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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 451 records · Page 25
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Dead Fuel Moisture Content Reanalysis Dataset for California (2000–2020)

This study presents a novel reanalysis dataset of dead fuel moisture content (DFMC) across California from 2000 to 2020 at a 2 km resolution. Utilizing a data assimilation system that integrates a simplified time-lag fuel moisture model with 10-h fuel moisture observations from remote automated weather stations (RAWS) allowed predictions of 10-h fuel moisture content by our method with a mean absolute error of 0.03 g/g compared to the widely used Nelson model, with a mean absolute error prediction of 0.05 g/g. For context, the values of DFMC in California are commonly between 0.05 g/g and 0.30 g/g. The presented product provides gridded hourly moisture estimates for 1-h, 10-h, 100-h, and 1000-h fuels, essential for analyzing historical fire activity and understanding climatological trends. The methodology presented here demonstrates significant advancements in the accuracy and robustness of fuel moisture estimates, which are critical for fire forecasting and management.

Farguell, Angel (ORCID:000000032395220X)↗
DOE OSTI2025

Dark Energy Survey: Modeling strategy for multiprobe cluster cosmology and validation for the Full Six-year Dataset

We introduce an updated To&Krause2021 model for joint analyses of cluster abundances and large-scale two-point correlations of weak lensing and galaxy and cluster clustering (termed CL+3x2pt analysis) and validate that this model meets the systematic accuracy requirements of analyses with the statistical precision of the final Dark Energy Survey (DES) Year 6 (Y6) dataset. The validation program consists of two distinct approaches, (1) identification of modeling and parameterization choices and impact studies using simulated analyses with each possible model misspecification (2) end-to-end validation using mock catalogs from customized Cardinal simulations that incorporate realistic galaxy populations and DES-Y6-specific galaxy and cluster selection and photometric redshift modeling, which are the key observational systematics. In combination, these validation tests indicate that the model presented here meets the accuracy requirements of DES-Y6 for CL+3x2pt based on a large list of tests for known systematics. In addition, we also validate that the model is sufficient for several other data combinations: the CL+GC subset of this data vector (excluding galaxy--galaxy lensing and cosmic shear two-point statistics) and the CL+3x2pt+BAO+SN (combination of CL+3x2pt with the previously published Y6 DES baryonic acoustic oscillation and Y5 supernovae data).

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

Dataset for "Large Language Models as molecular design engines"

This dataset contains data and results associated with the paper "Large Language Models as molecular design engines" The paper investigates the use of large language models, specifically Claude 3 Opus, for generating and analyzing chemical structures based on various prompts from A-H (as mentioned in the manuscript), and guided design related to electron-withdrawing groups (EWG), electron-donating groups (EDG).

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Dataset for "Climatic and socioeconomic drivers of water use and their spatio-temporal patterns for small and mid-sized cities in the Contiguous United States"

This dataset contains all code for calibrating and analyzing machine learning models for "Climatic and socioeconomic drivers of water use and their spatio-temporal patterns for small and mid-sized cities in the Contiguous United States". Please unzip the folders and follow the instructions from 'README.txt'. Required python modulessklearn=1.2.2numpy=1.23.3xgboost=2.0.2joblib=1.2.0 Required R libraryshapFlex:devtools::install_github("nredell/shapFlex")library(shapFlex)

Dave, Hari [Civil and Environmental Engineering De↗
DOE OSTI2026

Manufacturing Facility Inventory National Dataset (M-FIND)

This asset provides a high-fidelity, validated inventory of manufacturing facilities across the United States, filling a critical gap in publicly available industrial data. By integrating and cross-referencing thirteen distinct data sources, this dataset moves beyond the limitations of single-source registries to provide a harmonized list that includes precise geographic coordinates, industrial subsector designations, and—crucially—parcel-level spatial boundaries.

Billings, Blake [ORNL] (ORCID:0000000186021600)↗
DOE OSTI2026

CCN PI sample dataset from two BNF facilities

This dataset contains CCN instrument data files showing the sample time series of CCN concentrations in #/cm^3. The file also shows the instrument records and CCN data. The CCN_S20_data_250605000000.csv data file contains information from the S20 facility at Courteland, and the CCN_S30_data_250605000000.csv data file contains information from the S30 facility at Falkville. See the attached CCN-200 Manual for information about the variables and units with the data files.

ccn↗
DOE OSTI2024

Datasets for Widespread Residential Space Heating Electrification in Texas

In this experiment, we explore long term patterns in electricity demand driven by the dual effects of full electrification of space heating in Texas (by adoption of electric heat pumps), and climate change. We use a predictive model of electricity demand, climate projections, and an open source nodal power system (DC Optimal Power Flow) model of the Electric Reliability Council of Texas (ERCOT) system. Heat pumps are a more energy efficient way of providing space heating and cooling in homes. We attempt to exhaustively investigate the impacts of full residential space heating electrification by adoption of heat pumps for the segment of Texas households that currently rely on fossil fuels (about 40%), while simultaneously incorporating climate change meteorological variables. We explore a range of scenarios of heat pump efficiency and climate uncertainty over a long period of future years (2020-2099). In total, the simulation experiment generates 1,280 simulation years of hourly data. We report and analyze results in form of impacts on residential load, total load, peak load, seasonality of peaking, and reliability measured by occurrence and frequency of loss of load events. While the experiment is for ERCOT, the insights and approach can be applied to other regions. The results from the analysis can inform system planners on a range of potential capacity requirements/ reliability implications and/or risks of full space heating electrification via the adoption of electric heat pumps, given the uncertainty in the scenarios/ climate futures. The dataset includes model output for residential, non residential and total load, and the results from the GO ERCOT model runs for 4 RCP Scenarios (RCP 4.5 Cooler, RCP 4.5 Hotter, RCP 8.5 Cooler, RCP 8.5 Hotter), 4 Heating electrification Scenarios (Base , Standard Efficiency HP, High Efficiency HP, Ultra-High Efficiency HP) over 80 years (2020-2099). The metrological variables at BA scale were weighted weighted using population projections consistent with the SSP3 scenario.

Climate Change↗
DOE OSTI2025

Synthetic Streamflow Datasets Derived from DOE 9505 for Select Texas Basins

This dataset is generated using a Bayesian Hidden Markov Model trained on the DOE 9505 streamflow projection ensemble. A set of 21,000 streamflow realizations are generated for the Colorado, Sabine, and Trinity river basins in Texas. Separate models are trained either using the full 9505 ensemble or a subset based on three hyperparameters: bias correction, downscaling, and hydrological model.

drought↗
DOE OSTI2026

The Zooplankton International Geospatial (ZIG) dataset: A global repository of spatiotemporal freshwater zooplankton community composition data to support ecological research

Zooplankton play critical roles in aquatic ecosystem function and food webs. Nevertheless, global syntheses of their abundance and community dynamics are challenging due to methodological differences across monitoring programs, taxonomic inconsistencies, and a lack of standardized metadata. To reconcile these challenges, we assembled, curated, validated, and harmonized the Zooplankton International Geospatial (ZIG) dataset, which includes co-located and contemporaneous zooplankton, water chemistry, and limnological data from 307 lakes and reservoirs. ZIG includes waterbodies from each major lake thermal region and range in size from 0.8-2,805,8600 hectares. Temporal coverage for individual waterbodies ranges between 1-60 years of data (median = 4 years) with sampling from once annually to weekly. ZIG is publicly available and can be used to understand freshwater biodiversity change and its drivers at unprecedented scales, and we consider it to be a cornerstone for future investigations of freshwater biology, chemistry, and ecology.

Figary, Stephanie [Cornell University, Ithaca, NY]↗
NASA NTRS1990

Geocoding and stereo display of tropical forest multisensor datasets

Concern about the future of tropical forests has led to a demand for geocoded multisensor databases that can be used to assess forest structure, deforestation, thermal response, evapotranspiration, and other parameters linked to climate change. In response to studies being conducted at the Braulino Carrillo National Park, Costa Rica, digital satellite and aircraft images recorded by Landsat TM, SPOT HRV, Thermal Infrared Multispectral Scanner, and Calibrated Airborne Multispectral Scanner sensors were placed in register using the Landsat TM image as the reference map. Despite problems caused by relief, multitemporal datasets, and geometric distortions in the aircraft images, registration was accomplished to within + or - 20 m (+ or - 1 data pixel). A digital elevation model constructed from a multisensor Landsat TM/SPOT stereopair proved useful for generating perspective views of the rugged, forested terrain.

Welch, R.↗
NASA NTRS1991

Global heating distributions for January 1979 calculated from GLA assimilated and simulated model-based datasets

This study surveys the large-scale distribution of heating for January 1979 obtained from five sources of information. Through intercomparison of these distributions, with emphasis on satellite-derived information, an investigation is conducted into the global distribution of atmospheric heating and the impact of observations on the diagnostic estimates of heating derived from assimilated datasets. The results indicate a substantial impact of satellite information on diagnostic estimates of heating in regions where there is a scarcity of conventional observations. The addition of satellite data provides information on the atmosphere's temperature and wind structure that is important for estimation of the global distribution of heating and energy exchange.

Schaack, Todd K.↗
NASA NTRS1992

Subvisual-thin cirrus lidar dataset for satellite verification and climatological research

A polarization (0.694 microns wavelength) lidar dataset for subvisual and thin (bluish-colored) cirrus clouds is drawn from project FIRE (First ISCCP Regional Experiment) extended time observations. The clouds are characterized by their day-night visual appearance; base, top, and optical midcloud heights and temperatures; measured physical and estimated optical cloud thicknesses; integrated linear depolarization ratios; and derived k/2 eta ratios. A subset of the data supporting 30 NOAA polar-orbiting satellite overpasses is given in tabular form to provide investigators with the means to test cloud retrieval algorithms and establish the limits of cirrus detectability from satellite measurements under various conditions. Climatologically, subvisual-thin cirrus appear to be higher, colder, and more strongly depolarizing than previously reported multilatitude cirrus, although similar k/2 eta that decrease with height and temperature are found.

Sassen, Kenneth↗
NASA NTRS1994

Arctic ocean radiative fluxes and cloud forcing estimated from the ISCCP C2 cloud dataset, 1983-1990

Radiative fluxes and cloud forcings for the ocean areas of the Arctic are computed from the monthly cloud product of the International Satellite Cloud Climatology Project (ISCCP) for 1983-90. Spatially averaged short-wave fluxes are compared well with climatological values, while downwelling longwave fluxes are significantly lower. This is probably due to the fact that the ISCCP cloud amounts are underestimates. Top-of-the-atmosphere radiative fluxes are in excellent agreement with measurements from the Earth Radiation Budget Experiment (ERBE). Computed cloud forcings indicate that clouds have a warming effect at the surface and at the top of the atmosphere during winter and a cooling effect during summer. The net radiative effect of clouds is larger at the surface during winter but greater at the top of the atmosphere during summer. Overall the net radiative effect of clouds at the top of the atmosphere is one of cooling. This is in contrast to a previous result from ERBE data showing arctic cloud forcings have a net warming effect. Sensitivities to errors in input parameters are generally greater during winter with cloud amount being the most important paarameter. During summer the surface radiation balance is most sensitive to errors in the measurements of surface reflectance. The results are encouraging, but the estimated error of 20 W/sq m in surface net radiative fluxes is too large, given that estimates of the net radiative warming effect due to a doubling of CO2 are on the order of 4 W/sq m. Because it is difficult to determine the accuracy of results with existing in situ observations, it is recommended that the development of improved algorithms for the retrieval of surface radiative properties be accompanied by the simultaneous assembly of validation datasets.

Schweiger, Axel J.↗
NASA NTRS1995

First global WCRP shortwave surface radiation budget dataset

Shortwave radiative fluxes that reach the earth's surface are key factors that influence atmospheric and oceanic circulations as well as surface climate. Yet, information on these fluxes is meager. Surface site data are generally available from only a limited number of observing stations over land. Much less is known about the large-scale variability of the shortwave radiative fluxes over the oceans, which cover most of the globe. Recognizing the need to produce global-scale fields of such fluxes for use in climate research, the World Climate Research Program has initiated activities that led to the establishment of the Surface Radiation Budget Climatology Project with the ultimate goal to determine various components of the surface radiation budget from satellite data. In this paper, the first global products that resulted from this activity are described. Monthly and daily data on a 280-km grid scale are available. Samples of climate parameters obtainable from the dataset are presented. Emphasis is given to validation and limitations of the results. For most of the globe, satellite estimates have bias values between +/- 20 W/sq m and root mean square (rms) values are around 25 W/sq m. There are specific regions with much larger uncertainties however.

Whitlock, C. H.↗
NASA NTRS1995

First global WCRP shortwave surface radiation budget dataset

Shortwave radiative fluxes that reach the Earth's surface are key factors that influence atmospheric and oceanic circulations as well as surface climate. Yet, information on these fluxes is meager. Surface site data are generally available from only a limited number of observing stations over land. Much less is known about the large-scale variability of the shortwave radiative fluxes over the oceans, which cover most of the globe. Recognizing the need to produce global-scale fields of such fluxes for use in climate research, the World Climate Research Program has initiated activities that led to the establishment of the Surface Radiation Budget Climatology Project with the ultimate goal to determine various components of the surface radiation budget from satellite data. In this paper, the first global products that resulted from this activity are described. Monthly and daily data on a 280-km grid scale are available. Samples of climate parameters obtainable from the dataset are presented. Emphasis is given to validation and limitations of the results. For most of the globe, satellite estimates have bias values between +/- 20 W/sq m and rms values are around 25 W/sq m. There are specific regions with much larger uncertainties however.

Whitlock, C. H.↗