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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 973 records · Page 54
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Improving “Domain-Relevant Metadata Requirements” for Supporting Open-Source Science Initiative

Implementation of the NASA Open-Source Science Initiative (OSSI) requires sharing of all relevant information to ensure “open reproducible science” [1]. However, there are several challenges in applying the OSSI to airborne field campaigns focused on atmospheric composition, which often involve a wide variety of in-situ measurements for trace gases, aerosol and cloud properties, meteorological parameters, and radiation fields. To ensure open reproducibility from airborne field campaigns, it is essential to obtain detailed measurement descriptions, which include the detection principle, sample procedure and treatment, and data processing and correction method. The challenge is that some information, e.g., sampling procedure and treatment, may be instrument-specific and campaign or platform-dependent. The data processing may also involve empirical corrections which may evolve over time. In addition, these details (especially operation- or campaign-specific ones) are often not given in journal publications. Given these issues, there is a need to leverage and improve the current “domain-relevant metadata requirements” to represent the measurement description in standardized metadata. These requirements can then facilitate systematic collection of measurement specific metadata and serve as a foundation to develop tools for making the information accessible and data more interoperable and usable or reusable. Here we show a review of existing metadata collections, use cases, and needs for new standards.

Sean Leavor↗
NASA NTRS1990

Assessment of tropical forest stand characteristics with multipolarization SAR data acquired over a mountainous region in Costa Rica

A digital terrain elevation data set was coregistered with radar data for assessing tropical forest stand characteristics. Both raw and topographically corrected L-band polarimetric radar data acquired over the tropical forests of Costa Rica were analyzed and correlated with field-collected tree parameter data to study the stand characteristics. The results of analyses using 18 out of 81 plots for sites A and B indicated that per-plot bole volume and tree volume are related to SAR data, particularly at site A. The topographically corrected SAR data appear to produce the same findings as those of uncorrected data.

Wu, Shih-Tseng↗
DOE OSTI2016

The Python ARM Radar Toolkit (Py-ART), a library for working with weather radar data in the Python programming language

The Python ARM Radar Toolkit is a package for reading, visualizing, correcting and analysing data from weather radars. Development began to meet the needs of the Atmospheric Radiation Measurement Climate Research Facility and has since expanded to provide a general-purpose framework for working with data from weather radars in the Python programming language. The toolkit is built on top of libraries in the Scientific Python ecosystem including NumPy, SciPy, and matplotlib, and makes use of Cython for interfacing with existing radar libraries written in C and to speed up computationally demanding algorithms. As a result, the source code for the toolkit is available on GitHub and is distributed under a BSD license.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1979

Effect of the atmosphere on the classification of LANDSAT data

The author has identified the following significant results. In conjunction with Turner's model for the correction of satellite data for atmospheric interference, the LOWTRAN-3 computer was used to calculate the atmospheric interference. Use of the program improved the contrast between different natural targets in the MSS LANDSAT data of Brasilia, Brazil. The classification accuracy of sugar canes was improved by about 9% in the multispectral data of Ribeirao Preto, Sao Paulo.

Dejesusparada, N.↗
DOE OSTI2020

Piezoresponse amplitude and phase quantified for electromechanical characterization

Piezoresponse force microscopy (PFM) is a powerful characterization technique to readily image and manipulate the ferroelectric domains. PFM gives an insight into the strength of local piezoelectric coupling and polarization direction through PFM amplitude and phase, respectively. Converting measured arbitrary units into units of effective piezoelectric constant remains a challenge, and insufficient methods are often used. While most quantification efforts have been spent on quantifying the PFM amplitude signal, little attention has been given to the PFM phase, which is often arbitrarily adjusted to fit expectations. This is problematic when investigating materials with unknown or negative sign of the probed effective electrostrictive coefficient or strong frequency dispersion of electromechanical responses, because assumptions about the PFM phase cannot be reliably made. The PFM phase can, however, provide important information on the polarization orientation and the sign of the effective electrostrictive coefficient probed by PFM. Most notably, the orientation of the PFM hysteresis loop is determined by the PFM phase. Moreover, when presenting PFM data as a combined signal, the resulting response can be artificially lowered or asymmetric if the phase data have not been correctly processed. Here, we explain the PFM amplitude quantification process and demonstrate a path to identify the phase offset required to extract correct meaning from the PFM phase data. We explore different sources of phase offsets including the experimental setup, instrumental contributions, and data analysis. We discuss the physical working principles of PFM and develop a strategy to extract physical meaning from the PFM amplitude and phase.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Validation of non-negative matrix factorization for rapid assessment of large sets of atomic pair distribution function data

The use of the non-negative matrix factorization (NMF) technique is validated for automatically extracting physically relevant components from atomic pair distribution function (PDF) data from time-series data such as in situ experiments. The use of two matrix-factorization techniques, principal component analysis and NMF, on PDF data is compared in the context of a chemical synthesis reaction taking place in a synchrotron beam, applying the approach to synthetic data where the correct composition is known and on measured PDFs from previously published experimental data. The NMF approach yields mathematical components that are very close to the PDFs of the chemical components of the system and a time evolution of the weights that closely follows the ground truth. Lastly, it is discussed how this would appear in a streaming context if the analysis were being carried out at the beamline as the experiment progressed.

36 MATERIALS SCIENCE↗
DOE OSTI2016

ORNL Interim Progress Report on Static CIRFT Testing Curvature Data Update

Since the CIRFT tests reported in NUREG-7198 were generated, a number of factors that influence the recorded curvature measurement data were identified. In 2016, a data reanalysis task was undertaken to implement the lessons learned. This letter report provides the revised results of previous CIRFT tests, after implementing the following data reanalysis procedures: (A) experimental data smoothing and LVDT reset, (B) LVDT probe contact and sensor spacing correction for curvature data, and (C) LVDT probe dynamic vibration adjustment procedure development.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
NASA NTRS1979

Radiometric correction and equalization of satellite digital data

Satellite digital data from Landsat and NOAA satellites is often marred by striping or streaking errors due to variations in the response of the radiometric sensors. In this paper, we discuss the equalization of the digital data as a preprocessing step, prior to image enhancement or automatic classification. The methods described make use of statistics of the data itself to generate nonlinear or linear memory-less equalization algorithms. These algorithms, by contrast to multidimensional filtering, do not result in a loss of spatial resolution. Examples of applications to Landsat and NOAA-3 thermal infrared data are given and illustrated.

Algazi, V. R.↗
NASA NTRS1981

Data Quality: 1979 Southeastern Virginia Urban Plume Study (SEV-UPS): Surface and Airborne studies

The SEV-UPS field study conducted during August 1979 involved the coordinated air quality data collection efforts of three different organizations operating a total of four aircraft and 12 ground stations. Incorporated into plans for that program were numerous quality assurance activities designed to identify potential hazards for data quality in time for their correction and to take data to allow assessment of the consistency of the data from different stations. The purpose of this study was to document QA procedures that were performed by participating agencies and revise data from all platforms for mutual compatibility. For the latter effort, cases were identified on 21 occasions on seven different days where two or more monitoring systems were operating in the same location at nearly the same time. Data from the systems involved were tabulated in 200 m altitude segments, averaged and compared to detect any bias between measurements made from different systems. The results of these comparisons along with the results of independent performance audits are presented.

White, J. H.↗
NASA NTRS1981

MAGSAT data processing: A report for investigators

The in-flight attitude and vector magnetometer data bias recovery techniques and results are described. The attitude bias recoveries are based on comparisons with a magnetic field model and are thought to be accurate to 20 arcsec. The vector magnetometer bias recoveries are based on comparisons with the scalar magnetometer data and are thought to be accurate to 3 nT or better. The MAGSAT position accuracy goals of 60 m radially and 300 m horizontally were achieved for all but the last 3 weeks of Magsat lifetime. This claim is supported by ephemeris overlap statistics and by comparisons with ephemerides computed with an independent orbit program using data from an independent tracking network. MAGSAT time determination accuracy is estimated at 1 ms. Several errors in prelaunch assumptions regarding data time tags, which escaped detection in prelaunch data tests, and were discovered and corrected postlaunch are described. Data formats and products, especially the Investigator-B tapes, which contain auxiliary parameters in addition to the basic magnetometer and ephemeris data, are described.

Langel, R. A.↗
NASA NTRS1982

Off-nadir antenna bias correction using Amazon rain sigma(0) data

The radar response from the Amazon rain forest was studied to determine the suitability of this region for use as a standard target to calibrate a scatterometer like that proposed for the National Oceanic Satellite System (NOSS). Backscattering observations made by the SEASAT Scatterometer System (SASS) showed the Amazon rain forest to be a homogeneous, azimuthally-isotropic, radar target which was insensitive to polarization. The variation with angle of incidence was adequately modeled as scattering coefficient (dB) = a theta b with typical values for the incidence-angle coefficient from 0.07 to 0.15 dB/deg. A small diurnal effect occurs, with measurements at sunrise being 0.5 dB to 1 dB higher than the rest of the day. Maximum-likelihood estimation algorithms presented here permit determination of relative bias and true pointing angle for each beam. Specific implementation of these algorithms for the proposed NOSS scatterometer system is also discussed.

Birrer, I. J.↗
NASA NTRS1990

Ground-based studies of emission-line variability: Recent results for NGC 5548 and future plans

Ever since emission-line variability was first detected in AGNs, it has been clearly understood that light-travel time effects in the broadline region (BLR) afford a tool for studying the structure of these spatially unresolved regions. However, only within the last few years has the observational problem become well defined in terms of the sampling rates and quality of data necessary to address the problem correctly. The amount of data required to extract structural information about the BLR from the continuum and emission-line light curves is so considerable that the most promising approach seemed to be to combine the observational efforts. The goal was to work together to produce a large, high-quality database which would then be released to individual investigators for more complete analysis. The cornerstone of this effort, was a joint NASA/ESA/SERC program to monitor the spectrum of NGC 5548 every four days with IUE from December 1988 through August 1988. A concurrent ground-based program was organized in an effort to enhance the scientific return on the project by extending the wavelength coverage and providing higher spectral resolution and signal to noise ratios than would be possible with IUE. It also turned out that the temporal resolution of the ground-based program was somewhat better than the temporal resolution of the IUE program, and the temporal baseline is longer and continues to grow. The database and initial results are presented by Peterson et al., (1991). The important results of the ground-based program are summarized, some additional applications of the existing data are described, and a few comments are made on what can be done in the future to expand on this work.

Peterson, Bradley M.↗
NASA NTRS1997

SBUV/2 Long-Term Measurements of Solar Spectral Variability

The NOAA-11 SBUV/2 spectral solar data have been corrected for long-term instrument changes to produce a 5.5 year data record during solar cycle 22 (December 1988 - October 1994). Residual drifts in the data at long wavelengths are +/- 1% or less. At 200-205 nm, where solar variations drive stratospheric photochemistry, these data indicate long-term solar changes of 5-7% from the maximum of Cycle 22 in April 1991 through the end of the NOAA-11 data record. Comparisons of NOAA-11 data with UARS SUSIM and SOLSTICE for the period October 1991 - October 1994, when all 3 instruments were operating simultaneously, show that the observed long-term variations in 200-205 nm irradiance agree to within 2%. This result is consistent with predictions from the Mg-2 proxy index. The SBUV/2 instruments represent a valuable resource for long-term solar UV activity studies because of their overlapping data records. In addition to the NOAA-11 data presented here, the NOAA-9 SBUV/2 instrument began taking data in March 1985 and is still operating, providing a complete record of Cycle 22 behavior from a single instrument. Three additional SBUV/2 instruments are scheduled to be launched between 1997 and 2003, which should permit full coverage of solar cycle 23.

DeLand, Matthew T.↗
NASA NTRS2012

Estimation and Bias Correction of Aerosol Abundance using Data-driven Machine Learning and Remote Sensing

Air quality information is increasingly becoming a public health concern, since some of the aerosol particles pose harmful effects to peoples health. One widely available metric of aerosol abundance is the aerosol optical depth (AOD). The AOD is the integrated light extinction coefficient over a vertical atmospheric column of unit cross section, which represents the extent to which the aerosols in that vertical profile prevent the transmission of light by absorption or scattering. The comparison between the AOD measured from the ground-based Aerosol Robotic Network (AERONET) system and the satellite MODIS instruments at 550 nm shows that there is a bias between the two data products. We performed a comprehensive analysis exploring possible factors which may be contributing to the inter-instrumental bias between MODIS and AERONET. The analysis used several measured variables, including the MODIS AOD, as input in order to train a neural network in regression mode to predict the AERONET AOD values. This not only allowed us to obtain an estimate, but also allowed us to infer the optimal sets of variables that played an important role in the prediction. In addition, we applied machine learning to infer the global abundance of ground level PM2.5 from the AOD data and other ancillary satellite and meteorology products. This research is part of our goal to provide air quality information, which can also be useful for global epidemiology studies.

Malakar, Nabin K.↗
NASA NTRS2021

Application of Gaussian Mixture Regression for the Correction of Low Cost PM2.5 Monitoring Data in Accra, Ghana

Low-cost sensors (LCSs) for air quality monitoring have enormous potential to improve air quality data coverage in resource-limited parts of the world such as sub-Saharan Africa. LCSs, however, are affected by environment and source conditions. To establish high-quality data, LCSs must be collocated and calibrated with reference grade PM2.5 monitors. From March 2020, a low-cost PurpleAir PM2.5 monitor was collocated with a Met One Beta Attenuation Monitor 1020 in Accra, Ghana. While previous studies have shown that multiple linear regression (MLR) and random forest regression (RF) can improve accuracy and correlation between PurpleAir and reference data, MLR and RF yielded suboptimal improvement in the Accra collocation (R2 = 0.81 and R2 = 0.81, respectively). We present the first application of Gaussian mixture regression (GMR) to air quality data calibration and demonstrate improvement over traditional methods by increasing the collocated PM2.5 correlation and accuracy to R2 = 0.88 and MAE = 2.2 μg/cu. m. Gaussian mixture models (GMMs) are a probability density estimator and clustering method from which nonlinear regressions that tolerate missing inputs can be derived. We find that even when given missing inputs, GMR provides better correlation than MLR and RF performed with complete data. GMR also allows us to estimate calibration certainty. When evaluated, 95% confidence intervals agreed with reference PM2.5 data 96% of the time, suggesting that the model accurately assesses its own confidence. Additionally, clustering within the GMM is consistent with climate characteristics, providing confidence that the calibration approach can learn underlying relationships in data.

Sensors↗
DOE OSTI2021

Data Product: Ka ARM Zenith Radar (KAZR), in CfRadial format: precipitation mode, corrected

The KAZRCFRCOR Value Added Product (VAP) performs several corrections to the ingested KAZR data and also creates a significant detection mask for each radar mode. The VAP computes gaseous attenuation as a function of time and radial distance from the radar antenna, based on ambient relative humidity, and corrects observed reflectivities for that effect. KAZR reflectivities are also corrected in the antenna ‘nearfield’ range to compensate for the near-field antenna pattern and gain. KAZRCFRCOR also dealiases mean Doppler velocities, to correct velocities whose magnitudes exceed the radar’s Nyquist velocity. This dataset contains the 'kazrpr' (precipitation) mode output from this VAP.

54 ENVIRONMENTAL SCIENCES↗