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NASA NTRS2015

Evaluating Coastal Landscape Response to Sea-Level Rise in the Northeastern United States - Approach and Methods

The U.S. Geological Survey is examining effects of future sea-level rise on the coastal landscape from Maine to Virginia by producing spatially explicit, probabilistic predictions using sea-level projections, vertical land movement rates (due to isostacy), elevation data, and land-cover data. Sea-level-rise scenarios used as model inputs are generated by using multiple sources of information, including Coupled Model Intercomparison Project Phase 5 models following representative concentration pathways 4.5 and 8.5 in the Intergovernmental Panel on Climate Change Fifth Assessment Report. A Bayesian network is used to develop a predictive coastal response model that integrates the sea-level, elevation, and land-cover data with assigned probabilities that account for interactions with coastal geomorphology as well as the corresponding ecological and societal systems it supports. The effects of sea-level rise are presented as (1) level of landscape submergence and (2) coastal response type characterized as either static (that is, inundation) or dynamic (that is, landform or landscape change). Results are produced at a spatial scale of 30 meters for four decades (the 2020s, 2030s, 2050s, and 2080s). The probabilistic predictions can be applied to landscape management decisions based on sea-level-rise effects as well as on assessments of the prediction uncertainty and need for improved data or fundamental understanding. This report describes the methods used to produce predictions, including information on input datasets; the modeling approach; model outputs; data-quality-control procedures; and information on how to access the data and metadata online.

terrain↗
NASA NTRS2015

Online Resource for Earth-Observing Satellite Sensor Calibration

The Radiometric Calibration Test Site (RadCaTS) at Railroad Valley Playa, Nevada is being developed by the University of Arizona to enable improved accuracy and consistency for airborne and satellite sensor calibration. Primary instrumentation at the site consists of ground-viewing radiometers, a sun photometer, and a meteorological station. Measurements made by these instruments are used to calculate surface reflectance, atmospheric properties and a prediction for top-of-atmosphere reflectance and radiance. This work will leverage research for RadCaTS, and describe the requirements for an online database, associated data formats and quality control, and processing levels.

RadCaTS↗
NASA NTRS2015

Soldier-Warfighter Operationally Responsive Deployer for Space

The Soldier-Warfighter Operationally Responsive Deployer for Space (SWORDS) project was a joint project between the U.S. Army Space & Missile Defense Command (SMDC) and NASA. The effort, lead by SMDC, was intended to develop a three-stage liquid bipropellant (liquid oxygen/liquid methane), pressure-fed launch vehicle capable of inserting a payload of at least 25 kg to a 750-km circular orbit. The vehicle design was driven by low cost instead of high performance. SWORDS leveraged commercial industry standards to utilize standard hardware and technologies over customized unique aerospace designs. SWORDS identified broadly based global industries that have achieved adequate levels of quality control and reliability in their products and then designed around their expertise and business motivations.

Davis, Benny↗
NASA NTRS2016

MERRA-2 Input Observations: Summary and Assessment

The Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2) is an atmospheric reanalysis, spanning 1980 through near-realtime, that uses state-of-the-art processing of observations from the continually evolving global observing system. The effectiveness of any reanalysis is a function not only of the input observations themselves, but also of how the observations are handled in the assimilation procedure. Relevant issues to consider include, but are not limited to, data selection, data preprocessing, quality control, bias correction procedures, and blacklisting. As the assimilation algorithm and earth system models are fundamentally fixed in a reanalysis, it is often a change in the character of the observations, and their feedbacks on the system, that cause changes in the character of the reanalysis. It is therefore important to provide documentation of the observing system so that its discontinuities and transitions can be readily linked to discontinuities seen in the gridded atmospheric fields of the reanalysis. With this in mind, this document provides an exhaustive list of the input observations, the context under which they are assimilated, and an initial assessment of selected core observations fundamental to the reanalysis.

Satellite Observations↗
NASA NTRS2016

How Universal is the Relationship Between Remotely Sensed Vegetation Indices and Crop Leaf Area Index? A Global Assessment

Leaf Area Index (LAI) is a key variable that bridges remote sensing observations to the quantification of agroecosystem processes. In this study, we assessed the universality of the relationships between crop LAI and remotely sensed Vegetation Indices (VIs). We first compiled a global dataset of 1459 in situ quality-controlled crop LAI measurements and collected Landsat satellite images to derive five different VIs including Simple Ratio (SR), Normalized Difference Vegetation Index (NDVI), two versions of the Enhanced Vegetation Index (EVI and EVI2), and Green Chlorophyll Index (CI(sub Green)). Based on this dataset, we developed global LAI-VI relationships for each crop type and VI using symbolic regression and Theil-Sen (TS) robust estimator. Results suggest that the global LAI-VI relationships are statistically significant, crop-specific, and mostly non-linear. These relationships explain more than half of the total variance in ground LAI observations (R2 greater than 0.5), and provide LAI estimates with RMSE below 1.2 m2/m2. Among the five VIs, EVI/EVI2 are the most effective, and the crop-specific LAI-EVI and LAI-EVI2 relationships constructed by TS, are robust when tested by three independent validation datasets of varied spatial scales. While the heterogeneity of agricultural landscapes leads to a diverse set of local LAI-VI relationships, the relationships provided here represent global universality on an average basis, allowing the generation of large-scale spatial-explicit LAI maps. This study contributes to the operationalization of large-area crop modeling and, by extension, has relevance to both fundamental and applied agroecosystem research.

Vegetation Index↗
NASA NTRS2016

Assimilation of Gridded GRACE Terrestrial Water Storage Estimates in the North American Land Data Assimilation System

The objective of the North American Land Data Assimilation System (NLDAS) is to provide best available estimates of near-surface meteorological conditions and soil hydrological status for the continental United States. To support the ongoing efforts to develop data assimilation (DA) capabilities for NLDAS, the results of Gravity Recovery and Climate Experiment (GRACE) DA implemented in a manner consistent with NLDAS development are presented. Following previous work, GRACE terrestrial water storage (TWS) anomaly estimates are assimilated into the NASA Catchment land surface model using an ensemble smoother. In contrast to many earlier GRACE DA studies, a gridded GRACE TWS product is assimilated, spatially distributed GRACE error estimates are accounted for, and the impact that GRACE scaling factors have on assimilation is evaluated. Comparisons with quality-controlled in situ observations indicate that GRACE DA has a positive impact on the simulation of unconfined groundwater variability across the majority of the eastern United States and on the simulation of surface and root zone soil moisture across the country. Smaller improvements are seen in the simulation of snow depth, and the impact of GRACE DA on simulated river discharge and evapotranspiration is regionally variable. The use of GRACE scaling factors during assimilation improved DA results in the western United States but led to small degradations in the eastern United States. The study also found comparable performance between the use of gridded and basin averaged GRACE observations in assimilation. Finally, the evaluations presented in the paper indicate that GRACE DA can be helpful in improving the representation of droughts.

Kumar, Sujay V.↗
NASA NTRS2016

The XXL Survey I. Scientific Motivations - Xmm-Newton Observing Plan - Follow-up Observations and Simulation Programme

The quest for the cosmological parameters that describe our universe continues to motivate the scientific community to undertake very large survey initiatives across the electromagnetic spectrum. Over the past two decades, the Chandra and XMM-Newton observatories have supported numerous studies of X-ray-selected clusters of galaxies, active galactic nuclei (AGNs), and the X-ray background. The present paper is the first in a series reporting results of the XXL-XMM survey; it comes at a time when the Planck mission results are being finalized. Aims. We present the XXL Survey, the largest XMM programme totaling some 6.9 Ms to date and involving an international consortium of roughly 100 members. The XXL Survey covers two extragalactic areas of 25 deg2 each at a point-source sensitivity of approx. 5 x 10(exp 15) erg/s/sq cm in the [0.5-2] keV band (completeness limit). The surveys main goals are to provide constraints on the dark energy equation of state from the space-time-distribution of clusters of galaxies and to serve as a pathfinder for future, wide-area X-ray missions. We review science objectives, including cluster studies, AGN evolution, and large-scale structure, that are being conducted with the support of approximately 30 follow-up programs. Methods. We describe the 542 XMM observations along with the associated multi- and numerical simulation programmes. We give a detailed account of the X-ray processing steps and describe innovative tools being developed for the cosmological analysis. Results. The paper provides a thorough evaluation of the X-ray data, including quality controls, photon statistics, exposure and background maps, and sky coverage. Source catalogue construction and multi-associations are briefly described. This material will be the basis for the calculation of the cluster and AGN selection functions, critical elements of the cosmological and science analyses. Conclusions. The XXL multi- data set will have a unique lasting legacy value for cosmological and extragalactic studies and will serve asa calibration resource for future dark energy studies with clusters and other X-ray selected sources. With the present article, we release the XMM XXL photon and smoothed images along with the corresponding exposure maps.

X-rays: general – large-scale structure of Unive↗
NASA NTRS2017

Aerosol Data Assimilation at GMAO

This presentation presents an overview of the aerosol data assimilation work performed at GMAO. The GMAO Forward Processing system and the biomass burning emissions from QFED are first presented. Then, the current assimilation of Aerosol Optical Depth (AOD), performed by means of the analysis splitting method is briefly described, followed by some results on the quality control of observations using a Neural Network trained using AERONET AOD. Some applications are shown such as the Mount Pinatubo eruption in 1991 using the MERRA-2 aerosol dataset. Finally preliminary results on the EnKF implementation for aerosol assimilation are presented.

Splitting↗
NASA NTRS2016

Validation of Ocean Color Remote Sensing Reflectance Using Autonomous Floats

The use of autonomous proling oats for observational estimates of radiometric quantities in the ocean is explored, and the use of this platform for validation of satellite-based estimates of remote sensing reectance in the ocean is examined. This effort includes comparing quantities estimated from oat and satellite data at nominal wavelengths of 412, 443, 488, and 555 nm, and examining sources and magnitudes of uncertainty in the oat estimates. This study had 65 occurrences of coincident high-quality observations from oats and MODIS Aqua and 15 occurrences of coincident high-quality observations oats and Visible Infrared Imaging Radi-ometer Suite (VIIRS). The oat estimates of remote sensing reectance are similar to the satellite estimates, with disagreement of a few percent in most wavelengths. The variability of the oatsatellite comparisons is similar to the variability of in situsatellite comparisons using a validation dataset from the Marine Optical Buoy (MOBY). This, combined with the agreement of oat-based and satellite-based quantities, suggests that oats are likely a good platform for validation of satellite-based estimates of remote sensing reectance.

In Situ Oceanic Observations↗
NASA NTRS2017

Operational Implementation of Sea Ice Concentration Estimates from the AMSR2 Sensor

An operation implementation of a passive microwave sea ice concentration algorithm to support NOAA's operational mission is presented. The NASA team 2 algorithm, previously developed for the NASA advanced microwave scanning radiometer for the Earth observing system (AMSR-E) product suite, is adapted for operational use with the JAXA AMSR2 sensor through several enhancements. First, the algorithm is modified to process individual swaths and provide concentration from the most recent swaths instead of a 24-hour average. A latency (time since observation) field and a 24-hour concentration range (maximum-minimum) are included to provide indications of data timeliness and variability. Concentration from the Bootstrap algorithm is a secondary field to provide complementary sea ice information. A quality flag is implemented to provide information on interpolation, filtering, and other quality control steps. The AMSR2 concentration fields are compared with a different AMSR2 passive microwave product, and then validated via comparison with sea ice concentration from the Suomi visible and infrared imaging radiometer suite. This validation indicates the AMSR2 concentrations have a bias of 3.9% and an RMSE of 11.0% in the Arctic, and a bias of 4.45% and RMSE of 8.8% in the Antarctic. In most cases, the NOAA operational requirements for accuracy are met. However, in low-concentration regimes, such as during melt and near the ice edge, errors are higher because of the limitations of passive microwave sensors and the algorithm retrieval.

Arctic↗
NASA NTRS2017

Contamination and Surface Preparation Effects on Composite Bonding

Results presented here demonstrate the effect of several prebond surface contaminants (hydrocarbon, machining fluid, latex, silicone, peel ply residue, release film) on bond quality, as measured by fracture toughness and failure modes of carbon fiber reinforced epoxy substrates bonded in secondary and co-bond configurations with paste and film adhesives. Additionally, the capability of various prebond surface property measurement tools to detect contaminants and potentially predict subsequent bond performance of three different adhesives is also shown. Surface measurement methods included water contact angle, Dyne solution wettability, optically stimulated electron emission spectroscopy, surface free energy, inverse gas chromatography, and Fourier transform infrared spectroscopy with chemometrics analysis. Information will also be provided on the effectiveness of mechanical and energetic surface treatments to recover a bondable surface after contamination. The benefits and drawbacks of the various surface analysis tools to detect contaminants and evaluate prebond surfaces after surface treatment were assessed as well as their ability to correlate to bond performance. Surface analysis tools were also evaluated for their potential use as in-line quality control of adhesive bonding parameters in the manufacturing environment.

Kutscha, Eileen O.↗
NASA NTRS2015

The Use of UML for Software Requirements Expression and Management

It is common practice to write English-language "shall" statements to embody detailed software requirements in aerospace software applications. This paper explores the use of the UML language as a replacement for the English language for this purpose. Among the advantages offered by the Unified Modeling Language (UML) is a high degree of clarity and precision in the expression of domain concepts as well as architecture and design. Can this quality of UML be exploited for the definition of software requirements? While expressing logical behavior, interface characteristics, timeliness constraints, and other constraints on software using UML is commonly done and relatively straight-forward, achieving the additional aspects of the expression and management of software requirements that stakeholders expect, especially traceability, is far less so. These other characteristics, concerned with auditing and quality control, include the ability to trace a requirement to a parent requirement (which may well be an English "shall" statement), to trace a requirement to verification activities or scenarios which verify that requirement, and to trace a requirement to elements of the software design which implement that requirement. UML Use Cases, designed for capturing requirements, have not always been satisfactory. Some applications of them simply use the Use Case model element as a repository for English requirement statements. Other applications of Use Cases, in which Use Cases are incorporated into behavioral diagrams that successfully communicate the behaviors and constraints required of the software, do indeed take advantage of UML's clarity, but not in ways that support the traceability features mentioned above. Our approach uses the Stereotype construct of UML to precisely identify elements of UML constructs, especially behaviors such as State Machines and Activities, as requirements, and also to achieve the necessary mapping capabilities. We describe this approach in the context of a space-based software application currently under development at the Jet Propulsion Laboratory.

model-based engineering↗
NASA NTRS2017

Electric Potential and Electric Field Imaging with Dynamic Applications & Extensions

The technology and methods for remote quantitative imaging of electrostatic potentials and electrostatic fields in and around objects and in free space is presented. Electric field imaging (EFI) technology may be applied to characterize intrinsic or existing electric potentials and electric fields, or an externally generated electrostatic field made be used for volumes to be inspected with EFI. The baseline sensor technology (e-Sensor) and its construction, optional electric field generation (quasi-static generator), and current e- Sensor enhancements (ephemeral e-Sensor) are discussed. Critical design elements of current linear and real-time two-dimensional (2D) measurement systems are highlighted, and the development of a three dimensional (3D) EFI system is presented. Demonstrations for structural, electronic, human, and memory applications are shown. Recent work demonstrates that phonons may be used to create and annihilate electric dipoles within structures. Phonon induced dipoles are ephemeral and their polarization, strength, and location may be quantitatively characterized by EFI providing a new subsurface Phonon-EFI imaging technology. Results from real-time imaging of combustion and ion flow, and their measurement complications, will be discussed. Extensions to environment, Space and subterranean applications will be presented, and initial results for quantitative characterizing material properties are shown. A wearable EFI system has been developed by using fundamental EFI concepts. These new EFI capabilities are demonstrated to characterize electric charge distribution creating a new field of study embracing areas of interest including electrostatic discharge (ESD) mitigation, manufacturing quality control, crime scene forensics, design and materials selection for advanced sensors, combustion science, on-orbit space potential, container inspection, remote characterization of electronic circuits and level of activation, dielectric morphology of structures, tether integrity, organic molecular memory, atmospheric science, weather prediction, earth quake prediction, and medical diagnostic and treatment efficacy applications such as cardiac polarization wave propagation and electromyography imaging.

Generazio, Ed↗
NASA NTRS2017

Analysis of CrIs/ATMS Using AIRS Version-7 Retrieval and QC Methodology

The objective of this research is to develop and implement an algorithm to analyze a long term data record of CrIS/ATMS observations so as to produce monthly mean gridded Level-3 products which are consistent with, and will serve as a seamless follow on to, those of AIRS Version-7. We feel the best way to achieve this result is to analyze CrIS/ATMS data using retrieval and Quality Control (QC) methodologies which are scientifically equivalent to those used in AIRS Version-7. We developed and implemented a single retrieval program that uses as input either AIRS/AMSU or CrIS/ATMS radiance observations, and has appropriate switches that take into account the spectral and radiometric differences between CrIS and AIRS. Our methodology is call CHART (Climate Heritage AIRS Retrieval Technique).

Susskind, Joel↗
NASA NTRS2010

Statistical Model Selection for TID Hardness Assurance

Radiation Hardness Assurance (RHA) methodologies against Total Ionizing Dose (TID) degradation impose rigorous statistical treatments for data from a part's Radiation Lot Acceptance Test (RLAT) and/or its historical performance. However, no similar methods exist for using "similarity" data - that is, data for similar parts fabricated in the same process as the part under qualification. This is despite the greater difficulty and potential risk in interpreting of similarity data. In this work, we develop methods to disentangle part-to-part, lot-to-lot and part-type-to-part-type variation. The methods we develop apply not just for qualification decisions, but also for quality control and detection of process changes and other "out-of-family" behavior. We begin by discussing the data used in ·the study and the challenges of developing a statistic providing a meaningful measure of degradation across multiple part types, each with its own performance specifications. We then develop analysis techniques and apply them to the different data sets.

Ladbury, R.↗
NASA NTRS2017

Portable Laser Spectrometer for Airborne and Ground-Based Remote Sensing of Geological CO2 Emissions

A 24 kilogram, suitcase-sized, CW (Continuous Wave) Laser Remote Sensing Spectrometer (LARSS) with an approximately 2-kilometer range has been developed. It has demonstrated its flexibility in measuring both atmospheric CO2 from an airborne platform and terrestrial emission of CO2 from a remote mud volcano, Bledug Kuwu, Indonesia, from a ground-based sight. This system scans the CO2 absorption line with 20 discrete wavelengths, as opposed to the typical two-wavelength online-offline instrument. This multi-wavelength approach offers an effective quality control, bias control, and confidence estimate of measured CO2 concentrations via spectral fitting. The simplicity, ruggedness, and flexibility in the design allow for easy transportation and use on different platforms with a quick setup in some of the most challenging climatic conditions. While more refinement is needed, the results represent a stepping stone towards widespread use of active one-sided gas remote sensing in the earth sciences.

_______________________↗
NASA NTRS2018

Fractographic Analysis of 60-Nitinol Bearing Races

Fracture surfaces on a failed bearing race made from 60-Nitinol (60 wt% Ni - 40 wt% Ti) were analyzed. The bearing had experienced excessive localized heat due to an issue during machining that may have resulted in dimensional distortion. However, the classical fracture features that were identified by scanning electron microscopy and the fracture origins coincided with high concentrations of TiC-based contamination. Lessons learned for processing and quality control are discussed. An attempt to use x-ray diffraction to directly measure residual stresses imparted from the required heat treatment is also discussed.

intermetallics↗
NASA NTRS2018

Synthesizing a New Launch Vehicle Failure Probability Based on Historical Flight Data

New launch vehicles have historically had significantly higher failure rates in early flights than what has been predicted using Probabilistic Risk Assessment - PRA. This is because PRAs typically model a mature vehicle where a significant portion of the early failure probability contributors have been eliminated due to testing and improvements after actual field operation. To capture a more accurate early flight failure probability estimate, this paper develops a method that estimates ascent failure probability starting with the first flight based on historical launch vehicle records. With new launch vehicles being developed, such as the Space Launch System - SLS, a PRA model must be extended to cover early flight failure probability contributions that are either not covered in the mature-vehicle PRA or are underestimated. These failure probability contributions include design errors, quality control deficiencies, installation errors, and environmental impacts. There are also failure dependencies due to systemic errors that still exist due to limited entire-system testing.

Cross, Robert B.↗