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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Effect of Environmental and Socioeconomic Factors on Increased Early Childhood Blood Lead Levels: A Case Study in Chicago

This study analyzes the prevalence of elevated blood lead levels (BLLs) in children across Chicagoland zip codes from 2019 to 2021, linking them to socioeconomic, environmental, and racial factors. Wilcoxon tests and generalized additive model (GAM) regressions identified economic hardship, reflected in per capita income and unemployment rates, as a significant contributor to increased lead poisoning (LP) rates. Additionally, LP rates correlate with the average age of buildings, particularly post the 1978 lead paint ban, illustrating policy impacts on health outcomes. The study further explores the novel area of land surface temperature (LST) effects on LP, finding that higher nighttime LST, indicative of urban heat island effects, correlates with increased LP. This finding gains additional significance in the context of anthropogenic climate change. When these factors are combined with the ongoing expansion of urban territories, a significant risk exists of escalating LP rates on a global scale. Racial disparity analysis revealed that Black and Hispanic/Latino populations face higher LP rates, primarily due to unemployment and older housing. The study underscores the necessity for targeted public health strategies to address these disparities, emphasizing the need for interventions that cater to the unique challenges of these at-risk communities.

Lee, Jangho (ORCID:0000000289421092)↗
DOE OSTI2021

Validation of the EDGES Low-band Antenna Beam Model

The response of the antenna is a source of uncertainty in measurements with the Experiment to Detect the Global Epoch of Reionization Signature (EDGES). We aim to validate the electromagnetic beam model of the low-band (50–100 MHz) dipole antenna with comparisons between models and against data. We find that simulations of a simplified model of the antenna over an infinite perfectly conducting ground plane are, with one exception, robust to changes in the numerical electromagnetic solver code or algorithm. For simulations of the antenna with the actual finite ground plane and realistic soil properties, we find that two out of three numerical solvers agree well. Applying our analysis pipeline to a simulated drift-scan observation from an early EDGES low-band instrument that had a 10 m × 10 m ground plane, we find residual levels after fitting and removing a five-term foreground model from the simulated data binned in local sidereal time (LST) average about 250 mK with ±40 mK variation between numerical solvers. A similar analysis of the primary 30 m × 30 m sawtooth ground plane reduced the LST-averaged residuals to about 90 mK with ±10 mK between the two viable solvers. More broadly we show that larger ground planes generally perform better than smaller ground planes. Simulated data have a power that is within 4% of real observations, a limitation of net accuracy of the sky and beam models. We observe that residual spectral structures after foreground model fits match qualitatively between simulated data and observations, suggesting that the frequency dependence of the beam is reasonably represented by the models. We find that a soil conductivity of 0.02 S m{sup -1} and relative permittivity of 3.5 yield good agreement between simulated spectra and observations. This is consistent with the soil properties reported by Sutinjo et al. for the Murchison Radio-astronomy Observatory, where EDGES is located.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2021

Interpreting the Diurnal Cycle of Clouds and Precipitation in the ARM GoAmazon Observations: Shallow to Deep Convection Transition

The Green Ocean Amazon (GoAmazon) 2014/5 field campaign data are used to study the diurnal cycle of clouds and precipitation. Through a careful classification of days with shallow cumulus, congestus and deep convection, we investigate the major differences among locally generated convection regimes and the most important environmental factors governing the shallow-to-deep convection transition. On shallow cumulus days, a greater sensible heat flux drives deeper boundary layer growth, which entrains drier free-tropospheric air and lowers the relative humidity, thus leading to a significantly higher cloud base than those on days with deeper convection. Congestus and deep convection regimes exhibit distinct cloud top height distributions with noticeable differences in the vertical wind shear in the mid-troposphere, suggesting an important role of wind shear in limiting the vertical extent of convection. On deep convection days, with preexisting nocturnal convection or cold-pools from external disturbances, the timing of peak surface precipitation (12:00–13:00 LST) tends to be in-phase with the diurnal variation in surface fluxes. However, it takes longer for local deep convection to develop without these disturbances. A plume model with thermodynamic and dynamical constraints is developed to explore the relative importance of various convection-controlling factors. Initial cloud-base vertical velocity and buoyancy are important in helping parcels ascend to the level of free convection (LFC). After parcels reach the LFC, entrainment of environmental air and lower free troposphere humidity become crucial in determining cloud top. Entrainment rate differentiates among convection regimes, which may be tied to the cloud size distribution at cloud base.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Warm‐Season Afternoon Precipitation Peak in the Central Bay of Bengal: Process‐Oriented Diagnostics

Abstract Past studies have indicated that precipitation over tropical open oceans generally peaks in the early morning. However, an intriguing departure from this pattern is observed in the central Bay of Bengal (CBoB), where rainfall exhibits a distinct afternoon peak during the South Asian summer monsoon season. By using a novel satellite‐based cloud classification and tracking data set, we found that more than 75% of the afternoon rainfall (15–17 LST) over the CBoB comes from mesoscale convective systems (MCSs). Most of the MCSs contributing to the CBoB afternoon rainfall peak originate either locally over the CBoB or near the west and east coasts of the BoB, in contrast to the northern BoB as highlighted in previous studies. Analyses show that MCSs initiated near coastlines are primarily influenced by land‐sea breezes, whereas MCSs initiated over the BoB open ocean during early morning are strongly associated with diurnal radiative forcings. In addition, there are clear diurnal propagating MCS initiation signals from the west and north coastlines of the BoB to the CBoB, which are related to diurnal gravity waves emitted from the coastlines. The thermodynamic conditions conducive to MCS initiation over different sub‐regions of the BoB are also investigated. No systematic differences found in environmental convective available potential energy between days with and without MCS initiation. However, over most sub‐regions, days with MCS initiation generally have higher total column water vapor than days without MCS initiation. This difference suggests that the lower‐free‐tropospheric moisture content plays an important role in MCS initiation over the BoB.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Wildfires Temperature Estimation by Complementary Use of Hyperspectral PRISMA and Thermal (ECOSTRESS & L8)

This paper deals with detection and temperature analysis and of wildfires using PRISMA imagery. Precursore IperSpettrale della Missione Applicativa (Hyperspectral Precursor of the Application Mission, PRISMA) is a new hyperspectral mission by ASI (Agenzia Spaziale Italiana, Italian Space Agency) launched in 2019. This mission provides hyperspectral images with a spectral range of 400–2,500 nm and an average spectral resolution less than 12 nm and a spatial resolution of 30 m/pixel. This study focuses on the wildfire temperature estimation over the Bootleg Fire, US 2021. The analysis starts by considering the Hyperspectral Fire Detection Index (HFDI) which is used to analyze the informative content of the images, along with the analysis of some specific visible, near-infrared and shortwave-infrared bands. This first analysis is used as input to perform a temperature estimation of the areas with active wildfire. Surface temperature is retrieved using PRISMA radiance and a linear mixing model based on two background components (vegetation and burn scar) and two active fire components. PRISMA temperatures are compared with LST (Land Surface Temperature) products from NASA's ECOSTRESS and Landsat 8 which imaged the Bootleg Fire before and after PRISMA. A critical discussion of the results obtained with PRISMA is presented, followed by the advantages and limitation of the proposed approach.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Effect of Rocky Mountains and Tibetan Plateau 1998 Spring Land Temperature on N. American and East Asian Summer Precipitation Anomalies

This work follows up on the GEWEX/LS4P Phase I (LS4P-I) experiments, a community effort highlighting the spring land surface temperature anomalies in the Tibetan Plateau (TP) as a useful source for subseasonal to seasonal (S2S) prediction of summer precipitation in global hot spot regions, particularly in East Asia and North America. This paper extends the investigation to both the US Rocky Mountain (RM) region and the TP, considering the 1998 summer drought/flood event in North America/East Asia, respectively, as a case study. A previously developed initialization method for land surface temperature/subsurface temperature (LST/SUBT) is used in the NCEP Global Forecast System, coupled with a land model, SSiB2 (GFS/SSiB2), to produce observed RM cold May temperature anomaly. Forward simulation yields June precipitation anomalies at five remote locations. Likewise, the TP warm May temperature anomaly also produces June precipitation anomalies at these five locations. The effects of RM (cold) and TP (warm) temperature anomalies are consistent in the US South Coastal regions and the south Yangtze River Basin, yielding 49% (42%) of observed drought and 34% (44%) of observed flood, respectively. These LST/SUBT effects in RM and TP induce a global large-scale wave train linking North America with the TP, affecting the subtropical westerly jet and thereby modulating summer precipitation. Global SST effect is examined for comparison but does not yield statistically significant June precipitation anomalies in GFS/SSiB2. Furthermore, this study adds to evidence that high-mountain LST effects in the RM and TP are first-order sources of S2S precipitation predictability in summer months.

Nayak, Hara Prasad [University of California, Los ↗
DOE OSTI2022

An Investigation of the Electrochemical Activity of (Ba/Sr)FeO 3-y Anodes

FeO x , SrFeO 3-y (SF), and Ba 0.5 Sr 0.5 FeO 3–y (BSF) were studied for application as fuel oxidation catalysts in solid oxide fuel cells (SOFC) anodes. Electrodes were prepared by impregnation into porous yttria-stabilized zirconia (YSZ), with La 0.3 Sr 0.7 TiO 3–y (LST) added for electronic conductivity. The electrode impedances decreased dramatically upon addition of SF and BSF and much less when only SrO or FeO x were added. Temperature Programmed Desorption (TPD) of O 2 from oxidized BSF showed O 2 desorbing between 200 °C and 700 °C, while no O 2 desorbed from Fe 2 O 3 below 900 °C. The results, together with thermodynamic analysis, suggest that stabilization of Fe +4 in the perovskite lattice plays an important role in enhancing the catalytic activity of SF and BSF by providing access to a Fe 3+ /Fe 4+ redox couple that can accept oxygen anions from the yttria-stabilized zirconia (YSZ) electrolyte. Oxygen is weakly bound to these sites facilitating its reaction with adsorbed H 2 .

Electrochemistry↗
DOE OSTI2021

Diurnal Variation of the Planetary Boundary Layer Height Observed from GNSS Radio Occultation and Radiosonde Soundings over the Southern Great Plains

>The planetary boundary layer (PBL) height (PBLH) is a key physical parameter of the PBL affected by numerous physical processes within the boundary layer. Specifically, the PBLH over land exhibits large spatial and temporal variation across different geographical regions. In this study, the Constellation Observing System for Meteorology, Ionosphere and Climate (COSMIC) radio occultation (RO) profiles and high-resolution radiosonde profiles from 2007 to 2013 were analyzed to estimate the diurnal cycle of the PBLH over the Southern Great Plains (SGP) in the United States. Large variations in PBLH derived from radiosonde temperature, moisture, and refractivity are observed on seasonal scales. COSMIC RO is capable of observing diurnal and seasonal variations in the terrestrial PBLH over the SGP region. Annual mean diurnal amplitude of approximately 250 m in the terrestrial PBLH was observed, with maxima occurring at around 1500 local solar time (LST) in both the collocated radiosondes and COSMIC RO profiles. Seasonal changes in the PBLH diurnal cycles ranging from approximately 100 to 400 m were also observed. Such PBL diurnal and seasonal changes can be further incorporated into PBL parameterizations to help improve weather and climate model prediction. Significance Statement. The atmospheric planetary boundary layer (PBL) and its height (PBLH) control many atmospheric processes that affect our everyday lives. Observations of the PBL are usually limited to radiosondes at limited time intervals. GNSS radio occultation (RO) provide high-vertical-resolution atmospheric observations that are ideal for PBL study. This study demonstrates that the GNSS RO is capable of capturing the diurnal and seasonal variations of the PBLH over the southern Great Plains (SGP) well as compared to the collocated radiosonde observations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Understanding Biases in Sample Preparation Techniques for Coupled Scanning Electron Microscopy and MAMA PuO 2 Morphological Analysis

In this project, the scanning electron microscopy (SEM) sampling method used during the statistical design study (SDS) was investigated to determine if any sampling biases were present in the analyzed data. Using standard particle size distribution powders from the National Institute of Standards and Technology (NIST 1984 standard reference material) with the origin wet dispersion method, it was determined that a bias to smaller particles was present. This was supported by theoretical calculations using Stokes’ law to determine the settling rate of spherical particles of roughly the same size and mass as those found in the SDS. Based on the theoretical calculations, it was determined that the settling rate for each of the 76 powder sets in the SDS could be unique based on specific particle shape and mass distributions, making a universal correction factor/formula not applicable. Therefore, priority shifted to developing an improved wet dispersion method that significantly reduced the particle settling rate for all particle size and shapes. This was achieved by replacing the original solvent (isopropyl alcohol) with a heavy liquid (lithium heteropolytungstates), which dramatically slowed the settling rate and allowed for the capture of a suitable homogeneous aliquot. SEM imaging and Morphological Analysis for Material Attribution (MAMA) software analysis were conducted on the NIST standard, and the SEM/MAMA data were compared to data captured by a dynamic image analysis particle size analyzer. The resulting data confirmed that the new wet dispersion method does indeed deliver an improved representative aliquot to the SEM stub. For instance, in the NIST certificate, the average particle size is ~17.1 µm ± 2.2 µm with a normal distribution. The initial wet dispersion method resulted in a drastically reduced average particle size of 6.1 µm in addition to a non-representative heavy bi-modal distribution whereas the improved LST wet dispersion method resulting in an average particle size that was much closer to the NIST certificate (12.7 µm) with a similar normal distribution. Although the improved method was still short of the NIST certificate average, atomic force microscopy analysis determined that the resulting ~20-25% reduction in size was due to particles sinking into the carbon sticky tape used for SEM imaging. It is believed that that this bias can be calibrated in a much more predicable manner than the original settling rate bias. In addition, the matching normal distribution curves between the NIST certificate and the heavy liquid method indicate a much-improved representative aliquot has been sampled and imaged. A surrogate CeO 2 powder was used to reflect PuO 2 more accurately and to aid in implementing radiological controls and shielding. The resulting data sets from the SEM/MAMA method and the particle size analyzer give almost identical average particle sizes and particle distribution statistics. Future work will re-analyze several select runs from the SDS to determine if morphological signatures can be found with the improved sampling method.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Land-Surface Temperature Transitions at the Bankhead National Forest (LASTT-BNF) Field Campaign Report

Land surface types that are common in the Southeast United States (e.g., forests, agriculture, urban) can play key roles in the regulation of surface-atmosphere energy exchange and boundary-layer evolution in the region (Hinkle et al. 2024). Heterogeneity in these land-surface types and associated emissivities can drive strong transitions in land surface temperature (LST), which is an important component of the surface energy balance. Within the forested canopy surrounding the U.S. Department of Energy Atmospheric Radiation Measurement (ARM) User Facility’s field deployment at the Bankhead National Forest (BNF) in Alabama main site (M1) (Kuang et al. 2026), the different components of the vegetation and ground surface can create strong gradients in surface temperature that can influence the canopy thermal environment and vegetative function (e.g., stress, transpiration), both of which can have important, yet poorly characterized, controls on lower atmospheric processes (e.g., thermal turbulence, secondary circulations).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

An Evaluation of AI Models’ Performance for Three Geothermal Sites

Current artificial intelligence (AI) applications in geothermal exploration are tailored to specific geothermal sites, limiting their transferability and broader applicability. This study aims to develop a globally applicable and transferable geothermal AI model to empower the exploration of geothermal resources. This study presents a methodology for adopting geothermal AI that utilizes known indicators of geothermal areas, including mineral markers, land surface temperature (LST), and faults. The proposed methodology involves a comparative analysis of three distinct geothermal sites—Brady, Desert Peak, and Coso. The research plan includes self-testing to understand the unique characteristics of each site, followed by dependent and independent tests to assess cross-compatibility and model transferability. The results indicate that Desert Peak and Coso geothermal sites are cross-compatible due to their similar geothermal characteristics, allowing the AI model to be transferable between these sites. However, Brady is found to be incompatible with both Desert Peak and Coso. The geothermal AI model developed in this study demonstrates the potential for transferability and applicability to other geothermal sites with similar characteristics, enhancing the efficiency and effectiveness of geothermal resource exploration. This advancement in geothermal AI modeling can significantly contribute to the global expansion of geothermal energy, supporting sustainable energy goals.

Energy & Fuels↗
DOE OSTI2022

Predictive Risk Mapping of Schistosomiasis in Madagascar Using Ecological Niche Modeling and Precision Mapping

Schistosomiasis is a neglected tropical disease (NTD) found throughout tropical and subtropical Africa. In Madagascar, the condition is widespread and endemic in 74% of all administrative districts in the country. Despite the significant burden of the disease, high-resolution risk maps have yet to be produced to guide national control programs. This study used an ecological niche modeling (ENM) and precision mapping approach to estimate environmental suitability and disease transmission risk. The results show that suitability for schistosomiasis is widespread and covers 264,781 km 2 (102,232 sq miles). Covariates of significance to the model were the accessibility to cities, distance to water, enhanced vegetation index (EVI), annual mean temperature, land surface temperature (LST), clay content, and annual precipitation. Disease transmission risk is greatest in the central highlands, tropical east coast, arid-southwest, and northwest. An estimated 14.9 million people could be at risk of schistosomiasis; 11.4 million reside in rural areas, while 3.5 million are in urban areas. This study provides valuable insight into the geography of schistosomiasis in Madagascar and its potential risk to human populations. Because of the focal nature of the disease, these maps can inform national surveillance programs while improving understanding of areas in need of medical interventions.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Morning boundary layer conditions for shallow to deep convective cloud evolution during the dry season in the central Amazon

Observations of the boundary layer (BL) processes are analyzed statistically for dry seasons of 2 years and in detail, as case studies, for 4 shallow convective days (ShCu) and 4 shallow-to-deep convective days (ShDeep) using a suite of ground-based measurements from the Observation and Modeling of the Green Ocean Amazon (GoAmazon 2014/5) Experiment. The BL stages in ShDeep days, from the nighttime to the cloudy mixing layer stage, are then described in comparison with ShCu days. Atmospheric thermodynamics and dynamics, environmental profiles, and surface turbulent fluxes were employed to compare these two distinct situations for each stage of the BL evolution. Particular attention is given to the morning transition stage, in which the BL changes from stable to unstable conditions in the early morning hours. Results show that the decrease in time duration of the morning transition on ShDeep days is associated with high humidity and well-established vertical wind shear patterns. Higher humidity since nighttime not only contributes to lowering the cloud base during the rapid growth of the BL but also contributes to the balance between radiative cooling and turbulent mixing during nighttime, resulting in higher sensible heat flux in the early morning. The sensible heat flux promotes rapid growth of the well-mixed layer, thus favoring the deeper BL starting from around 08:00 LST (UTC-4 h). Under these conditions, the time duration of morning transition is used to promote convection, having an important effect on the convective BL strength and leading to the formation of shallow cumulus clouds and their subsequent evolution into deep convective clouds. Statistical analysis was used to validate the conceptual model obtained from the case studies. Despite the case-to-case variability, the statistical analyses of the processes in the BL show that the described processes are very representative of cloud evolution during the dry season.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Impact of Initialized Land Surface Temperature and Snowpack on Subseasonal to Seasonal Prediction Project, Phase I (LS4P-I): organization and experimental design

Subseasonal-to-seasonal (S2S) prediction, especially the prediction of extreme hydroclimate events such as droughts and floods, is not only scientifically challenging, but also has substantial societal impacts. Motivated by preliminary studies, the Global Energy and Water Exchanges (GEWEX)/Global Atmospheric System Study (GASS) has launched a new initiative called “Impact of Initialized Land Surface Temperature and Snowpack on Subseasonal to Seasonal Prediction” (LS4P) as the first international grass-roots effort to introduce spring land surface temperature (LST)/subsurface temperature (SUBT) anomalies over high mountain areas as a crucial factor that can lead to significant improvement in precipitation prediction through the remote effects of land–atmosphere interactions. LS4P focuses on process understanding and predictability, and hence it is different from, and complements, other international projects that focus on the operational S2S prediction. More than 40 groups worldwide have participated in this effort, including 21 Earth system models, 9 regional climate models, and 7 data groups.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Climate projections over the Great Lakes Region: using two-way coupling of a regional climate model with a 3-D lake model

Abstract. Warming trends in the Laurentian Great Lakes and surrounding areas have been observed in recent decades, and concerns continue to rise about the pace and pattern of future climate change over the world's largest freshwater system. To date, most regional climate models used for Great Lakes projections either neglected the lake-atmosphere interactions or are only coupled with a 1-D column lake model to represent the lake hydrodynamics. This study presents a Great Lakes climate change projection that has employed the two-way coupling of a regional climate model with a 3-D lake model (GLARM) to resolve 3-D hydrodynamics essential for large lakes. Using the three carefully selected Coupled Model Intercomparison Project Phase 5 (CMIP5) general circulation models (GCMs), we show that the GLARM ensemble average substantially reduces surface air temperature and precipitation biases of the driving GCM ensemble average in present-day climate simulations. The improvements are not only displayed from an atmospheric perspective but are also evident in the accurate simulations of lake temperature and ice coverage. We further present the GLARM projected climate change for the mid-21st century (2030–2049) and the late 21st century (2080–2099) in the Representative Concentration Pathway (RCP) 4.5 and RCP 8.5 scenarios. Under RCP 8.5, the Great Lakes basin is projected to warm by 1.3–2.1 ∘C by the mid-21st century and 4.1–5.0 ∘C by the end of the century relative to the early century (2000–2019). Moderate mitigation (RCP 4.5) reduces the mid-century warming to 0.8–1.8 ∘C and late-century warming to 1.8–2.7 ∘C. Annual precipitation in GLARM is projected to increase for the entire basin, varying from 0 % to 13 % during the mid-century and from 9 % to 32 % during the late century in different scenarios and simulations. The most significant increases are projected in spring and fall when current precipitation is highest and a minimal increase in winter when it is lowest. Lake surface temperatures (LSTs) are also projected to increase across the five lakes in all of the simulations, but with strong seasonal and spatial variability. The most significant LST increases occur in Lakes Superior and Ontario. The strongest warming is projected in spring that persists into the summer, resulting from earlier and more intense stratification in the future. In addition, diminishing winter stratification in the future suggests the transition from dimictic lakes to monomictic lakes by the end of the century. In contrast, a relatively smaller increase in LSTs during fall and winter is projected with heat transfer to the deep water due to the strong mixing and energy required for ice melting. Correspondingly, the highest monthly mean ice cover is projected to reduce to 3 %–15 % and 10 %–40 % across the lakes by the end of the century in RCP 8.5 and RCP 4.5, respectively. In the coastal regions, ice duration is projected to decrease by up to 60 d.

Xue, Pengfei (ORCID:000000025702421X)↗
DOE OSTI2021

Programs and Code for Geothermal Exploration Artificial Intelligence

The scripts below are used to run the Geothermal Exploration Artificial Intelligence developed within the "Detection of Potential Geothermal Exploration Sites from Hyperspectral Images via Deep Learning" project. It includes all scripts for pre-processing and processing, including: - Land Surface Temperature K-Means classifier - Labeling AI using Self Organizing Maps (SOM) - Post-processing for Permanent Scatterer InSAR (PSInSAR) analysis with SOM - Mineral marker summarizing - Artificial Intelligence (AI) Data splitting: creates data set from a single raster file - Artificial Intelligence Model: creates AI from a single data set, after splitting in Train, Validation and Test subsets - AI Mapper: creates a classification map based on a raster file

15 GEOTHERMAL ENERGY↗
DOE OSTI2021

Brady Geodatabase for Geothermal Exploration Artificial Intelligence

These files contain the geodatabases related to Brady's Geothermal Field. It includes all input and output files for the Geothermal Exploration Artificial Intelligence. Input and output files are sorted into three categories: raw data, pre-processed data, and analysis (post-processed data). In each of these categories there are six additional types of raster catalogs which are titled Radar, SWIR, Thermal, Geophysics, Geology, and Wells. These inputs and outputs were used with the Geothermal Exploration Artificial Intelligence to identify indicators of blind geothermal systems at the Brady Hot Springs Geothermal Site. The included zip file is a geodatabase to be used with ArcGIS and the tar file is an inclusive database that encompasses the inputs and outputs for the Brady Hot Springs Geothermal Site.

15 GEOTHERMAL ENERGY↗
DOE OSTI2021

Desert Peak Geodatabase for Geothermal Exploration Artificial Intelligence

These files contain the geodatabases related to the Desert Peak Geothermal Field. It includes all input and output files used in the project. The files include data categories of raw data, pre-processed data, and analysis (post-processed data). In each of these categories there are six additional types of raster catalogs including Radar, SWIR, Thermal, Geophysics, Geology, and Wells. The files for the Desert Peak Geothermal Site are used with the Geothermal Exploration Artificial Intelligence to identify indicators of blind geothermal systems. The included zip file is a geodatabase to be used with ArcGIS and the tar file is an inclusive database that encompasses the inputs and outputs for the Desert Peak Geothermal Field.

15 GEOTHERMAL ENERGY↗