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At least 271 records · Page 15
All sourcesNASA NTRSDOE OSTI
NASA NTRS2021

Investigating the Potential of A Global Precipitation Forecast to Inform Landslide Prediction

Extreme rainfall events within landslide-prone areas can be catastrophic, resulting in loss of property, infra-structure, and life. A global Landslide Hazard Assessment for Situational Awareness (LHASA) model provides routine near-real time estimates of landslide hazard using Integrated Multi-Satellite Precipitation Retrievals for the Global Precipitation Mission (IMERG). However, it does not provide information on potential landslide hazard in the future. Forecasting potential landslide events at a global scale presents an area of open research. This study compares a global precipitation forecast provided by NASA’s Goddard Earth Observing System (GEOS) with near-real time satellite precipitation estimates. The Multi-Radar Multi-Sensor gauge corrected (MRMS-GC) reference is used to assess the performance of both satellite and model-based precipitation products over the contiguous United States (CONUS). The forecast lead time of 24hrs is considered, with a focus on extreme precipitation events. The performance of IMERG and GEOS-Forecast products is assessed in terms of the probability of detection, success ratio, critical success index and hit bias as well as continuous statistics. The results show that seasonality influences the performance of both satellite and model-based precipitation products. Comparison of IMERG and GEOS-Forecast globally as well as in several event case studies (Colombia, southeast Asia, and Tajikistan) reveals that GEOS-Forecast detects extreme rainfall more frequently relative to IMERG for these specific analyses. For recent landslide points across the globe, the 24hr accumulated precipitation forecast >100 mm corresponds well with near-real time daily accumulated IMERG precipitation estimates. GEOS-Forecast and IMERG precipitation match more closely for tropical cyclones than for other types of storms. The main intention of this study is to assess the viability of using a global forecast for landslide predictions and understand the extent of the variability between these products to inform where we would expect the landslide modeling results to most prominently diverge. Results of this study will be used to inform how forecasted precipitation estimates can be incorporated into the LHASA model to provide the first global predictive view of landslide hazards.

S. Khan↗
DOE OSTI2021

Fatigue of laser powder bed fusion processed 17-4 stainless steel using prior process exposed powder feedstock

The rapid pace of development seen in the metal additive manufacturing (AM) process of laser powder-bed fusion (LPBF) requires in-step advances in processes qualification to enable full-scale adoption. This particularly applies to quantifying how powder feedstock conditions impact end-component quality. Here this study examines how in-machine 17-4 stainless steel powder feedstocks are affected by prior LPBF processes, and how these effects impact subsequent builds. Examinations of powder morphology, chemistry, flowability, and rheology were conducted to characterize the powder conditions. The resultant effects of powder feedstock condition on produced component quasi-static tensile and high-cycle fatigue properties were analyzed. Fatigue life was analyzed using a reliability modeling approach that enabled a robust statistical comparison of life. Powder characteristics were found to evolve with powder exposure to prior LPBF processes, particularly in the extremes of powder size distribution and measures of bulk flow. No significant effects of these changes on tensile properties were observed. Reliability modeling methods, including the lognormal and Weibull distributions as well as the empirical survival function, are shown to be effective tools for modeling fatigue variability in LPBF manufactured components. Through these tools, fatigue life was found to be invariant with changes in powder condition.

42 ENGINEERING↗
DOE OSTI2024

Vapor kinetic energy for the detection and understanding of atmospheric rivers

Poleward water vapor transport in the midlatitudes mainly occurs in meandering filaments of intense water vapor transport, spanning thousands of kilometers long and hundreds of kilometers wide and moving eastward. The water vapor filaments are known as atmospheric rivers (ARs). They can cause extreme wind gusts, intense precipitation, and flooding along densely populated coastal regions. Many recent studies about ARs focused on the statistical analyses of ARs, but a process-level understanding of ARs remains elusive. Here we show that ARs are streams of air with enhanced vapor kinetic energy (VKE) and derive a governing equation for Integrated VKE to understand what contributes to the evolution of ARs. We find that ARs grow mainly because of potential energy conversion to kinetic energy, decay largely owing to condensation and turbulence, and the eastward movement is primarily due to horizontal advection of VKE. Our VKE framework complements the integrated vapor transport framework, which is popular for identifying ARs but lacks a prognostic equation for understanding the physical processes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Metrics for evaluating tropical cyclones in climate data

This manuscript describes a software suite that can be used for objective evaluation of tropical cyclones (TCs) in gridded climate data. Using cyclone trajectories derived from 6-hourly data, a comprehensive set of metrics is defined to systematically compare and contrast products to one another. In addition to annual TC climatologies, attention is paid to spatial and temporal patterns of storm occurrence and intensity. Assessment can be performed either on the global scale or regional domains. Simple to visualize ‘scorecards’ allow for rapid credibility assessment. We showcase three key findings enabled by this suite. First, we compare the representation of TCs in seven current-generation global reanalyses and conclude that higher resolution models and those with TC-specific assimilation contain more accurate storm climatologies. Second, using a free-running Earth system model (ESM) we find that full basin refinement is required in variable-resolution configurations to adequately simulate North Atlantic TC frequency. Furthermore, upstream refinement over northern Africa offers little benefit in simulating storm occurrence but spatial genesis patterns are improved. Finally, we show that TCs simulated by ESMs can be highly sensitive to individual parameterizations in climate models, with North Atlantic TC metrics varying greatly depending on version of the Morrison-Gettelman microphysics package.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

A Search of the Full Six Years of the Dark Energy Survey for Outer Solar System Objects

We present a search for outer solar system objects in the 6 yr of data from the Dark Energy Survey (DES). The DES covered a contiguous 5000 deg 2 of the southern sky with ≈80,000 3 deg 2 exposures in the grizY filters between 2013 and 2019. This search yielded 812 trans-Neptunian objects (TNOs), one Centaur and one Oort cloud comet, 458 reported here for the first time. We present methodology that builds upon our previous search on the first 4 yr of data. All images were reprocessed with an optimized detection pipeline that leads to an average completeness gain of 0.47 mag per exposure, as well as improved transient catalog production and algorithms for linkage of detections into orbits. All objects were verified by visual inspection and by the “sub-threshold significance,” the signal-to-noise ratio in the stack of images in which its presence is indicated by the orbit, but no detection was reported. This yields a pure catalog complete to r ≈ 23.8 mag and distances 29 < d < 2500 au. The TNOs have minimum (median) of 7 (12) nights’ detections and arcs of 1.1 (4.2) yr, and will have grizY magnitudes available in a further publication. We present software for simulating our observational biases for comparisons of models to our detections. Initial inferences demonstrating the catalog’s statistical power are: the data are inconsistent with the CFEPS-L7 model for the classical Kuiper Belt; the 16 “extreme” TNOs (a > 150 au, q > 30 au) are consistent with the null hypothesis of azimuthal isotropy; and nonresonant TNOs with q > 38 au, a > 50 au show a significant tendency to be sunward of major mean-motion resonances.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
NASA NTRS2022

The Origins of Long-Term Variability in Martian Upper Atmospheric Densities

We quantify and interpret the long-term variability of dayside Martian upper thermosphere and lower exosphere densities within 180–275 km altitudes. Atmospheric CO2, N2, O, and Ar densities are from NASA Mars Atmosphere and Volatile EvolutioN (MAVEN) observations during the time period of 2015–2020 near solar minimum. These neutral measurements, together with contemporaneous solar irradiance measurements at Mars, enable disentanglement of the orbital effect (due to the annual Sun-Mars distance change with solar longitude) and the solar extreme ultraviolet (EUV) effect in atmospheric density variations. The relative importance of these two effects, which is obtained using a statistical method of Dominance Analysis, reveals the competition between the indirect effect of solar infrared (via the upward coupling from the middle atmosphere) and the direct effect of solar EUV (due to local heating). Our results show that, unlike the orbital effect which is relatively constant at low altitudes and then decreases with increasing altitude, the solar EUV effect nearly monotonically increases. These two effects are comparable at high altitudes (about 240/270/205 km for CO2/N2/O). This analysis is extended to include long-term exospheric mass density estimates near 400 km from Mars Global Surveyor and Mars Odyssey data, with a focus on representative solar cycle phases of solar minimum and maximum. It is found that near 400 km, the orbital effect is always a key driver regardless of the solar cycle phase, while the solar EUV effect plays a minor role during solar minimum and is greatly enhanced and slightly exceeds the orbital effect during solar maximum.

Xiaohua Fang↗
DOE OSTI2025

The Profiled Feldman-Cousins Method for Confidence Interval Construction for the Nova 3-Flavor Oscillation Analysis

The small interaction cross-section of neutrinos makes experimental neutrino physics particularly responsive to technological advancements. A significant development leveraged by the NOvA experiment is large-scale parallel processing, enabling novel computational approaches to longstanding experimental challenges. Central to managing the resulting high-throughput data is NOvA’s implementation of the Freight Train model, designed for efficient data production and handling.This dissertation details the methodology and execution of the NOvA 2024 3-Flavor Oscillation Analysis, supported by a comprehensive dataset spanning ten years. It emphasizes frequentist results refined through the Feldman-Cousins (FC) technique, specifically addressing confidence interval corrections in parameter estimation. The computational intensity associated with Feldman-Cousins arises from extensive Monte Carlo simulations, which were substantially mitigated through parallel computing on the Perlmutter supercomputer at the National Energy Research Scientific Computing Center (NERSC), employing the MPI framework.To further enhance computational efficiency, an Importance Sampling method is introduced and evaluated, demonstrating significant potential to reduce complexity, particularly in exploring extreme parameter space regions. This thesis presents both the successful application of advanced computational resources and the development of sophisticated statistical techniques, aiming to enhance the precision and scope of neutrino oscillation analyses.

Dye ajdye11190@gmail.com, Andrew Joseph [Mississip↗
DOE OSTI2020

The effect of geographic sampling on evaluation of extreme precipitation in high-resolution climate models

Traditional approaches for comparing global climate models and observational data products typically fail to account for the geographic location of the underlying weather station data. For modern global high-resolution models with a horizontal resolution of tens of kilometers, this is an oversight since there are likely grid cells where the physical output of a climate model is compared with a statistically interpolated quantity instead of actual measurements of the climate system. In this paper, we quantify the impact of geographic sampling on the relative performance of high-resolution climate model representations of precipitation extremes in boreal winter (December–January–February) over the contiguous United States (CONUS), comparing model output from five early submissions to the HighResMIP subproject of the CMIP6 experiment. We find that properly accounting for the geographic sampling of weather stations can significantly change the assessment of model performance. Across the models considered, failing to account for sampling impacts the different metrics (extreme bias, spatial pattern correlation, and spatial variability) in different ways (both increasing and decreasing). We argue that the geographic sampling of weather stations should be accounted for in order to yield a more straightforward and appropriate comparison between models and observational data sets, particularly for high-resolution models with a horizontal resolution of tens of kilometers. While we focus on the CONUS in this paper, our results have important implications for other global land regions where the sampling problem is more severe.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Impact of solar geoengineering on temperatures over the Indonesian Maritime Continent

Climate change has been projected to increase the intensity and magnitude of extreme temperature in Indonesia. Solar radiation management (SRM) has been proposed as a strategy to temporarily combat global warming, buying time for negative emissions. Though the global impacts of SRM have been extensively studied in recent years, regional impacts, especially in the tropics, have received much less attention. This paper investigates the potential stratospheric sulfate aerosol geoengineering to modify mean and extreme temperature, as well as the relative humidity and wet bulb temperature (WBT) change over Indonesian Maritime Continent (IMC) based on simulations from three different earth system models. We applied statistical downscaling and corrected the bias of model output to reproduce historical temperatures and relative humidity over IMC. We compared SRM as specified by the Geoengineering Model Intercomparison Project (GeoMIP) G4 experiment – 5 Tg of SO 2 into the lower stratosphere between 2020 and 2069 – concurrent with the RCP4.5 emissions scenario. SRM is able to significantly reduce the temperature means and extremes, and although differences in magnitude of response and spatial pattern occur, there is a generally consistent response. Additionally, the spatial response of changes forced by RCP4.5 scenario and SRM impact is notably heterogeneous in the archipelago, highlighting uncertainties that would be critical in assessing socio-economic consequences of both doing, and not doing SRM. In general, SRM has bigger impacts in reducing temperatures over land than oceans, and the southern monsoon region shows more variability. SRM is also effective at reducing the likelihood of WBT > 27OC events compared to RCP4.5 after some years of SRM deployment as well as during the post-termination period of SRM. Regional downscaling may be an effective tool in obtaining policy-relevant information about local effects of different future scenarios involving SRM.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2020

Comparison of Extreme Value Distributions for Electrostatic Discharge Magnitudes in Spacecraft Charging Tests

Electrostatic Discharge (ESD) induced by the accumulation of charge in the space environment is known to cause spacecraft anomalies and failures. While it is critical to estimate the worst-case expected ESD, sensitive radar instruments can be impacted by high rates of occurrence of very small ESD. To estimate the correct extreme behavior, it is important to select the correct extreme value distribution for extrapolation of test results limited in time and sensitivity. Quantile-quantile (Q-Q) analysis is used to compare electron beam-induced ESD test data to several statistical distributions used in the published literature. The best-fit distribution is shown to vary from between tests on different materials; however, it is clear that power law distributions are not good approximations for low amplitude events. Q-Q analysis is a convenient graphical method for evaluating multiple theoretical extreme-value distributions simultaneously.

Kim, Wousik↗
DOE OSTI2022

Implications of Microstructure in Helium-Implanted Nanocrystalline Metals

Helium bubbles are known to form in nuclear reactor structural components when displacement damage occurs in conjunction with helium exposure and/or transmutation. If left unchecked, bubble production can cause swelling, blistering, and embrittlement, all of which substantially degrade materials and—moreover—diminish mechanical properties. On the mission to produce more robust materials, nanocrystalline (NC) metals show great potential and are postulated to exhibit superior radiation resistance due to their high defect and particle sink densities; however, much is still unknown about the mechanisms of defect evolution in these systems under extreme conditions. Here, the performances of NC nickel (Ni) and iron (Fe) are investigated under helium bombardment via transmission electron microscopy (TEM). Bubble density statistics are measured as a function of grain size in specimens implanted under similar conditions. While the overall trends revealed an increase in bubble density up to saturation in both samples, bubble density in Fe was over 300% greater than in Ni. To interrogate the kinetics of helium diffusion and trapping, a rate theory model is developed that substantiates that helium is more readily captured within grains in helium-vacancy complexes in NC Fe, whereas helium is more prone to traversing the grain matrices and migrating to GBs in NC Ni. Our results suggest that (1) grain boundaries can affect bubble swelling in grain matrices significantly and can have a dominant effect over crystal structure, and (2) an NC-Ni-based material can yield superior resistance to irradiation-induced bubble growth compared to an NC-Fe-based material and exhibits high potential for use in extreme environments where swelling due to He bubble formation is of significant concern.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Modeling Spatial Asymmetries in Teleconnected Extreme Temperatures

Abstract Combining strengths from deep learning and extreme value theory can help describe complex relationships between variables where extreme events have significant impacts (e.g., environmental or financial applications). Neural networks learn complicated nonlinear relationships from large datasets under limited parametric assumptions. By definition, the number of occurrences of extreme events is small, which limits the ability of the data-hungry, nonparametric neural network to describe rare events. Inspired by recent extreme cold winter weather events in North America caused by atmospheric blocking, we examine several probabilistic generative models for the entire multivariate probability distribution of daily boreal winter surface air temperature. We propose metrics to measure spatial asymmetries, such as long-range anticorrelated patterns that commonly appear in temperature fields during blocking events. Compared to vine copulas, the statistical standard for multivariate copula modeling, deep learning methods show improved ability to reproduce complicated asymmetries in the spatial distribution of ERA5 temperature reanalysis, including the spatial extent of in-sample extreme events.

Krock, Mitchell L.↗
DOE OSTI2024

UFNet: Joint U-Net and Fully Connected Neural Network to Bias Correct Precipitation Predictions from

Paper information. Shuang Yu, Indrasis Chakraborty, Gemma J. Anderson, Donald D. Lucas, Yannic Lops, and Daniel Galea. UFNet: Joint U-Net and fully connected neural network to bias correct precipitation predictions from climate models. Artificial Intelligence for the Earth Systems, 2024. Overview. This work develops the UFNet methodology to correct E3SM historical precipitation projection bias. The UFNet deep learning framework consists of a two-part architecture: a U-Net convolutional network to capture the spatiotemporal distribution of precipitation and a fully connected network to capture the distribution of higher-order statistics. The joint network, termed UFNet, can simultaneously improve the spatial structure of the modeled precipitation and capture the distribution of extreme precipitation values. Below we provide guidance for applying UFNet to correct the Energy Exascale Earth System Model (E3SM; Golaz et al. 2019) daily precipitation projection over the contiguous United States (CONUS). Getting started 1. Obtain the historical climate simulation and observation data. The E3SM historical simulation data are available through https://aims2.llnl.gov/search/cmip6/. The CPC unified gauge-based analysis of daily precipitation can be found through https://psl.noaa.gov/data/gridded/data.cpc.globalprecip.html. The ECMWF atmospheric reanalysis of the 20th century (ERA-20C) data are available through https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era-20c. The spatial resolution of E3SM and observed datasets are both regridded to a common 1° resolution grid using conservative interpolation. The regridded E3SM, CPC and ERA-20C with 1° resolution can be found throught ./data/. 2. Train the fully connected network (DNN) Python train_dnn.py 3. Train the UFNet Python train_ufnet.py 4. Evaluation and compared with the baseline Python evaluation.py

Lucas, DonaldD↗
NASA NTRS1981

Design allowables for T300/5208 graphite/epoxy composite materials

Material coupon tests were conducted to formulate design allowables for T300/5208 graphite/epoxy unidirectional tape and bidirectional fabric composites. This paper gives a description of the tests conducted, representative ply level and laminate test data, and results of the statistical analysis. The design allowables for tension and compression strength are given and include the effect of notches, impact damage, temperature extremes and moisture absorption.

Ekvall, J. C.↗
NASA NTRS2023

An ALMA Glimpse of Dense Molecular Filaments Associated with High-mass Protostellar Systems in the Large Magellanic Cloud

Recent millimeter/submillimeter facilities have revealed the physical properties of filamentary molecular clouds in relation to high-mass star formation. A uniform survey of the nearest, face-on star-forming galaxy, the Large Magellanic Cloud (LMC), complements the Galactic knowledge. We present ALMA survey data with a spatial resolution of ∼0.1 pc in the 0.87 mm continuum and HCO+ (4–3) emission toward 30 protostellar objects with luminosities of 104–105.5L⊙ in the LMC. The spatial distributions of the HCO+ (4–3) line and thermal dust emission are well correlated, indicating that the line effectively traces dense, filamentary gas with an H2 volume density of ≳105 cm−3 and a line mass of ∼103–104M⊙ pc−1. Furthermore, we obtain an increase in the velocity line widths of filamentary clouds, which follows a power-law dependence on their H2 column densities with an exponent of ∼0.5. This trend is consistent with observations toward filamentary clouds in nearby star-forming regions within ≲1 kpc from us and suggests enhanced internal turbulence within the filaments due to surrounding gas accretion. Among the 30 sources, we find that 14 are associated with hub-filamentary structures, and these complex structures predominantly appear in protostellar luminosities exceeding ∼5 × 104L⊙. The hub-filament systems tend to appear in the latest stages of their natal cloud evolution, often linked to prominent H II regions and numerous stellar clusters. Our preliminary statistics suggest that the massive filaments accompanied by hub-type complex features may be a necessary intermediate product in forming extremely luminous high-mass stellar systems capable of ultimately dispersing the parent cloud.

Marta M Sewilo↗
DOE OSTI2020

Deployable sensor system using mesh networking and satellite communication

A sensor system may be configured for continuous operation in a low resource environment and/or in extreme environmental conditions. The sensor system may have sufficient processing capabilities to provide scientific computing for pre-processing, quality control, statistical analysis, event classification, data compression and corrections (e.g., spikes in the data), autonomous decisions and actions, triggering other nodes, and information assurance functions that provide data confidentiality, data integrity, authentication, and non-repudiation. The hardware may have both mesh networking and satellite and cellular communication capability, and may be available for relatively low cost. Such a network provides the flexibility to have potentially any number of nodes be completely independent from one another. Thus, the network may scale across a diverse terrain.

Frigo, Janette↗