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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 1,063 records · Page 59
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

The SPARC-Reanalysis Intercomparison Project: Summary of Phase 1 and Plans for Phase 2 (S-RIP2): Chemical Reanalyses & Air Quality, Tropospheric Circulation, Extreme Events, and More

Reanalysis datasets are widely used to understand numerous atmospheric processes. Different reanalyses may give very different results for the same diagnostics. The Stratosphere-troposphere Processes And their Role in Climate (SPARC) Reanalysis Intercomparison Project (S-RIP, https://s-rip.github.io/) is a coordinated activity to compare key diagnostics among available reanalyses, identify differences among reanalyses and their underlying causes, provide guidance on appropriate usage of reanalyses in scientific studies, and contribute to future improvements in the reanalysis products via collaborations with reanalysis centers and data users. S-RIP Phase 1 (completed in early 2022) focused primarily on the upper troposphere through the middle atmosphere and processes linking these regions to the troposphere and surface. We look forward to broadening our efforts in Phase 2 (S-RIP2), with new foci including studies of tropospheric circulation, extreme weather events, and their links to the stratosphere, and focusing on evaluation of chemical reanalyses, both those with a stratosphere / upper troposphere focus and those that focus on air quality applications. This presentation will provide a summary of Phase 1 results and discussion of future directions for S-RIP2, emphasizing applications to composition and chemistry studies as well as fostering capacity building for Early Career Scientists.

Jonathan S Wright↗
NASA NTRSDate not supplied

Broadening Systematic Reanalysis lntercomparisons in the SPARC-Reanalysis lntercomparison Project Phase 2 (S-RIP2): Chemical Reanalyses & Air Quality, Tropospheric Circulation, Extreme Events, and More

Reanalysis datasets are widely used to understand numerous atmospheric processes. Different reanalyses may give very different results for the same diagnostics. The Stratosphere-troposphere Processes And their Role in Climate (SPARC) Reanalysis Intercomparison Project (S-RIP, https://s-rip.github.io/) is a coordinated activity to compare key diagnostics among available reanalyses, identify differences among reanalyses and their underlying causes, provide guidance on appropriate usage of reanalyses in scientific studies, and contribute to future improvements in the reanalysis products via collaborations with reanalysis centers and data users. S-RIP Phase 1 (completed in early 2022) focused primarily on the upper troposphere through the middle atmosphere and processes linking these regions to the troposphere and surface. We look forward to broadening our efforts in Phase 2 (S-RIP2), with new foci including studies of tropospheric circulation, extreme weather events, and their links to the stratosphere, and focusing on evaluation of chemical reanalyses, both those with a stratosphere / upper troposphere focus and those that focus on air quality applications. This presentation will provide a summary of Phase 1 results and discussion of future directions for S-RIP2, emphasizing applications to composition and chemistry studies as well as fostering capacity building for Early Career Scientists.

Sean M Davis↗
NASA NTRSDate not supplied

Broadening Systematic Reanalysis Intercomparisons in the SPARC-Reanalysis Intercomparison Project Phase 2 (S-RIP2): Chemical Reanalyses & Air Quality, Tropospheric Circulation, Extreme Events, and More

Reanalysis datasets are widely used to understand numerous atmospheric processes. Different reanalyses may give very different results for the same diagnostics. The Stratosphere-troposphere Processes And their Role in Climate (SPARC) Reanalysis Intercomparison Project (S-RIP, https://s-rip.github.io/) is a coordinated activity to compare key diagnostics among available reanalyses, identify differences among reanalyses and their underlying causes, provide guidance on appropriate usage of reanalyses in scientific studies, and contribute to future improvements in the reanalysis products via collaborations with reanalysis centers and data users. S-RIP Phase 1 (completed in early 2022) focused primarily on the upper troposphere through the middle atmosphere and processes linking these regions to the troposphere and surface. We look forward to broadening our efforts in Phase 2 (S-RIP2), with new foci including studies of tropospheric circulation, extreme weather events, and their links to the stratosphere, and focusing on evaluation of chemical reanalyses, both those with a stratosphere / upper troposphere focus and those that focus on air quality applications. This presentation will provide a summary of Phase 1 results and discussion of future directions for S-RIP2, emphasizing applications to composition and chemistry studies as well as fostering capacity building for Early Career Scientists.

Sean M Davis↗
NASA NTRSDate not supplied

Big Data Challenges at the Community Coordinated Modeling Center (CCMC)

Like other research centers, the Community Coordinated Modeling Center (CCMC, https://ccmc.gsfc.nasa.gov) at NASA Goddard Space Flight Center (GSFC) is also experiencing the big data challenges. CCMC hosts over 80 space weather models for Runs On Request (ROR), Continuous Runs and Instant Runs simulation services for the research community. In addition, CCMC has started to support simulation output onboarding in response to the Open Science initiative. Overall, we have accumulated over petabytes of simulation output data and are rapidly growing. In this presentation, we will discuss our data and storage challenges. We will present our attempts to address our challenges and any associated lessons learned. CCMC uses Apache Airflow to ensure data transfer is consistent. We will give a brief overview on how we leverage Apache Airflow to enhance our environment.

space weather↗
NASA NTRS2014

Analyzing a 35-Year Hourly Data Record: Why So Difficult?

At the Goddard Distributed Active Archive Center, we have recently added a 35-Year record of output data from the North American Land Assimilation System (NLDAS) to the Giovanni web-based analysis and visualization tool. Giovanni (Geospatial Interactive Online Visualization ANd aNalysis Infrastructure) offers a variety of data summarization and visualization to users that operate at the data center, obviating the need for users to download and read the data themselves for exploratory data analysis. However, the NLDAS data has proven surprisingly resistant to application of the summarization algorithms. Algorithms that were perfectly happy analyzing 15 years of daily satellite data encountered limitations both at the algorithm and system level for 35 years of hourly data. Failures arose, sometimes unexpectedly, from command line overflows, memory overflows, internal buffer overflows, and time-outs, among others. These serve as an early warning sign for the problems likely to be encountered by the general user community as they try to scale up to Big Data analytics. Indeed, it is likely that more users will seek to perform remote web-based analysis precisely to avoid the issues, or the need to reprogram around them. We will discuss approaches to mitigating the limitations and the implications for data systems serving the user communities that try to scale up their current techniques to analyze Big Data.

computational performance↗
NASA NTRS1973

Station Data Acquisition and Control System.

The Station Data Acquisition and Control System (STADAC), a multicomputer data handling and control system, is expected to be installed in six network stations, the first station to be established at Goddard Space Flight Center. Network control data, in the form of schedules and acquisition pointing data, flow from the Network Control Center to the stations, while station status data flow in the other direction. The system will format spacecraft telemetry data for real-time transmission of control and status data to the project operations control centers and for transmission of experiment data to the data processing facilities. The system will also schedule station equipment resources, perform prepass tests to assure data quality, and automatically set up selected station equipment.

Kiebler, J. W.↗
NASA NTRS1994

Venus' center of figure-center of mass offset

Magellan altimetry data reveal that the center of figure (CF) of Venus is displaced approximately 280 m from its center of mass (CM) toward 4.4 deg S, 135.8 deg E, a location in Aphrodite Terra. This offset is smaller than those of other terrestrial planets but larger than the estimated error, which is no more than a few tens of meters. We examine the possibility that the CF-CM offset is related to specific geologic provinces on Venus by deriving three simple models for the offset: a thick-crust model, a hotspot model, and a thick-lithosphere model. The offset caused by a region of thick crust depends upon the region's extent, the crust-mantle density contrast, and the thickness of excess crust. A hotspot-related offset depends on the extent of the thermally anomalous region and the magnitude of the thermal anomaly. Offset due to a region of thick lithosphere depends upon the extent of the region, the average temperature contrast across the lithosphere, and the amount of excess lithosphere. We apply the three models to Venus plateau-shaped highlands, volcanic rises, and lowlands, respectively, in an attempt to match the observed CF-CM offset location and magnitude. The influence of most volcanic rises and of Ishtar Terra on the CF-CM offset must be quite small if we are to explain the direction of the observed offset. The lack of influence of volcanic rises can be explained if the related thermal anomalies are limited to a few hundred degrees or less and are plume-shaped (i.e., characterized by a flattened sublithospheric `head' with a narrow cylindrical feeder `tail'). The unimportance of Ishtar Terra is most easily explained if it lies atop a significant mantle downwelling.

Bindschadler, Duane L.↗
NASA NTRS2018

Automatic Speech Recognition for Launch Control Center Communication Using Recurrent Neural Networks with Data Augmentation and Custom Language Model

Transcribing voice communications in NASA’s launch control center is important for information utilization. However, automatic speech recognition in this environment is particularly challenging due to the lack of training data, unfamiliar words in acronyms, multiple different speakers and accents, and conversational characteristics of speaking. We used bidirectional deep recurrent neural networks to train and test speech recognition performance. We showed that data augmentation and custom language models can improve speech recognition accuracy. Transcribing communications from the launch control center will help the machine analyze information and accelerate knowledge generation.

Chow, Edward↗
NASA NTRS2017

Evolution of NASA's Earth Science Digital Object Identifier Registration System

NASA's Earth Science Data and Information System (ESDIS) Project has implemented a fully automated system for assigning Digital Object Identifiers (DOIs) to Earth Science data products being managed by its network of 12 distributed active archive centers (DAACs). A key factor in the successful evolution of the DOI registration system over last 7 years has been the incorporation of community input from three focus groups under the NASA's Earth Science Data System Working Group (ESDSWG). These groups were largely composed of DOI submitters and data curators from the 12 data centers serving the user communities of various science disciplines. The suggestions from these groups were formulated into recommendations for ESDIS consideration and implementation. The ESDIS DOI registration system has evolved to be fully functional with over 5,000 publicly accessible DOIs and over 200 DOIs being held in reserve status until the information required for registration is obtained. The goal is to assign DOIs to the entire 8000+ data collections under ESDIS management via its network of discipline-oriented data centers. DOIs make it easier for researchers to discover and use earth science data and they enable users to provide valid citations for the data they use in research. Also for the researcher wishing to reproduce the results presented in science publications, the DOI can be used to locate the exact data or data products being cited.

DOI; digital object identifier; mappin↗
NASA NTRS1998

The Data Acquisition and Control Systems of the Jet Noise Laboratory at the NASA Langley Research Center

The features of the data acquisition and control systems of the NASA Langley Research Center's Jet Noise Laboratory are presented. The Jet Noise Laboratory is a facility that simulates realistic mixed flow turbofan jet engine nozzle exhaust systems in simulated flight. The system is capable of acquiring data for a complete take-off assessment of noise and nozzle performance. This paper describes the development of an integrated system to control and measure the behavior of model jet nozzles featuring dual independent high pressure combusting air streams with wind tunnel flow. The acquisition and control system is capable of simultaneous measurement of forces, moments, static and dynamic model pressures and temperatures, and jet noise. The design concepts for the coordination of the control computers and multiple data acquisition computers and instruments are discussed. The control system design and implementation are explained, describing the features, equipment, and the experiences of using a primarily Personal Computer based system. Areas for future development are examined.

Jansen, B. J., Jr.↗
NASA NTRS2015

FJET Database Project: Extract, Transform, and Load

The Data Mining & Knowledge Management team at Kennedy Space Center is providing data management services to the Frangible Joint Empirical Test (FJET) project at Langley Research Center (LARC). FJET is a project under the NASA Engineering and Safety Center (NESC). The purpose of FJET is to conduct an assessment of mild detonating fuse (MDF) frangible joints (FJs) for human spacecraft separation tasks in support of the NASA Commercial Crew Program. The Data Mining & Knowledge Management team has been tasked with creating and managing a database for the efficient storage and retrieval of FJET test data. This paper details the Extract, Transform, and Load (ETL) process as it is related to gathering FJET test data into a Microsoft SQL relational database, and making that data available to the data users. Lessons learned, procedures implemented, and programming code samples are discussed to help detail the learning experienced as the Data Mining & Knowledge Management team adapted to changing requirements and new technology while maintaining flexibility of design in various aspects of the data management project.

excel vba↗
NASA NTRS1986

PCDS as a tool in teaching and research at the University of Michigan

The Space Physics Research Laboratory's (SPRL) use of the Pilot Climate Data System (PCDS) is discussed. For this purpose, a computer center was established to provide the hardware and software necessary to fully utilize existing data bases for research and teaching purposes. A schematic of the SPRL network is given. The core of the system consists of two VAX 11/750s and a VAX 8600, networked through ETHERNET to several LSI 11/23 microprocessors. Much of the system is used for external communications with major networks and data centers. A VAX 11/750 provides DECNET services through the SPAN network to the PCDS. A functional diagram of PCDS usage is given. The browsing capabilities of the PCDS are used to generate data files, which are later transferred to the SPRL center for further data manipulation and display. This mode of operation for classroom instruction will be used to effectively use terminals and to simplify usage of the data base. The Atmosphere Explorer data base has been used successfully in a similar manner in courses related to the thermosphere and ionosphere. The main motivation to access the PCDS was to complement research efforts related to the High Resolution Doppler Imager (HRDI), to be flown on the Upper Atmosphere Research Satellite (UARS).

Abreu, V.↗
NASA NTRS2016

Big Data Analytics and Machine Intelligence Capability Development at NASA Langley Research Center: Strategy, Roadmap, and Progress

In 2014, a team of researchers, engineers and information technology specialists at NASA Langley Research Center developed a Big Data Analytics and Machine Intelligence Strategy and Roadmap as part of Langley's Comprehensive Digital Transformation Initiative, with the goal of identifying the goals, objectives, initiatives, and recommendations need to develop near-, mid- and long-term capabilities for data analytics and machine intelligence in aerospace domains. Since that time, significant progress has been made in developing pilots and projects in several research, engineering, and scientific domains by following the original strategy of collaboration between mission support organizations, mission organizations, and external partners from universities and industry. This report summarizes the work to date in Data Intensive Scientific Discovery, Deep Content Analytics, and Deep Q&A projects, as well as the progress made in collaboration, outreach, and education. Recommendations for continuing this success into future phases of the initiative are also made.

Ambur, Manjula Y.↗
NASA NTRS1981

Visible and infrared spin scanning radiometer /VISSR/ atmospheric sounder /VAS/ ground data system

The interactive system developed at NASA/Goddard Space Flight Center to receive data from the infrared radiometer on GOES-4 in near real time and to perform interactive display and analysis of the 12-channel infrared imagery is described. The system is minicomputer based and uses a menu approach in guiding the analyst through spacecraft instrument programming, area and band selection, image acquisition, enhancement, analysis, and presentation of results. The system is linked by dual port disks to Goddard's Atmospheric and Oceanographic Information Processing System for comparing the sounding results with parameters derived from conventional data and from time lapse analysis of visible and IR imagery from other geostationary satellites. It is pointed out that the system hardware and software are being expanded to add capabilities for the integration and assimilation of VAS data with data from other sources, the comparison of severe storm observations from space with special ground network data, and the development of diagnostic models.

Dalton, J. T.↗
NASA NTRS2004

MIT Haystack Observatory Analysis Center Report

The data from twenty years of the NCEP numerical weather model have been used to calculate the IMF hydrostatic mapping function for several sites distributed in latitude from -66 degrees to +78 degrees. Comparison of heights estimated with the NMF hydrostatic mapping function demonstrates that using NMFh results in height errors at annual and semi-annual periods with amplitudes as large as approximately 8 mm and 4 mm, respectively, when data down to 5 degrees are included. The errors are smallest at the equator and increase towards the poles.

Niell, Arthur↗
NASA NTRS2015

Explore GPM IMERG and Other Global Precipitation Products with GES DISC GIOVANNI

New features and capabilities in the newly released GIOVANNI allow exploring GPM IMERG (Integrated Multi-satelliE Retrievals for GPM) Early, Late and Final Run global half-hourly and monthly precipitation products as well as other precipitation products distributed by the GES DISC such as TRMM Multi-Satellite Precipitation Analysis (TMPA), MERRA (Modern Era Retrospective-Analysis for Research and Applications), NLDAS (North American Land Data Assimilation Systems), GLDAS (Global Land Data Assimilation Systems), etc. GIOVANNI is a web-based tool developed by the GES DISC (Goddard Earth Sciences and Data Information Services Center) to visualize and analyze Earth science data without having to download data and software. The new interface in GIOVANNI allows searching and filtering precipitation products from different NASA missions and projects and expands the capabilities to inter-compare different precipitation products in one interface. Knowing differences in precipitation products is important to identify issues in retrieval algorithms, biases, uncertainties, etc. Due to different formats, data structures, units and so on, it is not easy to inter-compare precipitation products. Newly added features and capabilities (unit conversion, regridding, etc.) in GIOVANNI make inter-comparisons possible. In this presentation, we will describe these new features and capabilities along with examples.

Giovanni↗