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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 739 records · Page 41
All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

2016 Waste Management Monitoring Report Area 3 and Area 5 Radioactive Waste Management Sites

Environmental monitoring data are collected at and around the Area 3 and Area 5 Radioactive Waste Management Sites (RWMSs) within the Nevada National Security Site (NNSS). These data include direct radiation exposure, as well as radiation from the air, groundwater, meteorology, and vadose zone. This report summarizes the 2016 environmental data to provide an overall evaluation of RWMS performance and to support environmental compliance and performance assessment (PA) activities. Some of these data (e.g., radiation exposure, air, and groundwater) are presented in other reports, developed by National Security Technologies, LLC Direct radiation monitoring data indicate exposure levels at the RWMSs are within the range of background levels measured at the NNSS. Slightly elevated exposure levels outside the Area 3 RWMS are attributed to nearby historical aboveground nuclear weapons tests. Air monitoring data show that tritium concentrations in water vapor and americium and plutonium concentrations in air particles are below Derived Concentration Standards for these radionuclides. Groundwater monitoring data indicate the groundwater in the uppermost aquifer beneath the Area 5 RWMS is not impacted by RWMS operations. Results of groundwater analysis from wells around the Area 5 RWMS were all below established investigation levels. Leachate samples collected from the leachate collection system at the mixed low-level waste cell were below established contaminant regulatory limits. During 2016, precipitation at the Area 3 RWMS was 8% below average, and precipitation at the Area 5 RWMS was 8% above average. Water balance measurements indicate that evapotranspiration from the vegetated weighing lysimeter dries the soil and prevents downward percolation of precipitation more effectively than evaporation as measured from the bare-soil weighing lysimeter. Vadose zone monitoring on Area 5 and Area 3 RWMS cell covers shows no evidence of precipitation percolating through the covers to the waste. Moisture from precipitation did not percolate below 150 centimeters (cm) (5 feet [ft]) in the vegetated final cover on the U-3ax/bl disposal unit at the Area 3 RWMS. During 2016, there was no drainage through 2.4 meters (8 ft) of soil as indicated from the Area 3 drainage lysimeters that received only natural precipitation. Forty-four percent of the applied precipitation and irrigation drained from the bare-soil drainage lysimeter, which received 3 times the natural precipitation. All 2016 monitoring data indicate that the Area 3 and Area 5 RWMSs are performing within expectations of the model and parameter assumptions for the facilities’ PAs

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2018

A global coupled ensemble data assimilation system using the Community Earth System Model and the Data Assimilation Research Testbed

In this paper, we present a description of the CESM/DART ensemble coupled data assimilation (DA) system based on the Community Earth System Model (CESM) and the Data Assimilation Research Testbed (DART) assimilation software. The CESM/DART should be viewed as a flexible system to support the DA needs of the CESM research community and not as a static reanalysis product. In this implementation of the CESM/DART, conventional insitu observations of the ocean and atmosphere are assimilated into the respective component models of the CESM using a 30-member ensemble adjustment Kalman filter (EAKF). CESM/DART is run in a “weakly coupled” configuration wherein observations native to each climate system component only directly impact the state vector for that component. Information is passed between components indirectly through the short-term coupled model forecasts that provide the EAKF background ensemble. This system leverages previous ensemble DA development for the Community Atmosphere Model and Parallel Ocean Program models using the DART EAKF. The CESM/DART project is a step towards providing increasingly useful DA capabilities for the CESM research community. Results are presented for our prototype 12-year reanalysis, run from 1970 to mid 1982. Multiple lines of evidence demonstrate that the system is capable of constraining the CESM coupled model to simulate the historical variability of the climate system in the well-observed Northern Hemisphere. A collection of monthly average variables, climate mode indices, observation diagnostics and snapshots of synoptic weather in the ocean and atmosphere are compared to established datasets, showing especially good agreement in the Northern Hemisphere. A discussion of the CESM/DART as a modular, community facility and the benefits and challenges associated with this vision is also included.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

Assessing the Wind Energy Potential in Bangladesh: Enabling Wind Energy Development with Data Products

USAID Bangladesh and the U.S. Department of Energy’s National Renewable Energy Laboratory partnered with the Government of Bangladesh to develop a national wind resource assessment. The assessment used sophisticated resource modeling that was validated by a ground measurement campaign. Results from the project included a long-term, correlated wind data set; validated high-resolution wind resource maps; and publicly available data accessible through the RE Data Explorer (https://www.re-explorer.org/). The Renewable Energy (RE) Data Explorer allows users to access and download the Bangladesh wind resource assessment data and related geographic information system (GIS) data sets and perform customized technical potential analyses. Figure ES-1 is an example wind resource map for Bangladesh that can be created with the RE Data Explorer. This project improved upon existing global data sets by using best-in-class modeling techniques and analysis. Global models are good first steps in predicting wind resources in various parts of the world, but they do not generate the accuracy needed to reduce project risk and stimulate renewable investment. This project improved the quality of modeled wind resource data for Bangladesh. In addition to the modeling effort, the project team used a multi-year, local data-collection campaign to validate the model and further improve the accuracy of the data sets. The measurement campaign consisted of nine meteorological sites representing all geographical regions of the country. Seven meteorological towers and one remote-sensing, sonic detection and ranging unit (deployed at two sites) collected the data. Site-selection criteria focused on geographic diversity and proximity to existing transmission lines. Figure ES-1 shows measurement locations. The measurement campaign spanned June 2014 through December 2017. The results of the Bangladesh wind resource assessment will help Bangladesh overcome significant energy challenges. Its energy sector suffers from power shortages, increasing demand, decreasing domestic natural gas reserves, and inadequate transmission infrastructure. As part of a comprehensive plan to overcome these challenges, Bangladesh has committed that 10% of the total generation capacity will be renewable energy by 2021 (Power Division 2016). High-quality renewable energy resource data and other GIS data, such as those developed in this assessment, are important if Bangladesh wishes to reach its 10% renewable energy capacity target. These data support informed decision making, ranging from policy and investment decisions to reliable power sector planning. Specifically, the Bangladesh wind resource assessment will help reduce technical risk and encourage private-sector interest in the nascent wind market in Bangladesh. In addition, the Government of Bangladesh may use the results of the wind resource assessment to develop well-designed policies that could encourage investment in wind energy. This report provides a comprehensive description of the Bangladesh wind resource assessment, including details on the modeling approach and methods, instrumentation, data quality-control techniques, and resulting data sets.

17 WIND ENERGY↗
DOE OSTI2016

Nevada National Security Site 2015 Waste Management Monitoring Report Area 3 and Area 5 Radioactive Waste Management Sites

Environmental monitoring data are collected at and around the Area 3 and Area 5 Radioactive Waste Management Sites (RWMSs) within the Nevada National Security Site (NNSS). These data include direct radiation exposure, as well as radiation from the air, groundwater, meteorology, and vadose zone. This report summarizes the 2015 environmental data to provide an overall evaluation of RWMS performance and to support environmental compliance and performance assessment (PA) activities. Some of these data (e.g., radiation exposure, air, and groundwater) are presented in other reports developed by National Security Technologies, LLC. Direct radiation monitoring data indicate exposure levels at the RWMSs are within the range of background levels measured at the NNSS. Slightly elevated exposure levels outside the Area 3 RWMS are attributed to nearby historical aboveground nuclear weapons tests. Air monitoring data show that tritium concentrations in water vapor and americium and plutonium concentrations in air particles are below Derived Concentration Standards for these radionuclides. Groundwater monitoring data indicate the groundwater in the uppermost aquifer beneath the Area 5 RWMS is not impacted by RWMS operations. Results of groundwater analysis from wells around the Area 5 RWMS were all below established investigation levels. Leachate samples collected from the leachate collection system at the mixed low-level waste cell were below established contaminant regulatory limits. During 2015, precipitation at the Area 3 RWMS was 0.9% above average, and precipitation at the Area 5 RWMS was 25% above average. Water balance measurements indicate that evapotranspiration from the vegetated weighing lysimeter dries the soil and prevents downward percolation of precipitation more effectively than evaporation as measured from the bare-soil weighing lysimeter. The 1.8 inches of precipitation in September reached the lowest sensors at 180 cm on the Cell 5S and 5N covers, however the sensors on the floor do not indicate any signs of the precipitation percolating thru the waste to the floor. Moisture from precipitation did not percolate below 150 centimeters (cm) (5 feet [ft]) in the vegetated final cover on the U-3ax/bl disposal unit at the Area 3 RWMS. A second remedial revegetation trial was done on the southern portion of the North Cover. In March 2015 it was determined that germination occurred on both the mulched and non-mulched treatments that were irrigated, but that no germination occurred on the non-irrigated treatments. By June the seedlings observed in March were gone, possibly grazed by rabbits which were observed inside the rabbit-proof fence. In August the entire area was sprayed with the herbicide glyphosate in an attempt to minimize weed seed production. During 2015, there was no drainage through 2.4 meters (8 ft) of soil as indicated from the Area 3 drainage lysimeters that received only natural precipitation. Thirty-one percent of the applied precipitation and irrigation drained from the bare-soil drainage lysimeter, which received 3 times the natural precipitation. All 2015 monitoring data indicate that the Area 3 and Area 5 RWMSs are performing within expectations of the model and parameter assumptions for the facilities’ PAs.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2011

Use of Advanced Meteorological Model Output for Coastal Ocean Modeling in Puget Sound

It is a great challenge to specify meteorological forcing in estuarine and coastal circulation modeling using observed data because of the lack of complete datasets. As a result of this limitation, water temperature is often not simulated in estuarine and coastal modeling, with the assumption that density-induced currents are generally dominated by salinity gradients. However, in many situations, temperature gradients could be sufficiently large to influence the baroclinic motion. In this paper, we present an approach to simulate water temperature using outputs from advanced meteorological models. This modeling approach was applied to simulate annual variations of water temperatures of Puget Sound, a fjordal estuary in the Pacific Northwest of USA. Meteorological parameters from North American Region Re-analysis (NARR) model outputs were evaluated with comparisons to observed data at real-time meteorological stations. Model results demonstrated that NARR outputs can be used to drive coastal ocean models for realistic simulations of long-term water-temperature distributions in Puget Sound. Model results indicated that the net flux from NARR can be further improved with the additional information from real-time observations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Explore Spatio‐Temporal Learning of Large Sample Hydrology Using Graph Neural Networks

Abstract Streamflow forecasting over gauged and ungauged basins play a vital role in water resources planning, especially under the changing climate. Increased availability of large sample hydrology data sets, together with recent advances in deep learning techniques, has presented new opportunities to explore temporal and spatial patterns in hydrological signatures for improving streamflow forecasting. The purpose of this study is to adapt and benchmark several state‐of‐the‐art graph neural network (GNN) architectures, including ChebNet, Graph Convolutional Network (GCN), and GraphWaveNet, for end‐to‐end graph learning. We explicitly represent river basins as nodes in a graph, learn the spatiotemporal nodal dependencies, and then use the learned relations to predict streamflow simultaneously across all nodes in the graph. The efficacy of the developed GNN models is investigated using the Catchment Attributes and MEteorology for Large‐sample Studies (CAMELS) data set under two settings, fixed graph topology (transductive learning), and variable graph topology (inductive learning), with the latter applicable to prediction in ungauged basins (PUB). Results indicate that GNNs are generally robust and computationally efficient, achieving similar or better performance than a baseline model trained using the long short‐term memory (LSTM) network. Further analyses are conducted to interpret the graph learning process at the edge and node levels and to investigate the effect of different model configurations. We conclude that graph learning constitutes a viable machine learning‐based method for aggregating spatiotemporal information from a multitude of sources for streamflow forecasting

Sun, Alexander Y.↗
DOE OSTI2020

Identifying a regional aerosol baseline in the eastern North Atlantic using collocated measurements and a mathematical algorithm to mask high-submicron-number-concentration aerosol events

Abstract. High-time-resolution measurements of in situ aerosol and cloud properties provide the ability to study regional atmospheric processes that occur on timescales of minutes to hours. However, one limitation to this approach is that continuous measurements often include periods when the data collected are not representative of the regional aerosol. Even at remote locations, submicron aerosols are pervasive in the ambient atmosphere with many sources. Therefore, periods dominated by local aerosol should be identified before conducting subsequent analyses to understand aerosol regional processes and aerosol–cloud interactions. Here, we present a novel method to validate the identification of regional baseline aerosol data by applying a mathematical algorithm to the data collected at the U.S. Department of Energy's (DOE) Atmospheric Radiation Measurement (ARM) user facility in the eastern North Atlantic (ENA). The ENA central facility (C1) includes an aerosol observing system (AOS) for the measurement of aerosol physical, optical, and chemical properties at time resolutions from seconds to minutes. A second temporary supplementary facility (S1), located ∼0.75 km from C1, was deployed for ∼1 year during the Aerosol and Cloud Experiments (ACE-ENA) campaign in 2017. First, we investigate the local aerosol at both locations. We associate periods of high submicron number concentration (Ntot) in the fine-mode condensation particle counter (CPC) and size distributions from the Ultra-High Sensitivity Aerosol Spectrometer (UHSAS) as a function of wind direction using a meteorology sensor with local sources. Elevated concentrations of Aitken-mode (< 100 nm diameter) particles were observed in correspondence with the wind directions associated with airport operations. At ENA, the Graciosa Airport and its associated activities were found to be the main sources of high-concentration aerosol events at ENA, causing peaks in 1 min Ntot that exceeded 8000 and 10 000 cm−3 at C1, in summer and winter, respectively, and 5000 cm−3 at S1 in summer. Periods with high Ntot not associated with these wind directions were also observed. As a result, the diverse local sources at ENA yielded a poor relationship between Ntot measurements collected at C1 and S1 (R2=0.03 with a slope =0.05±0.001). As a first approach to mask these events, the time periods when the wind direction was associated with the airport operations (west to northwest and southeast to south at C1 and east to south at S1) were applied. The meteorological masks removed 38.9 % of the data at C1 and 43.4 % at S1, and they did not significantly improve the relationship between the two sites (R2=0.18 with a slope =0.06±0.001). Due to the complexity of high-Ntot events observed at ENA, we develop and validate a mathematical ENA Aerosol Mask (ENA-AM) to identify high-Ntot events using 1 min resolution data from the AOS CPC at C1 and S1. After its parameterization and application, ENA-AM generated a high correlation between Ntot in the summer at C1 and S1 (R2=0.87 with a slope =0.84±0.001). We identified the regional baseline at ENA to be 428±228 cm−3 in the summer and 346±223 cm−3 in the winter. Lastly, we compared masked measurements from the AOS with the ARM Aerial Facility (AAF) during flights over C1 in the summer to understand submicron aerosol vertical mixing over C1. The high correlation (R2=0.71 with a slope of 1.04±0.01) observed between C1 and the AAF Ntot collected within an area of 10 km surrounding ENA and at altitudes < 500 m indicated that the submicron aerosol at ENA was well mixed within the first 500 m of the marine boundary layer during the month of July during ACE-ENA. Our novel method for determining a regional aerosol baseline at ENA can be applied to other time periods and at other locations with validation by a secondary site or additional collocated measurements.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2013

Regulation of Carbon Sequestration and Water Use in an Ozark Forest: Proposing a New Strategically Located Ameriflux Tower Site in Missouri

by June 14, 2004, the MOFLUX site was fully instrumented and data streams started to flow. A primary accomplished deliverable for the project period was the data streams of CO 2 and water vapor fluxes and numerous meteorological variables (from which prepared datasets have been submitted to the AmeriFlux data archive for 2004-2006, Additionally, measurements of leaf biochemistry and physiology, biomass inventory, tree allometry, successional trends other variables were obtained.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Model Data Archive Associated with Manuscript "Fire-altered Carbon Pools Create Disturbance Memory in Stream Dissolved Organic Carbon"

This data package supports the publication “Fire-altered Carbon Pools Create Disturbance Memory in Stream Dissolved Organic Carbon” by Li et al. (2026). The package contains processed model inputs, configuration files, restart files, simulation outputs, scripts, and visualization products used to evaluate post-fire dissolved organic carbon (DOC) dynamics in the Naches River Watershed, Washington, USA, following the 2021 Schneider Springs Fire. The modeling workflow couples ELM-BGC, the biogeochemistry-enabled Energy Exascale Earth System Model Land Model; ATS, the Advanced Terrestrial Simulator for integrated surface-subsurface hydrology; and PFLOTRAN, a reactive transport model for multicomponent aqueous geochemistry. Together, these models simulate how wildfire-induced changes in vegetation, litter, coarse woody debris, and soil organic matter influence DOC production, transport, and reaction from burned hillslopes to stream networks. The archive includes preprocessed meteorological, geospatial, hydrologic, and biogeochemical forcing data; ELM-BGC-derived DOC source terms; ATS mesh files; PFLOTRAN reactive-transport inputs; model configuration files; spin-up and transient restart files; watershed-scale diagnostic outputs; stream concentration time series; and figures or visualization files used to inspect and reproduce key results. File types include Hierarchical Data Format 5 (HDF5) files for gridded forcing and model-coupling data, model input and configuration files for ELM-BGC, ATS, and PFLOTRAN, restart and simulation-output files generated by the modeling workflow, tabular or time-series diagnostic outputs, scripts for post-processing and figure generation, and image or visualization products associated with the manuscript. Use of the package depends on the intended task. Re-running the simulations requires the relevant modeling software, including ELM-BGC, ATS, and PFLOTRAN as ATS's geochemical engine. Inspecting outputs and reproducing figures requires Python with scientific plotting libraries such as Matplotlib, and three-dimensional model outputs may be viewed with ParaView. Geographic information system files or maps may be inspected with ArcGIS Pro or comparable GIS software. The data package is intended to enable traceability, reuse, and partial reproduction of the coupled land-to-watershed hydro-biogeochemical modeling workflow used to test how wildfire disturbance affects terrestrial carbon pools and downstream DOC dynamics.

ATS↗
DOE OSTI2018

NREL Renewable Energy Resource Data

This data is derived from the Wind Integration National Dataset (WIND) Toolkit and the Solar Integration National Dataset (SIND). The WIND Toolkit provides meteorological conditions and turbine power for more than 126,000 land-based and offshore wind sites across the continental United States, and the SIND Toolkit provides subhourly solar power data designed to assist researchers and utilities in solar generation integration studies This data set includes meteorological variables, including wind speed, direction, temperature, pressure, air density, irradiance, and surface temperature. Parameters such as wind profiles, atmospheric stability, and solar radiation data are also included.

P↗
DOE OSTI2018

NREL Renewable Energy Resource Data

This data is derived from the Wind Integration National Dataset (WIND) Toolkit and the Solar Integration National Dataset (SIND). The WIND Toolkit provides meteorological conditions and turbine power for more than 126,000 land-based and offshore wind sites across the continental United States, and the SIND Toolkit provides subhourly solar power data designed to assist researchers and utilities in solar generation integration studies This data set includes meteorological variables, including wind speed, direction, temperature, pressure, air density, irradiance, and surface temperature. Parameters such as wind profiles, atmospheric stability, and solar radiation data are also included.

Rossol, Michael↗
DOE OSTI2014

U.S. Virgin Islands Wind Resources Update 2014

This report summarized the data collected from two 60-meter (m) meteorological towers (met masts or met towers) and three sonic detection and ranging (SODAR) units on St. Thomas and St. Croix in 2012 and 2013. These results are an update to the previous feasibility study; the collected data are critical to the successful development of a wind project at either site (Lantz et al. 2012). The wind maps for the territory were updated based on these improved data. The National Renewable Energy Laboratory (NREL) conducted measurements from met towers and SODAR units for several locations in the U.S. Virgin Islands (USVI). Both met masts were deployed for more than a year and produced high-quality data (standard industry practice in the wind industry). The Longford (St. Croix) and Bovoni (St. Thomas) sites are estimated to have long-term corrected wind speeds that exceed 7 meters per second (m/s), which may produce financially viable wind projects for both sites. The levelized cost of energy for both sites was estimated at $\$$0.08–$\$$0.14 per kilowatt-hour for typical utility-scale wind turbine with the uncertainty in the estimate driven by uncertainty in the installed capital costs of the proposed wind farms. These estimates assume that the wind production tax credit is not reinstated, but that accelerated depreciation is in place.

17 WIND ENERGY↗
DOE OSTI2023

Improving Tropical Cyclogenesis Forecasts of Hurricane Irma (2017) through the Assimilation of All-Sky Infrared Brightness Temperatures

The assimilation of satellite all-sky infrared (IR) brightness temperatures (BTs) has been shown in previous studies to improve intensity forecasts of tropical cyclones. Here, in this study, we examine whether assimilating all-sky IR BTs can also potentially improve tropical cyclogenesis forecasts by improving the pregenesis cloud and moisture fields. By using an ensemble-based data assimilation system, we show that the assimilation of upper-tropospheric water vapor channel BTs observed by the Meteosat-10 SEVIRI instrument two days before the formation of a tropical depression improves the genesis forecast of Hurricane Irma (2017), a classic Cape Verde storm, by up to 24 h while also capturing its later rapid intensification in deterministic forecasts. In an experiment that withholds the assimilation of all-sky IR BTs, the assimilation of conventional observations from the Global Telecommunications System (GTS) leads to the premature genesis of Hurricane Irma by at least 24 h. This premature genesis is shown to result from an overestimation of the spatial coverage of deep convection within the African easterly wave (AEW) from which Irma eventually forms. The gross overestimation of deep convection without all-sky IR BTs is accompanied by higher column saturation fraction, stronger low-level convergence, and the earlier spinup of a low-level meso- β -scale vortex within the AEW that ultimately becomes Hurricane Irma. Through its adjustment to the initial moisture and cloud conditions, the assimilation of all-sky IR BTs leads to a more realistic convective evolution in forecasts and ultimately a more realistic timing of genesis.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Assessment of Spatiotemporal Variability of Evapotranspiration and Its Governing Factors in a Mountainous Watershed at the East River, Colorado, Water: Dataset

This data package contains simulation results of daily evapotranspiration for 17 locations of meteorological stations with different precipitation, air temperature and vegetation within the East River Watershed, Colorado (CLMResult.csv). Daily precipitation data is included for reference. These are the main results of the paper Tran, 2019. Meteorological stations names, latitude and longitude coordinates of meteorological stations, types of lands from the National Land Cover Database, and a comparison with the community land model (CLM) data are also included (Met_LC.csv). The purpose of this data is to analyze the spatio-temporal variations of evapotranspiration at the East River Watershed and understand the factors that control these variations. More information regarding the study, methods, and findings can be found in the Related Reference paper.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

Probable Maximum Precipitation in the U.S. Pacific Northwest in a Changing Climate

The safety of large and aging water infrastructures is gaining attention in water management given the accelerated rate of change in landscape, climate and society. In current engineering practice, such safety is ensured by the design of infrastructure for the Probable Maximum Precipitation (PMP). Recently, several physics-based numerical modeling approaches have been proposed to modernize the conventional and ad hoc PMP estimation approach. However, the underlying physics has not been investigated and thus differing PMP estimates are obtained without clarity on their interpretation. In this study, we present a hybrid approach that takes advantage of both traditional engineering wisdom and modern climate science to estimate PMP for current and future climate conditions. The traditional PMP approach is improved and applied to outputs from an ensemble of five CMIP5 models. This hybrid approach is applied in the Pacific Northwest (PNW) to produce ensemble PMP estimation for the historical (1970-2016) and future (2050-2099) time periods. The new historical PMP estimates are verified by comparing them with the traditional estimates. PMP in the PNW will increase by 50% of the current level by 2099 under the RCP8.5 scenario. Most of the increase is caused by warming, which mainly affects moisture availability, with minor contributions from changes in storm efficiency in the future. Moist track change tends to reduce the future PMP. Compared with extreme precipitation, ensemble PMP exhibits higher internal variation. Thus high-quality data of both precipitation and related meteorological fields (temperature, wind fields) are required to reduce uncertainties in the ensemble PMP estimates.

54 ENVIRONMENTAL SCIENCES↗