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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 OSTI2026

Data for: A hybrid biophysical-machine learning framework for diurnal surface energy flux estimation using proximal sensing

Thermal-based remote sensing of surface energy fluxes has traditionally relied on high spatial resolution satellite data with revisit frequencies on the order of weeks. In this study, we evaluate a biophysics-based analytical surface energy balance model for predicting latent energy (LE) and sensible heat (H) fluxes using proximal sensing observations. The Surface Temperature Initiated Closure (STIC1.2) model has been extensively validated across a wide range of spatial and temporal scales using various satellite-derived thermal datasets. Here we extend this validation by applying STIC at sub-hourly temporal resolution over multiple growing seasons for four distinct agricultural systems. We further develop and evaluate novel STIC variants that incorporate machine learning (ML) techniques to eliminate the need for specific surface energy balance observations, specifically net radiation and soil heat flux, thereby enhancing model applicability in data-sparse settings. The integration of an ML component to estimate surface available energy is shown to have strong predictive performance for both LE (R2 = 0.81-0.94) and H (R2 = 0.46-0.72) across all agricultural systems examined here, demonstrating the potential of hybrid biophysical – machine learning approaches for surface energy balance modeling with minimal data requirements. This study concludes with a novel application of explainable machine learning (exML) to diagnose sources of model error. This exML framework attributes residual prediction errors to both model input variables and environmental drivers not explicitly included in the simulation experiments. This approach provides a new pathway for improving model design and integrating previously overlooked yet influential variables into future model iterations.

Agricultural Sciences↗
DOE OSTI2026

Surface Quantitative Precipitation Estimates (SQUIRE) of Snow Water Equivalent from the Surface Atmospheric Integrated Field Laboratory

The upper Colorado River basin is the primary source of water for 40 million people. With declining snowpack in the basin, forecasting hydrological budgets in the Southwest United States is more important than ever. However, due in part, to a lack of reliable observations of precipitation in complex terrain, hydrological models struggle to assess and forecast snowpack snow water equivalent (SWE) in the upper Colorado River basin (UCRB). Therefore, the need for more reliable SWE forecasts in the UCRB motivated the U.S. Department of Energy Atmospheric Radiation Measurement Facility’s Surface Atmospheric Integrated Field Laboratory (SAIL) that occurred from June 2021 to June 2023. During SAIL, the X-band precipitation radar from Colorado State University conducted volume scans sampling the precipitation properties over the UCRB. The ARM facility developed a gridded Surface Quantitative Precipitation Estimates (SQUIRE) product from the radar observations. To do this, various daily SWE estimates from radar using the radar reflectivity factor Z e and specific differential phase K dp were compared against ground-based precipitation gauges. SWE in precipitation calculated from Wolfe and Snider’s S–Z e estimator was in best agreement with the rain gauges for the days when SWE < 12 mm. For days with SWE > 12 mm, the WSR-88D Intermountain West relationship had the best agreement with the precipitation gauges. Airborne snow depth observations show that SQUIRE captures regions of orographic enhancement in the mountains to the west and northwest of the SAIL study area, indicating that the scientific community should focus on understanding and ultimately simulating orographic atmospheric precipitation processes to improve UCRB snowpack SWE assessment and forecasting.

Hydrology↗
DOE OSTI2026

A Hybrid Biophysical‐Machine Learning Framework for Diurnal Surface Energy Flux Estimation Using Proximal Sensing

Thermal infrared-based remote sensing of surface energy fluxes has traditionally relied on high spatial resolution satellite data with revisit frequencies on the order of weeks. In this study, we evaluate a biophysics-based analytical surface energy balance model for predicting latent energy ( LE ) and sensible heat ( H ) fluxes using proximal sensing observations. The Surface Temperature Initiated Closure (STIC1.2) model has been extensively validated across a wide range of spatial and temporal scales using various satellite-derived thermal infrared data sets. Here we extend this validation by applying STIC at sub-hourly temporal resolution over multiple growing seasons for four distinct agricultural systems. We further develop and evaluate novel STIC variants that incorporate machine learning (ML) techniques to eliminate the need for surface energy balance observations, specifically net radiation and soil heat flux, thereby enhancing model applicability in data-sparse settings. The integration of a ML component to estimate surface available energy is shown to have strong predictive performance for both LE (R 2 = 0.81–0.94) and H (R 2 = 0.46–0.72) across all agricultural systems examined here, demonstrating the potential of hybrid biophysical-machine learning approaches for surface energy balance modeling with minimal data requirements. This study concludes with a novel application of explainable machine learning (exML) to diagnose sources of model error. This exML framework attributes residual prediction errors to both model input variables and environmental drivers not explicitly included in the simulation experiments. This approach provides a new pathway for improving model design and integrating previously overlooked yet influential variables into future model iterations.

evapotranspiration↗
DOE OSTI2025

Recent Widespread Deceleration of Global Surface Urban Heat Islands Unveiled by Satellites

Tracking the temporal dynamics of urban heat island (UHI) is critical for urban heat adaptation and mitigation strategies. However, whether UHI trends have shifted recently and their underlying drivers remain unknown. Here we investigate the variabilities in surface UHI trends and their associated determinants in 2,104 cities worldwide from 2000 to 2022. Our findings reveal that approximately half of the world's cities have experienced notable shifts in surface UHI trends, predominantly characterized by UHI deceleration. These shifts can be primarily attributed to alterations in vegetation trends during the day and to modifications in surface albedo and local warming trends at night. Our study challenges the conventional linear models commonly employed to estimate surface UHI trends, suggesting potential biases in such estimates. Our findings underscore the need for nuanced policies to curtail UHI growth by considering changes in urban underlying surfaces and background climate, particularly from a nonlinear perspective.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Local-Regional Surface Wave Dispersion Analysis using Ambient Noise Data of the Southern Nevada National Security Site Area

This report details seismic ambient noise analysis to improve seismic velocity estimates of the southeastern Nevada National Security Site (NNSS). We compare two different methods for estimating surface wave dispersion curves from ambient noise cross-correlations: frequency time analysis (FTAN) and Aki’s cross-spectral method (XSpec). We find that XSpec performs better for our local-regional dataset and frequency content. Using phase velocity estimates from XSpec, we build a preliminary phase velocity dispersion dataset, which contains data for 1,054 station pairs and 8,905 discrete phase velocity measurements for periods between 0.5 and 13 s. This phase velocity dataset will be incorporated into an updated local-regional P and S wave velocity model of the southeastern NNSS in the future and is expected to improve upon shallow velocity estimates.

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

Satellite Clear‐Sky Observations Overestimate Surface Urban Heat Islands in Humid Cities

Satellite-based thermal infrared (TIR) land surface temperature (LST) is hindered by cloud cover and is applicable solely under clear-sky conditions for estimating surface urban heat island intensity (SUHII). Clear-sky SUHII may not accurately represent all-sky conditions, potentially introducing quantitative biases in assessing urban heat islands. However, the differences between clear-sky and all-sky SUHIIs and their spatiotemporal variations are still poorly understood. Our analysis of over 600 global cities demonstrates that clear-sky SUHII is mostly higher than all-sky SUHII, particularly in summer, daytime, and precipitation-rich regions. Besides, clear-sky SUHII typically exhibits stronger seasonal and diurnal contrasts than all-sky SUHII, especially for cities located in humid regions. These discrepancies can be attributed mainly to the increased missing LST data caused by cloud enhancement in urban areas. Our findings highlight the tendency for clear-sky observations to overestimate SUHII, providing valuable insights for standardizing the quantification of surface urban heat islands.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Algorithmically detected rain-on-snow flood events in different climate datasets: a case study of the Susquehanna River basin

Abstract. Rain-on-snow (RoS) events in regions of ephemeral snowpack – such as the northeastern United States – can be key drivers of cool-season flooding. We describe an automated algorithm for detecting basin-scale RoS events in gridded climate data by generating an area-averaged time series and then searching for periods of concurrent precipitation, surface runoff, and snowmelt exceeding predefined thresholds. When evaluated using historical data over the Susquehanna River basin (SRB), the technique credibly finds RoS events in published literature and flags events that are followed by anomalously high streamflow as measured by gauge data along the river. When comparing four different datasets representing the same 21-year period, we find large differences in RoS event magnitude and frequency, primarily driven by differences in estimated surface runoff and snowmelt. Using dataset-specific thresholds improves agreement between datasets but does not account for all discrepancies. We show that factors such as meteorological forcing and coupling frequency, as well as choice of land surface model, play roles in how data products capture these compound extremes and suggest care is to be taken when climate datasets are used by stakeholders for operational decision-making.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Probing basal planes and edge sites in polygranular nuclear graphite by gas adsorption: Estimation of active surface area

Polygranular nuclear graphite, manufactured at 2800–3000 °C from a carbonized filler and binder mix, has high graphitization degree, crystalline structure close to perfect graphite, and about 20% porosity. The pore surfaces expose large regions of rough, defective prismatic edges of graphite crystallites which are the locus of graphite materials surface sites active for oxidation, chemisorption, and electron transfer. However, we show that high-resolution N 2 and Kr first monolayer adsorption on polygranular graphite (P/P 0 < 0.015) occurs in many ways like adsorption on graphitized carbon blacks. This proves the presence of energetically homogeneous basal planes domains in graphite porosity, which was not fully acknowledged before. Using classical analysis methods (Langmuir, Hill-de Boer, adsorption potential distribution) we quantity the basal plane area (BPA) of several polygranular graphite types and correlate it with their microstructure. Finally, we propose that gas adsorption is uniquely positioned to reliably estimate the active surface area of graphite by subtracting BPA from the BET total surface area. Direct estimation based on adsorption is preferable to indirect calculations based on microstructural information.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Y(III) Sorption at the Orthoclase (001) Surface Measured by X-ray Reflectivity

Interactions of heavy metals with charged mineral surfaces control their mobility in the environment. Here, we investigate the adsorption of Y(III) onto the orthoclase (001) basal plane, the former as a representative of rare earth elements and an analogue of trivalent actinides and the latter as a representative of naturally abundant K-feldspar minerals. In this study, we apply in situ high-resolution X-ray reflectivity to determine the sorption capacity and molecular distribution of adsorbed Y species as a function of the Y 3+ concentration, [Y 3+ ], at pH 7 and 5. With [Y 3+ ] ≥ 1 mM at pH 7, we observe an inner-sphere (IS) sorption complex at a distance of ~1.5 Å from the surface and an outer-sphere (OS) complex at 3–4 Å. Based on the adsorption height of the IS complex, a bidentate, binuclear binding mode, in which Y 3+ binds to two terminal oxygens, is proposed. In contrast, mostly OS sorption is observed at pH 5. The observed maximum Y coverage is ~1.3 Y 3+ /A UC (A UC : area of the unit cell = 111.4 Å 2 ) for all the investigated pH values and Y concentrations, which is in the expected range based on the estimated surface charge of orthoclase (001).

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Size-Dependent Energy and Adhesion of Pd Nanoparticles on Graphene on Ni(111) by Pd Vapor Adsorption Calorimetry

Carbon-supported late transition-metal nanoparticles are promising catalysts and electrocatalysts for wide-ranging applications. However, experimental investigations of the bonding energetics of metal nanoparticles on carbon supports are very limited. Here, in this work, we report heats of adsorption of Pd vapor deposited onto single-layer graphene(0001) supported on Ni(111) at 100 and 300 K as Pd grows particles of well-defined size in the range from three atom clusters to 6 nm diameter. Sizes were determined from He + low-energy ion scattering (LEIS). In this size range, the differential heat of Pd adsorption increases from 228 kJ/mol to within 10 kJ/mol of the heat of sublimation of bulk Pd (377 kJ/mol). The chemical potential of metal atoms in these nanoparticles as a function of average particle size was determined from these results. The adhesion energy at the Pd/graphene(0001)/Ni(111) interface was extracted from these data and found to be 3.5 J/m 2 for the largest Pd particles. For the three metal elements that have now been studied (Pd, Ni, and Ag), we found an increase in metal/graphene(0001)/Ni(111) adhesion energy with metal carbophilicity, which we defined here as the heat of C atom adsorption on that metal’s (111) surface estimated from published density functional theory calculations.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Exploring Mission Design for Imaging Spectroscopy Retrievals for Land and Aquatic Ecosystems

The retrieval algorithms used for optical remote sensing satellite data to estimate Earth's geophysical properties have specific requirements for spatial resolution, temporal revisit, spectral range and resolution, and instrument signal-to-noise ratio (SNR) performance to meet biogeoscience objectives. Studies to estimate surface properties from hyperspectral data use a range of algorithms sensitive to various sources of spectroscopic uncertainty, which are in turn influenced by mission architecture choices. Retrieval algorithms vary across scientific fields and may be more or less sensitive to mission architecture choices that affect spectral, spatial, or temporal resolutions and spectrometer SNR. We used representative remote sensing algorithms across terrestrial and aquatic study domains to inform aspects of mission design that are most important for impacting accuracy in each scientific area. We simulated the propagation of uncertainties in the retrieval process including the effects of different instrument configuration choices. We found that retrieval accuracy and information content degrade consistently at >10 nm spectral resolution, >30 m spatial resolution, and >8-day revisit. In these studies, the noise reduction associated with lower spatial resolution improved accuracy vis à vis high spatial resolution measurements. The interplay between spatial resolution, temporal revisit, and SNR can be quantitatively assessed for imaging spectroscopy missions and used to identify key components of algorithm performance and mission observing criteria.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Comparing measurements of the mixing layer height to modeled values

The mixing layer height (MLH) is the top layer of turbulent mixing within the lower atmosphere, above the Earth’s surface. Estimates of the mixing layer height allow us to determine the volume available for the dispersion of pollutants throughout the atmosphere. Our goal is to identify the most suitable mixing layer height input for our dispersion modeling tool (AERMOD). For this project we evaluated two different methods of estimating the local mixing layer height. First, we use 3 estimates of the mixing layer height obtained from a ceilometer, each height estimate corresponds to a different gradient in aerosol backscatter which is used as a proxy for mixing layer height identification. Second, we use AERMET, our AERMOD modeling system preprocessor for meteorological data, which estimates the mixing layer height using several equations combined with measured meteorological data. We evaluate the ceilometer and AERMET estimates over a 5-year period (2015-2019) to see how well aligned the estimated mixing layer heights are. Our results suggest that our model, AERMET, is on average, aligned with ceilometer estimates during the daytime hours. However, daily maximum MLH estimates by AERMET occur earlier in the day than those estimated by the ceilometer. We believe that the ceilometer struggles with accurately measuring the MLH during nighttime hours as a result of sensor limitations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

The effect of interfacial phenomena on gas solubility measurements in molten salts

The behavior of fission gases in molten fuel salt reactors governs activity transport from the reactor and can also affect the performance of the reactor itself. The gas solubility can be described thermodynamically by Henry’s law. However, the coupling of the condensed and gas phases depends on the interfacial area, which is difficult to measure or even to estimate. Surfaces of materials in the reactor will include disperse phases in the salt and porosity within the structural materials, covering a range of compositions and sizes. These attributes can affect measurements of fundamental properties such as gas solubility. Methods to obtain gas solubility, surface tension, interfacial energies, and bubble gas transport are reviewed. Recent data from manometric experiments are interpreted based on xenon sorption onto salt-wetted quartz.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

Utility of near‐surface phenology in estimating productivity and evapotranspiration across diverse ecosystems

Abstract Agroecosystems, which include row crops, pasture, and grass and shrub grazing lands, are sensitive to changes in management, weather, and genetics. To better understand how these systems are responding to changes, we need to improve monitoring and modeling carbon and water dynamics. Vegetation Indices (VIs) are commonly used to estimate gross primary productivity (GPP) and evapotranspiration (ET), but these empirical relationships are often location and crop specific. There is a need to evaluate if VIs can be effective and, more general, predictors of ecosystem processes through time and across different agroecosystems. Near‐surface photographic (red‐green‐blue) images from PhenoCam can be used to calculate the VI green chromatic coordinate (G CC ) and offer a pathway to improve understanding of field‐scale relationships between VIs and GPP and ET. We synthesized observations spanning 76 site‐years across 15 agroecosystem sites with PhenoCam G CC and GPP or ET estimates from eddy covariance (EC) to quantify interannual variability (IAV) in the relationship between GPP and ET and G CC across. We uncovered a high degree of variability in the strength and slopes of the G CC ∼ GPP and ET relationships (R 2 = 0.1 ‐ 0.9) within and across production systems. Overall, G CC is a better predictor of GPP than ET (R 2 = 0.64 and 0.54, respectively), performing best in croplands (R 2 = 0.91). Shrub‐dominated systems exhibit the lowest predictive power of G CC for GPP and ET but have less IAV in slope. We propose that PhenoCam estimates of G CC could provide an alternative approach for predictions of ecosystem processes.

Environmental Sciences & Ecology↗
DOE OSTI2022

Estimation of the Surface Fluxes for Heat and Momentum in Unstable Conditions with Machine Learning and Similarity Approaches for the LAFE Data Set

Abstract Measurements of three flux towers operated during the land atmosphere feedback experiment (LAFE) are used to investigate relationships between surface fluxes and variables of the land–atmosphere system. We study these relations by means of two machine learning (ML) techniques: multilayer perceptrons (MLP) and extreme gradient boosting (XGB). We compare their flux derivation performance with Monin–Obukhov similarity theory (MOST) and a similarity relationship using the bulk Richardson number (BRN). The ML approaches outperform MOST and BRN. Best agreement with the observations is achieved for the friction velocity. For the sensible heat flux and even more so for the latent heat flux, MOST and BRN deviate from the observations while MLP and XGB yield more accurate predictions. Using MOST and BRN for latent heat flux, the root mean square errors (RMSE) are 107 Wm $$^{-2}$$ - 2 and 121 Wm $$^{-2}$$ - 2 , respectively, as well as the intercepts of the regression lines are $$\approx 110$$ ≈ 110 Wm $$^{-2}$$ - 2 . For the ML methods, the RMSEs reduce to 31 Wm $$^{-2}$$ - 2 for MLP and 33 Wm $$^{-2}$$ - 2 for XGB as well as the intercepts to just 4 Wm $$^{-2}$$ - 2 for MLP and $$-1$$ - 1 Wm $$^{-2}$$ - 2 for XGB with slopes of the regression lines close to 1, respectively. These results indicate significant deficiencies of MOST and BRN, particularly for the derivation of the latent heat flux. In fact, in contrast to the established theories, feature importance weighting demonstrates that the ML methods base their improved derivations on net radiation, the incoming and outgoing shortwave radiations, the air temperature gradient, and the available water contents, but not on the water vapor gradient. The results imply that further studies of surface fluxes and other turbulent variables with ML techniques provide great promise for deriving advanced flux parameterizations and their implementation in land–atmosphere system models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

SAIL Field Campaign X-Band Precipitation Radar Surface Quantitative Precipitation Estimation (SQUIRE) Value-Added Product Report

In 2010, the U.S. Department of Energy Atmospheric Radiation Measurement (ARM) user facility procured 3- and 5-cm wavelength radars for documenting the macrophysical, microphysical, and dynamical structure of precipitating systems. In order to maximize the scientific impact, ARM supported the development of an application chain to correct for various phenomena in order to retrieve the lowest retrieved value on a Cartesian grid. This report details the motivation, science, and progress to date, as well as charting a path forward.

54 ENVIRONMENTAL SCIENCES↗