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NASA NTRS2011

Applied Meteorology Unit (AMU)

The AMU Team began four new tasks in this quarter: (1) began work to improve the AMU-developed tool that provides the launch weather officers information on peak wind speeds that helps them assess their launch commit criteria; (2) began updating lightning climatologies for airfields around central Florida. These climatologies help National Weather Service and Air Force forecasters determine the probability of lightning occurrence at these sites; (3) began a study for the 30th Weather Squadron at Vandenberg Air Force Base in California to determine if precursors can be found in weather observations to help the forecasters determine when they will get strong wind gusts in their northern towers; and (4) began work to update the AMU-developed severe weather tool with more data and possibly improve its performance using a new statistical technique. Include is a section of summaries and detail reporting on the quarterly tasks: (1) Peak Wind Tool for user Meteorological Interactive Data Display System (LCC), Phase IV, (2) Situational Lightning climatologies for Central Florida, Phase V, (3) Vandenberg AFB North Base Wind Study and (4) Upgrade Summer Severe Weather Tool Meteorological Interactive Data Display System (MIDDS).

Bauman, William↗

NASA NTRS2011

Monitoring Areal Snow Cover Using NASA Satellite Imagery

The objective of this project is to develop products and tools to assist in the hydrologic modeling process, including tools to help prepare inputs for hydrologic models and improved methods for the visualization of streamflow forecasts. In addition, this project will facilitate the use of NASA satellite imagery (primarily snow cover imagery) by other federal and state agencies with operational streamflow forecasting responsibilities. A GIS software toolkit for monitoring areal snow cover extent and producing streamflow forecasts is being developed. This toolkit will be packaged as multiple extensions for ArcGIS 9.x and an opensource GIS software package. The toolkit will provide users with a means for ingesting NASA EOS satellite imagery (snow cover analysis), preparing hydrologic model inputs, and visualizing streamflow forecasts. Primary products include a software tool for predicting the presence of snow under clouds in satellite images; a software tool for producing gridded temperature and precipitation forecasts; and a suite of tools for visualizing hydrologic model forecasting results. The toolkit will be an expert system designed for operational users that need to generate accurate streamflow forecasts in a timely manner. The Remote Sensing of Snow Cover Toolbar will ingest snow cover imagery from multiple sources, including the MODIS Operational Snowcover Data and convert them to gridded datasets that can be readily used. Statistical techniques will then be applied to the gridded snow cover data to predict the presence of snow under cloud cover. The toolbar has the ability to ingest both binary and fractional snow cover data. Binary mapping techniques use a set of thresholds to determine whether a pixel contains snow or no snow. Fractional mapping techniques provide information regarding the percentage of each pixel that is covered with snow. After the imagery has been ingested, physiographic data is attached to each cell in the snow cover image. This data can be obtained from a digital elevation model (DEM) for the area of interest.

Harshburger, Brian J.↗

NASA NTRS2011

Determining the Optimal Number of Clusters with the Clustergram

Cluster analysis aids research in many different fields, from business to biology to aerospace. It consists of using statistical techniques to group objects in large sets of data into meaningful classes. However, this process of ordering data points presents much uncertainty because it involves several steps, many of which are subject to researcher judgment as well as inconsistencies depending on the specific data type and research goals. These steps include the method used to cluster the data, the variables on which the cluster analysis will be operating, the number of resulting clusters, and parts of the interpretation process. In most cases, the number of clusters must be guessed or estimated before employing the clustering method. Many remedies have been proposed, but none is unassailable and certainly not for all data types. Thus, the aim of current research for better techniques of determining the number of clusters is generally confined to demonstrating that the new technique excels other methods in performance for several disparate data types. Our research makes use of a new cluster-number-determination technique based on the clustergram: a graph that shows how the number of objects in the cluster and the cluster mean (the ordinate) change with the number of clusters (the abscissa). We use the features of the clustergram to make the best determination of the cluster-number.

Fluegemann, Joseph K.↗

NASA NTRS2012

Analysis of Fine-Mode Aerosol Retrieval Capabilities by Different Passive Remote Sensing Instrument Designs

Remote sensing of aerosol optical properties is difficult, but multi-angle, multi-spectral, polarimetric instruments have the potential to retrieve sufficient information about aerosols that they can be used to improve global climate models. However, the complexity of these instruments means that it is difficult to intuitively understand the relationship between instrument design and retrieval success. We apply a Bayesian statistical technique that relates instrument characteristics to the information contained in an observation. Using realistic simulations of fine size mode dominated spherical aerosols, we investigate three instrument designs. Two of these represent instruments currently in orbit: the Multiangle Imaging SpectroRadiometer (MISR) and the POLarization and Directionality of the Earths Reflectances (POLDER). The third is the Aerosol Polarimetry Sensor (APS), which failed to reach orbit during recent launch, but represents a viable design for future instruments. The results show fundamental differences between the three, and offer suggestions for future instrument design and the optimal retrieval strategy for current instruments. Generally, our results agree with previous validation efforts of POLDER and airborne prototypes of APS, but show that the MISR aerosol optical thickness uncertainty characterization is possibly underestimated.

Knobelspiesse, Kirk↗

NASA NTRS2012

The Geology of the Marcia Quadrangle of Asteroid 4Vesta: An Integrated Mapping Study Using Dawn Spacecraft Data

We used geologic mapping applied to Dawn data as a tool to understand the geologic history of the Marcia quadrangle of Vesta. This region hosts a set of relatively fresh craters and surrounding ejecta field, an unusual dark hill named Arisia Tholus, and a orange (false color) diffuse material surrounding the crater Octavia. Stratigraphically, from oldest to youngest, three increasingly larger impact craters named Minucia, Calpurnia, and Marcia make up a snowmanlike feature, which is surrounded by a zone of dark material interpreted to consist of impact ejecta and possibly impact melts. The floor of Marcia contains a pitted terrain thought to be related to release of volatiles (1). The dark ejecta field has an enhanced signature of H, possibly derived from carbonaceous chondritic material that accumulated in Vesta s crust (2,3). The dark ejecta has a spectrally distinctive behavior with shallow pyroxenes band depths. Outside the ejecta field this quadrangle contains various cratered terrains, with increasing crater abundance moving south to north away from the Rheasilvia basin. Arisia Tholus, originally suggested as an ancient volcano, appears to be an impact-sculpted basin rim fragment with a superposed darkrayed impact crater. There remains no unequivocal evidence of volcanic features on Vesta s surface, likely because basaltic material of the HED meteorite suite demonstrates magmatism ended very early on Vesta (4). Ongoing work includes application of crater statistical techniques to obtain model ages of surface units, and more detailed estimates of the compositional variations among the surface units.

Williams, David A.↗

NASA NTRS2011

Estuarine Sediment Deposition during Wetland Restoration: A GIS and Remote Sensing Modeling Approach

Restoration of the industrial salt flats in the San Francisco Bay, California is an ongoing wetland rehabilitation project. Remote sensing maps of suspended sediment concentration, and other GIS predictor variables were used to model sediment deposition within these recently restored ponds. Suspended sediment concentrations were calibrated to reflectance values from Landsat TM 5 and ASTER using three statistical techniques -- linear regression, multivariate regression, and an Artificial Neural Network (ANN), to map suspended sediment concentrations. Multivariate and ANN regressions using ASTER proved to be the most accurate methods, yielding r2 values of 0.88 and 0.87, respectively. Predictor variables such as sediment grain size and tidal frequency were used in the Marsh Sedimentation (MARSED) model for predicting deposition rates for three years. MARSED results for a fully restored pond show a root mean square deviation (RMSD) of 66.8 mm (<1) between modeled and field observations. This model was further applied to a pond breached in November 2010 and indicated that the recently breached pond will reach equilibrium levels after 60 months of tidal inundation.

Newcomer, Michelle↗

NASA NTRS2009

Data Mining and Complex Problems: Case Study in Composite Materials

Data mining is defined as the discovery of useful, possibly unexpected, patterns and relationships in data using statistical and non-statistical techniques in order to develop schemes for decision and policy making. Data mining can be used to discover the sources and causes of problems in complex systems. In addition, data mining can support simulation strategies by finding the different constants and parameters to be used in the development of simulation models. This paper introduces a framework for data mining and its application to complex problems. To further explain some of the concepts outlined in this paper, the potential application to the NASA Shuttle Reinforced Carbon-Carbon structures and genetic programming is used as an illustration.

Rabelo, Luis↗

NASA NTRS2013

Orbital Debris Engineering Model (ORDEM) v.3

A model of the manmade orbital debris environment is required by spacecraft designers, mission planners, and others in order to understand and mitigate the effects of the environment on their spacecraft or systems. A manmade environment is dynamic, and can be altered significantly by intent (e.g., the Chinese anti-satellite weapon test of January 2007) or accident (e.g., the collision of Iridium 33 and Cosmos 2251 spacecraft in February 2009). Engineering models are used to portray the manmade debris environment in Earth orbit. The availability of new sensor and in situ data, the re-analysis of older data, and the development of new analytical and statistical techniques has enabled the construction of this more comprehensive and sophisticated model. The primary output of this model is the flux [#debris/area/time] as a function of debris size and year. ORDEM may be operated in spacecraft mode or telescope mode. In the former case, an analyst defines an orbit for a spacecraft and "flies" the spacecraft through the orbital debris environment. In the latter case, an analyst defines a ground-based sensor (telescope or radar) in terms of latitude, azimuth, and elevation, and the model provides the number of orbital debris traversing the sensor's field of view. An upgraded graphical user interface (GUI) is integrated with the software. This upgraded GUI uses project-oriented organization and provides the user with graphical representations of numerous output data products. These range from the conventional flux as a function of debris size for chosen analysis orbits (or views), for example, to the more complex color-contoured two-dimensional (2D) directional flux diagrams in local spacecraft elevation and azimuth.

Matney, Mark↗

NASA NTRS2014

Missing Data and Multiple Imputation: An Unbiased Approach

The default method of dealing with missing data in statistical analyses is to only use the complete observations (complete case analysis), which can lead to unexpected bias when data do not meet the assumption of missing completely at random (MCAR). For the assumption of MCAR to be met, missingness cannot be related to either the observed or unobserved variables. A less stringent assumption, missing at random (MAR), requires that missingness not be associated with the value of the missing variable itself, but can be associated with the other observed variables. When data are truly MAR as opposed to MCAR, the default complete case analysis method can lead to biased results. There are statistical options available to adjust for data that are MAR, including multiple imputation (MI) which is consistent and efficient at estimating effects. Multiple imputation uses informing variables to determine statistical distributions for each piece of missing data. Then multiple datasets are created by randomly drawing on the distributions for each piece of missing data. Since MI is efficient, only a limited number, usually less than 20, of imputed datasets are required to get stable estimates. Each imputed dataset is analyzed using standard statistical techniques, and then results are combined to get overall estimates of effect. A simulation study will be demonstrated to show the results of using the default complete case analysis, and MI in a linear regression of MCAR and MAR simulated data. Further, MI was successfully applied to the association study of CO2 levels and headaches when initial analysis showed there may be an underlying association between missing CO2 levels and reported headaches. Through MI, we were able to show that there is a strong association between average CO2 levels and the risk of headaches. Each unit increase in CO2 (mmHg) resulted in a doubling in the odds of reported headaches.

Foy, M.↗

NASA NTRS2013

Downscaling MODIS Land Surface Temperature for Urban Public Health Applications

This study is part of a project funded by the NASA Applied Sciences Public Health Program, which focuses on Earth science applications of remote sensing data for enhancing public health decision-making. Heat related death is currently the number one weather-related killer in the United States. Mortality from these events is expected to increase as a function of climate change. This activity sought to augment current Heat Watch/Warning Systems (HWWS) with NASA remotely sensed data, and models used in conjunction with socioeconomic and heatrelated mortality data. The current HWWS do not take into account intra-urban spatial variation in risk assessment. The purpose of this effort is to evaluate a potential method to improve spatial delineation of risk from extreme heat events in urban environments by integrating sociodemographic risk factors with estimates of land surface temperature (LST) derived from thermal remote sensing data. In order to further improve the consideration of intra-urban variations in risk from extreme heat, we also developed and evaluated a number of spatial statistical techniques for downscaling the 1-km daily MODerate-resolution Imaging Spectroradiometer (MODIS) LST data to 60 m using Landsat-derived LST data, which have finer spatial but coarser temporal resolution than MODIS. In this paper, we will present these techniques, which have been demonstrated and validated for Phoenix, AZ using data from the summers of 2000-2006.

Al-Hamdan, Mohammad↗

NASA NTRS2013

Downscaling MODIS Land Surface Temperature for Urban Public Health Applications

Al-Hamdan, Mohammad↗

NASA NTRS2012

The Herschel Multi-tiered Extragalactic Survey: HerMES

The Herschel Multi-tiered Extragalactic Survey, HerMES, is a legacy program designed to map a set of nested fields totalling approx. 380 deg(exp 2). Fields range in size from 0.01 to approx. 20 deg (exp 2), using Herschel-SPIRE (at 250, 350 and 500 micron), and Herschel-PACS (at 100 and 160 micron), with an additional wider component of 270 deg. (exp. 2) with SPIRE alone. These bands cover the peak of the redshifted thermal spectral energy distribution from interstellar dust and thus capture the re-processed optical and ultra-violet radiation from star formation that has been absorbed by dust, and are critical for forming a complete multi-wavelength understanding of galaxy formation and evolution. The survey will detect of order 100,000 galaxies at 5-sigma in some of the best studied fields in the sky. Additionally, HerMES is closely coordinated with the PACS Evolutionary Probe survey. Making maximum use of the full spectrum of ancillary data, from radio to X-ray wavelengths, it is designed to: facilitate redshift determination; rapidly identify unusual objects; and understand the relationships between thermal emission from dust and other processes. Scientific questions HerMES will be used to answer include: the total infrared emission of galaxies; the evolution of the luminosity function; the clustering properties of dusty galaxies; and the properties of populations of galaxies which lie below the confusion limit through lensing and statistical techniques. This paper defines the survey observations and data products, outlines the primary scientific goals of the HerMES team, and reviews some of the early results.

multi-tiered↗

NASA NTRS2010

Kriging as a Means of Improving WAAS Availability

The Wide Area Augmentation System (WAAS), an augmentation of the Global Positioning System (GPS), provides safe and reliable use of GPS signals for airline navigation over much of North America. Currently the largest source of positioning error in the system is signal delay caused by the ionosphere. To allow the user to take account of such error, WAAS computes and broadcasts ionospheric vertical delays at a set of regularly-spaced grid points. In addition, WAAS computes and broadcasts a safety-critical integrity bound at each ionospheric grid point (IGP) called the Grid Ionospheric Vertical Error (GIVE). GIVEs are constructed to be sufficiently large to protect the user against positioning error due to the presence of ionospheric irregularity. In the initial operating capability (IOC) of WAAS, the vertical delay estimate at each IGP is determined from a planar fit of neighboring slant delay measurements, projected to vertical using an obliquity factor specified by the standard thin-shell model of the ionosphere. In WAAS Follow-On (WFO) Release 3, however, the vertical delay will be estimated by an established, geo-statistical technique known as kriging. Compared to the planar fit model, the kriging model is found, in general, to match better the observed random structure of the vertical delay. This paper presents the kriging methodology that will be used to estimate the vertical delay and its uncertainty at each IGP, and it assesses the subsequent improvement in WAAS availability enabled by kriging.

GPS↗

NASA NTRS2016

In-Space Manufacturing Baseline Property Development

The In-Space Manufacturing (ISM) project at NASA Marshall Space Flight Center currently operates a 3D FDM (fused deposition modeling) printer onboard the International Space Station. In order to enable utilization of this capability by designer, the project needs to establish characteristic material properties for materials produced using the process. This is difficult for additive manufacturing since standards and specifications do not yet exist for these technologies. Due to availability of crew time, there are limitations to the sample size which in turn limits the application of the traditional design allowables approaches to develop a materials property database for designers. In this study, various approaches to development of material databases were evaluated for use by designers of space systems who wish to leverage in-space manufacturing capabilities. This study focuses on alternative statistical techniques for baseline property development to support in-space manufacturing.

Stockman, Tom↗

NASA NTRS2016

Improved Test Planning and Analysis Through the Use of Advanced Statistical Methods

The goal of this work is, through computational simulations, to provide statistically-based evidence to convince the testing community that a distributed testing approach is superior to a clustered testing approach for most situations. For clustered testing, numerous, repeated test points are acquired at a limited number of test conditions. For distributed testing, only one or a few test points are requested at many different conditions. The statistical techniques of Analysis of Variance (ANOVA), Design of Experiments (DOE) and Response Surface Methods (RSM) are applied to enable distributed test planning, data analysis and test augmentation. The D-Optimal class of DOE is used to plan an optimally efficient single- and multi-factor test. The resulting simulated test data are analyzed via ANOVA and a parametric model is constructed using RSM. Finally, ANOVA can be used to plan a second round of testing to augment the existing data set with new data points. The use of these techniques is demonstrated through several illustrative examples. To date, many thousands of comparisons have been performed and the results strongly support the conclusion that the distributed testing approach outperforms the clustered testing approach.

Green, Lawrence L.↗

NASA NTRS2016

Confidence Intervals for Laboratory Sonic Boom Annoyance Tests

Commercial supersonic flight is currently forbidden over land because sonic booms have historically caused unacceptable annoyance levels in overflown communities. NASA is providing data and expertise to noise regulators as they consider relaxing the ban for future quiet supersonic aircraft. One deliverable NASA will provide is a predictive model for indoor annoyance to aid in setting an acceptable quiet sonic boom threshold. A laboratory study was conducted to determine how indoor vibrations caused by sonic booms affect annoyance judgments. The test method required finding the point of subjective equality (PSE) between sonic boom signals that cause vibrations and signals not causing vibrations played at various amplitudes. This presentation focuses on a few statistical techniques for estimating the interval around the PSE. The techniques examined are the Delta Method, Parametric and Nonparametric Bootstrapping, and Bayesian Posterior Estimation.

Rathsam, Jonathan↗

NASA NTRS2015

North American Extreme Temperature Events and Related Large Scale Meteorological Patterns: A Review of Statistical Methods, Dynamics, Modeling, and Trends

The objective of this paper is to review statistical methods, dynamics, modeling efforts, and trends related to temperature extremes, with a focus upon extreme events of short duration that affect parts of North America. These events are associated with large scale meteorological patterns (LSMPs). The statistics, dynamics, and modeling sections of this paper are written to be autonomous and so can be read separately. Methods to define extreme events statistics and to identify and connect LSMPs to extreme temperature events are presented. Recent advances in statistical techniques connect LSMPs to extreme temperatures through appropriately defined covariates that supplement more straightforward analyses. Various LSMPs, ranging from synoptic to planetary scale structures, are associated with extreme temperature events. Current knowledge about the synoptics and the dynamical mechanisms leading to the associated LSMPs is incomplete. Systematic studies of: the physics of LSMP life cycles, comprehensive model assessment of LSMP-extreme temperature event linkages, and LSMP properties are needed. Generally, climate models capture observed properties of heat waves and cold air outbreaks with some fidelity. However they overestimate warm wave frequency and underestimate cold air outbreak frequency, and underestimate the collective influence of low-frequency modes on temperature extremes. Modeling studies have identified the impact of large-scale circulation anomalies and landatmosphere interactions on changes in extreme temperatures. However, few studies have examined changes in LSMPs to more specifically understand the role of LSMPs on past and future extreme temperature changes. Even though LSMPs are resolvable by global and regional climate models, they are not necessarily well simulated. The paper concludes with unresolved issues and research questions.

Cold Spells↗

NASA NTRS2017

The MSFC Solar Activity Future Estimation (MSAFE) Model

The MSAFE model provides forecasts for the solar indices SSN, F10.7, and Ap. These solar indices are used as inputs to space environment models used in orbital spacecraft operations and space mission analysis. Forecasts from the MSAFE model are provided on the MSFC Natural Environments Branch's solar web page and are updated as new monthly observations become available. The MSAFE prediction routine employs a statistical technique that calculates deviations of past solar cycles from the mean cycle and performs a regression analysis to calculate the deviation from the mean cycle of the solar index at the next future time interval. The forecasts are initiated for a given cycle after about 8 to 9 monthly observations from the start of the cycle are collected. A forecast made at the beginning of cycle 24 using the MSAFE program captured the cycle fairly well with some difficulty in discerning the double peak that occurred at solar cycle maximum.

Suggs, Ron↗