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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 307 records · Page 17
All sourcesNASA NTRSDOE OSTI
NASA NTRS1973

Machine processing methods for earth observational data

A brief review of the development over the last decade of earth resource information systems is presented. Machine data preprocessing and analysis methods are surveyed and illustrated. These include preprocessing steps intended to modify geometric and radiometric aspects of earth observational image data to enhance the ability of either human interpreters or machine algorithms to extract information from the data. Illustrations of processed and analyzed images from spaceborne sensors including the Earth Resources Technology Satellite are discussed.

Landgrebe, D. A.↗
NASA NTRS1989

Earth observing system: 1989 reference handbook

NASA is studying a coordinated effort called the Mission to Planet Earth to understand global change. The goals are to understand the Earth as a system, and to determine those processes that contribute to the environmental balance, as well as those that may result in changes. The Earth Observing System (Eos) is the centerpiece of the program. Eos will create an integrated scientific observing system that will enable multidisciplinary study of the Earth including the atmosphere, oceans, land surface, polar regions, and solid Earth. Science goals, the Eos data and information system, experiments, measuring instruments, and interdisciplinary investigations are described.

Source record↗
NASA NTRS1975

Observing earth from Skylab

Skylab technology and observations of earth resources are discussed. Special attention was given to application of Skylab data to mapmaking, geology/geodesy, water resources, oceanography, meteorology, and geography/ecology.

Source record↗
NASA NTRS1993

Earth Observing System overview

The paper discusses major objectives and the primary components of the Earth Observing System (EOS), an integrated scientific observing system which will carry out remote sensing measurements for at least 15 years, providing comprehensive data andinformation in support of NASA's Mission to Planet Earth and the international Global Change Research Program. Particular attention is given to the functions of 18 instruments selected for EOS for the definition-phase investigations and to the mission profile.

Walton, Barbara A.↗
NASA NTRS2017

A Hybrid Traveling Salesman Problem - Squeaky Wheel Optimization Planner for Earth Observational Scheduling

We outline a hybrid planner for scheduling Observa- tion Requests on an Earth observing satellite, subject to a variety of constraints for the ASPEN (Chien et al. 2000) Eagle Eye adaptation (Knight, Donnellan, and Green 2013) that combines Squeaky Wheel Optimiza- tion (Joslin and Clements 1999) with sliding observa- tion planning (Aldinger et al. 2013). The Earth Ob- serving Satellite (EOS) planning problem (Globus et al. 2004) is reformulated as time-varying travel time TSP with interval constraints (Ichoua, Gendreau, and Potvin 2003). The replanning/ ll stage of the hybrid scheduler marginally improves schedule quality for all bus agilities examined, but has more impact on lower agility observers. The squeaky wheel stage primarily a ects overall schedule quality by satisfying high pri- ority requests, while the replanner reduces starvation of lower value requests.

Trowbridge, Michael↗
NASA NTRS1983

Interactive analysis of a large aperture Earth observations satellite

A system level design and analysis has been conducted on an Earth Observation Satellite (EOS) system using the Interactive Design and Evaluation of Advanced Spacecraft (IDEAS) computer-aided design and analysis program. The IDEAS program consists of about 40 user-friendly technical modules and an interactive graphics display. The reflector support system and feed mast of the EOS spacecraft are constructed with box-truss structural concept, a lattice configuration which can be packaged for delivery in a single Shuttle flight and deployed in orbit. The deployed spacecraft consists of a 120-m by 60-m parabolic focal axis. The spacecraft was modeled for structural, thermal, and control systems analysis and structural elements were designed. On-orbit dynamic and thermal loading analyses were conducted; spacecraft weights and developmental and first unit costs were determined.

Wright, R. L.↗
NASA NTRS1983

Interactive analysis of a large aperture Earth Observations Satellite

A system level design and analysis has been conducted on an Earth Observation Satellite (EOS) system using the Interactive Design and Evaluation of Advanced Spacecraft (IDEAS) computer-aided design and analysis program. The IDEAS program consists of about 40 user-friendly technical modules and an interactive graphics display. The reflector support system and feed mast of the EOS spacecraft are constructed with a box-truss structural concept, a lattice configuration which can be packaged for delivery in a single Shuttle flight and deployed in orbit. The deployed spacecraft consists of a 120-m by 60-m parabolic reflector, a 120-m-long support structure, and a 30-m feed arc beam on the focal axis. The spacecraft was modeled for structural, thermal, and control systems analysis and structural elements were designed. On-orbit dynamic and thermal loading analyses were conducted and spacecraft weights were determined.

Wright, R. L.↗
NASA NTRS2008

Performance of Versions 1,2 and 3 of the Goddard Earth Observing System (GEOS) Chemistry-Climate Model (CCM)

Version 1 of the Goddard Earth Observing System Chemistry-Climate Model (GEOS CCM) was used in the first CCMVa1 model evaluation and forms the basis for several studies of links between ozone and the circulation. That version of the CCM was based on the GEOS-4 GCM. Versions 2 and 3 of the GEOS CCM are based on the GEOS-5 GCM, which retains the "Lin-Rood" dynamical core but has a totally different set of physical parameterizatiOns to GEOS-4. In Version 2 of the GEOS CCM the Goddard stratospheric chemistry module is retained. Difference between Versions 1 and 2 thus reflect the physics changes of the underlying GCMs. Several comparisons between these two models are made, several of which reveal improvements in Version 2 (including a more realistic representation of the interannual variability of the Antarctic vortex). In Version 3 of the GEOS CCM, the stratospheric chemistry mechanism is replaced by the "GMI COMBO" code that includes tropospheric chemistry and different computational approaches. An advantage of this model version. is the reduction of high ozone biases that prevail at low chlorine loadings in Versions 1 and 2. This poster will compare and contrast various aspects of the three model versions that are relevant for understanding interactions between ozone and climate.

Pawson, Steven↗
NASA NTRS2016

Big Data in the Earth Observing System Data and Information System

Approaches that are being pursued for the Earth Observing System Data and Information System (EOSDIS) data system to address the challenges of Big Data were presented to the NASA Big Data Task Force. Cloud prototypes are underway to tackle the volume challenge of Big Data. However, advances in computer hardware or cloud won't help (much) with variety. Rather, interoperability standards, conventions, and community engagement are the key to addressing variety.

data systems↗
NASA NTRS2017

Earth Observing System Covariance Realism Updates

This presentation will be given at the International Earth Science Constellation Mission Operations Working Group meetings June 13-15, 2017 to discuss the Earth Observing System Covariance Realism updates.

realism↗
NASA NTRS2017

Archive Management of NASA Earth Observation Data to Support Cloud Analysis

NASA collects, processes and distributes petabytes of Earth Observation (EO) data from satellites, aircraft, in situ instruments and model output, with an order of magnitude increase expected by 2024. Cloud-based web object storage (WOS) of these data can simplify the execution of such an increase. More importantly, it can also facilitate user analysis of those volumes by making the data available to the massively parallel computing power in the cloud. However, storing EO data in cloud WOS has a ripple effect throughout the NASA archive system with unexpected challenges and opportunities. One challenge is modifying data servicing software (such as Web Coverage Service servers) to access and subset data that are no longer on a directly accessible file system, but rather in cloud WOS. Opportunities include refactoring of the archive software to a cloud-native architecture; virtualizing data products by computing on demand; and reorganizing data to be more analysis-friendly.

Lynnes, Christopher↗
NASA NTRS2017

Archive Management of NASA Earth Observation Data to Support Cloud Analysis

NASA collects, processes and distributes petabytes of Earth Observation (EO) data from satellites, aircraft, in situ instruments and model output, with an order of magnitude increase expected by 2024. Cloud-based web object storage (WOS) of these data can simplify the execution of such an increase. More importantly, it can also facilitate user analysis of those volumes by making the data available to the massively parallel computing power in the cloud. However, storing EO data in cloud WOS has a ripple effect throughout the NASA archive system with unexpected challenges and opportunities. One challenge is modifying data servicing software (such as Web Coverage Service servers) to access and subset data that are no longer on a directly accessible file system, but rather in cloud WOS. Opportunities include refactoring of the archive software to a cloud-native architecture; virtualizing data products by computing on demand; and reorganizing data to be more analysis-friendly. Reviewed by Mark McInerney ESDIS Deputy Project Manager.

Lynnes, Christopher↗
NASA NTRSDate not supplied

Automated Metadata Scoring Approaches for Earth Observation Data

The Common Metadata Repository (CMR) contains metadata records describing NASA’s Earth observation data products which are archived across 12 data centers also known as Distributed Active Archive Centers (DAACs). To ensure that NASA’s data is discoverable, accessible, and usable, the Analysis and Review of CMR (ARC) Team, located at Marshall Space Flight Center, assesses the quality of these metadata records. The ARC team currently uses a combination of automated and manual methods to check metadata records for quality dimensions such as completeness, correctness, and consistency. In addition to these quality assessments, the team is currently exploring various metadata scoring methods in order to provide normalized results across the twelve DAACs. This method is conducted by using automated methods to assess metadata fields and then provide a numeric score, or grade, based on the analysis. To implement this process, two different approaches have been theorized and are currently being explored by the ARC team. This presentation will describe ARC's two proposed methodologies in more detail, and the pros and cons to using these metadata scoring methods.

Jenny Wood↗
NASA NTRS1978

Earth observations and photography experiment: Summary of significant results

Observation and photographic data from the Apollo Soyuz Test Project are analyzed. The discussion is structured according to the fields of investigation including: geology, desert studies, oceanography, hydrology, and meteorology. The data were obtained by: (1) visual observations of selected Earth features, (2) hand-held camera photography to document observations, and (3) stereo mapping photography of areas of significant scientific interest.

El-Baz, F.↗
NASA NTRS2006

Coloration Determination of Spectral Darkening Occurring on a Broadband Earth Observing Radiometer: Application to Clouds and the Earth's Radiant Energy System (CERES)

It is estimated that in order to best detect real changes in the Earth s climate system, space based instrumentation measuring the Earth Radiation Budget (ERB) must remain calibrated with a stability of 0.3% per decade. Such stability is beyond the specified accuracy of existing ERB programs such as the Clouds and the Earth s Radiant Energy System (CERES, using three broadband radiometric scanning channels: the shortwave 0.3 - 5microns, total 0.3. > 100microns, and window 8 - 12microns). It has been shown that when in low earth orbit, optical response to blue/UV radiance can be reduced significantly due to UV hardened contaminants deposited on the surface of the optics. Since typical onboard calibration lamps do not emit sufficient energy in the blue/UV region, this darkening is not directly measurable using standard internal calibration techniques. This paper describes a study using a model of contaminant deposition and darkening, in conjunction with in-flight vicarious calibration techniques, to derive the spectral shape of darkening to which a broadband instrument is subjected. Ultimately the model uses the reflectivity of Deep Convective Clouds as a stability metric. The results of the model when applied to the CERES instruments on board the EOS Terra satellite are shown. Given comprehensive validation of the model, these results will allow the CERES spectral responses to be updated accordingly prior to any forthcoming data release in an attempt to reach the optimum stability target that the climate community requires.

Matthews, Grant↗
NASA NTRS2018

NASA Crew Earth Observations: A Long-Term Dataset for Earth System Research, Applications, and Education

Polar orbit; Sun-synchronous - designed for long term repeatability of data, Typically nadir viewing, crosses every point on Earth ~ 12-14 days near local solar noon/local midnight, Landsat series collecting data since 1972, Pointing capability, satellite constellations. Inclined Equatorial Orbit: ISS; Sun-asynchronous - similar illumination 3-4 days every 90 days, Nadir to highly oblique imagery possible from hand-held cameras, WORF, external sensors, Provides opportunity to collect unique datasets for scientific study, disaster response, Data is complementary to polar-orbiting satellite data.

Stefanov, William L.↗