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At least 91 records · Page 5

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NASA NTRS1998

Detection of Polar Stratospheric Clouds Using AVHRR Imagery

In recent years, the role of PSCs in the ozone depletion process has become better understood. PSCs provide the surfaces upon which heterogeneous reactions take place that affect the gas phase partitioning between active and reservoir chlorine and nitrogen species. Present methods of PSC detection include in situ measurements by lidar and various satellite-borne instruments such as the Stratospheric Aerosol Measurement II (SAM II) on the Nimbus 7 spacecraft, which produced PSC measurements from 1978 to 1994 and several instruments onboard the Upper Atmospheric Research Satellite (UARS) such as the Cryogenic Limb Array Etalon Spectrometer (CLAES) which provided measurements for 1991-1993. All of the PSC-detection methods devised so far have been hampered by incomplete sampling of the places and times in which PSCs are likely to form. There is a need to understand the climatology of PSCS, in particular the timing of their onset and duration, their vertical distribution, geographic extent, annual variability and responses to volcanic aerosol forcing. Poole and Pitts [1994] assembled a PSC climatology based on SAM II data, but this climatology is incomplete, as it is limited to the edge of the polar night due to the limitations of the solar occultation scan geometry. The Advanced Very High Resolution Radiometer (AVHRR) five- channel sensors onboard the NOAA polar-orbiting satellites have been collecting data over the polar regions continuously since 1979. These operational satellites provide unmatched coverage in space and time of both polar regions, but were not designed for the detection of optically-thin PSCS. However, the AVHRR data archive would be an invaluable source for the construction of a long-term climatology of PSCs if techniques can be developed and tested to detect PSCs in AVHRR data. In the last few years, the members of our group at San Francisco State University and NASA Ames Research Center have been engaged in the development of various PSC detection methods using AVHRR data. There is strong evidence that a subset of PSCS, those that are optically thick, can be readily identified in the AVHRR data set. Our group has also made significant progress in the identification of optically thinner PSCs using a variety of techniques.

Garcia, Oswaldo↗

NASA NTRS1999

1978-1988 Total Solar Irradiance (TSI) Variability Trends

Total solar irradiance (TSI), normalized to the mean earth-sun distance, is analyzed to assess long-term solar variability which may affect climate. TSI data sets are reviewed primarily from the 1984-1999 Earth Radiation Budgets Satellite (ERBS), 1978-1993 Nimbus7, 1980-1989 Solar Maximum Mission (SMM), 19911998 Upper Atmospheric Research Satellite (UARS), and 1996-1998 Solar and Heliospheric Observatory (SOHO)/ Variability of solar IRradiance and Gravity Oscillations (VIRGO) Spacecraft missions. The data sets indicate that 1365 W/sq m [Watts per meter square] is the most likely TSI amplitude at minimum solar magnetic activity as indicated by minimum sunspot numbers. The TSI long-term variability component was found to vary with a period of approximately 10 years and with an amplitude of 2 W/sq m. An empirical TSI fit model, based upon 10.7-cm solar radio fluxes and prompt photometric sunspot indices, was used to characterize TSI variability. Comparisons among TSI measurements and empirical fit trends are reviewed as well as inconsistencies among current spacecraft TSI data set trends. The 1996-1998, SOHO/VIRGO measurement indicate stronger TSI increasing trends than those suggested by the corresponding ERBS and UARS measurement and by the empirical model fit. 1978-1999 TSI data sets are analyzed to identify the probable existence of another long-term TSI variability component.

Lee, Robert B., III↗

NASA NTRS2004

Clustering with Missing Values: No Imputation Required

Clustering algorithms can identify groups in large data sets, such as star catalogs and hyperspectral images. In general, clustering methods cannot analyze items that have missing data values. Common solutions either fill in the missing values (imputation) or ignore the missing data (marginalization). Imputed values are treated as just as reliable as the truly observed data, but they are only as good as the assumptions used to create them. In contrast, we present a method for encoding partially observed features as a set of supplemental soft constraints and introduce the KSC algorithm, which incorporates constraints into the clustering process. In experiments on artificial data and data from the Sloan Digital Sky Survey, we show that soft constraints are an effective way to enable clustering with missing values.

constraints↗

NASA NTRS2011

Range 7 Scanner Integration with PaR Robot Scanning System

An interface bracket and coordinate transformation matrices were designed to allow the Range 7 scanner to be mounted on the PaR Robot detector arm for scanning the heat shield or other object placed in the test cell. A process was designed for using Rapid Form XOR to stitch data from multiple scans together to provide an accurate 3D model of the object scanned. An accurate model was required for the design and verification of an existing heat shield. The large physical size and complex shape of the heat shield does not allow for direct measurement of certain features in relation to other features. Any imaging devices capable of imaging the entire heat shield in its entirety suffers a reduced resolution and cannot image sections that are blocked from view. Prior methods involved tools such as commercial measurement arms, taking images with cameras, then performing manual measurements. These prior methods were tedious and could not provide a 3D model of the object being scanned, and were typically limited to a few tens of measurement points at prominent locations. Integration of the scanner with the robot allows for large complex objects to be scanned at high resolution, and for 3D Computer Aided Design (CAD) models to be generated for verification of items to the original design, and to generate models of previously undocumented items. The main components are the mounting bracket for the scanner to the robot and the coordinate transformation matrices used for stitching the scanner data into a 3D model. The steps involve mounting the interface bracket to the robot's detector arm, mounting the scanner to the bracket, and then scanning sections of the object and recording the location of the tool tip (in this case the center of the scanner's focal point). A novel feature is the ability to stitch images together by coordinates instead of requiring each scan data set to have overlapping identifiable features. This setup allows models of complex objects to be developed even if the object is large and featureless, or has sections that don't have visibility to other parts of the object for use as a reference. In addition, millions of points can be used for creation of an accurate model [i.e. within 0.03 in. (=0.8 mm) over a span of 250 in. (=635 mm)].

Schuler, Jason↗

NASA NTRS1986

Archival of Poker Flat MST radar data

The Poker Flat MST (mesosphere stratosphere troposphere) radar has operated from early 1979 to 1985. The data recorded during that time resides on some 1100 magnetic tapes. A second set containing only the derived parameters of velocity, width and signal to noise of the primary echo at each height, plus the noise spectra, occupies another 250 tapes. While processing to generate the compressed data set does correct some known errors, no attempt has been made to identify or remove spurious echoes. Because the Poker Flat data set is such a unique and valuable resource, a proposal is made to archive the data in forms more useful for analysis. The archived data set would contain only the parameters for significant echoes with contamination from airplanes, unwanted ionospheric returns, frequently aliased Doppler signals and other sources removed. The archived set should be as good or better than the example shown and may occupy only 25 to 50 tapes.

Riddle, A. C.↗

NASA NTRS1983

Status of the geopotential

Satellite laser ranging, satellite altimetry, and improved measurements of surface gravitational anomalies have broadened the data base on intermediate and short wavelength regions of the earth gravity field. The global data set served to develop new geopotential models with a resolution in spherical harmonics out to degree 180. The resolution was made possible using Seasat altimetry data containing 56,761 values of 1 x 1 deg gravity anomalies. Satellite-to-satellite tracking techniques involving the Geos-3 and Apollo spacecraft data for the sea surface temperature have yielded accurate intermediate wavelength gravity variations which correlate well with residual depth anomalies. Oceanic gravity anomalies have been computed directly from satellite altimetry or through statistical estimation using oceanic geoid heights. The data sets for gravimetric geoids have been compared with altimetric surfaces to identify areas which were of interest for geophysical investigation. Future data sets could become available from a proposed satellite-to-satellite Doppler tracking system (Gravsat) launched by NASA.

Lerch, F. J.↗

NASA NTRSDate not supplied

The Use of Atmospheric Composition Variable Standard Names in Airborne and Field Data Products

The number of variables measured during airborne field campaigns has increased more than tenfold over the last thirty years. With this increase in measurements, the complexity for distributed active archive centers (DAACs) to distribute the data and for data users to search for and find measurements of interest has also increased. Part of this complexity arises from the unique variable names in suborbital atmospheric composition field studies. With limited guidelines related to variable naming, variable names and structures can vary significantly, even for the same type of variable. It is common for instrument scientists to use their intended measurable quantity as the data variable name. This can make it difficult for users to locate and interact with a particular variable across multiple data sets. One effective solution to this problem, identified by the Earth Science Data System (ESDS) ICARTT Refresh Working Group [1], was to introduce variable standard names that can be used as tags for each data variable. This allows similar measurements (e.g., dew point) to be categorized and located across field campaigns, regardless of what variable name the instrument scientist has used. From this the atmospheric composition variable standard names were developed with the goal to use Findable, Accessible, Interoperable, and Reusable (FAIR) principles [2] and provide context for all users, while remaining connected to those in the subject area. These standard names have been successfully implemented in FIREX-AQ, CAMP 2EX, ACTIVATE, and DCOTSS field campaigns.

metadata↗

DOE OSTI2026

Generative AI for Grid Operations [Slides]

In the last few years, the development and use of generative artificial intelligence (AI) and large-language models (LLMs) have changed the landscape of how AI and machine learning (ML) are being used in power systems. LLMs are built on foundational models based on large data sets that can be trained to provide information rapidly and through simple natural language prompts. Generative AI can then perform human-like tasks using ML models to identify and mimic pattens in the data sets. This presentation explores how generative AI can enhance grid operations by improving forecasts, enabling rapid contingency analyses, and offering real-time operational suggestions. By providing grid operators with valuable insights, generative AI will empower them to manage power systems more effectively.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2025

Satellite Imagery of PV Site Storm Damage

"This repository contains multiple data sets focused on visible damage to photovoltaic (PV) installations following extreme weather events such as hailstorms and hurricanes. Data sets are split into two categories: the first category, the ‘manually labeled’ data, was compiled by researchers manually, and contains manually identified PV sites exposed to storms. The second data set, the ‘aggregated’ data, is a compilation of the manually labeled PV sites and deep learning-identified PV sites. The hail damage data set focuses on post-storm PV damage following a September 24, 2023 hailstorm in Austin, TX, which caused over $600 million in damages in the Austin metro area. The hurricane damage data set focuses on post-storm PV damage following Hurricanes Irma and Maria in Puerto Rico and the US Virgin Islands. Hurricanes Irma and Maria were back-to-back category 5 hurricanes, which pummeled the Caribbean and southeastern United States in September 2017, causing an estimated $115.2 billion in damages."

14 SOLAR ENERGY↗

NASA NTRS1983

Identification of general linear mechanical systems

Previous work in identification theory has been concerned with the general first order time derivative form. Linear mechanical systems, a large and important class, naturally have a second order form. This paper utilizes this additional structural information for the purpose of identification. A realization is obtained from input-output data, and then knowledge of the system input, output, and inertia matrices is used to determine a set of linear equations whereby we identify the remaining unknown system matrices. Necessary and sufficient conditions on the number, type and placement of sensors and actuators are given which guarantee identificability, and less stringent conditions are given which guarantee generic identifiability. Both a priori identifiability and a posteriori identifiability are considered, i.e., identifiability being insured prior to obtaining data, and identifiability being assured with a given data set.

Sirlin, S. W.↗

DOE OSTI2024

Geothermal Play Fairway Analysis of Low-Temperature Resources for Sedimentary Basin Geothermal Play Types: An Example in the Denver Basin

This project is part of a nationwide effort to highlight the advantages of incorporating low-temperature geothermal resource evaluation into the implementation of combined heat and power (CHP), and geothermal direct use (GDU) technologies (e.g., space heating and/or cooling). The initiative aims to hasten the nation's decarbonization process by exploring the potential for using low-temperature geothermal resources (< 150 Degrees Celsius) in selected sedimentary basins that have several population centers. The Play Fairway Analysis (PFA) techniques were modified from earlier studies of sedimentary basin geothermal play types (SBGPTs) that assessed the viability of low-temperature resources. The decision-making process for leveraging low-temperature geothermal resources for GDU and CHP applications is complex and considers a variety of factors, including geological, economic, and risk criteria. This study covers workflows, relevant datasets, python code, and both common and composite maps used to create low-temperature geothermal resource favorability maps for the Denver Basin, which extends across Colorado, Nebraska, and Wyoming. The replication of these methodologies in other SBGPTs can evaluate potential for low-temperature resources. The proposed geothermal PFA approach for low-temperature geothermal resources includes: (1) identifying available relevant data and grouping data sets into PFA criteria (e.g., geological, economic, and risk criteria); (2) analyzing data gaps enable future focalized exploration; (3) performing uncertainty quantification; (4) weighting relevant data; (5) developing favorability and common risk maps for low-temperature geothermal resources to identify potential locations for more focused data collection. This project will facilitate future deployment of CHP and GDU by providing data, tools, and a workflow applicable to low-temperature geothermal resources in sedimentary basins.

15 GEOTHERMAL ENERGY↗

NASA NTRS2024

QSF18 Nonresponse Follow-up Reminders Survey Data Supplemental File

This minimal data set contains anonymized study subject identifier (PARTICIPANT_ID) and non-response follow up type (group) from the single events surveys conducted during the Quiet Supersonic Flights 2018 risk reduction study in Galveston, Texas, in November 2018. Nonresponse follow up groups and procedures are defined and discussed in Page et al. 2020, Section 6.2 (NASA/CR-2020-220589/Volume I). The data cleaning conventions are consistent with the assumptions of Lee et al. in the treatment of the single events survey data (Lee, Rathsam, Wilson (2020). Journal of the Acoustical Society of America. 147, doi: 10.1121/10.0001021). Filename: reminder_groups.csv Dimensions: 371 rows by 2 columns. Variables: PARTICIPANT_ID, group PARTICPANT_ID: numeric (integer, six digits) group: character string taking one of four values ('Email - No Reminder'; 'Email - Reminder'; 'Text - No Reminder'; 'Text - Reminder').

sample survey↗

NASA NTRS2010

Atmospheric Risk Assessment for the Mars Science Laboratory Entry, Descent, and Landing System

In 2012, the Mars Science Laboratory (MSL) mission will pioneer the next generation of robotic Entry, Descent, and Landing (EDL) systems, by delivering the largest and most capable rover to date to the surface of Mars. As with previous Mars landers, atmospheric conditions during entry, descent, and landing directly impact the performance of MSL's EDL system. While the vehicle's novel guided entry system allows it to "fly out" a range of atmospheric uncertainties, its trajectory through the atmosphere creates a variety of atmospheric sensitivities not present on previous Mars entry systems and landers. Given the mission's stringent landing capability requirements, understanding the atmosphere state and spacecraft sensitivities takes on heightened importance. MSL's guided entry trajectory differs significantly from recent Mars landers and includes events that generate different atmospheric sensitivities than past missions. The existence of these sensitivities and general advancement in the state of Mars atmospheric knowledge has led the MSL team to employ new atmosphere modeling techniques in addition to past practices. A joint EDL engineering and Mars atmosphere science and modeling team has been created to identify the key system sensitivities, gather available atmospheric data sets, develop relevant atmosphere models, and formulate methods to integrate atmosphere information into EDL performance assessments. The team consists of EDL engineers, project science staff, and Mars atmospheric scientists from a variety of institutions. This paper provides an overview of the system performance sensitivities that have driven the atmosphere modeling approach, discusses the atmosphere data sets and models employed by the team as a result of the identified sensitivities, and introduces the tools used to translate atmospheric knowledge into quantitative EDL performance assessments.

EDL↗

DOE OSTI2026

Characterizing skyrmion flow phases with principal component analysis

Principal component analysis (PCA) is a powerful method that can identify patterns in large, complex data sets by constructing low-dimensional order parameters from higher-dimensional feature vectors. There are increasing efforts to use space-and-time-dependent PCA to detect transitions in nonequilibrium systems that are difficult to characterize with equilibrium methods. Here, we demonstrate that feature vectors incorporating the position and velocity information of driven skyrmions moving through random disorder permit PCA to resolve different types of disordered skyrmion motion as a function of driving force and the ratio of the Magnus force to the dissipation. Since the Magnus force creates gyroscopic motion and a finite Hall angle, skyrmions can exhibit a greater range of flow phases than what is observed in overdamped driven systems with quenched disorder. We show that in addition to identifying previously known skyrmion flow phases, PCA detects several additional phases, including different types of channel flow, moving fluids, and partially ordered states. Guided by the PCA analysis, we further characterize the disordered flow phases to elucidate the different microscopic dynamics and show that the changes in the PCA-derived order parameters can be connected to features in bulk transport measures, including the transverse and longitudinal velocity-force curves, differential conductivity, topological defect density, and changes in the skyrmion Hall angle as a function of drive. We discuss how asymmetric feature vectors can be used to improve the resolution of the PCA analysis, and how this technique can be extended to find disordered phases in other nonequilibrium systems with time-dependent dynamics.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Structural basis of differential gene expression at eQTLs loci from high-resolution ensemble models of 3D single-cell chromatin conformations

Abstract Motivation Techniques such as high-throughput chromosome conformation capture (Hi-C) have provided a wealth of information on nucleus organization and genome important for understanding gene expression regulation. Genome-Wide Association Studies have identified numerous loci associated with complex traits. Expression quantitative trait loci (eQTL) studies have further linked the genetic variants to alteration in expression levels of associated target genes across individuals. However, the functional roles of many eQTLs in noncoding regions remain unclear. Current joint analyses of Hi-C and eQTLs data lack advanced computational tools, limiting what can be learned from these data. Results We developed a computational method for simultaneous analysis of Hi-C and eQTL data, capable of identifying a small set of nonrandom interactions from all Hi-C interactions. Using these nonrandom interactions, we reconstructed large ensembles (×105) of high-resolution single-cell 3D chromatin conformations with thorough sampling, accurately replicating Hi-C measurements. Our results revealed many-body interactions in chromatin conformation at the single-cell level within eQTL loci, providing a detailed view of how 3D chromatin structures form the physical foundation for gene regulation, including how genetic variants of eQTLs affect the expression of associated eGenes. Furthermore, our method can deconvolve chromatin heterogeneity and investigate the spatial associations of eQTLs and eGenes at subpopulation level, revealing their regulatory impacts on gene expression. Together, ensemble modeling of thoroughly sampled single-cell chromatin conformations combined with eQTL data, helps decipher how 3D chromatin structures provide the physical basis for gene regulation, expression control, and aid in understanding the overall structure-function relationships of genome organization. Availability and implementation It is available at https://github.com/uic-liang-lab/3DChromFolding-eQTL-Loci.

Du, Lin (ORCID:0009000289869812)↗

NASA NTRS2014

Detection of Unexpected High Correlations between Balance Calibration Loads and Load Residuals

An algorithm was developed for the assessment of strain-gage balance calibration data that makes it possible to systematically investigate potential sources of unexpected high correlations between calibration load residuals and applied calibration loads. The algorithm investigates correlations on a load series by load series basis. The linear correlation coefficient is used to quantify the correlations. It is computed for all possible pairs of calibration load residuals and applied calibration loads that can be constructed for the given balance calibration data set. An unexpected high correlation between a load residual and a load is detected if three conditions are met: (i) the absolute value of the correlation coefficient of a residual/load pair exceeds 0.95; (ii) the maximum of the absolute values of the residuals of a load series exceeds 0.25 % of the load capacity; (iii) the load component of the load series is intentionally applied. Data from a baseline calibration of a six-component force balance is used to illustrate the application of the detection algorithm to a real-world data set. This analysis also showed that the detection algorithm can identify load alignment errors as long as repeat load series are contained in the balance calibration data set that do not suffer from load alignment problems.

strain-gage balance↗

DOE OSTI2026

SAIL Field Campaign X-Band Precipitation Radar Seasonal Surface Hydrometeor Phase Classification Value-Added Product Report

The Surface Atmosphere Integrated Field Laboratory (SAIL) campaign generated an unprecedented, multisensory record of cold-season precipitation in the Upper Colorado River Basin (UCRB). To complement the Surface Quantitative Precipitation Estimation (SQUIRE) snow water equivalent (SWE) product derived from the Colorado State University (CSU) X-band radar, a hydrometeor phase classification data set was developed to systematically identify phase of the near-surface precipitation. The algorithm uses Corrected Moments in Antenna Coordinates (CMAC) fields with semi-supervised classification (SSC) and fuzzy-logic hydrometeor classification (FHC) methods and maps hydrometeor classes to a compact set of phase classes suitable for Quantitative Precipitation Estimation (QPE) and hydrologic applications. We used SQUIRE grid projections to mitigate beam-blockage issues by selecting the near-surface gates for each beam as well as to make it directly usable for the QPE products.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS1979

Evaluation of several schemes for classification of remotely sensed data: Their parameters and performance

The author has identified the following significant results. Data sets for corn, soybeans, winter wheat, and spring wheat were used to evaluate the following schemes for crop identification: (1) per point Gaussian maximum classifier; (2) per point sum of normal densities classifiers; (3) per point linear classifier; (4) per point Gaussian maximum likelihood decision tree classifiers; and (5) texture sensitive per field Gaussian maximum likelihood classifier. Test site location and classifier both had significant effects on classification accuracy of small grains; classifiers did not differ significantly in overall accuracy, with the majority of the difference among classifiers being attributed to training method rather than to the classification algorithm applied. The complexity of use and computer costs for the classifiers varied significantly. A linear classification rule which assigns each pixel to the class whose mean is closest in Euclidean distance was the easiest for the analyst and cost the least per classification.

Scholz, D.↗