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At least 667 records · Page 37

Evaluation of multiband, multitemporal, and transformed LANDSAT MSS data for land cover area estimation

Sample segments of ground-verified land cover data collected in conjunction with the USDA/ESS June Enumerative Survey were merged with LANDSAT data and served as a focus for unsupervised spectral class development and accuracy assessment. Multitemporal data sets were created from single-date LANDSAT MSS acquisitions from a nominal scene covering an eleven-county area in north central Missouri. Classification accuracies for the four land cover types predominant in the test site showed significant improvement in going from unitemporal to multitemporal data sets. Transformed LANDSAT data sets did not significantly improve classification accuracies. Regression estimators yielded mixed results for different land covers. Misregistration of two LANDSAT data sets by as much and one half pixels did not significantly alter overall classification accuracies. Existing algorithms for scene-to scene overlay proved adequate for multitemporal data analysis as long as statistical class development and accuracy assessment were restricted to field interior pixels.

Stoner, E. R.↗

The analytical representation of viscoelastic material properties using optimization techniques

This report presents a technique to model viscoelastic material properties with a function of the form of the Prony series. Generally, the method employed to determine the function constants requires assuming values for the exponential constants of the function and then resolving the remaining constants through linear least-squares techniques. The technique presented here allows all the constants to be analytically determined through optimization techniques. This technique is employed in a computer program named PRONY and makes use of commercially available optimization tool developed by VMA Engineering, Inc. The PRONY program was utilized to compare the technique against previously determined models for solid rocket motor TP-H1148 propellant and V747-75 Viton fluoroelastomer. In both cases, the optimization technique generated functions that modeled the test data with at least an order of magnitude better correlation. This technique has demonstrated the capability to use small or large data sets and to use data sets that have uniformly or nonuniformly spaced data pairs. The reduction of experimental data to accurate mathematical models is a vital part of most scientific and engineering research. This technique of regression through optimization can be applied to other mathematical models that are difficult to fit to experimental data through traditional regression techniques.

Hill, S. A.↗

Using PCDS to study the influence of UV flux variation on the middle atmosphere

The value of the Pilot Climate Data System (PCDS) in examining the influences of short-period solar flux variations on the middle atmosphere was demonstrated. Several Nimbus satellite data sets proposed for the study exist in the PCDS. Planned for retrieval through the PCDS are ozone mixing ratios and cumulative ozone profiles available from the Backscatter Ultraviolet Spectrophotometer on Nimbus-4. Also to be accessed are Nimbus-7 data sets that will provide ozone and nitrogen dioxide mixing ratios, temperature profiles from the Limb Infrared Monitor of the Stratosphere, and ozone mixing ratio profiles from the Solar Backscatter Ultraviolet Spectrophotometer. Bypassing the time-consuming process of reading raw data tapes, the researchers plan to transfer the PCDS processed data to an IBM PC at Iowa State University. The IBM PC will serve as an intermediate vehicle for transferring the data to the NOAA CYBER 840 at Boulder, Colorado, where research will continue on both an eight-layer radiative-photochemical numerical model and on a nonlinear dynamical model. in this research are the FGGE and ERB data sets. These relevant data sets also reside in the PCDS. Problems associated with remote access to the PCDS were discussed in regard to all the PCDS data sets.

Heilman, W.↗

An interactive Barnes objective map analysis scheme for use with satellite and conventional data

The Barnes (1973) objective map analysis scheme is employed to develop an interactive analysis package for assessing the impact of satellite-derived data on analyses of conventional meteorological data sets. The method permits modification of the values of input parameters in the objective analysis within objectively determined, internally set limits. The effects of the manipulations are rapidly displayed, and methods are included for assimilating the spatially clustered characteristics of satellite data and the various horizontal resolutions of the data types. Data sets from the SESAME rawinsonde wind data with uniform spatial distribution, with the same data set plus satellite cloud motion data, and a data set from the atmospheric sounder radiometer on the GOES satellite were analyzed as examples. The scheme is demonstrated to recover details after two iterations through the data.

Koch, S. E.↗

Relationship Between Column-Density and Surface Mixing Ratio: Statistical Analysis of O3 and NO2 Data from the July 2011 Maryland DISCOVER-AQ Mission

To investigate the ability of column (or partial column) information to represent surface air quality, results of linear regression analyses between surface mixing ratio data and column abundances for O3 and NO2 are presented for the July 2011 Maryland deployment of the DISCOVER-AQ mission. Data collected by the P-3B aircraft, ground-based Pandora spectrometers, Aura/OMI satellite instrument, and simulations for July 2011 from the CMAQ air quality model during this deployment provide a large and varied data set, allowing this problem to be approached from multiple perspectives. O3 columns typically exhibited a statistically significant and high degree of correlation with surface data (R(sup 2) > 0.64) in the P- 3B data set, a moderate degree of correlation (0.16 < R(sup 2) < 0.64) in the CMAQ data set, and a low degree of correlation (R(sup 2) < 0.16) in the Pandora and OMI data sets. NO2 columns typically exhibited a low to moderate degree of correlation with surface data in each data set. The results of linear regression analyses for O3 exhibited smaller errors relative to the observations than NO2 regressions. These results suggest that O3 partial column observations from future satellite instruments with sufficient sensitivity to the lower troposphere can be meaningful for surface air quality analysis.

nitrogen dioxide↗

SPARC Intercomparison of Middle Atmosphere Climatologies

This atlas presents detailed incomparisons of several climatological wind and temperature data sets which cover the middle atmosphere (over altitudes approx. 10-80 km). A number of middle atmosphere climatologies have been developed in the research community based on a variety of meteorological analyses and satellite data sets. Here we present comparisons between these climatological data sets for a number of basic circulation statistics, such as zonal mean temperature, winds and eddy flux statistics. Special attention is focused on tropical winds and temperatures, where large differences exist among separate analyses. We also include comparisons between the global climatologies and historical rocketsonde wind and temperature measurements, and also with more recent lidar temperature data. These comparisons highlight differences and uncertainties in contemporary middle atmosphere data sets, and allow biases in particular analyses to be isolated. In addition, a brief atlas of zonal mean temperature and wind statistics is provided to highlight data availability and as a quick-look reference. This technical report is intended as a companion to the climatological data sets held in archive at the SPARC Data Center (http://www.sparc.sunysb.edu).

Randel, William↗

The Rings Node for the Planetary Data System

The Planetary Data System's Rings Node is devoted to the archiving and distributing of scientific data sets relevant to planetary ring systems. The two major classes of ring data are images and occultation profiles, although a variety of additional data types (e.g. spectra, particle absorption signatures, etc.) are also of interest. A large fraction of our data sets are from the Voyager missions to the outer planets, but Earth-based and Hubble Space Telescope data sets are also represented. Archiving work often includes re-formatting the data into standardized formats and reconstructing some of the data processing steps. The Rings Node also performs a variety of services to support research into these data sets. These services include developing on-line catalogs and information systems, filling orders for data, developing software tools, and coordinating special observing campaigns.

Showalter, Mark R.↗

User Directed Tools for Exploiting Expert Knowledge in an Immersive Segmentation and Visualization Environment

Volumetric data sets have become common in medicine and many sciences through technologies such as computed x-ray tomography (CT), magnetic resonance (MR), positron emission tomography (PET), confocal microscopy and 3D ultrasound. When presented with 2D images humans immediately and unconsciously begin a visual analysis of the scene. The viewer surveys the scene identifying significant landmarks and building an internal mental model of presented information. The identification of features is strongly influenced by the viewers expectations based upon their expert knowledge of what the image should contain. While not a conscious activity, the viewer makes a series of choices about how to interpret the scene. These choices occur in parallel with viewing the scene and effectively change the way the viewer sees the image. It is this interaction of viewing and choice which is the basis of many familiar visual illusions. This is especially important in the interpretation of medical images where it is the expert knowledge of the radiologist which interprets the image. For 3D data sets this interaction of view and choice is frustrated because choices must precede the visualization of the data set. It is not possible to visualize the data set with out making some initial choices which determine how the volume of data is presented to the eye. These choices include, view point orientation, region identification, color and opacity assignments. Further compounding the problem is the fact that these visualization choices are defined in terms of computer graphics as opposed to language of the experts knowledge. The long term goal of this project is to develop an environment where the user can interact with volumetric data sets using tools which promote the utilization of expert knowledge by incorporating visualization and choice into a tight computational loop. The tools will support activities involving the segmentation of structures, construction of surface meshes and local filtering of the data set. To conform to this environment tools should have several key attributes. First, they should be only rely on computations over a local neighborhood of the probe position. Second, they should operate iteratively over time converging towards a limit behavior. Third, they should adapt to user input modifying they operational parameters with time.

Senger, Steven O.↗

Uncertainty Analysis of Historical Hurricane Data

An analysis of variance (ANOVA) study was conducted for historical hurricane data dating back to 1851 that was obtained from the U. S. Department of Commerce National Oceanic and Atmospheric Administration (NOAA). The data set was chosen because it is a large, publicly available collection of information, exhibiting great variability which has made the forecasting of future states, from current and previous states, difficult. The availability of substantial, high-fidelity validation data, however, made for an excellent uncertainty assessment study. Several factors (independent variables) were identified from the data set, which could potentially influence the track and intensity of the storms. The values of these factors, along with the values of responses of interest (dependent variables) were extracted from the data base, and provided to a commercial software package for processing via the ANOVA technique. The primary goal of the study was to document the ANOVA modeling uncertainty and predictive errors in making predictions about hurricane location and intensity 24 to 120 hours beyond known conditions, as reported by the data set. A secondary goal was to expose the ANOVA technique to a broader community within NASA. The independent factors considered to have an influence on the hurricane track included the current and starting longitudes and latitudes (measured in degrees), and current and starting maximum sustained wind speeds (measured in knots), and the storm starting date, its current duration from its first appearance, and the current year fraction of each reading, all measured in years. The year fraction and starting date were included in order to attempt to account for long duration cyclic behaviors, such as seasonal weather patterns, and years in which the sea or atmosphere were unusually warm or cold. The effect of short duration weather patterns and ocean conditions could not be examined with the current data set. The responses analyzed were the storm latitude, longitude and intensity, as recorded in the data set, 24 or 120 hours beyond the current state. Several ANOVA modeling schemes were examined. Two forms of validation were used: 1) comparison with official hurricane prediction performance metrics and 2) cases studies conducted on hurricanes from the 2005 season, which were not included within the model construction and ANOVA assessment. In general, the ANOVA technique did not perform as well as the established official prediction performance metrics published by NOAA; still, the technique did remarkably well in this demonstration with a difficult data set and could probably be made to perform better with more knowledge of hurricane development and dynamics applied to the problem. The technique provides a repeatable prediction process that eliminates the need for judgment in the forecast.

Green, Lawrence L.↗

Multisensor data analysis and its application to monitoring of cropland, forest, strip mines and cultural targets

Seasat L-band and aircraft X-band dual polarized synthetic aperture (SAR) data of the Western Kentucky Coal Region were examined, preprocessed and combined with Landsat Multispectral Scanner (MSS) data to form a seven-band multisensor data set. Analysis of classified data sets show that the three-band SAR data contain moderate discrimination accuracy for the strip mine class but low accuracy for the residential class. The four-band MSS data contain low classification accuracy for the strip mine and residential classes. The integrated five-band SAR/MSS data show that significant improvement in classification accuracy is obtained for both strip mine and residential classes.

Wu, S. T.↗

International Space Station (ISS) Anomalies Trending Study: Appendices - Volume II

The NASA Engineering and Safety Center (NESC) set out to utilize data mining and trending techniques to review the anomaly history of the International Space Station (ISS) and provide tools for discipline experts not involved with the ISS Program to search anomaly data to aid in identification of areas that may warrant further investigation. Additionally, the assessment team aimed to develop an approach and skillset for integrating data sets, with the intent of providing an enriched data set for discipline experts to investigate that is easier to navigate, particularly in light of ISS aging and the plan to extend its life into the late 2020s. This document contains the Appendices to the Volume I report.

Beil, Robert J.↗

International Space Station (ISS) Anomalies Trending Study

The NASA Engineering and Safety Center (NESC) set out to utilize data mining and trending techniques to review the anomaly history of the International Space Station (ISS) and provide tools for discipline experts not involved with the ISS Program to search anomaly data to aid in identification of areas that may warrant further investigation. Additionally, the assessment team aimed to develop an approach and skillset for integrating data sets, with the intent of providing an enriched data set for discipline experts to investigate that is easier to navigate, particularly in light of ISS aging and the plan to extend its life into the late 2020s. This report contains the outcome of the NESC Assessment.

Beil, Robert J.↗

Historical and Future Global Irrigation Energy Consumption by Fuel and Region

Irrigation energy use is a significant component of agricultural production costs, contributing directly to the energy and emissions intensity of crop production and ultimately to food prices. Understanding the existing structure of irrigation energy consumption help achieve food-energy-water security and environmental goals. We present a comprehensive global data set detailing country-level irrigation energy consumption, emphasizing the comparative use of electric, diesel, and emerging solar pumps. To our knowledge, no such data set exists. We draw from a literature review to develop a logistic transformed regression model to estimate the shares of fuel sources for irrigation across countries over historical years to construct a global data set of country-level irrigation energy consumption by multiple fuel sources. Additionally, we compare our estimates of irrigation energy use with agricultural energy use as reported by the International Energy Agency and other external sources. We then use this data to project future irrigation energy use with the Global Change Analysis Model, which is a multisector dynamics model, to showcase the usage of this data set. Projections under the reference scenario show a global shift in fuel types for irrigation pumping, while patterns vary across regions, with India and Pakistan leading in solar-powered irrigation growth and countries like the USA and China continuing to rely primarily on grid electricity. This data set provides a resource to understand the role of irrigation fuel choices within the broader energy sector, as well as the connected agricultural, land use, and water sectors under alternative future scenarios, enabling informed decision making toward efficient agricultural practices.

Global Change Analysis Model (GCAM)↗

A system identification approach for non-intrusive reduced order modeling of radiation-induced photocurrents

In this study, development of compact photocurrent models is currently dominated by analytical techniques that rely on physical assumptions to render the governing equations solvable in a closed form. Violation of these assumptions can reduce the accuracy of the models and/or limit their scope. In this paper we show that system identification of nonlinear state-space systems can serve as an alternative numerical basis for non-intrusive reduced order modeling of photocurrent effects. To that end we develop a compact gray box photocurrent model (GBPM) by using a state-space representation with a low-dimensional latent state equation that mimics a mathematical model for the response of an idealized class of devices to ionizing radiation. In so doing we obtain a model that learns the dynamics of a quantity of interest directly from its measurements without requiring snapshots of the internal device state or its discretized model, and can be inferred from very small data sets. To demonstrate the approach we train the GBPM using a small experimental data set for a Z5236 Zener diode and a small synthetic data set obtained by simulating a synthetic pn-junction device. We then compare the GBPMs with black box models trained on the same data and show that performance of the latter is limited by the size of the data set, while the former are able to achieve excellent performance in both the reproductive and the predictive regimes.

97 MATHEMATICS AND COMPUTING↗

Tool for Automated Retrieval of Generic Event Tracks (TARGET)

Methods have been developed to identify and track tornado-producing mesoscale convective systems (MCSs) automatically over the continental United States, in order to facilitate systematic studies of these powerful and often destructive events. Several data sources were combined to ensure event identification accuracy. Records of watches and warnings issued by National Weather Service (NWS), and tornado locations and tracks from the Tornado History Project (THP) were used to locate MCSs in high-resolution precipitation observations and GOES infrared (11-micron) Rapid Scan Operation (RSO) imagery. Thresholds are then applied to the latter two data sets to define MCS events and track their developments. MCSs produce a broad range of severe convective weather events that are significantly affecting the living conditions of the populations exposed to them. Understanding how MCSs grow and develop could help scientists improve their weather prediction models, and also provide tools to decision-makers whose goals are to protect populations and their property. Associating storm cells across frames of remotely sensed images poses a difficult problem because storms evolve, split, and merge. Any storm-tracking method should include the following processes: storm identification, storm tracking, and quantification of storm intensity and activity. The spatiotemporal coordinates of the tracks will enable researchers to obtain other coincident observations to conduct more thorough studies of these events. In addition to their tracked locations, their areal extents, precipitation intensities, and accumulations all as functions of their evolutions in time were also obtained and recorded for these events. All parameters so derived can be catalogued into a moving object database (MODB) for custom queries. The purpose of this software is to provide a generalized, cross-platform, pluggable tool for identifying events within a set of scientific data based upon specified criteria with the possibility of storing identified events into a searchable database. The core of the application uses an implementation of the connected component labeling (CCL) algorithm to identify areas of interest, then uses a set of criteria to establish spatial and temporal relationships between identified components. The CCL algorithm is used for identifying objects within images for computer vision. This application applies it to scientific data sets using arbitrary criteria. The most novel concept was applying a generalized CCL implementation to scientific data sets for establishing events both spatially and temporally. The combination of several existing concepts (pluggable components, generalized CCL algorithm, etc.) into one application is also novel. In addition, how the system is designed, i.e., its extensibility with pluggable components, and its configurability with a simple configuration file, is innovative. This allows the system to be applied to new scenarios with ease.

Clune, Thomas↗

Aerosol and Cloud Observations and Data Products by the GLAS Polar Orbiting Lidar Instrument

The Geoscience Laser Altimeter System (GLAS) launched in 2003 is the first polar orbiting satellite lidar. The instrument was designed for high performance observations of the distribution and optical scattering cross sections of clouds and aerosol. The backscatter lidar operates at two wavelengths, 532 and 1064 nm. Both receiver channels meet and exceed their design goals, and beginning with a two month period through October and November 2003, an excellent global lidar data set now exists. The data products for atmospheric observations include the calibrated, attenuated backscatter cross section for cloud and aerosol; height detection for multiple cloud layers; planetary boundary layer height; cirrus and aerosol optical depth and the height distribution of aerosol and cloud scattering cross section profiles. The data sets are now in open release through the NASA data distribution system. The initial results on global statistics for cloud and aerosol distribution has been produced and in some cases compared to other satellite observations. The sensitivity of the cloud measurements is such that the 70% global cloud coverage result should be the most accurate to date. Results on the global distribution of aerosol are the first that produce the true height distribution for model inter-comparison.

Spinhirne, J. D.↗

Earth observing system. Output data products and input requirements, version 2.0. Volume 3: Algorithm summary tables and non-EOS data products

Volume 3 assists Earth Observing System (EOS) investigators in locating required non-EOS data products by identifying their non-EOS input requirements and providing the information on data sets available at various Distributed Active Archive Centers (DAAC's), including those from Pathfinder Activities and Earth Probes. Volume 3 is intended to complement, not to duplicate, the the EOSDIS Science Data Plan (SDP) by providing detailed data set information which was not presented in the SDP. Section 9 of this volume discusses the algorithm summary tables containing information on retrieval algorithms, expected outputs and required input data. Section 10 describes the non-EOS input requirements of instrument teams and IDS investigators. Also described are the current and future data holdings of the original seven DAACS and data products planned from the future missions and projects including Earth Probes and Pathfinder Activities. Section 11 describes source of information used in compiling data set information presented in this volume. A list of data set attributes used to describe various data sets is presented in section 12 along with their descriptions. Finally, Section 13 presents the SPSO's future plan to improve this report .

Lu, Yun-Chi↗

Mars' global properties - Maps and applications

Mars data sets from many sources have been put into a common digital format and global maps at constant scale have been produced. These formatted data sets form the basis of the Mars Consortium. They currently include maps of geology, volcanic units, channels, wind features, topography, gravity, Viking approach and apoapsis color, predawn temperature residuals, thermal inertia, radiometric and solar albedo, water vapor abundance, 1.4 micrometers albedo, crater abundance, Earth-based radar and photographic telescope observations, and terrestrial spectral observations. The generation of these data sets is briefly described. As an example of their use, the mapped geologic units are characterized in terms of color, predawn temperature and elevation, and unusual areas identified. The Oxia Palus region is examined as an example of combining higher resolution data with the Consortium data sets. Use of the existing data sets and submission of new ones are encouraged.

Kieffer, H. H.↗