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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 1,207 records · Page 67
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Comparison of fiber-optic distributed temperature sensing and high-sensitivity sensor spatial surveying of stream temperature

Measuring surface water temperature spatial variability is needed to estimate the interaction between surface water and groundwater, evaluate fish habitat and thermal inertia, and to estimate streamflow frequency and duration. Fiber optic distributed temperature sensing (FO-DTS) has been used in rivers and lakes, providing high-resolution and sensitive temperature monitoring over large temporal and spatial scales. However, in streams with cobbly or bedrock-lined streambeds and variable bathymetry, use of FO-DTS to measure temperature close to the surface water and groundwater interface can be challenging if even feasible. FO-DTS can also be costly, involve difficult installations, and require an advanced understanding of the technology, calibration, and data processing. In this study, we compared FO-DTS stream temperature survey results to an alternative temperature survey method employing a towed transect of high-resolution temperature loggers spaced at 1-m and transported in the stream along the study reach, to measure the spatial distribution of stream-water temperature in East Fork Poplar Creek near Oak Ridge, Tennessee, USA. We assessed the applicability and limitations of the two methods, and quantitatively compared in-situ temperature survey results measured simultaneously with each method. Regression results showed strong temporal and spatial correlation between the two methods. Differences were only elevated near the stream banks in areas that were coincident with correlation slope deviations from unity, which was attributed to shallower water and lower data density. Kriging standard errors were also low at channel center with minor increases near the stream banks. Furthermore, the results suggested that the array of the individual temperature sensors can provide a practical alternative to FO-DTS for thermal characterization of surface water, providing slightly lower spatial and temporal resolution, but with higher accuracy of temperature measurement, with greater simplicity, and with a broader range of conditions where it may be applied.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Photoelectron spectroscopy of cryogenically cooled NiO 2 – via slow photoelectron velocity-map imaging

In this work, high-resolution anion photoelectron spectra of cryogenically cooled NiO 2 – anions, obtained using slow photoelectron velocity-map imaging (cryo-SEVI), are presented in tandem with coupled cluster electronic structure calculations including relativistic effects. The experimental spectra encompass the $\tilde{\text{X}}^1Σ_{\text{g}}^+ ← \tilde{\text{X}}^2Π_{\text{g}}, ã^3Π{\text{g}} ← \tilde{\text{X}}^2Π_{\text{g}}$, and $\tilde{\text{A}}^1Π_{\text{g}} ← \tilde{\text{X}}^2Π_{\text{g}}$ photodetachment transitions of linear ONiO 0/– , revealing previously unobserved vibrational structure in all three electronic bands. The high-resolution afforded by cryo-SEVI allows for the extraction of vibrational frequencies for each state, consistent with those previously measured in the ground state and in good agreement with scalar-relativistic coupled-cluster calculations. Previously unobserved vibrational structure is observed in the $ã^3Π_{\text{g}}$ and $Ã^1Π_{\text{g}}$ states and is tentatively assigned. Further, a refined electron affinity of 3.0464(7) eV for NiO 2 is obtained as well as precise term energies for the ã and à states of NiO 2 of 0.3982(7) and 0.7422(10) eV, respectively. Numerous Franck–Condon forbidden transitions involving the doubly degenerate $ν_2$ bending mode are observed and ascribed to Herzberg-Teller coupling to an excited electronic state.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

ADRIANO2 Calorimeter Performance from 2022 Test Beams

A novel high-granularity dual-readout calorimetric technique was developed as part of the T1604 collaboration. The ADRIANO2 Calorimeter Prototype consists of a pair of optically isolated, small sized tiles made of scintillating plastic and lead glass. Čerenkov light from the lead glass are exploited to for high resolution timing measurements, while high granularity from scintillating plastic can be used to probe the spatial component of the particle shower. This setup works for excellent energy resolution and particle detection for REDTOP as it is crucial for a calorimeter to detect the decay products of eta/eta-prime mesons. Measurements were collected on ADRIANO2 between February to December 2022 to evaluate the detector performance at Fermilab’s Test Beam Facility. The key metrics extracted from my analysis are the detector’s efficiency for various tile configuration and light-yield which will then be used as parameters for an upgraded REDTOP monte-carlo simulation campaign. An in-depth analysis of ADRIANO2 performance are detailed in this presentation.

43 PARTICLE ACCELERATORS↗
DOE OSTI2020

Direct observation of dual-step twinning nucleation in hexagonal close-packed crystals

Design and processing of advanced lightweight structural alloys based on magnesium and titanium rely critically on a control over twinning which, however, remains elusive to date and is dependent on an explicit understanding on the twinning nucleation mechanism in hexagonal close-packed (HCP) crystals. Here, by using in-situ high resolution transmission electron microscopy, we directly reveal a “dual-step” twinning nucleation mechanism in HCP rhenium nanocrystals. It is found that nucleation of the predominant {10 -12} twinning is initiated by disconnections on the Prismatic¦Basal interfaces which establish the lattice correspondence of the twin with a minor deviation from the ideal orientation. Subsequently, the minor deviation is corrected by the formation of coherent twin boundaries through rearrangement of the disconnections on the Prismatic¦Basal interface; thereafter, the coherent twin boundaries propagate by twinning disconnections. The findings provide much-needed high-resolution evidences to the twinning nucleation mechanism in HCP crystals, with crucial implications for engineering advanced structural alloys.

36 MATERIALS SCIENCE↗
DOE OSTI2020

A Numerical Study of Hypoxia in Chesapeake Bay Using an Unstructured Grid Model: Validation and Sensitivity to Bathymetry Representation

A three-dimensional unstructured-grid hydrodynamic and water quality model (Semi-implicit Cross-scale Hydroscience Intergrated System Model-Integrated Compartment Model) is applied successfully for Chesapeake Bay. The model is validated with observations of salinity, chlorophyll-a, dissolved oxygen, nutrients, and phytoplankton productions from the year 1991 to 1995 for the mainstem and some major tributaries, based on multiple model skill scores. Model experiments are conducted to test the importance of having (1) an accurate representation of bathymetry to correctly predict hypoxia and other processes and (2) a high-resolution model grid for tributaries to correctly simulate water quality variables. Further, comparison with the model experiment results with bathymetry smoothing indicates that bathymetry smoothing, as commonly used for many systems, changes the stratification and lateral circulation pattern, resulting in more salt intrusion into shallow water regions, and an increase in the freshwater age. Consequently, a model with bathymetry smoothing can lead to an unrealistic prediction of the distribution of hypoxia and phytoplankton production. Local grid refinement shows significant improvement of model simulations on local stratification and water quality variables. Overall, the use of high-resolution unstructured grid model leads to a faithful representation of the complex geometry, and thus a seamless cross-scale capability for simulating water quality processes in the Bay including tributaries and tidal creeks.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Structural characterization of a GNAT family acetyltransferase from Elizabethkingia anophelis bound to acetyl-CoA reveals a new dimeric interface

General control non-repressible 5 (GCN5)-related N-acetyltransferases (GNATs) catalyse the acetylation of a diverse range of substrates, thereby orchestrating a variety of biological processes within prokaryotes and eukaryotes. GNAT enzymes can catalyze the transfer of an acetyl group from acetyl coenzyme A to substrates such as aminoglycoside antibiotics, amino acids, polyamines, peptides, vitamins, catecholamines, and large macromolecules including proteins. Although GNATs generally exhibit low to moderate sequence identity, they share a conserved catalytic fold and conserved structural motifs. In this current study we characterize the high-resolution X-ray crystallographic structure of a GNAT enzyme bound with acetyl-CoA from Elizabethkingia anophelis , an important multi-drug resistant bacterium. The tertiary structure is comprised of six α-helices and nine β-strands, and is similar with other GNATs. We identify a new and uncharacterized GNAT dimer interface, which is conserved in at least two other unpublished GNAT structures. This suggests that GNAT enzymes can form at least five different types of dimers, in addition to a range of other oligomers including trimer, tetramer, hexamer, and dodecamer assemblies. The high-resolution structure presented in this study is suitable for future in-silico docking and structure–activity relationship studies.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

LandScan Mosaic

The LandScan program at Oak Ridge National Laboratory (ORNL), in collaboration with the National Geospatial-Intelligence Agency (NGA), continues to deliver the most accurate and up to date global, high resolution gridded population data. Additionally, the latest advancements in the LandScan HD methodology led to reduced latency in development of rapid updates for geopolitical events. With momentum towards reporting more up to date population estimates, feedback from the user community expressed interest in reporting population estimates in ranges - whether to express a level of uncertainty or confirm to leadership and stakeholders the modeled data are estimates. Building upon the need to understand uncertainty or confidence in the modeled data and report ranges at the global scale, LandScan Mosaic was developed. LandScan Mosaic represents the next generation of high-resolution population modeling, building upon the established success of previous LandScan HD iterations. While LandScan HD employed a deterministic big data fusion approach, LandScan Mosaic enhances this methodology by integrating advanced machine learning techniques to impute missing, yet crucial, population model parameters. This advancement allows for probabilistic modeling of building occupancy and population distribution, incorporating uncertainty quantification through Monte Carlo sampling methods. By combining big data fusion with machine learning-driven imputation and stochastic modeling, LandScan Mosaic provides a more comprehensive and robust representation of population dynamics. LandScan Mosaic will be following the in the footsteps of its longstanding counterpart LandScan Global and releasing a global gridded population raster, at the 3-arcsecond resolution. This technical report documents the current stage of development of LandScan Mosaic, detailing the methodologies and data sources behind the modeling. Stakeholders are encouraged to use this document as an authoritative reference for insight into Mosaic’s data development processes. However, readers should note that LandScan Mosaic remains in a late-stage research and development phase, and methodologies and data presented here are subject to refinements ahead of the anticipated global release in Summer 2025. Feedback and inquiries from users and stakeholders are welcomed as we continue to refine and enhance this important population resource.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Aminobenzoic Acid Derivatives Obstruct Induced Fit in the Catalytic Center of the Ribosome

The Escherichia coli (E. coli) ribosome can incorporate a variety of non-l-α-amino acid monomers into polypeptide chains in vitro but with poor efficiency. Although these monomers span a diverse set of compounds, there exists no high-resolution structural information regarding their positioning within the catalytic center of the ribosome, the peptidyl transferase center (PTC). Thus, details regarding the mechanism of amide bond formation and the structural basis for differences and defects in incorporation efficiency remain unknown. Within a set of three aminobenzoic acid derivatives–3-aminopyridine-4-carboxylic acid (Apy), ortho-aminobenzoic acid (oABZ), and meta-aminobenzoic acid (mABZ)–the ribosome incorporates Apy into polypeptide chains with the highest efficiency, followed by oABZ and then mABZ, a trend that does not track with the nucleophilicity of the reactive amines. Here, we report high-resolution cryo-EM structures of the ribosome with each of these three aminobenzoic acid derivatives charged on tRNA bound in the aminoacyl-tRNA site (A-site). The structures reveal how the aromatic ring of each monomer sterically blocks the positioning of nucleotide U2506, thereby preventing rearrangement of nucleotide U2585 and the resulting induced fit in the PTC required for efficient amide bond formation. They also reveal disruptions to the bound water network that is believed to facilitate formation and breakdown of the tetrahedral intermediate. Together, the cryo-EM structures reported here provide a mechanistic rationale for differences in reactivity of aminobenzoic acid derivatives relative to l-α-amino acids and each other and identify stereochemical constraints on the size and geometry of non-monomers that can be accepted efficiently by wild-type ribosomes.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Characteristics of Ice Cloud–Precipitation of Warm Season Mesoscale Convective Systems over the Great Plains

Abstract In this study, the mesoscale convective systems (MCSs) are tracked using high-resolution radar and satellite observations over the U.S. Great Plains during April–August from 2010 to 2012. The spatiotemporal variability of MCS precipitation is then characterized using the Stage IV product. We found that the spatial variability and nocturnal peaks of MCS precipitation are primarily driven by the MCS occurrence rather than the precipitation intensity. The tracked MCSs are further classified into convective core (CC), stratiform rain (SR), and anvil clouds regions. The spatial variability and diurnal cycle of precipitation in the SR regions of MCSs are not as significant as those of MCS precipitation. In the SR regions, the high-resolution, long-term ice cloud microphysical properties [ice water content (IWC) and ice water paths (IWPs)] are provided. The IWCs generally decrease with height. Spatially, the IWC, IWP, and precipitation are all higher over the southern Great Plains than over the northern Great Plains. Seasonally, those ice and precipitation properties are all higher in summer than in spring. Comparing the peak timings of MCS precipitation and IWPs from the diurnal cycles and their composite evolutions, it is found that when using the peak timing of IWPSR as a reference, the heaviest precipitation in the MCS convective core occurs earlier, while the strongest SR precipitation occurs later. The shift of peak timings could be explained by the stratiform precipitation formation process. The IWP and precipitation relationships are different at MCS genesis, mature, and decay stages. The relationships and the transition processes from ice particles to precipitation also depend on the low-level humidity.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

The National Solar Radiation Database (NSRDB) Fiscal Years 2019-2021(Final Report)

The National Solar Radiation Database (NSRDB) is the leading public source of high-resolution solar resource data in the United States, with more than 166,000 users annually. This database represents the state of the art in satellite-based estimation of solar resource information and uses a unique physics-based modeling approach that enables improvements in accuracy with the deployment of the next-generation geostationary satellites. Making the highest quality, state-of-the-art, regularly updated data sets available on a timely basis for users reduces costs of solar deployment by providing accurate information for siting studies and system output prediction, and thereby reduces levelized cost of energy. Also, high-resolution information from the NSRDB enables moving beyond levelized cost of energy when valuing the impact of renewables on the grid. Additionally, the NSRDB enables the integration of large amounts of solar on the grid by providing critical information about solar availability and variability that is used to enhance grid reliability and power quality.

14 SOLAR ENERGY↗
DOE OSTI2023

Improving Subsurface Stress Characterization for Carbon Dioxide Storage Projects by Incorporating Machine Learning Techniques

The overall objective of this project is to develop a framework for reliable characterization and prediction of the state of stress in the overburden and underburden (including the basement) in CO 2 storage reservoirs using machine learning and integrated geomechanics and geophysical methods. Specifically, we propose to develop workflow encompassing of technologies and/or methods to predict stress and pressure changes due to CO 2 injection in an active tertiary recovery site and their impacts on subtle fault activation, fractures and occurrence of microseismic events and compare responses to field observations. In this project, we anticipate using dataset from the Farnsworth field Unit (FWU) which is operated by Purdure Petroleum. A novel elastic-waveform VSP inversion technique will be used to estimate high-resolution spatial and temporal changes of elastic moduli in CO 2 storage reservoirs, which will be combined with velocity-stress relationship derived from laboratory tests to obtain subsurface pressure and stress. Clustered microseismic data will be jointly inverted for improved focal mechanisms. Least-squares reverse-time migration of microseismic waveform data will be performed to directly image fracture/fault zones. Additionally, a deep neural network machine learning technique with convolutional and recurrent layers will be used for learning the spectro-temporal structures in microseismic waveforms. The results of this geotechnical data analysis will be integrated to develop a high-resolution 3D mechanical earth model extending from the overburden sealing formations to the underburden including the basement. Mechanical properties will be derived through integration of mechanical logs, tests, available results from chemo-mechanical laboratory tests, and elastic inversion of seismic data using a combination of Bayesian and stochastic methods as well as machine learning technique. Failure features (faults/fractures) will be represented and/or modeled based on seismic and core data analysis. A transient hydrodynamic-geomechanical model will be developed through coupling with the calibrated FWU reservoir simulation model. The full physics coupled model will be used to train a reduced order proxy model using machine learning algorithm for estimating stress which will then be used with appropriate constitutive relationships and forward seismological models to simulate pressure changes and induced microseismicity. An advanced optimization framework will be developed to perform a history match to minimize error between field observations and simulated. The history matched proxy model will be verified against the full-physics equivalent. The field observations that will be used in the coupled model calibration process include pressure/stress inverted from VSP, moment magnitude from microseismic analysis, real time downhole pressure measurements, production and injection data. Parameter sensitivity and uncertainty analysis will be performed to characterize the impact of model parameter uncertainty on stress estimates. The proposed project will have significant impact on future field implementation of the proposed technology. Because the project field site is an ongoing CO 2 EOR development, the value of the new technology will be demonstrated in an operational context and evaluated as a viable risk mitigation strategy. Cost/benefit will be evaluated together with the various commercial incentives for CO 2 sequestration available to oil and gas operators. The extensive available dataset and ongoing data acquisition under the SWP Phase III work plan provides flexibility for investigation of multiple approaches and reduces technical risk.

58 GEOSCIENCES↗
DOE OSTI2020

Wet-chemical etching of FIB lift-out TEM lamellae for damage-free analysis of 3-D nanostructures

Reducing ion beam damage from the focused ion beam (FIB) during fabrication of cross sections is a well-known challenge for materials characterization, especially cross sectional characterization of nanostructures. To address this, a new method has been developed for cross section fabrication enabling high resolution transmission electron microscopy (TEM) analysis of 3-D nanostructures free of surrounding material and free of damage detectable by TEM analysis. Before FIB processing, nanopillars are encapsulated in a sacrificial oxide which acts as a protective layer during FIB milling. Furthermore, the cross sectional TEM lamella containing the nanopillars is then mounted and thinned with some modifications to conventional FIB sample preparation that provide stability for the lamella during the following wet-chemical dip etch. The wet-chemical etch of the TEM lamella removes the sacrificial oxide layer, freeing the nanopillars from any material that would obscure TEM imaging. Both high resolution TEM and aberration corrected scanning TEM images of Si/SiGe pillars with diameters down to 30 nm demonstrate the successful application of this approach.

3-d nanostructure analysis↗
DOE OSTI2024

Improving Stratocumulus Cloud Amounts in a 200‐m Resolution Multi‐Scale Modeling Framework Through Tuning of Its Interior Physics

Abstract High‐Resolution Multi‐scale Modeling Frameworks (HR)—global climate models that embed separate, convection‐resolving models with high enough resolution to resolve boundary layer eddies—have exciting potential for investigating low cloud feedback dynamics due to reduced parameterization and ability for multidecadal throughput on modern computing hardware. However low clouds in past HR have suffered a stubborn problem of over‐entrainment due to an uncontrolled source of mixing across the marine subtropical inversion manifesting as stratocumulus dim biases in present‐day climate, limiting their scientific utility. We report new results showing that this over‐entrainment can be partly offset by using hyperviscosity and cloud droplet sedimentation. Hyperviscosity damps small‐scale momentum fluctuations associated with the formulation of the momentum solver of the embedded large eddy simulation. By considering the sedimentation process adjacent to default one‐moment microphysics in HR, condensed phase particles can be removed from the entrainment zone, which further reduces entrainment efficiency. The result is an HR that can produce more low clouds with a higher liquid water path and a reduced stratocumulus dim bias. Associated improvements in the explicitly simulated sub‐cloud eddy spectrum are observed. We report these sensitivities in multi‐week tests and then explore their operational potential alongside microphysical retuning in decadal simulations at operational 1.5° exterior resolution. The result is a new HR having desired improvements in the baseline present‐day low cloud climatology, and a reduced global mean bias and root mean squared error of absorbed shortwave radiation. We suggest it should be promising for examining low cloud feedbacks with minimal approximation.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

X-ray escape peaks in gamma-ray spectrum acquired by perovskite detector

The X-ray escape peaks are clearly visible in the high-resolution gamma spectra acquired by CsPbBr 3 , credit to the rapidly improving energy resolution of perovskite gamma detectors. But it also raises concerns. If this peak remains as significant as it is, perovskite gamma detectors would have very limited use in complex gamma spectrometry applications. This paper discusses the origin of the escape peaks in CsPbBr 3 detectors, benchmarked against Ge and CZT detectors. The Pb escape peak consists of four escape peaks that are merged together, while the Cs and Br escape peaks are hidden inside the photopeak of 137 Cs (662 keV). These combined effects either create a noticeable gamma peak observed to the left of the 662 keV photopeak when acquired using a high-resolution CsPbBr 3 detector or raise the left slope of the photopeak. We also explore potential methods to mitigate these peaks, such as applying anti-coincidence method. The height of the escape peaks diminishes as the detector volume increases but will not go away entirely. The relationship between the observed Pb escape peak and the detector's dimensions is demonstrated through Monte Carlo simulation.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2026

4D-STEM Coupled with Unsupervised Machine Learning to Reveal at Large-Scale the Microstructural Evolution in Li- and Mn-Rich Cathodes

Li- and Mn-rich (LMR) layered oxides are known to exhibit a thin surface reconstruction layer, which grows during electrochemical cycling in a manner that depends on exposed crystallographic facets, cycling conditions, and electrolyte chemistry. Direct characterization of this layer has traditionally relied on high-resolution electron microscopy, which is inherently limited to small fields of view. Here, we employ four-dimensional scanning transmission electron microscopy (4D-STEM) combined with unsupervised machine-learning clustering to quantitatively map phase distributions over large areas and track their evolution in LMR cathodes during electrochemical aging. Our results show that the surface reconstruction layer consists predominantly of a rocksalt phase, whose thickness varies across different facets following activation cycling and becomes substantially thicker and more uniform during calendar aging. In contrast, a spinel-like phase is observed within the particle bulk. Large-area phase mapping and correlative high-resolution imaging reveal that this spinel-like phase preferentially nucleates at bulk crystallographic defects, including boundaries between 60°-rotated layered domains and associated mixed-phase regions, rather than exclusively at the particle surface. Our findings establish a mechanistic distinction between surface-driven rocksalt formation and bulk-defect-mediated spinel nucleation while demonstrating the unique capability of 4D-STEM to provide statistically robust, mesoscale insight into complex phase-evolution processes in LMR cathodes.

4D-STEM↗
DOE OSTI2021

HAIMOS Ensemble Forecasts for Intra-day and Day- Ahead GHI, DNI and Ramps

The objective of this research is to develop a hybrid physics-based/data-driven forecast model to improve direct normal and global horizontal irradiance (DNI and GHI) prediction for horizons ranging from 1 to 72 hours. Project objectives also address key gaps in state-of-the-art solar forecasting: accurate probabilistic solar forecasts and the forecasting of large irradiance ramps (ramp onset and magnitude). The proposed model ensembles Numerical Weather Prediction (NWP) forecasts, determinist physics-based algorithms, and new-generation cloud cover products (high-resolution rapid refresh satellite images and Large Eddy Simulations). The result is the Hybrid Adaptive Input Model Objective Selection (HAIMOS) ensemble model. HAIMOS blends state of the art machine learning methodologies with physics-based models for cloud cover and cloud optical depth forecasts. The technical activities followed a two-pronged strategy. First, the preprocessing of data, the selection of inputs to the nonlinear approximators, the type of approximator and objective functions, and post-processing ensembling techniques included in HAIMOS were all optimized adaptively to find the best model for a specific goal (reduce DNI/GHI forecast error, improve the prediction of ramp onset, etc.). Second, a large effort was put in improving cloud identification and the forecast of cloud cover and cloud optical depth. To this end, new-generation cloud parametrization products were developed in this work. These include improved algorithms to assist in cloud identification, cloud classification and cloud parametrization from satellite images – three key factors in the accuracy of 1 to 6-hours irradiance forecasts and prediction of ramp onset. Furthermore, we also included cloud information extracted from high resolution rapid refresh satellite images (GOES-16) and Large Eddy Simulations (LES). LES was used to model the atmosphere in detail over locations of interest and produce cloud optical depth forecasts. Once these data streams were validated, they were used as input data to the HAIMOS forecast. The model was developed using data from several climatologically distinct locations with potential for high solar penetration. In the last year of the project, we conducted a validation campaign according to the guidelines stipulated by the Topic Area 1 project as described in the FOA. This effort brings, for the first time, proven machine-learning methodologies for generating state-of-the-art solar forecasts interweaved with detailed physics-based models for cloud detection, and cloud optical depth forecasts. HAIMOS will generate accurate irradiance probabilistic forecast to assist in reducing solar generation prediction error. Globally optimized solar forecast models are more likely to impact solar energy stakeholders. The goal of this project was to increase the state-of-the-art forecast skill from their present values of 10 to 35%. At the end of the project, we achieved between 30% and 50% forecast skill across a wide range of horizons for both GHI and DNI.

14 SOLAR ENERGY↗
DOE OSTI2024

Weakened Orographic Influence on Cool-Season Precipitation in Simulations of Future Warming Over the Western US

High-resolution regional climate model (RCM) simulations of global warming consistently predict larger percentage increases in precipitation in the lee of midlatitude mountain ranges than on their windward slopes, indicating a weakening of the orographic rain shadow. This redistribution of precipitation could have profound consequences for water resources and ecosystems, but its underlying mechanisms are unknown. Here we show that rain-shadow weakening is just one manifestation of a more general decrease in the influence of orography on precipitation under global warming. We introduce a simple model of precipitation change based on this principle, and find that it agrees well with an ensemble of high-resolution simulations performed over the western United States. We argue that diminished orographic influence can be explained by the unique vertical structure of orographically forced ascent, which tends to maximize in the lower atmosphere where condensation is thermodynamically less sensitive to warming.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Evaluating Automated Face Identity-Masking Methods with Human Perception and a Deep Convolutional Neural Network

Face de-identification (or “masking”) algorithms have been developed in response to the prevalent use of video recordings in public places. Here, we evaluated the success of face identity masking for human perceivers and a deep convolutional neural network (DCNN). Eight de-identification algorithms were applied to videos of drivers’ faces, while they actively operated a motor vehicle. These masks were pre-selected to be applicable to low-quality video and to maintain coarse information about facial actions. Humans studied high-resolution images to learn driver identities and were tested on their recognition of active drivers in low-resolution videos. Faces in the videos were either unmasked or were masked by one of the eight algorithms. When participants were tested immediately after learning (Experiment 1), all masks reduced identification, with six of eight masks reducing identification to extremely poor performance. In a second experiment, two of the most effective masks were tested after a delay of 7 or 28 days. The delay did not further reduce identification of the masked faces. In all masked conditions, participants maintained stringent decision criteria, with low confidence in recognition, further indicating the effectiveness of the masks. Next, the DCNN performed an identity-matching task between high-resolution images and masked videos—a task analogous to that done by humans. The pattern of accuracy for the DCNN mirrored some, but not all, aspects of human performance, highlighting the need to test the effectiveness of identity masking for both humans and machines. The DCNN was also tested on its ability to match identity between masked and unmasked versions of the same video, based only on the face. DCNN performance for the eight masks offers insight into the nature of the information in faces that is coded in these networks.

97 MATHEMATICS AND COMPUTING↗