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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 235 records · Page 13
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

A network of soil moisture, soil temperature, air temperature, net radiation, ground heat flux and ground water for Chicago, Illinois

This dataset contains environmental monitoring data collected using solar-powered Multi-Function Research (MFR) Long Range Wide Area (LoRaWAN)-enabled nodes at 11 sites in Chicago, Illinois, as part of the DOE Urban Integrated Field Lab CROCUS project. The MFR node system consists of an Input/Output Digital Input Module (IB8) interface box (ICT International) providing wired connections for environmental sensors and an MFR-Node-L data logger that manages power, data processing, and LoRaWAN communication. The wireless data are ingested via Sage network (https://sagecontinuum.org/) nodes that contain LoRaWAN antennae. Measurements were collected from 11 MFR nodes deployed across Chicago State University (CSU), Northeastern Illinois University (NEIU), Northwestern University (NU), University of Illinois Chicago (UIC), West Woodlawn "Blacks in Green" (BIG), and Indian Boundary Prairies (IBP). Each MFR node supports a consistent suite of sensors measuring atmospheric, soil, and hydrological variables. Atmospheric measurements include 2m air temperature (°C), 2m vapor pressure deficit (kPa), and 2m shortwave/longwave radiation (incoming and outgoing, W/m²) measured using ATH-VPD and Apogee SN500 sensors. Soil measurements include volumetric water content (VWC, %) and temperature (°C) at four depths (15, 30, 45, and 60 cm below surface) using Meter Teros54 sensors, and heat flux (W/m²) at 10 cm depth using Huske HFP01-05 sensors. At selected locations, Meter Hydros21 sensors measure groundwater depth (mm), specific conductivity (dS/m), and temperature (°C). The dataset includes timestamps, site identifiers with location names, device IDs, Global Positioning System (GPS) coordinates, variable names with units, measurement depths, values, sensor names, and Sage node identifiers. All timestamps are in local Chicago time (CDT/CST). Quality control flags are provided using a 6-bit binary system indicating physical range violations, step spikes, 24-hour flat-line conditions, 6-hour jitter, 7-day ultra-low variance, and persistent high offset. Data is provided in CSV and CF-compliant NetCDF formats. This dataset is part of a larger collection of CROCUS environmental monitoring data, including linked datasets from Air Quality Transmitter (AQT) sensors, Weather Transmitter (WXT) sensors, and Sap Flow Meter (SFM1x) sensors.

Chicago↗
DOE OSTI2021

A Functional All-Hazard Approach to Critical Infrastructure Dependency Analysis

The critical infrastructures protection landscape is a vast and varied pattern of independent, but interconnected infrastructure systems that are essential to the function of our modern society. The U.S. policy on critical infrastructure protection has been continually evolving since the “President’s Commission on Critical Infrastructure Protection” was published in 1997. In response to these policies, federal, state, and local governments, along with research institutions, have invested a substantial amount of time and effort into identifying and analyzing critical infrastructure, their functions, and dependencies/interdependencies to better understand their vulnerabilities. To date, the ability to assess vulnerabilities, resiliency, and priorities for protecting interdependent critical infrastructure systems from an all-hazards perspective remains a difficult problem. In this paper we introduce the All-Hazards Analysis (AHA) methodology, which provides an integrated functional basis across infrastructure systems, through the implementation of a common language and a scalable level of decomposition to effectively evaluate the resilience of interconnected infrastructure systems. AHA models infrastructure systems as directed multidimensional graphs, which enable the evaluation of cross-sector interdependencies prior to, during, and after disruptive events. Finally, and by design, AHA enables the cross linking of data taxonomies to enable more effective data sharing, such as the National Critical Functions (NCF) and Infrastructure Data Taxonomy (IDT).

02 PETROLEUM↗
DOE OSTI2022

Discovery of Innovative Polymers for Next-Generation Gas-Separation Membranes using Interpretable Machine Learning

Polymer membranes perform innumerable separations with far-reaching environmental implications. Despite decades of research on membrane technologies, design of new membrane materials remains a largely Edisonian process. To address this shortcoming, we demonstrate a generalizable, accurate machine-learning (ML) implementation for the discovery of innovative polymers with ideal separation performance. Specifically, multitask ML models are trained on available experimental data to link polymer chemistry to gas permeabilities of He, H2, O2, N2, CO2, and CH4. Here, we interpret the ML models and extract chemical heuristics for membrane design, through Shapley Additive exPlanations (SHAP) analysis. We then screen over nine million hypothetical polymers through our models and identify thousands of candidates that lie well above current performance upper bounds. Notably, we discover hundreds of never-before-seen ultrapermeable polymer membranes with O2 and CO2 permeability greater than 104 and 105 Barrer, respectively. These hypothetical polymers are capable of overcoming undesirable trade-off relationship between permeability and selectivity, thus significantly expanding the currently limited library of polymer membranes for highly efficient gas separations. High-fidelity molecular dynamics simulations confirm the ML-predicted gas permeabilities of the promising candidates, which suggests that many can be translated to reality.

Yang, Jason↗
DOE OSTI2012

Vehicle Technologies' Fact of the Week 2011

Each week the U.S. Department of Energy s Vehicle Technology Program (VTP) posts a Fact of the Week on their website: http://www1.eere.energy.gov/vehiclesandfuels/. These Facts provide statistical information, usually in the form of charts and tables, on vehicle sales, fuel economy, gasoline prices, and other transportation-related trends. Each Fact is a stand-alone page that includes a graph, text explaining the significance of the data, the supporting information on which the graph was based, and the source of the data. A link to the current Fact is available Monday through Friday on the VTP homepage, but older Facts are archived and still available at: http://www1.eere.energy.gov/vehiclesandfuels/facts/. This report is a compilation of the Facts that were posted during calendar year 2011. The Facts were written and prepared by staff in Oak Ridge National Laboratory's Center for Transportation Analysis.

02 PETROLEUM↗
DOE OSTI2013

Vehicle Technologies' Fact of the Week 2012

Each week the U.S. Department of Energy's Vehicle Technology Office (VTO) posts a Fact of the Week on their website (http://www1.eere.energy.gov/vehiclesandfuels/). These Facts provide statistical information, usually in the form of charts and tables, on vehicle sales, fuel economy, gasoline prices, and other transportation-related trends. Each Fact is a stand-alone page that includes a graph, text explaining the significance of the data, the supporting information on which the graph was based, and the source of the data. A link to the current week s Fact is available on the VTO homepage, but older Facts are archived and still available at: (http://www1.eere.energy.gov/vehiclesandfuels/facts/). This report is a compilation of the Facts that were posted during calendar year 2012. The Facts were written and prepared by staff in Oak Ridge National Laboratory's Center for Transportation Analysis.

Transportation Vehicles↗
DOE OSTI2014

Commercial and Residential Hourly Load Profiles for all TMY3 Locations in the United States

Note: This dataset has been superseded by the dataset found at "End-Use Load Profiles for the U.S. Building Stock" (submission 4520; linked in the submission resources), which is a comprehensive and validated representation of hourly load profiles in the U.S. commercial and residential building stock. The End-Use Load Profiles project website includes links to data viewers for this new dataset. For documentation of dataset validation, model calibration, and uncertainty quantification, see Wilson et al. (2022). These data were first created around 2012 as a byproduct of various analyses of solar photovoltaics and solar water heating (see references below for are two examples). This dataset contains several errors and limitations. It is recommended that users of this dataset transition to the updated version of the dataset posted in the resources. This dataset contains weather data, commercial load profile data, and residential load profile data. Weather The Typical Meteorological Year 3 (TMY3) provides one year of hourly data for around 1,000 locations. The TMY weather represents 30-year normals, which are typical weather conditions over a 30-year period. Commercial The commercial load profiles included are the 16 ASHRAE 90.1-2004 DOE Commercial Prototype Models simulated in all TMY3 locations, with building insulation levels changing based on ASHRAE 90.1-2004 requirements in each climate zone. The folder names within each resource represent the weather station location of the profiles, whereas the file names represent the building type and the representative city for the ASHRAE climate zone that was used to determine code compliance insulation levels. As indicated by the file names, all building models represent construction that complied with the ASHRAE 90.1-2004 building energy code requirements. No older or newer vintages of buildings are represented. Residential The BASE residential load profiles are five EnergyPlus models (one per climate region) representing 2009 IECC construction single-family detached homes simulated in all TMY3 locations. No older or newer vintages of buildings are represented. Each of the five climate regions include only one heating fuel type; electric heating is only found in the Hot-Humid climate. Air conditioning is not found in the Marine climate region. One major issue with the residential profiles is that for each of the five climate zones, certain location-specific algorithms from one city were applied to entire climate zones. For example, in the Hot-Humid files, the heating season calculated for Tampa, FL (December 1 - March 31) was unknowingly applied to all other locations in the Hot-Humid zone, which restricts heating operation outside of those days (for example, heating is disabled in Dallas, TX during cold weather in November). This causes the heating energy to be artificially low in colder parts of that climate zone, and conversely the cooling season restriction leads to artificially low cooling energy use in hotter parts of each climate zone. Additionally, the ground temperatures for the representative city were used across the entire climate zone. This affects water heating energy use (because inlet cold water temperature depends on ground temperature) and heating/cooling energy use (because of ground heat transfer through foundation walls and floors). Representative cities were Tampa, FL (Hot-Humid), El Paso, TX (Mixed-Dry/Hot-Dry), Memphis, TN (Mixed-Humid), Arcata, CA (Marine), and Billings, MT (Cold/Very-Cold). The residential dataset includes a HIGH building load profile that was intended to provide a rough approximation of older home vintages, but it combines poor thermal insulation with larger house size, tighter thermostat setpoints, and less efficient HVAC equipment. Conversely, the LOW building combines excellent thermal insulation with smaller house size, wider thermostat setpoints, and more efficient HVAC equipment. However, it is not known how well these ...

Array↗
DOE OSTI2018

Monthly Atmospheric 13C/12C Isotopic Ratios for 11 SIO Stations (1977-2008)

Stable isotopic measurements for atmospheric 13C/12C and 18O/16O at global sampling sites were initiated by Dr. C.D. Keeling and co-workers at Scripps Institution of Oceanography (SIO) in 1977. These isotopic measurements complement the continuing global atmospheric and oceanic CO2 measurements initiated by Keeling in 1957. This work is currently being continued under the direction of R.F. Keeling, who also runs a parallel program at SIO to measure changes in atmospheric O2 and Ar abundances (Scripps O2 Program). A more complete set of 13CO2 data is found online at http://scrippsco2.ucsd.edu/data/atmospheric_co2.htmlFor access to the data files, click this link to the CDIAC data transition website: http://cdiac.ess-dive.lbl.gov/trends/co2/iso-sio/iso-sio.html

12C↗
DOE OSTI2018

Globally Averaged Atmospheric CFC-11 Concentrations: Monthly and Annual Data for the Period 1975-1992 (DB1010)

This data set presents globally averaged atmospheric concentrations of chlorofluorocarbon 11, known also as CFC-11 or F-11 (chemical name: trichlorofluoromethane; formula: CCl3F). The monthly global average data are derived from flask air samples collected at eight sites in six locations over the period August 1980-July 1992. The sites are Barrow (Alaska), Cape Meares (Oregon), Cape Kumukahi and Mauna Loa (Hawaii), Cape Matatula (American Samoa), Cape Grim (Tasmania), Palmer Station, and the South Pole (Antarctica). At each collection site, monthly averages were obtained from three flask samples collected every week. In addition to the monthly global averages available for 1980-992, this data set also contains annual global average data for 1975-1985. These annual global averages were derived from January measurements at the South Pole and in the Pacific Northwest of the United States (specifically, Washington state and the Oregon coast).For access to the data files, click this link to the CDIAC data transition website: http://cdiac.ess-dive.lbl.gov/ndps/db1010.html

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2012

Daily Temperature and Precipitation Data for 518 Russian Meteorological Stations (1881 - 2010)

Over the past several decades, many climate datasets have been exchanged directly between the principal climate data centers of the United States (NOAA's National Climatic Data Center (NCDC)) and the former-USSR/Russia (All-Russian Research Institute for Hydrometeorological Information-World Data Center (RIHMI-WDC)). This data exchange has its roots in a bilateral initiative known as the Agreement on Protection of the Environment (Tatusko 1990). CDIAC has partnered with NCDC and RIHMI-WDC since the early 1990s to help make former-USSR climate datasets available to the public. The first former-USSR daily temperature and precipitation dataset released by CDIAC was initially created within the framework of the international cooperation between RIHMI-WDC and CDIAC and was published by CDIAC as NDP-040, consisting of data from 223 stations over the former USSR whose data were published in USSR Meteorological Monthly (Part 1: Daily Data). The database presented here consists of records from 518 Russian stations (excluding the former-USSR stations outside the Russian territory contained in NDP-040), for the most part extending through 2010. Records not extending through 2010 result from stations having closed or else their data were not published in Meteorological Monthly of CIS Stations (Part 1: Daily Data). The database was created from the digital media of the State Data Holding. The station inventory was arrived at using (a) the list of Roshydromet stations that are included in the Global Climate Observation Network (this list was approved by the Head of Roshydromet on 25 March 2004) and (b) the list of Roshydromet benchmark meteorological stations prepared by V.I. Kodratyuk, Head of the Department at Voeikov Main Geophysical Observatory.For access to the data files, click this link to the CDIAC data transition website: http://cdiac.ess-dive.lbl.gov/ndps/russia_daily518.html

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2012

DayRec: An Interface for Exploring United States Record-Maximum/Minimum Daily Temperatures

Like politics, you might say that all climate is local. As researchers seek to help the public better understand climate and climate change, a sensible approach would include helping people know more about changes in their own backyards. High and low temperatures are something that all of us pay attention to each day; when they are extreme (flirting with or setting records) they generate tremendous interest, largely because of the potential for significant impacts on human health, the environment, and built infrastructure. Changes through time in record high and low temperatures (extremes) are also an important manifestation of climate change (Sect. 3.8 in Trenberth et al. 2007; Peterson et al. 2008; Peterson et al. 2012). Meehl et al. (2009) found that currently, about twice as many high temperature records are being set as low temperature records over the conterminous U.S. (lower 48 states) as a whole. As the climate warms further, this ratio is expected to multiply, mainly because when the whole temperature distribution for a location or region shifts, it changes the "tails" of the distribution (in the case of warming this means fewer extreme cold temperatures and more extreme hot temperatures; see Page 2, Figure ES.1 of Karl et al. 2008). The Meehl et al. (2009) findings were covered pretty well by the online media, but, as is the case for all types of scientifc studies, it's safe to say that most of the public are not aware of these basic findings, and they would benefit from additional ways to get climate extremes information for their own areas and assess it. One such way is the National Climatic Data Center's (NCDC) U.S. Records Look-Up page. But how do most people typically hear about their area's high and low temperature records? Likely via the evening news, when their local on-air meteorologist notes the high/low for the day at a nearby airport then gives the years when the all-time high and low for the date were set (perhaps not at that same airport). The year of the record is an interesting bit of information on its own but it doesn't do much to place things in context. What about the local history of record temperatures and how things may be changing? Here we present a daily temperature records data product that we hope will serve the scientist and non-scientist alike in exploring and analyzing high and low temperature records and trends at hundreds of locations across the U.S. For access to the data files, click this link to the CDIAC data transition website: http://cdiac.ess-dive.lbl.gov/climate/temp/us_recordtemps/dayrec.html

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

Tree Ring Chronology Indexes and Reconstructions of Precipitation in Central Iowa, USA (1984) (NDP-002)

Tree core samples (4 mm in diameter) were extracted from the trunks of white oak (Quercus alba) at three sites in central Iowa (Duvick Back Woods, Ledges State Park, and Pammel). At least 60 trees were sampled at each site, and at least two cores were taken from each tree. The growth rings of each core were dated by calendar year and measured; the measurements were then transformed into dimensionless ring-width indices and correlated with annual precipitation. Data were collected for the years 1680 through 1979. Each tree ring was characterized by the site, year, tree-ring-width index, number of core samples, decade year, and the annual reconstructed precipitation estimate. These data have more than 50% of their variance in common with the known annual statewide average precipitation for Iowa and serve as useful indicators of the precipitation and drought history of the region for the past 300 years. The data are in two files: tree-ring-chronology data (8 kB) and the annual reconstructed precipitation data for central Iowa (2 kB).For access to the data files, click this link to the CDIAC data transition website: http://cdiac.ess-dive.lbl.gov/ndps/ndp002.html

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

An Updated Global Grid Point Surface Air Temperature Anomaly Data Set: 1851-1990 (revised 1991) (NDP-020)

This NDP presents land-based monthly surface-air-temperature anomalies (departures from a 1951-1970 reference period mean) on a 5° latitude by 10° longitude global grid. Monthly surface-air-temperature anomalies (departures from a 1957-1975 reference period mean) for the Antarctic (grid points from 65°S to 85°S) are presented in a similar way as a separate data set. The data were derived primarily from the World Weather Records and from the archives of the United Kingdom Meteorological Office. This long-term record of temperature anomalies may be used in studies addressing possible greenhouse-gas-induced climate changes. To date, the data have been employed in producing regional, hemispheric, and global time series for determining whether recent (i.e., post-1900) warming trends have taken place. The present updated version of this data set is identical to the earlier version for all records from 1851-1978 except for the addition of the Antarctic surface-air-temperature anomalies beginning in 1957. Beginning with the 1979 data, this package differs from the earlier version in several ways. Erroneous data for some sites have been corrected after a review of the actual station temperature data, and inconsistencies in the representation of missing values have been removed. For some grid locations, data have been added from stations that had not contributed to the original set. Data from satellites have also been used to correct station records where large discrepancies were evident. The present package also extends the record by adding monthly surface-air-temperature anomalies for the Northern (grid points from 85°N to 0°) and Southern (grid points from 5°S to 60°S) Hemispheres for 1985-1990. In addition, this updated package presents the monthly-mean-temperature records for the individual stations that were used to produce the set of gridded anomalies. The periods of record vary by station. Northern Hemisphere data have been corrected for inhomogeneities, while Southern Hemisphere data are presented in uncorrected form.For access to the data files, click this link to the CDIAC data transition website: http://cdiac.ess-dive.lbl.gov/ndps/ndp020.html

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

Historical Sunshine and Cloud Data in the United States (revised 1991) (NDP-021)

This data base presents monthly sunshine data from 240 U.S. stations (including Puerto Rico and nine Pacific Islands) and monthly cloud amount data from 197 U.S. stations. The longest periods of record are 1891 through 1987 for the sunshine data and 1871 through 1987 for the cloud data. The sunshine data were derived from measurements taken by a variety of sunshine-recording instruments. The cloud data were derived from land-based estimates of fractional cloud amount, which were made with observation practices that have varied during the period of record. Station number, station name, latitude, and longitude are given for all stations in each network. The sunshine data include monthly and annual total hours of recorded sunshine, monthly and annual maximum possible hours of sunshine, monthly and annual percentages of possible sunshine (hours recorded/hours possible), and dates of use for specific types of sunshine recorders at each station. The cloud data contain monthly and annual cloud amount (in percent of sky cover).For access to the data files, click this link to the CDIAC data transition website: http://cdiac.ess-dive.lbl.gov/ndps/ndp021.html

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

Monthly Mean Pressure Reconstructions for Europe (1780-1980) and North America (1858-1980) (1987) (NDP-025)

Real and reconstructed measurements of monthly mean pressure data have been constructed for Europe for 1780 through 1980 and North America for 1858 through 1980. The reconstructions use early pressure, temperature, and precipitation data from a variety of sources including World Weather Records, meteorological and national archives, circulation maps, and daily chart series. Each record contains the year, monthly mean pressure, quality code, and annual mean pressure. These reconstructed gridded monthly pressures provide a reliable historical record of mean sea-level pressures for Europe and North America. The data are in two files: pressure reconstructions for Europe (1.47 MB) and for North America (0.72 MB).For access to the data files, click this link to the CDIAC data transition website: http://cdiac.ess-dive.lbl.gov/ndps/ndp025.html

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2012

Cloud Climatology for Land Stations Worldwide, 1971-2009 (NDP-026D)

Surface synoptic weather reports for 39 years have been processed to provide a climatology of clouds for each of over 5000 land-based weather stations with long periods of record both day and night. For each station, this digital archive includes: multi-year annual, seasonal and monthly averages for day and night separately; seasonal and monthly averages by year; averages for eight times per day; and analyses of the first harmonic for the annual and diurnal cycles. Averages are given for total cloud cover, clear-sky frequency, and 9 cloud types: 5 in the low level (fog, St, Sc, Cu, Cb), 3 in the middle level (Ns, As, Ac) and one in the high level (all cirriform clouds combined). Cloud amounts and frequencies of occurrence are given for all types. In addition, non-overlapped amounts are given for middle and high cloud types, and average base heights are given for low cloud types. Nighttime averages were obtained by using only those reports that met an "illuminance criterion" (i.e., made under adequate moonlight or twilight), thus making possible the determination of diurnal cycles and nighttime trends for cloud types.The authors have also produced an online, gridded atlas of the cloud observations contained in NDP-026D. The Online Cloud Atlas containing NDP-026D data is available via the University of Washington.For access to the data files, click this link to the CDIAC data transition website: http://cdiac.ess-dive.lbl.gov/epubs/ndp/ndp026d/ndp026d.html

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

A Global Geographic Information System Data Base of Storm Occurrences and Other Climatic Phenomena Affecting Coastal Zones (1991) (NDP-035)

This NDP is unique in that it represents CDIAC's first offering of ARC/INFOTM export data files and equivalent flat ASCII data files that may be used by raster or vector geographic information systems (GISs). The data set contains 61 variables, including information on tropical storms, hurricanes, super typhoons, extratropical cyclogeneses, polar lows, cyclonicity, influence of winds in monsoon regions, and sea-ice concentrations. Increased availability of source data has made it possible to extend the area of these data variables to regional or global coverages. All data variables except five are referenced to 1° × 1° or 5° × 5° grid cells of latitude and longitude. These data help meet the demand for new and improved climatologies of storm events and may be used in climate research studies, including the verification of general circulation models and the calculation of storm-recurrence intervals.For access to the data files, click this link to the CDIAC data transition website: http://cdiac.ess-dive.lbl.gov/ndps/ndp035.html

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

Daily Temperature and Precipitation Data for 223 Former-USSR Stations (NDP-040)

The stations in this dataset are considered by RIHMI to comprise one of the best networks suitable for temperature and precipitation monitoring over the the former-USSR. Factors involved in choosing these 223 stations included length or record, amount of missing data, and achieving reasonably good geographic coverage. There are indeed many more stations with daily data over this part of the world, and hundreds more station records are available through NOAA's Global Historical Climatology Network - Daily (GHCND) database. The 223 stations comprising this database are included in GHCND, but different data processing, updating, and quality assurance methods/checks mean that the agreement between records will vary depending on the station. The relative quality and accuracy of the common station records in the two databases also cannot be easily assessed. As of this writing, most of the common stations contained in the GHCND have more recent records, but not necessarily records starting as early as the records available here. This database contains four variables: daily mean, minimum, and maximum temperature, and daily total precipitation (liquid equivalent). Temperature were taken three times a day from 1881-1935, four times a day from 1936-65, and eight times a day since 1966. Daily mean temperature is defined as the average of all observations for each calendar day. Daily maximum/minimum temperatures are derived from maximum/minimum thermometer measurements. See the measurement description file for further details. Daily precipitation totals are also available (to the nearest tenth of a millimeter) for each station. Throughout the record, daily precipitation is defined as the total amount of precipitation recorded during a 24-h period, snowfall being converted to a liquid total by melting the snow in the gauge. From 1936 on, rain gauges were checked several times each day; the cumulative total of all observations during a calendar day was presumably used as the daily total. Again, see the measurement description file for further details. For access to the data files, click this link to the CDIAC data transition website: http://cdiac.ess-dive.lbl.gov/ndps/ndp040.html

54 ENVIRONMENTAL SCIENCES↗