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At least 973 records · Page 54
All sourcesNASA NTRSDOE OSTI
NASA NTRS2022

A New Machine Learning Based Analysis for Improving Satellite Retrieved Atmospheric Composition Data: OMI SO2 as an Example

Despite recent progress, satellite retrievals of anthropogenic SO2 still suffer from relatively low signal-tonoise ratios. In this study, we demonstrate a new machine learning data analysis method to improve the quality of satellite SO2 products. In the absence of large ground-truth datasets for SO2, we start from SO2 slant column densities (SCDs) retrieved from the Ozone Monitoring Instrument (OMI) using a data-driven, physically based algorithm and calculate the ratio between the SCD and the root mean square (rms) of the fitting residuals for each pixel. To build the training data, we select presumably clean pixels with small SCD / rms ratios (SRRs) and set their target SCDs to zero. For polluted pixels with relatively large SRRs, we set the target to the original retrieved SCDs. We then train neural networks (NNs) to reproduce the target SCDs using predictors including SRRs for individual pixels, solar zenith, viewing zenith and phase angles, scene reflectivity, and O3 column amounts, as well as the monthly mean SRRs. For data analysis, we employ two NNs: (1) one trained daily to produce analyzed SO2 SCDs for polluted pixels each day and (2) the other trained once every month to produce analyzed SCDs for less polluted pixels for the entire month. Test results for 2005 show that our method can significantly reduce noise and artifacts over background regions. Over polluted areas, the monthly mean NN-analyzed and original SCDs generally agree to within ±15 %, indicating that our method can retain SO2 signals in the original retrievals except for large volcanic eruptions. This is further confirmed by running both the NN-analyzed and original SCDs through a topdown emission algorithm to estimate the annual SO2 emissions for ∼ 500 anthropogenic sources, with the two datasets yielding similar results. We also explore two alternative approaches to the NN-based analysis method. In one, we employ a simple linear interpolation model to analyze the original SCD retrievals. In the other, we develop a PCA–NN algorithm that uses OMI measured radiances, transformed and dimension-reduced with a principal component analysis (PCA) technique, as inputs to NNs for SO2 SCD retrievals. While the linear model and the PCA–NN algorithm can reduce retrieval noise, they both underestimate SO2 over polluted areas. Overall, the results presented here demonstrate that our new data analysis method can significantly improve the quality of existing OMI SO2 retrievals. The method can potentially be adapted for other sensors and/or species and enhance the value of satellite data in air quality research and applications.

Can Li↗
DOE OSTI2019

Evaluation of Geologic Carbon Storage Resource Estimates (SREs) of Cambrian Ordovician Units within the CarbonSAFE Prefeasibility Study Region Subtasks 4.2 and 4.3 – Topical Report

This report by the Indiana Geological & Water Survey (IGWS) assessed the storage resource estimate (SRE) of Cambrian-Ordovician stratigraphic units located within the CarbonSAFE prefeasibility area within the Illinois Basin. A comprehensive data set of wireline logs and petrophysical information was used. These data include core analysis for porosity and permeability, and existing well data including location and stratigraphic information. This report includes storage resource estimates (SREs) for four potential storage reservoirs (Upper Ordovician Trenton Limestone/Black River Group, Middle Ordovician St. Peter Sandstone, Lower Ordovician and Upper Cambrian Knox Supergroup, including a separate assessment for the lower Knox Potosi Dolomite. The resultant SREs volumes are displayed using georeferenced maps and boxplots. The latter allow for comparing data statistics (mean values and variability) between five methods employed for the SRE calculations. Differences observed in SRE results from the five methods are mainly attributable to differences in the data used for porosity in each method. ArcMap 10.5.1 software was used to portray SREs to help visualize spatial variance of estimates for each methodology, and more importantly, to highlight those areas having the greatest total storage potential estimates.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2021

Method for determining a histogram of variable sample rate waveforms

A computer-implemented method comprises receiving a plurality of sampled data points, each data point including a y value and a t value; defining an array of bins, each bin identified by a unique number and including histogram data for a range of y values; for each consecutive pair of data points including a current data point and a next data point, determining a corresponding one of a plurality of linear equations, each linear equation defining a line between the current data point and the next data point; for each line, determining an amount of time that the y value of the line is within the range of values for each bin from the current data point to the next data point; and adding the time to the histogram data for each bin.

Tohlen, Michael Aaron↗
NASA NTRS2019

Intercomparison of Aerosol Volume Size Distributions Derived from AERONET Ground-Based Remote Sensing and LARGE in Situ Aircraft Profiles During the 2011–2014 DRAGON and DISCOVER-AQ Experiments

Aerosol volume size distribution (VSD) retrievals from the Aerosol Robotic Network (AERONET) aerosol monitoring network were obtained during multiple DRAGON (Distributed Regional Aerosol Gridded Observational Network) campaigns conducted in Maryland, California, Texas and Colorado from 2011 to 2014. These VSD retrievals from the field campaigns were used to make comparisons with near-simultaneous in situ samples from aircraft profiles carried out by the NASA Langley Aerosol Group Experiment (LARGE) team as part of four campaigns comprising the DISCOVER-AQ (Deriving Information on Surface conditions from Column and Vertically Resolved Observations Relevant to Air Quality) experiments. For coincident (1 h) measurements there were a total of 91 profile-averaged fine-mode size distributions acquired with the LARGE ultra-high sensitivity aerosol spectrometer (UHSAS) instrument matched to 153 AERONET size distributions retrieved from almucantars at 22 different ground sites. These volume size distributions were characterized by two fine-mode parameters, the radius of peak concentration (rpeak_conc) and the VSD fine-mode width (widthpeak_conc). The AERONET retrievals of these VSD fine-mode parameters, derived from ground-based almucantar sun photometer data, represent ambient humidity values while the LARGE aircraft spiral profile retrievals provide dried aerosol (relative humidity; RH< 20 %) values. For the combined multiple campaign dataset, the average difference in rpeak_conc was 0:0330:035 μm (ambient AERONET values were 15.8% larger than dried LARGE values), and the average difference in widthpeak_conc was 0:0420:039 μm (AERONET values were 25.7% larger). For a subset of aircraft data, the LARGE data were adjusted to account for ambient humidification. For these cases, the AERONET–LARGE average differences were smaller, with rpeak_conc differing by 0:0110:019 μm (AERONET values were 5.2% larger) and widthpeak_conc average differences equal to 0:0300:037 μm (AERONET values were 15.8% larger).

Schafer, Joel S.↗
DOE OSTI2026

Water isotope data for the TEMPEST study site, 2023-2026

This dataset contains water isotope (deuterium (dD) and oxygen (d18O)) data from porewater, experimental source water, and aquifer sources from the Terrestrial Ecosystem Manipulation to Probe the Effects of Storm Treatments (TEMPEST) experimental field site in Edgewater, MD. Samples were taken from 2023-2026 and included in file "TEMPEST_Water_Isotope_LANL_2023-2026.csv". Porewater samples were taken from 15cm depth at multiple locations in each experimental plot (i.e., control, freshwater, and saltwater). Water samples were stored in 2 ml amber glass vials with septum caps under refrigeration until analysis. A L2140-i Picarro cavity ringdown spectrometer connected to an A0211 Picarro vaporization module was used to run water samples. Standards (United States Geological Survey (USGS) 47 and 48) were used to check instrument accuracy during each run of samples. Post-processing codes were run to calculate isotopic values from raw data. Reported isotope data is post-processed. Raw data CSV files from the Picarro were processed using a Python script and output as excel files containing data calibrated to Vienna Standard Mean Ocean Water (VSMOW). Standardization is based on USGS47 and USGS48 standards that are measured before and after every 5-10 unknowns. Every measurement is comprised of 10 injections. Processing involved discarding the first 4 injections due to a known memory effect. Raw means of the last 6 injections are taken for each measurement and then corrected from the linear calibration relationship of the USGS standards expected vs measured values to obtain corrected compositions relative to VSMOW. Reported isotope data are corrected mean results.

Aquifer↗
DOE OSTI2018

Nuclear Weapons Data Strategy

This document describes the data strategy for the Nuclear Weapons Program Management Unit (NW PMU), with emphasis on functions related to the product realization lifecycle. It describes a vision to more effectively value and utilize data as an asset: data often is our product, and when it isn't, our products are made possible only through the data produced and consumed throughout each product's lifecycle. True confidence in the nuclear deterrent requires a clear understanding of how our products perform against requirements; both in the near term as well as over long periods of time. Data is the foundation for this understanding, and as such is a key enabler of the success of the NW mission. This document defines principles that enable a "culture of care" around NW data and drive the specific approaches to create a data-centric environment that is sustainable, agile, and responsive to the needs of the NW mission. The strategy outlined within this document must guide the definition of a high-level roadmap to achieve this vision, which in turn will be supported by separate detailed implementation plans.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
NASA NTRS1976

All-sky monitor observations of flares from Aquila X-1

The All-Sky Monitor on Ariel 5 has observed the Aquila-Serpens region on a quasi-continuous basis since launch in 1974 October. During this time Aql X-1 exhibited major flares in 1975 June and 1976 June, with the measured X-ray intensities increasing more than an order of magnitude to approximately that measured from the Crab nebula, and remaining above the experiment threshold (approximately 0.1 x s crab) for almost two months in both instances. These outbursts resemble the X-ray light curves of transient sources such as A0620-00 and are interpreted in terms of episodic accretion in a dwarf nova-like binary. Combination of the epoch of phase minimum for the 1.3d period (1976) with the 1975 data yields a value of P = 1.28d + or - 0.02d with a corresponding modulation of approximately 3% (3-6 keV). Modulation at this period is not apparent in the 1976 data, with an upper limit of 2% during that time.

Kaluzienski, L. J.↗
NASA NTRS1977

Correlation of total, diffuse, and direct solar radiation

Present requirements for realistic solar energy system evaluations necessitate a comprehensive body of solar-radition data. The data should include both diffuse and direct solar radiation as well as their total on an hourly (or shorter) basis. In general, however, only the total solar radiation values were recorded. This report presents a correlation that relates the diffuse component of an hourly total solar radiation value to the total radiation ratio of the maximum value attainable. The data used were taken at the Blue Hill Observatory in Milton, Massachusetts, for the period 1952. The relation - in the form of the data plots - can be used in situations in which only the hourly total radiation data are available but the diffuse component is desired.

Buyco, E. H.↗
NASA NTRS1982

Global mean sea surface computation using GEOS 3 altimeter data

A mean sea surface map has been determined for the global ocean areas between +62 deg and -62 deg latitude using GEOS 3 altimeter data. A grid of laser reference orbits computed using the GEM 10B gravity model has been used to orient the altimeter data in a center of mass coordinate system. The density of the altimeter tracks has enabled the computation of the sea surface heights above the reference ellipsoid on 1 deg x 1 deg grid in most of the oceanic areas. In the northwest Atlantic the dense coverage has enabled computations on a 0.25 deg x 0.25 deg grid. Comparisons of the global surface with an independently computed mean sea surface based upon SEASAT altimeter data indicate an rms agreement of a little over a meter. Comparisons of the regional solution in the northwest Atlantic with SEASAT profiles indicate a precision of a few decimeters in this surface. An analysis of the global crossover differences has indicated the possibility of a timing error in the altimeter data. Timing bias values of within 0.5 msec of 9.2 msec for 1975 and within 0.6 msec of 18.7 msec for 1976 have been recovered from an analysis of the altimeter data.

Marsh, J. G.↗
NASA NTRS1987

Manual for Getdata Version 3.1: a FORTRAN Utility Program for Time History Data

This report documents version 3.1 of the GetData computer program. GetData is a utility program for manipulating files of time history data, i.e., data giving the values of parameters as functions of time. The most fundamental capability of GetData is extracting selected signals and time segments from an input file and writing the selected data to an output file. Other capabilities include converting file formats, merging data from several input files, time skewing, interpolating to common output times, and generating calculated output signals as functions of the input signals. This report also documents the interface standards for the subroutines used by GetData to read and write the time history files. All interface to the data files is through these subroutines, keeping the main body of GetData independent of the precise details of the file formats. Different file formats can be supported by changes restricted to these subroutines. Other computer programs conforming to the interface standards can call the same subroutines to read and write files in compatible formats.

Maine, Richard E.↗
NASA NTRS1981

A gradient model of vegetation and climate utilizing NOAA satellite imagery. Phase 1: Texas transect

A new experimental climatological model/variable termed the sponge, a measure of moisture availability based on daily temperature maxima and minima and precipitation, is tested for potential biogeographic, ecological, and agro-climatological applications. Results, depicted in tabular and graphic from, suggest that, as a generalized climatic index, sponge's simplicity and sensitivity make particularly appropriate for trans-regional biogeographic studies (e.g., large-area and global vegetation monitoring). The feasibility of utilizing NOAA/AVHRR data for vegetation classification was investigated and a vegetation gradient model that utilizes sponge, and AVHRR pixel data (channels 1 and 2) were obtained for 12 locations. The normalized difference values for the AVHRR data when plotted against vegetation characteristics (biomass, net productivity, leaf area) and sponge values suggest that a multivariate gradient model incorporating AVHRR and sponge data may indeed be useful in global vegetation stratification and monitoring.

Greegor, D. H.↗
DOE OSTI2023

Quality Assessment of Molten Salt Thermochemical Property Data

Recommendations are made to enhance user confidence in property values included in the Molten Salt Thermal Database-Thermochemical Properties (MSTDB-TC). Specifically, it is recommended that all available measured values be included with a transparent assessment of each to arrive at a preferred value for each salt composition in the database. The use of a quality assessment and ranking system is recommended for data and property values compiled in the MSTDB-TC that is analogous to the approach developed for data and property values included in the MSTDB-Thermophysical Properties (TP) database. The approach includes assessments of five features of data collection: the method used to measure the property value, calibration of devices used to make the measurements, the quantified uncertainty of the results, control of the environmental conditions, and determination of the salt composition. The quality of each facet is ranked as High, Moderate, or Incomplete based on objective metrics. The overall quality of the dataset is ranked based on rankings of the five data features and traceability of the reported property value, with rankings of A and B indicating the data and property value are deemed suitable for quantitative use, C indicating the value is computed and not validated and U indicating uncertainties restrict the recommended use to qualitative purposes. Sufficient information should be provided or cited within each data set included in the database to verify the reported property value. The recommended approach is suitable for assessing data already in the MSTDB-TC and screening new entries.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2011

Determining strain in amorphous alloys: Uncertainties with analyzing structural changes during deformation

Molecular dynamics simulations were utilized to test the reliability of strain values obtained from diffraction data for noncrystalline alloys. We found that in the case of a one-component system, the strain value obtained from the pair correlation functions underestimates the actual value because of a small degree of atomic relaxations, which minimize the effects of the applied deformation. In the case of multicomponent systems, the different pairs are affected by applied deformation to different extents; moreover, this implies that the strain value determined from diffraction data should depend on the type of scattering.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
NASA NTRS1974

Government-Industry Data Exchange Program (GIDEP)

The Government-Industry Data Exchange Program (GIDEP) was originated in 1959 by the Army, Navy, and Air Force Ballistic Missile Agencies. Known at that time as IDEP--Interservice Data Exchange Program--its intent was to eliminate duplicate testing of parts and components by disseminating pertinent test data among Department of Defense contractors and various government agencies. In 1966 both the National Aeronautics and Space Administration and the Canadian Military Electronics Standards Agency (CAMESA) recognized the value of the data provided by the program and became participants. Today, GIDEP provides the interchange of specialized technical data to all the military services, participating government contractors and numerous government agencies such as the Atomic Energy Commission, Federal Aviation Administration, Defense Supply Agency, and the Small Business Administration.

Richards, E. T.↗
DOE OSTI2018

Geocellular Model of Mt. Simon Sandstone for University of Illinois at Urbana-Champaign DDU feasibility study

The geocellular model of the Mt. Simon Sandstone was constructed for the University of Illinois at Urbana-Champaign DDU feasibility study. Starting with the initial area of review (18.0 km by 18.1 km [11.2 miles by 11.3 miles]) the boundaries of the model were trimmed down to 9.7 km by 9.7 km (6 miles by 6 miles) to ensure that the model enclosed a large enough volume so that the cones of depression of both the production and injection wells would not interact with each other, while at the same time minimizing the number of cells to model to reduce computational time. The grid-cell size was set to 61.0 m by 61.0 m (200 feet by 200 feet) for 160 nodes in the X and Y directions. Within the model, 67 layers are represented that are parameterized with their sediment/rock properties and petrophysical data. The top surface of the Mt. Simon Sandstone was provided by geologists working on the project, and the average thickness of the formation was taken from the geologic prospectus they provided. An average thickness of 762 m (2500 feet) was used for the Mt. Simon Sandstone, resulting in 60 layers for the model. Petrophysical data was taken from available rotary sidewall core data (Morrow et al., 2017). As geothermal properties (thermal conductivity, specific heat capacity) are closely related to mineralogy, specifically the percentage of quartz, available mineralogical data was assembled and used with published data of geothermal values to determine these properties (Waples and Waples, 2004; Robertson, 1988). The Mt. Simon Sandstone was divided into three separate units (lower, middle, upper) according to similar geothermal and petrophysical properties, and distributed according to available geophysical log data and prevailing interpretations of the depositional/diagenetic history (Freiburg et al. 2016). Petrophysical and geothermal properties were distributed through geostatistical means according to the associated distributions for each lithofacies. The formation temperature was calculated, based on data from continuous temperature geophysical log from a deep well drilled into the Precambrian basement at the nearby Illinois Basin Decatur Project (IBDP) where CO2 is currently being sequestered (Schlumberger, 2012). Salinity values used in the model were taken from regional studies of brine chemistry in the Mt. Simon Sandstone, including for the IBDP (e.g., Panno et al. 2018). After being reviewed by the project's geologists, the model was then passed onto the geological engineers to begin simulations of the geothermal reservoir and wellbores.

15 GEOTHERMAL ENERGY↗
NASA NTRS1992

Determination of mean rainfall from the Special Sensor Microwave/Imager (SSM/I) using a mixed lognormal distribution

Global estimates of monthly, seasonal, and annual oceanic rainfall are computed for a period of one year using data from the Special Sensor Microwave/Imager (SSM/I). Instantaneous rainfall estimates are derived from brightness temperature values obtained from the satellite data using the Hughes D-matrix algorithm. The instantaneous rainfall estimates are stored in 1 deg square bins over the global oceans for each month. A mixed probability distribution combining a lognormal distribution describing the positive rainfall values and a spike at zero describing the observations indicating no rainfall is used to compute mean values. The resulting data for the period of interest are fitted to a lognormal distribution by using a maximum-likelihood. Mean values are computed for the mixed distribution and qualitative comparisons with published historical results as well as quantitative comparisons with corresponding in situ raingage data are performed.

Berg, Wesley↗