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All sources NASA NTRS DOE OSTI

DOE OSTI2024

Pan-tropical daily L-band microwave land surface emissivity retrieval from GNSS-R observations

The uncertainties in microwave land surface emissivity (MLSE) measurements have long limited the use of spaceborne microwave radiometer data. As an emerging observation method, Global Navigation Satellite System Reflectometry (GNSS-R) has demonstrated great potential in several land and ocean applications. In this study, a method for obtaining daily MLSE dataset in the pan-tropical region from Cyclone GNSS (CYGNSS) observations is presented and evaluated. The CYGNSS observations are first aggregated into the Equal-Area-Scalable-Earth (EASE) 2.0 36 km grid by a combined weight function of distance, time, and signal-to-noise ratio variance. Then, the method employs a pixel-by-pixel regression algorithm to conduct the daily MLSE retrieval using reference emissivity derived from the Soil Moisture Active Passive (SMAP) brightness temperature. The CYGNSS MLSE shows good agreement with SMAP MLSE, delivering an overall root-mean-square error (RMSE) of 0.022 and 0.017 for horizontal and vertical polarization, respectively, during the training set spanning the whole year of 2018. Furthermore, on the test set from January 2019 to May 2019, the RMSE values amounted to 0.030 and 0.023 for horizontal and vertical polarization, respectively. Temperature records from the International Soil Moisture Network are employed to calculate the emissivity and for in-situ validation, which yield an RMSE of 0.034 and 0.026 for the two polarizations, respectively. The proposed algorithm provides an encouraging approach to obtain accurate daily MLSE dataset for microwave remote sensing. Compared to the SMAP MLSE, the CYGNSS MLSE has a remarkable improvement of 86% in temporal resolution, greatly complementing the existing microwave emissivity datasets.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

Assessing variation in faecal glucocorticoid concentrations in gray whales exposed to anthropogenic stressors

Understanding how individual animals respond to stressors behaviourally and physiologically is a critical step towards quantifying long-term population consequences and informing management efforts. Glucocorticoid (GC) metabolite accumulation in various matrices provides an integrated measure of adrenal activation in baleen whales and could thus be used to investigate physiological changes following exposure to stressors. In this study, we measured GC concentrations in faecal samples of Pacific Coast Feeding Group (PCFG) gray whales (Eschrichtius robustus) collected over seven consecutive years to assess the association between GC content and metrics of exposure to sound levels and vessel traffic at different temporal scales, while controlling for contextual variables such as sex, reproductive status, age, body condition, year, time of year and location. We develop a Bayesian Generalized Additive Modelling approach that accommodates the many complexities of these data, including non-linear variation in hormone concentrations, missing covariate values, repeated samples, sampling variability and some hormone concentrations below the limit of detection. Estimated relationships showed large variability, but emerging patterns indicate a strong context-dependency of physiological variation, depending on sex, body condition and proximity to a port. Our results highlight the need to control for baseline hormone variation related to context, which otherwise can obscure the functional relationship between faecal GCs and stressor exposure. Therefore, extensive data collection to determine sources of baseline variation in well-studied populations, such as PCFG gray whales, could shed light on cetacean stress physiology and be used to extend applicability to less-well-studied taxa. GC analyses may offer greatest utility when employed as part of a suite of markers that, in aggregate, provide a multivariate measure of physiological status, better informing estimates of individuals’ health and ultimately the consequences of anthropogenic stressors on populations.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2023

OpenCSP v.1.0

SAND2024-08987O OpenCSP analyzes and improves the performance of optical mirrors that reflect sunlight and concentrate it onto a receiver. The algorithms in this library analyze the flow of light from an original source—such as the sun—reflecting off one or more mirrors, and onto a receiver, optical screen, or camera. OpenCSP also analyzes optical systems using other techniques, such as shape or slope comparison. The software calculates aggregate properties of the optical system, such as its efficiency, manufacturing error, pointing control error, expected performance, and other useful parameters. OpenCSP also includes high-level tools, such as Open SOFAST, a program that obtains high-resolution measurement of CSP mirrors, full heliostats, and other CSP collectors. OpenCSP supports producing meaningful output data to help CSP designers and field operators improve the performance of their systems. OpenCSP also includes classes and functions that support these calculations, including input utilities, analysis output libraries, geometric constraint analysis for drone flight planning, and more. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Brost, Randolph↗

NASA NTRS2010

Identifying Aerosol Type/Mixture from Aerosol Absorption Properties Using AERONET

Aerosols are generated in the atmosphere through anthropogenic and natural mechanisms. These sources have signatures in the aerosol optical and microphysical properties that can be used to identify the aerosol type/mixture. Spectral aerosol absorption information (absorption Angstrom exponent; AAE) used in conjunction with the particle size parameterization (extinction Angstrom exponent; EAE) can only identify the dominant absorbing aerosol type in the sample volume (e.g., black carbon vs. iron oxides in dust). This AAE/EAE relationship can be expanded to also identify non-absorbing aerosol types/mixtures by applying an absorption weighting. This new relationship provides improved aerosol type distinction when the magnitude of absorption is not equal (e.g, black carbon vs. sulfates). The Aerosol Robotic Network (AERONET) data provide spectral aerosol optical depth and single scattering albedo - key parameters used to determine EAE and AAE. The proposed aerosol type/mixture relationship is demonstrated using the long-term data archive acquired at AERONET sites within various source regions. The preliminary analysis has found that dust, sulfate, organic carbon, and black carbon aerosol types/mixtures can be determined from this AAE/EAE relationship when applying the absorption weighting for each available wavelength (Le., 440, 675, 870nm). Large, non-spherical dust particles absorb in the shorter wavelengths and the application of 440nm wavelength absorption weighting produced the best particle type definition. Sulfate particles scatter light efficiently and organic carbon particles are small near the source and aggregate over time to form larger less absorbing particles. Both sulfates and organic carbon showed generally better definition using the 870nm wavelength absorption weighting. Black carbon generation results from varying combustion rates from a number of sources including industrial processes and biomass burning. Cases with primarily black carbon showed improved definition in the 870nm wavelength absorption weighting due to the increased absorption in the near-infrared wavelengths, while the 440nm wavelength provided better definition when black carbon mixed with dust. Utilization of this particle type scheme provides necessary information for remote sensing applications, which needs a priori knowledge of aerosol type to model the retrieved properties especially over semi-bright surfaces. In fact, this analysis reveals that the aerosol types occurred in mixtures with varying magnitudes of absorption and requires the use of more than one assumed aerosol mixture model. Furthermore, this technique will provide the aerosol transport model community a data set for validating aerosol type.

Giles, D. M.↗

DOE OSTI2021

A meta-learning based distribution system load forecasting model selection framework

This paper presents a meta-learning based, automatic distribution system load forecasting model selection framework. Furthermore, the framework includes the following processes: feature extraction, candidate model preparation and labeling, offline training, and online model recommendation. Using load forecasting needs and data characteristics as input features, multiple metalearners are used to rank the candidate load forecast models based on their forecasting accuracy. Then, a scoring-voting mechanism is proposed to weights recommendations from each meta-leaner and make the final recommendations. Heterogeneous load forecasting tasks with different temporal and technical requirements at different load aggregation levels are set up to train, validate, and test the performance of the proposed framework. Simulation results demonstrate that the performance of the meta-learning based approach is satisfactory in both seen and unseen forecasting tasks.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2016

The study of the structural properties of very low viscosity sodium alginate by small-angle neutron scattering

Sodium alginate is a linear polymer extract from brown algae and it is used in the biomedical, food, cosmetics and pharmaceutical industries as solution property modifiers and gelling agents. But despite the extensive studies of the alginate gelation process, still some fundamental questions remain unresolved. The fractal behavior of very low viscosity sodium alginate solutions and their influence on the critical gelation of alginate induced by Ca{sup 2+} ions were investigated using Small-Angle Neutron Scattering (SANS) measurements. SANS data are interpreted using both standard linear plots and the Beaucage model. The scattering intensity is dependent by alginate concentration and Ca{sup 2+} concentration. From a critical concentration of 1.0 % w/w our polymer swelled forming spherical structures with rough surfaces. Also the addition of the salt induces the collapse and the appearance of the aggregation and clusters formation.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2018

Status and Trends in the U.S. Voluntary Green Power Market (2017 Data)

Green power refers to renewable electricity voluntarily purchased by retail electricity customers. Renewable energy sources for green power include solar, wind, biomass, geothermal, and small-scale hydropower. This report summarizes data on the various ways in which voluntary purchasers - including residential, commercial, and institutional customers - purchase green power. We summarize key historic trends in U.S. voluntary green power markets and the current status of green power sales through seven products: utility green pricing programs, utility renewable contracts, competitive suppliers, unbundled renewable energy certificates, community choice aggregations, power purchase agreements, and community solar.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

NASA NTRS2021

Cities Exacerbate Climate Warming

Urbanization is a complex land transformation, not explicitly resolved within large-scale climate models. Long-term timeseries of high-resolution satellite data are essential to characterize it within land surface models and assess its contribution to surface temperature changes. The potential for additional surface warming from urbanization-induced land use change is investigated and de-coupled from that due to change in climate over the continental US using a decadal timescale. We show that, aggregated over the US, the summer mean urban-induced surface temperature increased by 0.15°C, with a warming of 0.24°C in cities built in vegetated areas and a cooling of 0.25°C in cities built in non-vegetated arid areas. This temperature change is comparable in magnitude to the 0.13°C/decade global warming trend observed over the last 50 years caused by increased CO2. We also show that the effect of urban-induced change on surface temperature is felt above and beyond that of the CO2 effect. Our results suggest that climate mitigation policies must consider urbanization feedback to put a limit on the worldwide mean temperature increase.

Lahouari Bounoua↗

DOE OSTI2018

State of the Voluntary Green Power Market

Green power refers to renewable electricity voluntarily purchased by retail electricity customers. Renewable energy sources for green power include solar, wind, biomass, geothermal, and small-scale hydropower. This presentation summarizes data on the various ways in which voluntary purchasers - including residential, commercial, and institutional customers - purchase green power. We summarize key historic trends in U.S. voluntary green power markets and the current status of green power sales through seven products: utility green pricing programs, utility renewable contracts, competitive suppliers, unbundled renewable energy certificates, community choice aggregations, power purchase agreements, and community solar.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2019

Status and Trends in the Voluntary Market (2018 Data)

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2020

Status and Trends in the Voluntary Market (2019 Data)

42 ENGINEERING↗

DOE OSTI2021

Status and Trends in the Voluntary Market (2020 Data)

community choice aggregation↗

DOE OSTI2022

Status and Trends in the Voluntary Market (2021 Data)

Green power refers to renewable electricity voluntarily purchased by retail electricity customers. Renewable energy sources for green power include solar, wind, biomass, geothermal, and small-scale hydropower. This report summarizes data on the various ways in which voluntary purchasers including residential, commercial, and institutional customers purchase green power. We summarize key historic trends in U.S. voluntary green power markets and the current status of green power sales through seven products: utility green pricing programs, utility renewable contracts, competitive suppliers, unbundled renewable energy certificates, community choice aggregations, and power purchase agreements. It includes discussion of how the voluntary market may impact the grid.

ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2024

Private, public, and bottled drinking water: Shared contaminant-mixture exposures and effects challenge

Background: Humans are primary drivers of environmental–contaminant exposures worldwide, including in drinking-water (DW). In the United States, point-of-use DW (POU–DW) is supplied via private tapwater (TW), public-supply TW, and bottled water (BW). Differences in management, monitoring, and messaging and lack of directly–intercomparable exposure data influence the actual and perceived quality and safety of different DW supplies and directly impact consumer decision–making. Objectives: The purpose of this paper is to provide a meta-analysis (quantitative synthesis) of POU–DW contaminant–mixture exposures and corresponding potential human–health effects of private-TW, public-TW, and BW by aggregating exposure results and harmonizing apical–health–benchmark–weighted and bioactivity–weighted effects predictions across previous studies by this research group. Discussion: Simultaneous exposures to multiple inorganic and organic contaminants of known or suspected human-health concern are common across all three DW supplies, with substantial variability observed in each and no systematic difference in predicted cumulative risk between supplies. Differences in contaminant or contaminant–class exposures, with important implications for DW–quality improvements, were observed and attributed to corresponding differences in regulation and compliance monitoring. Conclusion: The results indicate that human-health risks from contaminant exposures are common to and comparable in all three DW–supplies, including BW. Importantly, this study’s target analytical coverage, which exceeds that currently feasible for water purveyors or homeowners, nevertheless is a substantial underestimation of the breadth of contaminant mixtures in the environment and potentially present in DW. Thus, the results emphasize the need for improved understanding of the adverse human-health implications of long-term exposures to low–level inorganic–/organic–contaminant mixtures across all three distribution pipelines and do not support commercial messaging of BW as a systematically safer alternative to public-TW. Regardless of the supply, increased public engagement in source-water protection and drinking–water treatment is necessary to reduce risks associated with long-term DW–contaminant exposures, especially in vulnerable populations, and to reduce environmental waste and plastics contamination.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2026

Scalable Federated Learning for Scientific Foundation Models on Leadership-Class Systems

Federated learning (FL) at leadership-class HPC systems remains largely unexplored, despite growing interest in deploying federated workflows on modern HPC systems. This paper provides the first system-level empirical characterization of federated fine-tuning of pretrained foundation models on an exascale supercomputer under a multi-node deployment. Using up to 96 concurrent FL clients deployed across Frontier nodes, we study the impact of client scale, model size, data heterogeneity, partial participation, and differential privacy on runtime, communication overhead, and convergence stability. Our results show that pretrained transformer models remain robust to heterogeneity, client dropout, and privacy noise, while system efficiency degrades rapidly with scale as synchronizat and orchestration dominate runtime. We further demonstrate that system-aware execution strategies, including intra-node aggregation and early aggregation, significantly reduce wall-clock time without degrading model quality. These findings establish a practical performance baseline and inform the design of communication-efficient FL systems on leadership-class HPC platforms.

Kotevska, Olivera [ORNL] (ORCID:0000000316772243)↗

DOE OSTI2017

Probabilistic Multi-Hazard Assessment of Dry Cask Structures

systems the concrete shall not only provide shielding but insures stability of the upright canister, facilitates anchoring, allows ventilation, and provides physical protection against theft, severe weather and natural (seismic) as well as man-made events (blast incidences). Given the need to remain functional for 40 years or even longer in case of interim storage, the concrete outerpack and the internal canister components need to be evaluated with regard to their long-term ability to perform their intended design functions. Just as evidenced by deteriorating concrete bridges, there are reported visible degradation mechanisms of dry storage systems especially when high corrosive environments are considered in maritime locations. The degradation of reinforced concrete is caused by multiple physical and chemical mechanisms, which may be summarized under the heading of environmental aging. The underlying hygro-thermal transport processes are accelerated by irradiation effects, hence creep and shrinkage need to include the effect of chloride penetration, alkali aggregate reaction as well as corrosion of the reinforcing steel. In light of the above, the two main objectives of this project are to (1) develop a probabilistic multi-hazard assessment framework, and (2) through experimental and numerical research perform a comprehensive assessment under combined earthquake loads and aging induced deterioration, which will also provide data for the development and validation of the probabilistic framework.

36 MATERIALS SCIENCE↗

NASA NTRS1973

Lunar soil grain size distribution

A comprehensive review has been made of the currently available data for lunar grain size distributions. It has been concluded that there is little or no statistical difference among the large majority of the soil samples from the Apollo 11, 12, 14, and 15 missions. The grain size distribution for these soils has reached a steady state in which the comminution processes are balanced by the aggregation processes. The median particle size for the steady-state soil is 40 to 130 microns. The predictions of lunar grain size distributions based on the Surveyor television photographs have been found to be quantitatively in error and qualitatively misleading.

Carrier, W. D., III↗

NASA NTRS1986

Correlations between Nimbus-7 Scanning Multichannel Microwave Radiometer data and an antecedent precipitation index

Passive microwave brightness temperatures from the Nimbus-7 Scanning Multichannel Microwave Radiometer (SMMR) can be used to infer the soil moisture content over agricultural areas such as the southern Great Plains of the United States. A linear regression analysis between three transforms of the five dual polarized SMMR wavelengths of 0.81, 1.36, 1.66, 2.80 and 4.54 cm and an antecedent precipitation index representing the precipitation history showed correlation coefficients greater than 0.90 for pixel aggregates of 25-50 km. The use of surface air temperatures to approximate the temperature of the emitting layer was not required to obtain high correlation coefficients between the transforms and the antecedent precipitation index.

Wilke, G. D.↗