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All sourcesNASA NTRSDOE OSTI
NASA NTRS2012

An Effort to Map and Monitor Baldcypress Forest Areas in Coastal Louisiana, Using Landsat, MODIS, and ASTER Satellite Data

This presentation discusses a collaborative project to develop, test, and demonstrate baldcypress forest mapping and monitoring products for aiding forest conservation and restoration in coastal Louisiana. Low lying coastal forests in the region are being negatively impacted by multiple factors, including subsidence, salt water intrusion, sea level rise, persistent flooding, hydrologic modification, annual insect-induced forest defoliation, timber harvesting, and conversion to urban land uses. Coastal baldcypress forests provide invaluable ecological services in terms of wildlife habitat, forest products, storm buffers, and water quality benefits. Before this project, current maps of baldcypress forest concentrations and change did not exist or were out of date. In response, this project was initiated to produce: 1) current maps showing the extent and location of baldcypress dominated forests; and 2) wetland forest change maps showing temporary and persistent disturbance and loss since the early 1970s. Project products are being developed collaboratively with multiple state and federal agencies. Products are being validated using available reference data from aerial, satellite, and field survey data. Results include Landsat TM- based classifications of baldcypress in terms of cover type and percent canopy cover. Landsat MSS data was employed to compute a circa 1972 classification of swamp and bottomland hardwood forest types. Landsat data for 1972-2010 was used to compute wetland forest change products. MODIS-based change products were applied to view and assess insect-induced swamp forest defoliation. MODIS, Landsat, and ASTER satellite data products were used to help assess hurricane and flood impacts to coastal wetland forests in the region.

Spruce, Joseph P.↗
NASA NTRS2013

Experimental Identification and Simulation of Time and/or Rate Dependent Reversible and Irreversible Deformation Regions for both a Titanium and Nickel Alloy

In this paper time and/or rate dependent deformation regions are experimentally mapped out as a function of temperature. It is clearly demonstrated that the concept of a threshold stress (a stress that delineate reversible and irreversible behavior) is valid and necessary at elevated temperatures and corresponds to the classical yield stress at lower temperatures. Also the infinitely slow modulus, (Es) i.e. the elastic modulus of the material if it was loaded at an infinitely slow strain rate, and the "dynamic modulus", modulus, Ed, which represents the modulus of the material if it is loaded at an infinitely fast rate are used to delineate rate dependent from rate independent regions. As demonstrated at elevated temperatures there is a significant difference between the two modulus values, thus indicating both significant time-dependence and rate dependence. In the case of the nickel-based super alloy, ME3, this behavior is also shown to be grain size specific. Consequently, at higher temperatures viscoelastic behavior exist below k (i.e., the threshold stress) and at stresses above k the behavior is viscoplastic. Finally a multi-mechanism, stress partitioned viscoelastic model, capable of being consistently coupled to a viscoplastic model is characterized over the full temperature range investigated for Ti-6-4 and ME3.

Arnold, Steven M.↗
NASA NTRSDate not supplied

Application of the Global Precipitation Forecast dataset for Global Landslide Forecasting System

Landslides are extremely damaging, pervasive and cause fatalities and economic impacts. Extreme rainfall events, coupled with inopportune surface conditions, are the primary triggers of landslides around the world. Forecasting landslide events represent an area of open research. A global Landslide Hazard Assessment for Situational Awareness (LHASA) model has been created that provides near real-time dynamic landslide characterization using Integrated Multi-Satellite Retrievals for Global Precipitation Mission (IMERG). However, it does not provide information on prediction of landslides into future. This study considers how global precipitation forecast data compares to satellite rainfall at different spatiotemporal scales and outlines the potential for its use in landslide hazard prediction/forecasting system. NASA Goddard Earth Observing System (GEOS)-Forecast model assimilates new observations every 6 hours, at 00, 06, 12, and 18 UTC. The GEOS-Forecast model is initialized at 00 UTC and is evaluated with IMERG Early, using Multi-Radar Multi-Sensor gauge corrected (MRMS-GC) precipitation product as a reference over contiguous United States (CONUS). Categorical and continuous statistics along with probability density functions and cumulative distribution functions are considered to assess the performance of the precipitation products, with a focus on landslide hotspots. Seasonality appears to influence the performance of both the GEOS-Forecast and IMERG Early product. Moreover, the global comparison between the GEOS-Forecast and IMERG Early is carried out in terms of percentile difference, correlation, and bias maps. For extreme rainfall events in regions such as Mekong, Colombia, and Tajikistan, GEOS-Forecast appears to resolve high rainfall relative to IMERG Early more frequently. Validation over landslide points reveal that for 24hr rainfall accumulations > 100mm, GEOS-Forecast and IMERG coincide. GEOS-Forecast and IMERG Early precipitation matches more closely for tropical cyclones than other types of storms. Overall, the performance varies with respect to location, rainfall intensities, and type of precipitation events.

Sana Khan↗
DOE OSTI2026

The DECam MAGIC Survey $-$ Mapping the Ancient Galaxy in CaHK: Overview and Summary of Early Science

We present the DECam Mapping the Ancient Galaxy in CaHK (MAGIC) survey, a 54-night NOIRLab Survey Program to image $\gtrsim$5,000$\,$deg$^2$ of the southern hemisphere using a metallicity-sensitive narrow-band filter covering the Ca$\,$ii$\,$H&K lines centered at 3955$\,$A. This filter is installed on the Dark Energy Camera (DECam), mounted on the 4-m NSF Víctor M. Blanco Telescope. The survey reaches typical $10σ$ depths of $\text{mag}_{\text{CaHK}} \approx 22.5$, 3$-$4$\,$mag deeper than comparable surveys in the southern hemisphere. By combining photometry from this Ca$\,$ii$\,$H&K filter with existing DECam $g,r,i$ broadband photometry from the DECam Local Volume Exploration (DELVE) survey, MAGIC is deriving photometric metallicities for red giant branch stars down to the magnitude limit of usable proper motions from Gaia data release 3 (DR3). MAGIC has already imaged $\sim$3,000$\,$deg$^2$, supplemented by other affiliated observing programs that have used this filter to image star clusters, dwarf galaxies, and stellar streams. We overview MAGIC's survey strategy, describe data processing through the derivation of metallicities and photometric distances, and summarize early science results that have been published with this dataset. In addition, we present several new results, including the confirmation of a distant ($>5\,r_h$) member of the Reticulum II ultra-faint dwarf galaxy, on-sky density maps of low-metallicity stars into the distant Milky Way halo ($\sim150\,$kpc) recovering 13/14 ultra-faint dwarf galaxies in the current footprint, and a validation of our initial targeting of extremely metal-poor stars. Collectively, these results demonstrate that the MAGIC dataset enables cutting-edge studies of the faint, low-metallicity regime of the Milky Way and its substructures.

Chiti, A. [KIPAC, Menlo Park] (ORCID:0000000271556↗
DOE OSTI2024

Simulating water dynamics related to pedogenesis across space and time: Implications for four-dimensional digital soil mapping

Digital soil mapping (DSM) relies on machine-learning and geostatistics to represent soil property observations across space. DSM techniques are powerful but often empirical, being limited to the quality and density of point samples. Water dynamics are closely related to soil variability, and the physics that govern water movement are well known. Hydrological properties can hence be simulated by physical models through space and time, unveiling key characteristics about soils. We propose the use of hydrologic models to map soils across the surface (2D), depth (1D), and time (1D)–which provides a 4D approach to digital soil mapping (4DSM). The Distributed Hydrology Soil Vegetation Model (DHSVM) was applied to a watershed currently under pasture. Moisture sensors and wells were installed at different depths in the watershed on summit, sideslope and toeslope positions to validate the model. DHSVM simulations of soil moisture distribution and depth to saturation were performed during the hydrological year (October 2008-September 2009). Clusters of similar pixels based on soil moisture values were determined using Dynamic Time Warping (DTW) to align temporal data and K-means. Clustering was performed both seasonally and for the entire year. Temporal patterns simulated by DHSVM matched measurements given by moisture sensors and wells. Seasonal clusters differed from the annual cluster. Distinct clusters were observed for each season and with depth, showing that spatiotemporal soil variability is lost when statically assessing soils. Spatiotemporal clusters corroborated field observations of fragipan occurrence not explicitly spatially mapped by Soil Survey Geographic Database (SSURGO). If a connection can be made between water and soils, static and dynamic soil variability can be predicted using physically based hydrologic models. Hydrologic models can benefit soil mapping by enabling reliable 4D simulation of water dynamics, which are fundamental to soil variability and soil classification and directly relate to biological, physical and chemical soil processes not captured by typical soil sampling protocols.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1995

Utilization of UARS Data in Validation of Photochemical and Dynamical Mechanism in Stratospheric Models

We proposed model-data intercomparison studies for UARS data. In the past three months, we have been working on constructing analysis tools to diagnose the UARS data. The 'Trajectory mapping' technique, which was developed by Morris (1994), is adaptable to generate synoptic maps of trace gas data from asynoptic observations. An in-house trajectory model (kinematic methods following Merrill et al., 1986 and Pickering et al., 1994) has been developed in AER under contract with NASA/ACMAP and the trajectory mapping tool has been applied to analyze UARS measurement.

Rodriquez, Jose M.↗
DOE OSTI2018

Efficient modeling of optically-complex, non-coplanar exterior shading: Validation of matrix algebraic methods

It has long been established that shading windows with overhangs, fins, and other types of non-coplanar systems (NCS) is one of the most effective ways of controlling solar heat gains in buildings because they intercept solar radiation prior to entry into the building. Designers however often specify non-opaque materials (e.g., louvers, fritted glass, expanded metal mesh) for these systems in order to admit daylight, reduce lighting energy use, and improve indoor environmental quality. Most simulation tools rely on geometric calculations and radiosity methods to model the solar heat gain impacts of NCS and cannot model optically-complex materials or geometries. For daylighting analysis, optically-complex NCS can be modeled using matrix algebraic methods, although time-efficient parametric analysis has not yet been implemented. Determining the best design and/or material for static or operable NCS that minimize cooling, heating, and lighting energy use and peak demand requires an iterative process. This study describes and validates a matrix algebraic method that enables parametric energy analysis of NCS. Such capabilities would be useful not only for design but also for development of prescriptive energy-efficiency standards, rating and labeling systems for commercial products, development of design guidelines, and development of more optimal NCS technologies. A facade or “F” matrix, which maps the transfer of flux from the NCS to the surface of the window, is introduced and its use is explained. A field study was conducted in a full-scale outdoor testbed to measure the daylight performance of an operable drop-arm awning. Simulated data were compared to measured data in order to validate the models. Results demonstrated model accuracy: simulated workplane illuminance was within 11–13%, surface luminance was within 16–18%, and the daylight glare probability was within 6–9% of measured results. Methods used to achieve accurate results are discussed. Results of the validation of daylighting performance are applicable to solar heat gain performance. Since exterior shading can also significantly reduce peak demand, these models enable stakeholders to more accurately assess HVAC and lighting impacts in support of grid management and resiliency goals.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
NASA NTRS2020

ICESat-2 Tracking App for Public Engagement

Information regarding the predicted ground tracks of Earth observing satellites is typically difficult to find and understand for the general public. This paper will describe and demonstrate an iOS mobile application for tracking NASA’s ICESat-2 (Ice, Cloud, and land Elevation Satellite 2), making it easier for users to see exactly when the satellite will be passing over any location in the world. ICESat-2, launched in 2018, uses green lasers to track elevation changes in polar ice and indirectly measure trees, land, and water, providing a precise height map of our planet. Most satellite tracking mobile apps display information about where a specific satellite is at that given moment and when it will be passing over the user’s location in the near future. ICESat-2 the app differs by allowing users to search for data about the satellite’s future flybys relative to a specific search location and radius that they get to choose. The search results include points up to three months into the future. By supplying user-centric results, users are provided relevant data in an easy-to- access manner. Additionally, this data is very clear to visualize in-app through maps, pins, and ground track lines. This becomes an incredibly useful tool not only to plan an observation of the satellite as it passes, but also for knowing when elevation data for a specific area will be available. In addition, this flyby information helps students and citizen scientists take more valuable tree height measurements to better validate the elevation data collected by the satellite. To achieve this outreach product, three key components were developed. These include a Python script to simplify the raw ground track data, a Node.js server that actively takes requests, and the iOS app itself. From initial beta tests, it has been noted that providing users with a map for visual awareness of search radius and result coordinates gives them a more comprehensive understanding of the data. Overall, by cleaning the raw data and making it available in a much more accessible and easier to understand way, ICESat-2 the app has exceeded expectations in providing educational and exciting data for all.

Harbeck, Kaitlin↗
DOE OSTI2022

The Use of Thermal Cameras for Pedestrian Detection

Visible-range camera sensors have been widely used for pedestrian detection. However, most of the methods, which employ visible-range color cameras, do not perform well under low-light and no-light conditions, e.g. during night time. Since the working principle of thermal camera sensors is mainly based on temperature and not light, they have been employed for person detection to overcome the drawbacks of visible-range sensors under these conditions. Every object gives off thermal energy, which is captured by a thermal camera sensor. When an object becomes hotter, it emits more thermal energy, and is therefore captured as much brighter or vice versa. Yet, compared to visible-range cameras, there are many additional challenges that need to be addressed when detecting pedestrians from thermal camera images. These challenges include bright hot objects close to humans, similar pixel values in an image due to weather conditions, or objects that block thermal cameras such as concrete or glass. Glass acts like a mirror for infrared radiation and reflects whatever is in front of the camera. Thus, novel methods are still required to accomplish pedestrian detection task from thermal camera images. To contribute to these efforts, we propose a new method and a modified object detection network incorporating saliency maps of thermal camera images. The features obtained from thermal images and their corresponding saliency maps are combined to obtain richer representations of pedestrian regions, and better detection performance. We perform extensive evaluations on five different datasets to compare the performance of the proposed approach with two baselines. Moreover, we evaluate and compare the transferability of these approaches by doing leave-one-out cross validation across different datasets. Furthermore, the results show that the proposed approach outperforms the baselines, and has better transferability properties across different thermal image datasets.

47 OTHER INSTRUMENTATION↗
DOE OSTI2019

Development and validation of a quantitative PCR assay method of assessing relative resistance of oat ( Avena sativa ) to crown rust ( Puccinia coronata f. sp. avenae )

Evaluation of oat crown rust resistance is usually based on visual assessment of disease severity or infection types. Visual assessment is subjective, prone to rater bias and requires expert knowledge. PCR‐based quantitative assays can overcome challenges associated with visual assessment. New TaqMan primers and probes were designed from Puccinia coronata f. sp. avenae (Pca) sequences. The primer–probe sets were specific to Pca, amplified using as little as 0.5 pg fungal DNA (fDNA) and allowed for scaling to variation in sample total DNA quantity. The quantitative PCR (qPCR) assay was validated using oat recombinant inbred lines (RILs) from the Provena × 94197A1‐9‐2‐2‐2‐5 cross evaluated under a controlled environment. For comparison with fDNA load, inoculation with the Pca race LCBB provided segregation data on the hypersensitive response, while Pca race LSLG provided data on segregation for reduced pustule number. fDNA content was positively correlated with both pustule number and infection type (IT). Composite interval mapping identified two quantitative trait loci (QTLs) on oat linkage groups Mrg12 and Mrg20 using visual and qPCR assessments (pustule number, IT and fDNA). In this study a qPCR assay method that can be used to assess the relative resistance of oat to crown rust was refined and validated, and single nucleotide polymorphisms (SNPs) closely linked with two QTLs derived from the crown rust resistant line 94197A1‐9‐2‐2‐2‐5 were identified.

Admassu‐Yimer, B.↗
DOE OSTI2021

A High-Throughput Method to Define Additive Manufacturing Process Parameters: Application to Haynes 282

This paper demonstrates how an analytical and experimental method can be used to rapidly define the 45 additive manufacturing settings for a new alloy where the process parameters were previously unknown. A nickel-based superalloy, Haynes 282, was chosen for the analysis. An experimental matrix of focused processing parameters was predicted with a dimensionless number and 100 samples were printed using the Selective Laser Melting technique. High-throughput measurements validated the predicted process conditions needed to achieve desired density and hardness. The whole process was completed in 16 hours. The new technique was confirmed with analytical processing maps adopted by the metal additive manufacturing community. With the predicted set of process parameters, microstructural characterizations and tensile testing were used to confirm the predictions. The resultant as-fabricated microstructures have refined length scales of both microsegregation and secondary phase distributions. As a result, the mechanical properties were comparable within the predicted processing window and exhibited high strength and high ductility.

Laser Material Processing↗
DOE OSTI2016

Data-driven probability concentration and sampling on manifold

A new methodology is proposed for generating realizations of a random vector with values in a finite-dimensional Euclidean space that are statistically consistent with a dataset of observations of this vector. The probability distribution of this random vector, while a priori not known, is presumed to be concentrated on an unknown subset of the Euclidean space. A random matrix is introduced whose columns are independent copies of the random vector and for which the number of columns is the number of data points in the dataset. The approach is based on the use of (i) the multidimensional kernel-density estimation method for estimating the probability distribution of the random matrix, (ii) a MCMC method for generating realizations for the random matrix, (iii) the diffusion-maps approach for discovering and characterizing the geometry and the structure of the dataset, and (iv) a reduced-order representation of the random matrix, which is constructed using the diffusion-maps vectors associated with the first eigenvalues of the transition matrix relative to the given dataset. The convergence aspects of the proposed methodology are analyzed and a numerical validation is explored through three applications of increasing complexity. The proposed method is found to be robust to noise levels and data complexity as well as to the intrinsic dimension of data and the size of experimental datasets. Both the methodology and the underlying mathematical framework presented in this paper contribute new capabilities and perspectives at the interface of uncertainty quantification, statistical data analysis, stochastic modeling and associated statistical inverse problems.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2026

A simulation framework for evaluating electronic order workflows in integrated health records

Electronic health record (EHR) systems are critical to modern healthcare delivery, yet the dynamic workflows that govern electronic order processing remain underexplored. Inefficiencies in these digital pathways can cause delays in care, repetitive workloads, and even patient harm. This study presents a discrete-event simulation framework used to reconstruct and evaluate EHR-based order workflows in a large integrated healthcare system. Using real-world data extracted from the Veterans Health Administration’s Corporate Data Warehouse, the authors mapped order events to standardized state transitions and modeled their progression across different facilities of varying complexity levels. After being calibrated with empirical distributions of transition times and validated against observed time-in-system metrics, the simulation demonstrates close alignment with historical performance. Scenario analyses reveal that resource capacity constraints significantly amplify the impact of electronic order surges, which are reflected in the disproportionate growth in backlogs and processing delays. Adjustments in transition probabilities further increased recirculation and extended workflow paths. Network-based analysis identified Reserved, InProgress, and Completed as structurally critical states that function as hubs within the process network but the transitions in-between also act as major bottlenecks. These results showcased the effectiveness of simulation-based approaches in monitoring EHR order processing performance and evaluating consequences of workflow changes on healthcare network resources planning. The proposed simulation framework provides a scalable data-driven tool to support operational decision-making and improve the efficiency of electronic order management in complex healthcare environments.

Engineering↗
DOE OSTI2016

SU-F-T-217: A Comprehensive Monte-Carlo Study of Out-Of-Field Secondary Neutron Spectra in a Scanned-Beam Proton Therapy Treatment Room

Purpose: To simulate secondary neutron radiation-fields produced at different positions during phantom irradiation inside a scanning proton therapy gantry treatment room. Further, to identify origin, energy distribution and angular emission as function of proton beam energy. Methods: GEANT4 and FLUKA Monte-Carlo codes were used to model the relevant parts of the treatment room in a gantry-equipped pencil beam scanning proton therapy facility including walls, floor, metallic gantry-components, patient table and the homogeneous PMMA target. The proton beams were modeled based on experimental beam ranges in water and spot shapes in air. Neutron energy spectra were simulated at 0°, 45°, 90° and 135° relative to the beam axis at 2m distance from isocenter, as well as 11×11 cm2 fields for 75MeV, 140MeV, 200MeV and for 118MeV with 5cm PMMA range-shifter. The total neutron energy distribution was recorded for these four positions and proton energies. Additionally, the room-components generating secondary neutrons in the room and their contributions to the total spectrum were identified and quantified. Results: FLUKA and GEANT4 simulated neutron spectra showed good general agreement in the whole energy range of 10{sup −}9 to 10{sup 2} MeV. Comparison of measured spectra with the simulated contributions of the various room components helped to limit the complexity of the room model, by identifying the dominant contributions to the secondary neutron spectrum. The iron of the bending magnet and counterweight were identified as sources of secondary evaporation-neutrons, which were lacking in simplified room models. Conclusion: Thorough Monte-Carlo simulations have been performed to complement Bonner-sphere spectrometry measurements of secondary neutrons in a clinical proton therapy treatment room. Such calculations helped disentangling the origin of secondary neutrons and their dominant contributions to measured spectra, besides providing a useful validation of widely used Monte-Carlo packages in comparison to experimental data. Cluster of Excellence of the German Research Foundation (DFG) “Munich-Centre for Advanced Photonics (MAP)”.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2019

Application of deep artificial neural networks to multi-dimensional flamelet libraries and spray flames

The “curse of dimensionality” has limited the applicability and expansion of tabulated combustion models. While the tabulated flamelet model and other multi-dimensional manifold approaches have shown predictive capability, the associated tabulation involves the storage of large lookup tables, requiring large memory as well as multi-dimensional interpolation subroutines, all implemented during runtime. As such, this work investigates the use of deep artificial neural networks to replace lookup tables in order to reduce the memory footprint and increase the computational speed of tabulated flamelets and related approaches. Specifically, different strategic approaches to training the artificial neural network models are explored and a grouped multi-target artificial neural network is introduced, which takes advantage of the ability of artificial neural networks to map an input space to multiple targets by classifying the species based on their correlation to one another. The grouped multi-target artificial neural network approach is validated by applying it to an n-dodecane spray flame using conditions of the Spray A flame from the Engine Combustion Network and comparing global flame characteristics for different ambient conditions using a well-established large-eddy simulation framework. The same framework is then extended to the simulations of methyl decanoate combustion in a compression ignition engine. The validation studies show that the grouped multi-target artificial neural networks are able to accurately capture flame liftoff, autoignition, two-stage heat release and other quantitative trends over a range of conditions. The use of neural networks in conjunction with the grouping mechanism as performed in the grouped multi-target artificial neural network produces a significant reduction in the memory footprint and computational costs for the code and, thus, widens the operating envelope for higher fidelity engine simulations with detailed mechanisms.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2014

A Laboratory Exposure System to Study the Effects of Aging on Super-micron Aerosol Particles

A laboratory system was constructed that allows the super-micron particles to be aged for long periods of time under conditions that can simulate a range of natural environments and conditions, including relative humidity, oxidizing chemicals, organics and simulated solar radiation. Two proof-of-concept experiments using a non-biological simulant for biological particles and a biological simulant demonstrate the utility of these types of aging experiments. Green Visolite®, which is often used as a tracer material for model validation experiments, does not degrade with exposure to simulated solar radiation, the actual biological material does. This would indicate that Visolite® should be a good tracer compound for mapping the extent of a biological release using fluorescence as an indicator, but that it should not be used to simulate the decay of a biological particle when exposed to sunlight. The decay in the fluorescence measured for B. thurengiensis is similar to what has been previously observed in outdoor environments.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

Development of a MODIS-Derived Surface Albedo Data Set: An Improved Model Input for Processing the NSRDB

A significant source of bias in the transposition of global horizontal irradiance to plane-of-array (POA) irradiance arises from inaccurate estimations of surface albedo. The current physics-based model used to produce the National Solar Radiation Database (NSRDB) relies on model estimations of surface albedo from a reanalysis climatalogy produced at relatively coarse spatial resolution compared to that of the NSRDB. As an input to spectral decomposition and transposition models, more accurate surface albedo data from remotely sensed imagery at finer spatial resolutions would improve accuracy in the final product. The National Renewable Energy Laboratory (NREL) developed an improved white-sky (bi-hemispherical reflectance) broadband (0.3-5.0 ..mu..m) surface albedo data set for processing the NSRDB from two existing data sets: a gap-filled albedo product and a daily snow cover product. The Moderate Resolution Imaging Spectroradiometer (MODIS) sensors onboard the Terra and Aqua satellites have provided high-quality measurements of surface albedo at 30 arc-second spatial resolution and 8-day temporal resolution since 2001. The high spatial and temporal resolutions and the temporal coverage of the MODIS sensor will allow for improved modeling of POA irradiance in the NSRDB. However, cloud and snow cover interfere with MODIS observations of ground surface albedo, and thus they require post-processing. The MODIS production team applied a gap-filling methodology to interpolate observations obscured by clouds or ephemeral snow. This approach filled pixels with ephemeral snow cover because the 8-day temporal resolution is too coarse to accurately capture the variability of snow cover and its impact on albedo estimates. However, for this project, accurate representation of daily snow cover change is important in producing the NSRDB. Therefore, NREL also used the Integrated Multisensor Snow and Ice Mapping System data set, which provides daily snow cover observations of the Northern Hemisphere for the temporal extent of the NSRDB (1998-2015). We provide a review of validation studies conducted on these two products and describe the methodology developed by NREL to remap the data products to the NSRDB grid and integrate them into a seamless daily data set.

14 SOLAR ENERGY↗
DOE OSTI2024

Idaho National Laboratory Integrated Multisite SSHAC Level 3: Probabilistic Volcanic Hazards Assessment

The Idaho National Laboratory (INL) resides on the eastern Snake River Plain (ESRP), part of the Snake River Plain (SRP) with a complex origin and geologic history of volcanism. Much of the Quaternary (last 2.58 million years) volcanism within 400 km of INL is genetically associated with a major thermal anomaly referred to as the Yellowstone hotspot, which is currently located more than 180 km northeast of INL. Near INL, local volcanic sources include silicic domes near its southern border, and numerous dike-fed basaltic vents of the ESRP, some of which are near or within the INL boundaries. Quantitative probabilistic assessments of screened-in volcanic hazardous phenomena for nine different facility complexes at the INL are presented for a Senior Seismic Hazard Analysis Committee (SSHAC) Level 3 (SL3) study. The comprehensive, integrated multisite SSHAC study consists of a single regional Probabilistic Volcanic Hazards Assessment (PVHA) that pertains to all nine INL facility complexes, with site-specific information developed at each respective area of interest (AOI), referred to as the "INL facility AOI" (Figure ES-1). Hazard products are generated for specified INL facility AOIs for use by multiple stakeholders from different agencies in risk-informed decision-making regarding site selection, operations, and design of nuclear facilities at INL, consistent with U.S. Department of Energy (DOE) and U.S. Nuclear Regulatory Commission (NRC) regulatory guidance. The study also serves as the basis for future periodic safety assessments required for existing DOE facilities, such as 10-year evaluations of natural-phenomena hazards. Elements of the INL PVHA including initial characterization, screening, quantitative assessments of eruption and hazard potential, and consideration of facility needs are conducted using the three phases of the SSHAC process: evaluation, integration, and documentation. As per regulatory guidance, the SSHAC framework provided the necessary processes and procedures for the PVHA Technical Integration (TI) team, at three workshops, five formal working meetings and many TI team remote meetings, to conduct initial characterization, screen the volcanic hazards, create PVHA model inputs, exercise those models in the PVHA, consider facility-specific volcanic hazard needs, and perform final hazard calculations. The Participatory Peer Review Panel (PPRP) provided independent oversight and performed process and technical reviews of the PVHA throughout its duration. The study included an extensive New Data Collection and Analyses (NDCA) program developed by the PVHA TI team to reduce uncertainties in hazard-significant elements in the PVHA model. NDCA activities generated 20 reports providing important contributory datasets and results to the project database for characterizing the ESRP. For example, a report compiling the dimensions of ESRP shield volcanoes and lava fields was used to construct volcanic footprints (areas of impact), was compared with data from INL subsurface cores, and was used to validate the results of lava-flow inundation modeling on the contemporary terrain. Another example is the acquisition of aeromagnetic data over INL and its surrounding area, with maps of buried magmatic features (e.g., subsurface volcanoes and swarms of feeder dikes) that informed the PVHA conceptual model of volcanism. The SSHAC evaluation process was used to conduct all elements of the PVHA including initial characterization and screening. Existing data and NDCA activities provided the foundation to develop the tectonomagmatic conceptual model of volcanism for the region of geographical interest in the SRP and Yellowstone hotspot volcanic system, and for considering volcanoes in the western US. Considering Quaternary volcanic sources active during this period, the screening approach identified and evaluated magma compositions, types of eruptive and intrusive phenomena, types of hazardous phenomena, and proximity of sources to INL. The PVHA TI team evaluated 18 types of magmatic sources in terms of 20 potentially hazardous phenomena, resulting in 360 screening decisions. The screening process resulted in 140 screened-in hazardous volcanic phenomena for Quaternary volcanic sources 1) proximal to INL facility complexes in the ESRP (64 basaltic and 54 silicic), 2) regional sources associated with the Yellowstone caldera system and Blackfoot Reservoir volcanic field (7), and 3) more distal sources from thirteen Cascade volcanoes and two volcanoes at Long Valley caldera (CA).

58 GEOSCIENCES↗