivating efforts to better understand and simulate these tropical disturbances. Here, a detailed review of subseasonal tropical convective behavior as simulated in the Energy Exascale Earth System Model version 2 (E3SMv2) is presented, with comparison to its predecessor version 1 (E3SMv1) and reference data sets. Model structural changes to the deep convective trigger function and surface fluxes, along with parametric tuning of the cloud and microphysics schemes, together result in an improved depiction of organized tropical convection across scales. In particular, E3SMv2 exhibits a more realistic Madden‐Julian oscillation (MJO) and low‐frequency Kelvin waves—owing to a sharper time mean equatorial meridional moisture gradient and improved convection‐circulation coupling —as well as a better depiction of MJO Northern Hemisphere teleconnections. Despite these improvements, subseasonal precipitation variance continues to be strongly underestimated in E3SMv2. Use of a cloud plume model also reveals that the coupling between daily averaged tropical precipitation and lower tropospheric instability in E3SM is inconsistent with observations, a bias that could potentially impact the simulation of intraseasonal disturbances.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2012

Aberration features in directional dark matter detection

The motion of the Earth around the Sun causes an annual change in the magnitude and direction of the arrival velocity of dark matter particles on Earth, in a way analogous to aberration of stellar light. In directional detectors, aberration of weakly interacting massive particles (WIMPs) modulates the pattern of nuclear recoil directions in a way that depends on the orbital velocity of the Earth and the local galactic distribution of WIMP velocities. Knowing the former, WIMP aberration can give information on the latter, besides being a curious way of confirming the revolution of the Earth and the extraterrestrial provenance of WIMPs. While observing the full aberration pattern requires extremely large exposures, we claim that the annual variation of the mean recoil direction or of the event counts over specific solid angles may be detectable with moderately large exposures. For example, integrated counts over Galactic hemispheres separated by planes perpendicular to Earth's orbit would modulate annually, resulting in Galactic Hemisphere Annual Modulations (GHAM) with amplitudes larger than the usual non-directional annual modulation.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2026

The Impact of Time-Aware Design Choices in ICS Anomaly Detection

Industrial control systems (ICS) remain vulnerable to increasingly sophisticated cyberattacks, yet evaluating anomaly detection models in these environments is challenging due to temporal dependencies, missing-not-at-random patterns, and extremely imbalanced datasets. These factors make common practices—especially random data splits and na¨ıve imputation— prone to severe temporal leakage, which can inflate reported performance and obscure real-world limitations. In this work, we systematically examine classical machine learning models, temporal deep learning architecture, and tensordecomposition– based methods on a gas-pipeline dataset using a fully temporally separated evaluation pipeline designed to mimic realistic deployment conditions. Our findings show that proper temporal handling and MNAR-aware preprocessing significantly alter the relative performance of popular anomaly-detection methods, providing practical guidance for designing reliable, leakage-resistant ICS intrusion-detection systems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Power Distribution Designing For Resilience Application

The number of power outages have been on the rise as extreme weather patterns have been occurring more frequently due to climate change. These outages have an economic impact in the billions of dollar. The modernization of the power grid and adoption of high penetration of distributed generation such as wind and solar have the potential to reduce the number and length of time of these power outages. However, planners and operators need a way to value the resilience the distributed assets provide to the grid. PowDDeR is a tool that allows them to evaluate the resilience each asset provides as well as how they provide resilience over the connected network.

Phillips, TylerB↗
DOE OSTI2021

Improving Short Term Predictability of Hydrologic Models with Deep Learning

Focal Area: Focal Area 2. Predictive modeling through the use of AI techniques and AI-derived model components; the use of AI and other tools to design a prediction system comprising of a hierarchy of models (e.g., AI driven model/component/parameterization selection). Science Challenge: A major challenge exists in the lack of reliable predictive modeling of water-cycle extremes using macro-scale hydrologic models that are driven by atmospheric climate data. This causes a critical knowledge gap in understanding the magnitudes, probabilities, and severity of droughts, rainfall, and flooding. Improved understanding of these extreme events requires more accurate modeling at high spatial and temporal resolutions. This shortcoming is especially apparent when complex couplings between atmospheric quantities and engineered systems emerge, such as during extreme precipitation or drought events and at intersections of atmospheric-land-fluvial-ocean systems. Added complexity around representing operations of engineered infrastructure leads to a limited capability to analyze the temporal and spatial implications of compounding extreme events across inland and coastal regions. This results in profound challenges for water and power systems operators, agriculture production, and regional infrastructure planning. The science challenge can be summarized as How can we use AI/ML to improve short term predictability of extreme water-cycle events and associated risk, especially compounding events and extreme rainfall patterns that result in flooding, to inland and coastal communities under different climate scenarios?

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Global Building Decarbonization Trends and Strategies

It is evident from extreme weather patterns that climate change is impacting the whole world. The consequences in the form of unpredicted severe weather conditions, hurricanes, floods, and wildfires have been disrupting national economies and affecting lives across the globe. As the global economy is recovering from the COVID-19 pandemic, the temporary drop in greenhouse gas (GHG) emissions mainly caused due to travel bans and economic slowdowns is no longer the case and, moving forward, controlling the increase in GHG emissions has emerged as a global challenge. The impact of climate change has enabled an acknowledgement of the challenge and this coincided with the rapid development of clean energy technologies for buildings, transportation, and industrial sectors, while ensuring a significant reduction in costs for their wide implementation.

54 ENVIRONMENTAL SCIENCES↗