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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 991 records · Page 55
All sourcesNASA NTRSDOE OSTI
NASA NTRS2022

Cause of a Lower-Tropospheric High-Ozone Layer in Spring Over Hanoi

The ozonesonde observations in Hanoi, Vietnam, over fourteen years since 2004 have confirmed the enhancement in lower tropospheric ozone concentration at about 3 km altitude in the spring season. We investigated the evolution of the ozone enhancement from analysis of meteorological data, backward trajectories, and model sensitivity experiments. In spring, air masses over Hanoi exhibit strong height dependence. At 3km, the high-ozone air masses originate from the land area to the west of Hanoi, while low-ozone air masses below about 1.5 km are from the oceanic area to the east. Above 4 km, the air masses are mostly traced back to the farther west area. The chemical transport model simulations revealed that precursor emissions from biomass burning in the inland Indochina Peninsula have the largest contribution to the lower tropospheric ozone enhancement, which is transported upward and eastward and overhangs the clean air intrusion from the ocean to the east of Hanoi. At this height level, the polluted air has the horizontal extent of about 20 degrees in longitude and latitude. The polluted air observed in Hanoi is transported further east and widely spread over the northern Pacific Ocean.

tropospheric ozone↗
NASA NTRS2022

Cause of a Lower-Tropospheric High-Ozone Layer in Spring Over Hanoi

The ozonesonde observations in Hanoi, Vietnam, over fourteen years since 2004 have confirmed the enhancement in lower tropospheric ozone concentration at about 3 km altitude in the spring season. We investigated the evolution of the ozone enhancement from analysis of meteorological data, backward trajectories, and model sensitivity experiments. In spring, air masses over Hanoi exhibit strong height dependence. At 3km, the high-ozone air masses originate from the land area to the west of Hanoi, while low-ozone air masses below about 1.5 km are from the oceanic area to the east. Above 4 km, the air masses are mostly traced back to the farther west area. The chemical transport model simulations revealed that precursor emissions from biomass burning in the inland Indochina Peninsula have the largest contribution to the lower tropospheric ozone enhancement, which is transported upward and eastward and overhangs the clean air intrusion from the ocean to the east of Hanoi. At this height level, the polluted air has the horizontal extent of about 20 degrees in longitude and latitude. The polluted air observed in Hanoi is transported further east and widely spread over the northern Pacific Ocean.

Hanoi↗
NASA NTRSDate not supplied

NASA Initiatives to Support Renewable Energy and Sustainable Infrastructure

Initiatives to build and manage climate-resilient infrastructure require spatially explicit environmental and climate information. NASA’s vantage point of Earth from space provides valuable observations that, in concert with additional information, can be used to inform both the design and operation of infrastructure that builds resilience to climate stressors. In this presentation, we provide an overview of how NASA Earth science is used to support decision-making associated with climate adaptation and mitigation, with a particular focus on renewable energy and sustainable infrastructure. Much of this work is currently delivered through NASA’s Prediction of Worldwide Energy Resources (POWER) project, which generates high quality solar and meteorological data products that are customized to the needs of user communities. Importantly, these products are delivered through a variety of web-based applications that make it easier for users to access relevant and timely information. We will highlight several examples of how the impact of POWER is scaled via a robust user community and discuss further plans to expand impact. Additionally, this presentation will highlight efforts of the NASA Earth Science Division’s Energy & Infrastructure Applications program to expand collaborative activities with researchers and practitioners that work on renewable energy and sustainable infrastructure.

Allison K Leidner↗
NASA NTRS2026

Measure sea level air pressure from space to improve knowledge and forecasting of the atmospheric state

Modern numerical weather prediction (NWP) and analysis models require globally-observed meteorological data including sea-level pressure (SLP) for accurate operations. Up until now, SLP has only been measured by in situ instruments from ships, buoys, and ocean platforms. These measurements are sparse with large gaps, leaving models starved of this critical information to constrain the atmospheric state. Recent advancements in differential absorption radar (DAR) provide a path to close this critical observation gap through spaceborne observations in the coming decade, improving the analysis models relied upon for atmospheric research and the weather forecasts depended upon daily for public safety and commerce.

Matthew L Walker McLinden↗
DOE OSTI2011

Gaseous dry deposition of atmospheric mercury: A comparison of two surface resistance models for deposition to semiarid vegetation

In the United States, atmospheric mercury (Hg) deposition, from regional and international sources, is the largest contributor to increased Hg concentrations in bodies of water leading to bioaccumulation of methyl mercury in fish. In this work, modeled dry deposition velocities (vd) for gaseous Hg are calculated using two surface resistance parameterizations found in the literature. The flux is then estimated as the product of the species concentration and modeled vd. The calculations utilize speciated atmospheric mercury concentrations measured during an annual monitoring campaign in southern Idaho. Gaseous elemental mercury (GEM) and reactive gaseous mercury (RGM) were monitored with Tekran models 2537A and 1130, respectively. Two anemometers collected meteorological data, including one fast-response three-dimensional sonic anemometer to measure turbulence parameters. For the flux calculation, three resistances are required to model the mechanisms that transport gaseous Hg from the atmosphere to the surface, with the surface resistance being the largest source of error. Results from two surface resistance models are presented. In particular, the downward flux is sensitive to the choice of model and input parameters such as seasonal category and mesophyll resistance. A comparison of annual GEM and RGM fluxes calculated using the two models shows good agreement for RGM (3.2% difference for annual deposition); however, for the low-solubility species of GEM, the models show a 64% difference in annual fluxes, with a range of 32% to 200% in seasonal fluxes. Results indicate the importance of understanding the diurnal variation of the physical processes modeled in the surface resistance parameterization for vd.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2012

DEPOSITION VELOCITY ESTIMATION WITH THE GENII V2 SOFTWARE

In 2010, the Department of Energy (DOE) Chief of Nuclear Safety and Office of Health, Safety and Security (HSS), with the support of industry experts in atmospheric sciences and accident dose consequences analysis, performed detailed analyses of the basis for the dry deposition velocity (DV) values used in the MACCS2 computer code. As a result of these analyses, DOE concluded that the historically used default DV values of 1 centimeter/second (cm/s) for unfiltered/unmitigated releases and 0.1 cm/s for filtered/mitigated releases may not be reasonably conservative for all DOE sites and accident scenarios. HSS recently issued Safety Bulletin 2011-02, Accident Analysis Parameter Update, recommending the use of the newly developed default DV, 0.1 cm/s for an unmitigated/unfiltered release. Alternatively site specific DV values can be developed using GENII version 2 (GENII v2) computer code. Key input parameters for calculating DV values include surface roughness, maximum wind speed for calm, particle size, particle density and meteorological data (wind speed and stability class). This paper will include reasonably conservative inputs, and a truncated parametric study. In lieu of the highly-conservative recommended DV value (0.1cm/s) for unmitigated/unfiltered release, GENII v2 has been used to justify estimated 95th percentile DV values. Also presented here are atmospheric dilution factors ({chi}/Q values) calculated with the MACCS2 code using the DV values form GENII v2, {chi}/Q values calculated directly with GENII v2, and a discussion of these results compare with one another. This paper will give an overview of the process of calculating DV with GENII v2 including a discussion of the sensitivity of input parameters.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2017

Understanding the Temporal and Spatial Variability of New Generation Gridded TMYs

Presentation at ASHRAE about the spatial and temporal variability of gridded TMYs, discussing advanced GIS and Web services that allow for direct access to data, surface-based observations for thousands of stations, climate reanalysis data, and products derived from satellite data; new developments in NREL's solar databases based on both observed data and satellite-derived gridded data, status of TMY3 weather files, and NREL's plans for the next-generation TMY weather files; and also covers what is new and different in the Climatic Design Conditions Table in the 2013 ASHRAE Handbook of Fundamentals.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Simulating Impacts of Extreme Events on Grids with High Penetrations of Wind Power Resources

As extreme weather events become more frequent and intense, the demand for connecting grid operation and infrastructure planning with extreme event models will increase as well. We present a methodology for creating damage contingencies and scenarios for electric transmission grids during a hurricane strike. Included is an example case study: Hurricane Dolly damaging a synthetic 2000 bus test system during its landing in Southern Texas. Using WIND Toolkit meteorological data in conjunction with fragility curves for various electric grid elements, we generate stochastic damage scenarios that can be used for short- and long-term planning problems including emergency asset management. We perform statistical analysis of damages and quantify topological effects on example synthetic grid. Also, we investigate loss-of-load events during two-day economic dispatch experiment. Finally, we point out various shortcomings of our method and suggest how it can be improved.

data-driven forecasting↗
DOE OSTI2022

Simulating Impacts of Extreme Events on Grids with High Penetrations of Wind Power Resources: Preprint

As extreme weather events become more frequent and intense, the demand for connecting grid operation and infrastructure planning with extreme event models will increase as well. We present a methodology for creating damage contingencies and scenarios for electric transmission grids during a hurricane strike. Included is an example case study: Hurricane Dolly damaging a synthetic 2000 bus test system during its landing in Southern Texas. Using WIND Toolkit meteorological data in conjunction with fragility curves for various electric grid elements, we generate stochastic damage scenarios that can be used for short- and long-term planning problems including emergency asset management. We perform statistical analysis of damages and quantify topological effects on example synthetic grid. Also, we investigate loss-of-load events during two-day economic dispatch experiment. Finally, we point out various shortcomings of our method and suggest how it can be improved.

contingencies↗
DOE OSTI2023

Emergency Asset Positioning for Resilient Transmission Grid Operation

In this paper we study a problem of hurricane emergency preparedness via placement of emergency generation assets prior to its strike. We present a two-stage stochastic model for choosing locations and quantities of emergency storage to help support the power grid through a hurricane event. The expectation of losses in our two-stage model is estimated using the sample average approximation. We construct damage scenarios for sample average approximation using WIND Toolkit meteorological data and fragility curves of various electric grid components. We demonstrate the efficacy of our two-stage planning model by simulating operations during Hurricane Dolly on the 2000-bus transmission test system. Our model, coupled with our scenario selection strategy, is effective at mitigating loss of load when compared to a model without emergency generation assets placed prior to an extreme event.

emergency preparedness↗
DOE OSTI2023

Emergency Generation Asset Positioning for Resilient Transmission Grid Operation: Preprint

In this paper we study a problem of hurricane emergency preparedness via placement of emergency generation assets prior to its strike. We present a two-stage stochastic model for choosing locations and quantities of emergency storage to help support the power grid through a hurricane event. The expectation of losses in our two-stage model is estimated using the sample average approximation. We construct damage scenarios for sample average approximation using WIND Toolkit meteorological data and fragility curves of various electric grid components. We demonstrate the efficacy of our two-stage planning model by simulating operations during Hurricane Dolly on the 2000-bus transmission test system. Our model, coupled with our scenario selection strategy, is effective at mitigating loss of load when compared to a model without emergency generation assets placed prior to an extreme event.

emergency preparedness↗
DOE OSTI2019

Initial Assessment of Cover Degradation Features at the Mexican Hat, Utah, UMTRCA Disposal Site - 19556

The Mexican Hat, Utah, Uranium Mill Tailings Radiation Control Act (UMTRCA) Title I Disposal Site is located on the Navajo Reservation in southeast Utah. The US Nuclear Regulatory Commission licensed the site to the US Department of Energy Office of Legacy Management (LM) for custody and long-term care. LM manages long-term surveillance and maintenance activities at the site with the principal goal of protecting human health and the environment. The disposal cell was designed to encapsulate low-level radioactive mill tailings and other residual radioactive materials (RRM) in a way that minimizes the need for active maintenance and limits radon gas emanation and precipitation infiltration. The disposal cell has a multicomponent cover, consisting of a 60 cm thick (24 inch), low-permeability, bentonite-amended radon barrier, overlain by a 15 cm thick (6 inch) sandy gravel bedding/filter layer and a 30 cm thick (12 inch) riprap erosion-protection layer. Under UMTRCA, designs intended to control and contain RRM shall be effective for up to 1000 years, to the extent reasonably achievable and, in any case, for at least 200 years. In 2016, multiple subtle depressions were identified on the rock cover along the toe and lower portions of the northeast side slope of the disposal cell. Due to concerns regarding potential impacts to disposal cell performance and erosion resistance, evaluations of the depressions and cell cover design and construction were initiated. The evaluations included reviews of the disposal cell as-built drawings and supporting design calculations for the rock cover components. Also, in 2017, a weather monitoring station was installed at the site to collect continuous site-specific meteorological data, and repeatable ground-based light imaging, detection, and ranging (lidar) topographical and aerial photogrammetry surveys were implemented to monitor for potential changes in the surface depression features. The evaluations also included visual inspections of the depressions, and in 2018, small-area manual excavations of the rock cover components (test pits) were performed to observe the conditions of the cover components, scan for radioactivity, and observe evidence of erosion on the surface of the radon barrier material. Voids and incisions were discovered within the radon barrier on the north and northeast side slopes of the disposal cell. Visual observations at test pits indicate that the bedding/filter material is highly segregated and may not be in conformance with the disposal cell engineering and construction design specifications. Post-construction development of a weak to strongly cemented material has also been observed immediately below the base of the bedding layer in test pits with observed radon barrier degradation. A series of radiological surveys have been performed at the site, and radiological readings have not exceeded natural background conditions in the vicinity of the site. Thus, the site remains protective of human health and the environment. Implementation of continuous radiological monitoring at the site will be initiated in fall 2018 through the deployment of radon cups and thermoluminescent dosimeters to obtain data that provides supporting evidence that the disposal cell remains protective of human health and the environment. Evaluations to determine the cause(s) of the depression features and associated radon barrier erosion are ongoing. Possible mechanisms of cover degradation identified thus far include segregated bedding material that would allow for higher precipitation runoff velocities, resulting in erosion of the radon barrier; post-construction cementation of radon barrier or aeolian material that could negatively affect the performance of the radon barrier; cation exchange leading to the development of cementation; and aeolian deposition affecting precipitation runoff. Moreover, geotechnical sampling and testing is planned to verify spatial cover conditions relative to design specifications. The resulting information will be used to identify possible causes for the surface depressions and associated radon barrier degradation, gain insight on potential long-term performance implications along the side slopes of the disposal cell, and identify potential corrective actions. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
NASA NTRS2008

Surface Meteorology and Solar Energy (SSE) Data Release 5.1

The Surface meteorology and Solar Energy (SSE) data set contains over 200 parameters formulated for assessing and designing renewable energy systems.The SSE data set is formulated from NASA satellite- and reanalysis-derived insolation and meteorological data for the 10-year period July 1983 through June 1993. Results are provided for 1 degree latitude by 1 degree longitude grid cells over the globe. Average daily and monthly measurements for 1195 World Radiation Data Centre ground sites are also available. [Location=GLOBAL] [Temporal_Coverage: Start_Date=1983-07-01; Stop_Date=1993-06-30] [Spatial_Coverage: Southernmost_Latitude=-90; Northernmost_Latitude=90; Westernmost_Longitude=-180; Easternmost_Longitude=180] [Data_Resolution: Latitude_Resolution=1 degree; Longitude_Resolution=1 degree].

Arctic heating↗
DOE OSTI2024

WFIP3 - RHOD Site - PNNL Surface Meteorological Suit / Raw Data

This dataset contains raw data from the WFIP3 RHOD site supplementary meteorological sensors: T/RH and barometer; 1-sec average. The supplementary meteorological suite was added to PNNL Surface Flux Station to provide independent measurements of air temperature, relative humidity, and atmospheric pressure.

17 WIND ENERGY↗
DOE OSTI2024

Surface Meteorological Station / Raw Data

This dataset contains raw data from the WFIP3 RHOD site supplementary meteorological sensors: T/RH and barometer; 1-sec average. The supplementary meteorological suite was added to PNNL Surface Flux Station to provide independent measurements of air temperature, relative humidity, and atmospheric pressure.

17 WIND ENERGY↗
DOE OSTI2025

RHOD Site - Surface Meteorological Station / Processed Data

This dataset contains raw data from the WFIP3 RHOD site supplementary meteorological sensors: T/RH and barometer; 1-sec average. The supplementary meteorological suite was added to PNNL Surface Flux Station to provide independent measurements of air temperature, relative humidity, and atmospheric pressure.

17 WIND ENERGY↗
DOE OSTI2023

LANL Meteorological Program: 2022 Data Completeness/Quality Report

Los Alamos National Laboratory (LANL) operates seven mesa-top instrumented meteorology towers: Technical Area (TA) 6, TA-49, TA-53, TA-54, TA-63, TA-54B, and TA-16B. An additional instrumented tower is located in Mortandad Canyon (TA-5 MDCN), and there is a rain gauge at North Community (NCOM), located within the town of Los Alamos. The 10 meter (m) towers at TA-63, TA- 54B, and TA-16B were installed in 2021, and will be included in the 2023 data completeness/quality report. A description of the meteorology monitoring network, prior to the installation of the TA-63, TA-54B, and TA-16B is found in Dewart and Boggs (2014). Four of the mesa-top towers (e.g., TA-6, TA-49, TA-53, and TA-54) are instrumented at the 1.2 m, 11.5 m, 23 m, and 46 m levels. In addition, the TA-6 tower is instrumented at the 92 m level. The TA-5 MDCN tower is 10 m in height and is instrumented at 1.2 m and 10 m. Data are collected and averaged every 15 minutes. Range checking is performed on each measurement every 15 minutes; data that are beyond normal ranges are eliminated from the data set and replaced by a code for missing data. In addition, data are reviewed weekly by qualified meteorologists to identify bad data not identified by the range checking technique. The data steward eliminates these data from the data set and replaces them with a code for missing data. The instrument technicians also review that data and schedule instrument replacement, as required. All instruments are calibrated at a frequency that meets the criteria identified in ANSI/ANS-3.11-2015. Data completeness is determined by the number of total 15-minute records available versus the number of possible measurements for the entire year. As a rule, the meteorologists do not attempt to estimate data that are eliminated as bad data. Original datalogger records, including bad data, can be recalled from program archival storage.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

LANL Meteorological Program: 2023 Data Completeness/Quality Report

Los Alamos National Laboratory (LANL) operates seven mesa-top instrumented meteorology towers: Technical Area (TA) 6, TA-49, TA-53, TA-54, TA-63, TA-54B, and TA-16B. An additional instrumented tower is located in Mortandad Canyon (TA-5 MDCN), and there is a rain gauge at North Community (NCOM), located within the town of Los Alamos. The 10 meter (m) towers at TA-63, TA-54B, and TA-16B have been in testing since they were installed in 2021, and will be included in a future data completeness report. A description of the meteorology monitoring network, prior to the installation of the TA-63, TA-54B, and TA-16B is found in Dewart and Boggs (2014). Four of the mesa-top towers (e.g., TA-6, TA-49, TA-53, and TA-54) are instrumented at the 1.2 m, 11.5 m, 23 m, and 46 m levels. In addition, the TA-6 tower is instrumented at the 92 m level. The TA-5 MDCN tower is 10 m in height and is instrumented at 1.2 m and 10 m. Data are collected and averaged every 15 minutes. Range checking is performed on each measurement every 15 minutes; data that are beyond normal ranges are eliminated from the data set and replaced by a code for missing data. In addition, data are reviewed weekly by qualified meteorologists to identify bad data not identified by the range checking technique. The data steward eliminates these data from the data set and replaces them with a code for missing data. The instrument technicians also review that data and schedule instrument replacement, as required. All instruments are calibrated at a frequency that meets the criteria identified in ANSI/ANS-3.11-2015. Data completeness is determined by the number of total 15-minute records available versus the number of possible measurements for the entire year. As a rule, the meteorologists do not attempt to estimate data that are eliminated as bad data. Original datalogger records, including bad data, can be recalled from program archival storage.

54 ENVIRONMENTAL SCIENCES↗