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At least 1,117 records · Page 62
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Studying Rate of Fire Spread Using Landscape Statistics and FIRETEC

Wildfires are multi-system and multi-scale processes that are influenced by the land and atmosphere. Here, we study the influence of land, in terms of topography and vegetation, on uphill and downhill fire spread using data of the state of California as a testbed. The California landscape is first quantified by reclassifying its dominant vegetation types into grass, trees, and shrubs. Next, statistics of slope, vegetation height, and live fuel moisture percentage for each dominant vegetation type are gathered to create ensembles of idealized computational fluid dynamics (CFD) model simulations representative of the landscape. Rate of spread values for each CFD simulation are recorded to observe the differences in perturbing slope and fuel moisture for each vegetation type. We have found that all vegetation types prefer slopes of less than 30 degrees. Trees in the California data are 10 to 40 meters high, whereas shrub heights are less than 3 meters and grass heights are less than 1 meter. The live fuel moisture percentage of both trees and shrubs fluctuates around approximately 100% over one year (2019-2020). Using these parameters as inputs into the CFD model, fire spread rate is found to be fastest in the grass ensemble (maximum 12 m s -1 ), followed by shrubs (maximum 10 m s -1 ) and lastly by trees (maximum 7 m s -1 ). Increasing slope increases the spread rate of the uphill fire but the downhill spread decreases for slopes greater than 15 degrees. Increasing fuel moisture decreases the spread rate both up and down the hill. This study highlights the impacts that topography and vegetation have on fire spread.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2001

Risk analysis simulation of rover operations for Mars surface exploration

Risk management advocates have long sought to directly influence the early stages of the systems engineering process through a more effective role in system design trade studies. The principal obstacle to this has been the lack of credible ways to represent and quantify mission risk—that is, a combination of the probability of mission success (“system safety”) and science value—for the project manager and the rest of the design team. If it were possible to quantify mission risk, then the effects of proposed mission and system design changes could be calculated, and along with life-cycle costs, could be used to explore the design space more extensively and select better designs. JPL has been working to build the capability to quantify the probability of mission success using a federation of diverse simulations and models, each of which contributes some vital piece of the puzzle. The initial institutional focus has been on Mars surface operations. This ensemble computing framework enables the diverse models and simulations to work together seamlessly. Recent work at JPL has demonstrated the capability to exercise this ensemble from end-to-end using an Oracle-based database to automatically move results from one model/simulation to the next stage in the analysis.

Shishko, Robert↗
DOE OSTI2022

Effects of Selenium Doping in Zinc Telluride from First Principles

We report point defects and compensation in zinc telluride have been subjects of study for several decades due to the compound’s scientific/technological applications. Despite this, there remains some disagreement in the literature as to the fine details of its defect chemistry. Recently, it has also come under renewed interest due to the emerging Cd( 1–x )Zn x Te( 1–y )Se y material family, used in nuclear radiation detector applications. In this article, we use state-of-the-art computational tools to re-examine the native defect chemistry of ZnTe and explore how it changes upon addition of selenium using density functional theory with screened hybrid exchange–correlation functionals, thermochemical models of the equilibration environment, and statistical mechanics models of the point defect ensemble.

II-VI semiconductors↗
NASA NTRS1999

Disturbance Dynamics in Transitional and Turbulent Flows

In order to expand the predictive capability of single-point turbulence closure models to account for the early-stage transition regime, a methodology for the formulation and calibration of model equations for the ensemble-averaged disturbance kinetic energy and energy dissipation rate is presented. First the decay of laminar disturbances and turbulence in mean shear-free flows is studied. In laminar flows, such disturbances are linear superpositions of modes governed by the Orr-Sommerfeld equation. In turbulent flows, disturbances are described through transport equations for representative mean quantities. The link between a description based on a deterministic evolution equation and a probability based mean transport equation is established. Because an uncertainty in initial conditions exists in the laminar as well as the turbulent regime, a probability distribution must be defined even in the laminar case. Using this probability distribution, it is shown that the exponential decay of the linear modes in the laminar regime can be related to a power law decay of both the (ensemble) mean disturbance kinetic energy and the dissipation rate. The evolution of these mean disturbance quantities is then described by transport equations similar to those for the corresponding turbulent decaying flow. Second, homogeneous shear flow, where disturbances can be described by rapid distortion theory (RDT), is studied. The relationship between RDT and linear stability theory is exploited in order to obtain a closed set of modeled equations. The linear disturbance equations are solved directly so that the numerical simulation yields a database from which the closure coefficients in the ensemble-averaged disturbance equations can be determined.

Grosch, Chester E.↗
DOE OSTI2022

Internal variability and forcing influence model–satellite differences in the rate of tropical tropospheric warming

Climate-model simulations exhibit approximately two times more tropical tropospheric warming than satellite observations since 1979. The causes of this difference are not fully understood and are poorly quantified. Here, we apply machine learning to relate the patterns of surface-temperature change to the forced and unforced components of tropical tropospheric warming. This approach allows us to disentangle the forced and unforced change in the model-simulated temperature of the midtroposphere (TMT). In applying the climate-model-trained machine-learning framework to observations, we estimate that external forcing has produced a tropical TMT trend of 0.25 ± 0.08 K⋅decade −1 between 1979 and 2014, but internal variability has offset this warming by 0.07 ± 0.07 K⋅decade −1 . Using the Community Earth System Model version 2 (CESM2) large ensemble, we also find that a discontinuity in the variability of prescribed biomass-burning aerosol emissions artificially enhances simulated tropical TMT change by 0.04 K⋅decade −1 . The magnitude of this aerosol-forcing bias will vary across climate models, but since the latest generation of climate models all use the same emissions dataset, the bias may systematically enhance climate-model trends over the satellite era. Our results indicate that internal variability and forcing uncertainties largely explain differences in satellite-versus-model warming and are important considerations when evaluating climate models.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1990

A CO survey of regions around 34 open clusters. II - Physical properties of cataloged molecular clouds

The physical properties of the 148 molecular clouds found in a CO survey of regions around 34 young open clusters have been examined. Expressions are given for the cloud size spectrum and the mass spectrum. The mass-radius relation implies that clouds of all size larger than a few pc have about the same mean volume density. Power laws with slopes of 0.6 and 3 describe, respectively, the relations of CO linewidth and cloud mass to cloud size. The clouds are distinctly nonspherical and appear to be randomly oriented with respect to the Galactic plane. The observations can be explained by a model for molecular clouds in which clouds are ensembles of dense clumps of gas. Based on such a model, it is shown that molecular clouds are perturbed on a time scale short compared to the time required for them to reestablish virial equilibrium.

Leisawitz, D.↗
NASA NTRS2012

Assimilating Remote Sensing Observations of Leaf Area Index and Soil Moisture for Wheat Yield Estimates: An Observing System Simulation Experiment

Observing system simulation experiments were used to investigate ensemble Bayesian state updating data assimilation of observations of leaf area index (LAI) and soil moisture (theta) for the purpose of improving single-season wheat yield estimates with the Decision Support System for Agrotechnology Transfer (DSSAT) CropSim-Ceres model. Assimilation was conducted in an energy-limited environment and a water-limited environment. Modeling uncertainty was prescribed to weather inputs, soil parameters and initial conditions, and cultivar parameters and through perturbations to model state transition equations. The ensemble Kalman filter and the sequential importance resampling filter were tested for the ability to attenuate effects of these types of uncertainty on yield estimates. LAI and theta observations were synthesized according to characteristics of existing remote sensing data, and effects of observation error were tested. Results indicate that the potential for assimilation to improve end-of-season yield estimates is low. Limitations are due to a lack of root zone soil moisture information, error in LAI observations, and a lack of correlation between leaf and grain growth.

Nearing, Grey S.↗
NASA NTRS2019

Mid-21st Century Ozone Air Quality and Health Burden in China Under Emissions Scenarios and Climate Change

Despite modest emissions reductions of air pollutants in recent years, China still suffers from poor air quality, and the outlook for future air quality in China is uncertain. We explore the impact of two disparate 2050 emissions scenarios relative to 2015 in the context of a changing climate with the Geophysical Fluid Dynamics Laboratory Atmospheric Model version 3 (GFDL-AM3) chemistry-climate model. We impose the same near-term climate change for both emission scenarios by setting global sea surface temperature (SST) and sea ice cover (SIC) to the average over 2010–2019 and 2046–2055, respectively, from a three-member ensemble of GFDL coupled climate model simulations under the RCP8.5 (Representative Concentration Pathway) scenario. By the 2050s, annual mean surface ozone increases throughout China by up to 8 ppbv from climate change alone (estimated by holding air pollutants at 2015 levels while setting SIC and SST to 2050 conditions in the model) and by 8–12 ppbv in a scenario in which emissions of ozone precursors nitrogen oxides (NO (sub x) ) and anthropogenic volatile organic compounds (VOCs) increase by ~10%. In a scenario in which NO (sub x) and anthropogenic VOC emissions decline by 60%, annual mean surface ozone over China decreases by 16–20 ppbv in the 2050s relative to the 2010s. The ozone increase from climate change alone results in an additional 62 000 premature deaths in China as compared to 330 000 fewer premature deaths by the 2050s under a strong emissions mitigation scenario. In springtime over Southwestern China in the 2050s, the model projects 9–12 ppbv enhancements to surface ozone from the stratosphere (diagnosed with a model tracer) and from international anthropogenic emissions (diagnosed by differencing AM3 simulations with the same emissions within China but higher versus lower emissions in the rest of the world). Our findings highlight the effectiveness of emissions controls in reducing the health burden in China due to air pollution, and also the potential for climate change and rising global emissions to offset, at least partially, some of the ozone decreases attained with regional emission reductions in China.

Westervelt, D. M.↗
DOE OSTI2025

Persistent Sampling: Enhancing the Efficiency of Sequential Monte Carlo

Sequential Monte Carlo (SMC) samplers are powerful tools for Bayesian inference but suffer from high computational costs due to their reliance on large particle ensembles for accurate estimates. We introduce persistent sampling (PS), an extension of SMC that systematically retains and reuses particles from all prior iterations to construct a growing, weighted ensemble. By leveraging multiple importance sampling and resampling from a mixture of historical distributions, PS mitigates the need for excessively large particle counts, directly addressing key limitations of SMC such as particle impoverishment and mode collapse. Crucially, PS achieves this without additional likelihood evaluations-weights for persistent particles are computed using cached likelihood values. This framework not only yields more accurate posterior approximations but also produces marginal likelihood estimates with significantly lower variance, enhancing reliability in model comparison. Furthermore, the persistent ensemble enables efficient adaptation of transition kernels by leveraging a larger, decorrelated particle pool. Experiments on high-dimensional Gaussian mixtures, hierarchical models, and non-convex targets demonstrate that PS consistently outperforms standard SMC and related variants, including recycled and waste-free SMC, achieving substantial reductions in mean squared error for posterior expectations and evidence estimates, all at reduced computational cost. PS thus establishes itself as a robust, scalable, and efficient alternative for complex Bayesian inference tasks.

Karamanis, Minas↗
NASA NTRSDate not supplied

AMOC Stability in GISS-E2-1-G: A Case of Stochastic Bifurcation

A 10-member ensemble simulation using the NASA GISS-E2-1-G climate model shows a clear bifurcation in the Atlantic Meridional Overturning Circulation (AMOC) strength under the SSP2-4.5 future scenario, leading to 8 stable AMOC-on and 2 stable AMOC-off climate states after 400 years of integration, despite identical forcing of each ensemble member. Ocean models have previously shown a tipping point where sufficient forcing (e.g. by freshwater inputs) can shut down the AMOC. A variety of fully coupled models have demonstrated this, either through hosing experiments or increased precipitation and warming at high latitudes due to increased concentrations of greenhouse gases. In the GISS simulations, there are no external freshwater perturbations. The bifurcation arises freely in the coupled system and is the result of stochastic variability associated with sea-ice transport and melting in the Irminger Sea following a slowing of the GHG concentration increase at the end of the twenty-first century. We believe this is the first time that a CMIP-class model has shown such a bifurcation across an initial condition ensemble.

GISS ModelE2.1↗
DOE OSTI2024

The Influence of ENSO Diversity on Future Atlantic Tropical Cyclone Activity

El Niño–Southern Oscillation (ENSO) influences seasonal Atlantic tropical cyclone (TC) activity by impacting environmental conditions important for TC genesis. However, the influence of future climate change on the teleconnection between ENSO and Atlantic TCs is uncertain, as climate change is expected to impact both ENSO and the mean climate state. We used the Weather Research and Forecasting Model on a tropical channel domain to simulate 5-member ensembles of Atlantic TC seasons in historical and future climates under different ENSO conditions. Experiments were forced with idealized sea surface temperature configurations based on the Community Earth System Model (CESM) Large Ensemble representing: a monthly varying climatology, eastern Pacific El Niño, central Pacific El Niño, and La Niña. The historical simulations produced fewer Atlantic TCs during eastern Pacific El Niño compared to central Pacific El Niño, consistent with observations and other modeling studies. For each ENSO state, the future simulations produced a similar teleconnection with Atlantic TCs as in the historical simulations. Specifically, La Niña continues to enhance Atlantic TC activity, and El Niño continues to suppress Atlantic TCs, with greater suppression during eastern Pacific El Niño compared to central Pacific El Niño. In addition, we found a decrease in the Atlantic TC frequency in the future relative to historical regardless of ENSO state, which was associated with a future increase in northern tropical Atlantic vertical wind shear and a future decrease in the zonal tropical Pacific sea surface temperature (SST) gradient, corresponding to a more El Niño–like mean climate state. Our results indicate that ENSO will remain useful for seasonal Atlantic TC prediction in the future.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Bayesian Optimized Deep Ensemble for Uncertainty Quantification of Deep Neural Networks: a System Safety Case Study on Sodium Fast Reactor Thermal Stratification Modeling

Deep neural networks (DNNs) are increasingly important to scientific computing and engineering system simulations. Accurate uncertainty quantification (UQ) for DNNs is critical in safety-sensitive engineering domains. Traditional Deep Ensemble (DE) methods, while easy to implement, frequently suffer from poorly calibrated uncertainty estimates and limited predictive accuracy due to reliance on fixed architectures with varied weight initializations. To address these issues, we introduce a workflow that combines Bayesian Optimization (BO) and DE. The workflow is modular, scalable, and integrates parallel BO initialized with Sobol sequences to individually optimize the hyperparameters of each ensemble member. This method enhances ensemble diversity, improves predictive accuracy, and provides reliable uncertainty estimates. We evaluate the proposed BODE approach in a sodium fast reactor thermal stratification modeling case study, where we used a densely connected convolutional neural network to predict turbulent viscosity during the reactor transient with consideration of data noise. We benchmark its performance against several optimization approaches, including baseline deep ensemble, evolutionary algorithm-optimized ensemble, ensemble formed via random search combined with greedy selection, and a BO ensemble using random initialization. Here, our results demonstrate superior performance of the developed BODE approach. In noise-free scenarios, BODE notably reduces incorrect aleatoric uncertainty and significantly enhances predictive accuracy. Under conditions of 5% and 10% Gaussian noise, BODE adaptively quantifies uncertainty proportional to data noise, achieving up to an 80% reduction in root mean square error compared to baseline methods and producing well-calibrated prediction intervals.

Bayesian optimization↗
DOE OSTI2023

Anomalous Loss Reduction Below Two‐Level System Saturation in Aluminum Superconducting Resonators

Superconducting resonators are widely used in many applications such as qubit readout for quantum computing, and kinetic inductance detectors. These resonators are susceptible to numerous loss and noise mechanisms, especially the dissipation due to two-level systems (TLS) which become the dominant source of loss in the few-photon and low temperature regime. In this study, capacitively-coupled aluminum half-wavelength coplanar waveguide resonators are investigated. Surprisingly, the loss of the resonators is observed to decrease with a lowering temperature at low excitation powers and temperatures below the TLS saturation. This behavior is attributed to the reduction of the TLS resonant response bandwidth with decreasing temperature and power to below the detuning between the TLS and the resonant photon frequency in a discrete ensemble of TLS. When response bandwidths of TLS are smaller than their detunings from the resonance, the resonant response and thus the loss is reduced. At higher excitation powers, the loss follows a logarithmic power dependence, consistent with predictions from the generalized tunneling model (GTM). A model combining the discrete TLS ensemble with the GTM is proposed and matches the temperature and power dependence of the measured internal loss of the resonator with reasonable parameters.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2025

Synthetic Streamflow Datasets Derived from DOE 9505 for Select Texas Basins

This dataset is generated using a Bayesian Hidden Markov Model trained on the DOE 9505 streamflow projection ensemble. A set of 21,000 streamflow realizations are generated for the Colorado, Sabine, and Trinity river basins in Texas. Separate models are trained either using the full 9505 ensemble or a subset based on three hyperparameters: bias correction, downscaling, and hydrological model.

drought↗
DOE OSTI2020

Probabilistic Forecast of All-Sky Solar Radiation Using Enhanced WRF-Solar: Preprint

This study presents enhancements of the Weather Research and Forecasting model with solar extensions (WRF-Solar) to provide probabilistic forecasts of solar radiation. Our approach builds ensemble WRF-Solar runs by introducing stochastic perturbations of variables that produce the largest uncertainties in predicting surface irradiance and clouds. The key variables are identified using tangent linear sensitivity analysis of six physics packages responsible for all-sky irradiance variability. An optimal strategy to stochastically perturb the selected variables is developed and applied to WRF-Solar to generate ensemble members for day-ahead solar prediction. The National Solar Radiation Database (NSRDB) is used to validate the ensemble forecast at arbitrary locations on the model grid. Preliminary results indicate that the proposed technique can potentially produce WRF-Solar ensembles providing reliable information of solar prediction uncertainty. This study describes the implemented methodology and initial results as well as future research to improve ensemble-based probabilistic forecasts with WRF-Solar.

all-sky↗
DOE OSTI2024

CMIP6-based Multi-model Streamflow Projections over the Conterminous US, Version 1.1

This dataset presents an ensemble of streamflow projections covering the conterminous United States (CONUS), developed to support the SECURE Water Act Section 9505 Assessment for the US Department of Energy (DOE) Water Power Technologies Office (WPTO). Multiple Coupled Models Intercomparison Project phase 6 (CMIP6) Global Climate Models (GCMs) were downscaled using either statistical (DBCCA) or dynamical (RegCM) downscaling methods, based on two meteorological reference datasets (Daymet and Livneh). Subsequently, the downscaled precipitation, temperature, and wind speed data were used to drive two calibrated hydrologic models (VIC and PRMS), with total runoff routed through the Routing Application for Parallel computatIon of Discharge (RAPID) routing model, producing an ensemble of streamflow projections across 2.7 million NHDPlusV2 stream reaches across the CONUS. Each ensemble member covers the 1980-2019 baseline and 2020-2059 near-future periods under the high-end (SSP585) emission scenario. Additionally, using only DBCCA and Daymet, the projections extend to the 2060-2099 far-future period and encompass three additional emission scenarios (SSP370, SSP245, and SSP126). This dataset is designed to support the SECURE Water Act Section 9505 Assessment for the US Department of Energy (DOE) Water Power Technologies Office (WPTO). For further details, refer to Kao et al. (2022), Rastogi et al. (2022), and Ghimire et al. (2023).

13 HYDRO ENERGY↗
DOE OSTI2021

Dataset and scripts for manuscript "Using Neural Network Ensembles to Separate Ocean Biogeochemical and Physical Drivers of Phytoplankton Biogeography in Earth System Models"

Please note: The title of this version contains an updated title for the manuscript compared to the previous version of this dataset. This is only due to title updates during the peer review process for the manuscript. The zip file contains the scripts, functions, and source files for the manuscript titled "Using Neural Network Ensembles to Separate Ocean Biogeochemical and Physical Drivers of Phytoplankton Biogeography in Earth System Models." The manuscript has been submitted for peer review. Please consult the README file for information on the specifications of the files. These files may occasionally be updated to add annotations to the scripts to make them more user friendly and to correct any errors.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Microstructure in matrix elements

We investigate the simple model of Pennington, Shenker, Stanford and Yang for modeling the density matrix of Hawking radiation, but further include dynamics for EOW branes behind the horizon. This allows interactions that scatter one interior state to another, and also allows EOW loops. At strong coupling, we find that EOW states are no longer random; the ensemble has collapsed, and coupling constants encode the microscopic matrix elements of Hawking radiation. This suggests strong interior dynamics are important for understanding evaporating black holes, without any ensemble average. In this concrete model the density matrix of the radiation deviates from the thermal state, small off-diagonal fluctuations encode equivalences between naively orthogonal states, and bound the entropy from above. For almost evaporated black holes the off-diagonal terms become as large as the diagonal ones, eventually giving a pure state. We also find the unique analytic formula for all Renyi entropies.

2D gravity↗