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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 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
NASA NTRS2021

Trend Detection of Atmospheric Time Series: Incorporating Appropriate Uncertainty Estimates and Handling Extreme Events

This paper is aimed at atmospheric scientists without formal training in statistical theory. Its goal is to, 1) provide a critical review of the rationale for trend analysis of the time series typically encountered in the field of atmospheric chemistry; 2) describe a range of trend-detection methods; and 3) demonstrate effective means of conveying the results to a general audience. Trend detections in atmospheric chemical composition data are often challenged by a variety of sources of uncertainty, which often behave differently to other environmental phenomena such as temperature, precipitation rate, or stream flow, and may require specific methods depending on the science questions to be addressed. Some sources of uncertainty can be explicitly included in the model specification, such as autocorrelation and seasonality, but some inherent uncertainties are difficult to quantify, such as data heterogeneity and measurement uncertainty due to the combined effect of short- and long-term natural variability, instrumental stability, and aggregation of data from sparse sampling frequency. Failure to account for these uncertainties might result in an inappropriate inference of the trends and their estimation errors. On the other hand, the variation in extreme events might be interesting for different scientific questions, for example, the frequency of extremely high surface ozone events and their relevance to human health. In this study we aim to, 1) review trend detection methods for addressing different levels of data complexity in different chemical species; 2) demonstrate that the incorporation of scientifically interpretable covariates can outperform pure numerical curve fitting techniques in terms of uncertainty reduction and improved predictability; 3) illustrate the study of trends based on extreme quantiles that can provide insight beyond standard mean or median based trend estimates; and 4) present an advanced method of quantifying regional trends based on the inter-site correlations of multi-site data. All demonstrations are based on time series of observed trace gases relevant to atmospheric chemistry, but the methods can be applied to other environmental data sets.

Trace gas↗
NASA NTRSDate not supplied

Updates of MERRA-2 Data and Services at NASA GES DISC

Over40 years of NASA climate reanalysis datasets from the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) are available at the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC). In addition to being used in traditional weather and climate research, MERRA-2 is also widely used in application studies of, e.g., wind and solar energy, air quality and health, food and drought, and heat waves. Two new MERRA-2 datasets were recently added at the GES DISC: (1) climate statistics derived fromMERRA-2 daily data to assist in the analysis of extreme temperature and precipitation events and of large-scale meteorological patterns from 1980 to the present and (2) gridded satellite and conventional observations processed in the MERRA-2 system, along with key statistics derived from the data assimilation, to help better understand how the quality of observations directly affect there analysis data. The GES DISC focuses its efforts on continually improving existing data services and to develop new data tools to satisfy various user communities. The newly added features include the following: Time series service: This is a new service for MERRA-2 data, which enables the easy and fast access of long-term hourly or daily time series at a location for popular parameters. The data is saved in a single le in Ascii format with a user- friendly structure. New analytic functions in the subsetter interface: Options for downloading daily minimum and maximum values have been added into the subsetter interface, in addition to the existing daily mean option, for all MERRA-2 and MERRA sub-daily products. Data format conversion to GeoTIFF has been implemented. New variables in Giovanni: Most monthly variables have been integrated into Giovanni, GESDISC’s online visualization and analysis tool. Due to the large data volume, hourly variables were selected based on user requests. More online information: New MERRA-2 documentation has been added: Data How-To, Data in Action, and FAQ. This presentation overviews two new MERRA-2 datasets and illustrates the new features of data services through a number of case studies. MERRA-2 data and services can be found at: https://disc.gsfc.gov/datasets?

Data management↗
DOE OSTI2022

A treatment-effect model to quantify human dimensions of disaster impacts: the case of Hurricane Maria in Puerto Rico

Here, we propose a supervised learning approach using publicly available panel data to statistically quantify the specific manifestations of human impacts of an extreme event, such as changes number of suicides, substance abuse, excess mortality, and unemployment. This allows us to conceptually focus our framework on human impacts and how by attributing them to disaster events along widely accepted psychological, economic, and social dimensions. Our modified treatment-effect model allows counterfactual baseline conditions to be posited for each manifestation from which an aggregated quantitative multi-faceted measure of human impacts can be determined. The developed statistical methodology could be beneficial to policymakers who must allocate scarce resources to those communities in greater need. We illustrate the applicability of our approach using annual and monthly panel data from 2012 to 2018 encompassing the 2017 Hurricane Maria event across various municipalities in Puerto Rico. Our statistical modeling methodology stands apart since (i) it explicitly and more realistically captures the effect of different human-oriented manifestations of an actual event and (ii) it is flexible enough to accommodate individual preferences of various stakeholders in how they assign importance to multiple manifestations of human impacts.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Extreme variation in fatigue: Fatigue life prediction and dependence on build volume location in laser powder bed fusion of 17-4 stainless steel

Laser powder bed fusion (LPBF), a metal additive manufacturing technology, is well-suited for design optimization but fatigue life is limited by manufacturing defects. In this work, 17-4 stainless steel components were manufactured in densely populated build volumes, simulating at-scale LPBF production. Tests revealed extreme variability in fatigue life data, analyzed via rigorous statistical tools. The El-Haddad model, modified for finite-life, enabled defect-based life prediction. Specimen location within the build volume correlated to life, which was heteroscedastic. Investigating defect concentration over the build volume explained typical life and scatter. Finally, these findings argue for qualification approaches which acknowledge high material lot variability.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Upper bounds for 21st-century surface air temperatures in the Western United States

The last decade has seen a large number of severe heatwaves that were unprecedented in the observational record, highlighting challenges associated with observationally-based statistical quantification of the likelihood and magnitude of future extreme temperatures. An alternative to such probabilistic assessments is identification of upper bounds that quantify the hottest surface air temperatures that can possibly be achieved by the end of the 21st century. Theory, simulations, and observational analyses support the existence of a finite upper bound for surface air temperature; however, estimates for future upper-bound values that are realistic and usable for planning remain unavailable. Here, we combine atmospheric theory with large ensembles of dynamically downscaled projections to estimate historical and end-of-century upper bounds for surface air temperatures. A number of physical mechanisms can influence upper bounds, and at the end of the 21st century, estimates based on mechanisms that yield more moderate upper-bounds produce values around 60∘C for much of the Western United States and in excess of 80∘C for the hottest parts of the domain. Even cooler high-altitude locations have end-of-century upper bounds over 50∘C. Although these upper-bound estimates might seem implausibly large, increases in the upper bounds over the 21st century are similar to increases in dynamically downscaled peak surface temperatures after adjusting those downscaled temperatures to eliminate the possibly biased model trends in surface specific humidity. While upper bound estimates are high relative to historical observations, they nonetheless suggest that heatwave intensity risk is bounded, with uncertainty dominated by projections of surface and upper-level humidity.

Risser, Mark D↗
NASA NTRS2017

Chapter 8: Droughts, Floods, and Wildfires

Recent droughts and associated heat waves have reached record intensity in some regions of the United States; however, by geographical scale and duration, the Dust Bowl era of the 1930s remains the benchmark drought and extreme heat event in the historical record (very high confidence). While by some measures drought has decreased over much of the continental United States in association with long-term increases in precipitation, neither the precipitation increases nor inferred drought decreases have been confidently attributed to anthropogenic forcing. The human effect on recent major U.S. droughts is complicated. Little evidence is found for a human influence on observed precipitation deficits, but much evidence is found for a human influence on surface soil moisture deficits due to increased evapotranspiration caused by higher temperatures. Future decreases in surface (top 10 cm) soil moisture from anthropogenic forcing over most of the United States are likely as the climate warms under higher scenarios. Substantial reductions in western U.S. winter and spring snowpack are projected as the climate warms. Earlier spring melt and reduced snow water equivalent have been formally attributed to human-induced warming (high confidence) and will very likely be exacerbated as the climate continues to warm (very high confidence). Under higher scenarios, and assuming no change to current water resources management, chronic, long-duration hydrological drought is increasingly possible by the end of this century. Detectable changes in some classes of flood frequency have occurred in parts of the United States and are a mix of increases and decreases. Extreme precipitation, one of the controlling factors in flood statistics, is observed to have generally increased and is projected to continue to do so across the United States in a warming atmosphere. However, formal attribution approaches have not established a significant connection of increased riverine flooding to human-induced climate change, and the timing of any emergence of a future detectable anthropogenic change in flooding is unclear. The incidence of large forest fires in the western United States and Alaska has increased since the early 1980s and is projected to further increase in those regions as the climate warms, with profound changes to certain ecosystems.

Wehner, M. F.↗
NASA NTRS2010

Statistical Properties of SGR J1550-5418 Bursts

Magnetars are slowly rotating neutron stars with extreme magnetic fields, over 10(exp 15) Gauss. Only few have been discovered in the last 30 years. These sources are dormant most of their lifetimes and become randomly active emitting multiple soft gamma-ray bursts. We present here our results on the temporal analysis of ~300 bursts from Soft Gamma Repeater SGR J1550-5418 recorded with the Gamma-ray Burst Monitor (GBM) onboard the Fermi Observatory during its activation on January 22-29, 2009. We employed an un-triggered burst search in the energy range 8-100keV to collect all events from the source, besides the ones that triggered GBM. For the entire sample of bursts we determined their durations, rise and decay times. We study here the statistical properties of these characteristics and discuss how these may help us better understand the physical characteristics of the magnetar model.

Gorgone, Nicholas M.↗
DOE OSTI2022

Rapid attribution analysis of the extraordinary heat wave on the Pacific coast of the US and Canada in June 2021

Towards the end of June 2021, temperature records were broken by several degrees Celsius in several cities in the Pacific Northwest areas of the US and Canada, leading to spikes in sudden deaths and sharp increases in emergency calls and hospital visits for heat-related illnesses. Here we present a multi-model, multi-method attribution analysis to investigate the extent to which human-induced climate change has influenced the probability and intensity of extreme heat waves in this region. Based on observations, modelling and a classical statistical approach, the occurrence of a heat wave defined as the maximum daily temperature (TXx) observed in the area 45–52 °N, 119–123 °W, was found to be virtually impossible without human-caused climate change. The observed temperatures were so extreme that they lay far outside the range of historical temperature observations. This makes it hard to state with confidence how rare the event was. Using a statistical analysis that assumes that the heat wave is part of the same distribution as previous heat waves in this region led to a first-order estimation of the event frequency of the order of once in 1000 years under current climate conditions. Using this assumption and combining the results from the analysis of climate models and weather observations, we found that such a heat wave event would be at least 150 times less common without human-induced climate change. Also, this heat wave was about 2 °C hotter than a 1-in-1000-year heat wave would have been in 1850–1900, when global mean temperatures were 1.2 °C cooler than today. Looking into the future, in a world with 2 °C of global warming (0.8 °C warmer than today), a 1000-year event would be another degree hotter. Our results provide a strong warning: our rapidly warming climate is bringing us into uncharted territory with significant consequences for health, well-being and livelihoods. Adaptation and mitigation are urgently needed to prepare societies for a very different future.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2013

Statistical Downscaling and Bias Correction of Climate Model Outputs for Climate Change Impact Assessment in the U.S. Northeast

Statistical downscaling can be used to efficiently downscale a large number of General Circulation Model (GCM) outputs to a fine temporal and spatial scale. To facilitate regional impact assessments, this study statistically downscales (to 1∕8deg spatial resolution) and corrects the bias of daily maximum and minimum temperature and daily precipitation data from six GCMs and four Regional Climate Models (RCMs) for the northeast United States (US) using the Statistical Downscaling and Bias Correction (SDBC) approach. Based on these downscaled data from multiple models, five extreme indices were analyzed for the future climate to quantify future changes of climate extremes. For a subset of models and indices, results based on raw and bias corrected model outputs for the present-day climate were compared with observations, which demonstrated that bias correction is important not only for GCM outputs, but also for RCM outputs. For future climate, bias correction led to a higher level of agreements among the models in predicting the magnitude and capturing the spatial pattern of the extreme climate indices. We found that the incorporation of dynamical downscaling as an intermediate step does not lead to considerable differences in the results of statistical downscaling for the study domain.

extreme climate index↗
DOE OSTI2025

Data-driven upper bounds and event attribution for unprecedented heatwaves

The last decade has seen numerous record-shattering heatwaves in all corners of the globe. In the aftermath of these devastating events, there is interest in identifying worst-case thresholds or upper bounds that quantify just how hot temperatures can become. Generalized Extreme Value theory provides a data-driven estimate of extreme thresholds; however, upper bounds may be exceeded by future events, which undermines attribution and planning for heatwave impacts. Here, we show how the occurrence and relative probability of observed yet unprecedented events that exceed a priori upper bound estimates, so-called “impossible” temperatures, has changed over time. We find that many unprecedented events are actually within data-driven upper bounds, but only when using modern spatial statistical methods. Furthermore, there are clear connections between anthropogenic forcing and the “impossibility” of the most extreme temperatures. Robust understanding of heatwave thresholds provides critical information about future record-breaking events and how their extremity relates to historical measurements.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

From Ensemble Climate to Ensemble Impacts

Many climate-risk tools rely on ensemble mean projections or endpoint climate snapshots to characterize future hazards. Although convenient for communication, these representations remove the statistical, temporal, and physical information that real infrastructure systems respond to. Infrastructure degradation and failure arise from extremes, sequences, cumulative stress, compound hazards, and nonlinear fragility relationships, none of which survive ensemble averaging or temporal compression. Power-system failure statistics and cascading failure models further show that infrastructure risk is dominated by tail events and path-dependent dynamics rather than by mean conditions. This paper demonstrates why ensemble mean or endpoint-only climate representations are mathematically and physically inconsistent with engineering-grade risk analysis. We outline a model-resolved, time-series-based workflow that preserves extremes, variability, and sequencing by propagating each climate-model realization independently through hazard formation, exposure, fragility, and cascading failure mechanisms. Taking the ensemble of impacts—rather than the ensemble of climate—provides a defensible, physically coherent foundation for infrastructure resilience planning, regulatory compliance, and long-term investment decisions.

54 - ENVIRONMENTAL SCIENCES/GLOBAL CLIMATE CHANGE ↗
NASA NTRS2020

Potential of GPM IMERG Precipitation Estimates to Monitor Natural Disaster Triggers in Urban Areas: the Case of Rio deJaneiro, Brazil

Extreme rainfall can be a catastrophic trigger for natural disaster events at urban scales. However, there remains large uncertainties as to how satellite precipitation can identify these triggers at a city scale. The objective of this study is to evaluate the potential of satellite-based rainfall estimates to monitor natural disaster triggers in urban areas. Rainfall estimates from the Global Precipitation Measurement (GPM) mission are evaluated over the city of Rio de Janeiro, Brazil, where urban floods and landslides occur periodically as a result of extreme rainfall events. Two rainfall products derived from the Integrated Multi-satellite Retrievals for GPM (IMERG), the IMERG Early and IMERG Final products, are integrated into the Noah Multi-Parameterization (Noah-MP) land surface model in order to simulate the spatial and temporal dynamics of two key hydrometeorological disaster triggers across the city over the wet seasons during 2001-2019. Here, total runoff (TR) and rootzone soil moisture (RZSM) are considered as flood and landslide triggers, respectively. Ground-based observations at 33 pluviometric stations are interpolated, and the resulting rainfall fields are used in an in-situ precipitation-based simulation, considered as the reference for evaluating the IMERG-driven simulations. The evaluation is performed during the wet seasons (November-April), when average rainfall over the city is 4.4mm/day. Results show that IMERG products show low spatial variability at the city scale, generally overestimate rainfall rates by 12-35%, and impacts on TR and RZSM vary spatially mostly as a function of land cover and soil types. Results based on statistical and categorical metrics show that IMERG skill in detecting extreme events is moderate, with IMERG Final performing slightly better for most metrics. By analyzing two recent storms, we observe that IMERG detects mostly hourly extreme events, but underestimates rainfall rates, resulting in underestimated TR and RZSM. An evaluation of normalized time series using percentiles shows that both satellite products have significantly improved skill in detecting extreme events when compared to the evaluation using absolute values, indicating that IMERG precipitation could be potentially used as a predictor for natural disasters in urban areas.

IMERG↗
DOE OSTI2025

Processing-dependent chemical ordering in Cu 3 Au characterized via non-destructive Bragg coherent diffraction imaging

Of current importance for alloy design is controlling chemical ordering through processing routes to optimize an alloy's mechanical properties for a desired application. However, characterization of chemical ordering remains an ongoing challenge, particularly when nondestructive characterization is needed. Here, in this study, Bragg coherent diffraction imaging is used to reconstruct morphology and lattice displacement in model Cu 3 Au nanocrystals that have undergone different heat treatments to produce variation in chemical ordering. The magnitudes and distributions of the scattering amplitudes (proportional to electron density) and lattice strains within these crystals are then analyzed to correlate them to the expected amount of chemical ordering present. Nanocrystals with increased amounts of ordering are found to generally have less extreme strains present and reduced strain distribution widths. In addition, statistical correlations are found between the spatial arrangement of scattering amplitude and lattice strains.

Warren, Nathaniel [Pennsylvania State Univ., Unive↗
DOE OSTI2025

Near-term heatwave risk in HighResMIP models across different temperature zones of West Africa

This study projects near-future (2031–2050) changes in heatwave (HW) risk across West Africa (WA) using an ensemble of eight high-resolution global climate models from the High-Resolution Model Intercomparison Project under a high-emission scenario. Using K-means clustering, we divided WA into four unique temperature zones and examined projected changes in extreme temperatures, HW occurrence and magnitude. Our results indicate a statistically significant increase in future HW events across most parts of WA, although considerable spread exists over the region and among individual models. The most pronounced increases are evident in the Sahel/Sahara and the Guinea Highlands subregions, with an ensemble mean increase of ∼10 HW events per year. In contrast, the lowest increase in HW events is projected in central WA, with increases ranging between 1 and 5 events per year. Similarly, the magnitude of HW events is projected to increase in most models, with Sahel/Sahara exhibiting the largest increases. Additionally, projections suggest that the strongest HWs will become more frequent, particularly in northern and southwestern WA. These findings highlight significant spatial heterogeneity in future HW risk across WA, emphasizing the need for targeted adaptation strategies.

HighResMIP↗
DOE OSTI2023

Reevaluating Contour Visualizations for Power Systems Data

Effective visual analytics tools are needed now more than ever as emerging energy systems data and models are rapidly growing in scale and complexity. Here we examined the suitability of colored contour maps to visually represent bus values in two different power system models: a dense 24k-bus distribution system and a 240-bus transmission system. In a quantitative analysis, we found that contour maps misrepresent power systems data, changing the statistical dispersion of the bus values, including the loss of extreme values. In a controlled empirical study with thirty professional power system research engineers, we found that these distortions significantly impact excursion identification tasks. Additionally, the engineers were less confident in their assessments using contour-based visualizations than glyph-based visualizations.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

mystic : software for autonomous discovery and design under uncertainty

Throughout the diverse range of science and engineering applications, there is a growing desire to develop computational methods that can reliably predict the behavior of complex systems. Specifically, there is a strategic need for tools that can robustly forecast the behavior of complex physical systems, where data may be high-dimensional, noisy, or sparse, and models of the system may be time-dependent or include uncertainty. We use mystic to build tools that leverage statistical learning, physics-informed learning, and active learning in the efficient generation of reliably predictive surrogates for complex physical systems. mystic is a robust, proven, open-source optimization and uncertainty quantification toolkit with over a decade of use in the design and optimization of neutron instrumentation, solar-powered drones, and gasguns, and in iterative tuning of models for Raman spectroscopy and elastoplastic materials strength. Recent developments have focused on automated learning of statistically robust surrogates under uncertainty, with applications in materials in extreme environments, nanostructures, materials simulations and strength models, and the failure of shielding under particle radiation. In 2020, McKerns demonstrated active learning of optimally robust surrogates with respect to new simulated data for molecular dynamics simulations of materials mixing in warm dense matter, and is currently applying active learning to the automated steering of particle accelerator beams and the optimal design and control of quantum optical sensor instrumentation.

42 ENGINEERING↗