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NASA NTRSDate not supplied

Observation impacts in the lower troposphere and challenges of Planetary Boundary Layer data assimilation

The Goddard Earth Observing System (GEOS) developed by the NASA Global Modeling and Assimilation Office assimilates a wide range of observations to support various NASA Earth Science missions. To set the stage for follow-on Planetary Boundary Layer (PBL) science and prepare for future observing systems of the next decade, we have assessed the effectiveness of the use of existing observing systems in the lower troposphere in GEOS, and analyzed model responses to the incremental analysis update (IAU) forcing. With a better understanding of the GEOS data assimilation algorithms in the PBL, we have developed strategies for improved PBL data assimilation in GEOS. The strategies to enhance data usages in both the data assimilation system and forecast model will be presented, and the utilization of PBL height data from multiple observing systems will be discussed as well.

Yanqiu Zhu↗

NASA NTRS2003

The Use of Sage Water Vapor Data for Investigating Climate Change Issues

SAGE water vapor data has proven valuable for addressing several of the important issues in climate change research. It has been used to investigate how the upper troposphere water vapor responds to warming and convection, a key question in understanding the water vapor feedback to anthropogenic global warming. In the case of summer versus winter differences, SAGE results showed that the upper tropospheric relative humidity remained approximately constant; this result was in general agreement with how a GCM handled the seasonal difference, and gave credence to the argument that the GCM was not overestimating the water vapor feedback associated with convection. In addition, the convection-water vapor relationship was investigated further using SAGE water vapor and ISCCP cloud data. The results showed that upper tropospheric drying did appear to occur simultaneously with deep convective events in the tropics, only to be replaced by moistening a few hours later, associated (most likely) with the reevaporation of moisture from anvil clouds. The total effect was, again, a moistening of the upper troposphere associated with convection. Calculation of the actual trends in upper tropospheric moisture is a potential goal for SAGE data usage; trends calculated with radiosonde data, or instruments calibrated with radiosonde data have the problem of the effect of changing radiosonde instruments. SAGE data have in effect been used to compare different radiosondes through comparisons, and could continue to do so. SAGE 3 should also help clarify the absolute accuracy of SAGE retrievals in the troposphere. and its consequences. Model results show that water vapor increases can help explain the observations of stratospheric cooling, along with increasing C02 and ozone reduction. SAGE has been shown to provide trends similar to those of some other satellite and in situ retrievals, with increasing water vapor over time. However, SAGE is impacted by aerosol contamination which must be removed from the data; approaches used in the past will be reviewed. The SAGE water vapor errors, if taken literally, would make it hard to justify using SAGE data for such trends, and the error bars must be investigated. Stratospheric water vapor increases, like tropospheric increases, would lead to noticeable warming at the surface, and their changes must be quantified.

Rind, D.↗

NASA NTRS2019

Reusability in NASA's Earth Observation System Data and Information System (EOSDIS)

NASA's Earth Observation System Data and Information System (EOSDIS) has been operational since 1994. The term FAIR is relatively recent compared to the operational life of EOSDIS. However, given the evolutionary nature of EOSDIS in response to both the state of the art and community expectations, it is useful to assess where EOSDIS stands relative to the FAIR data management. In this presentation we evaluate EOSDIS against the Reusability principle, which calls for a clear and accessible data usage license, rich description of data, detailed provenance, and compliance with domain-relevant community standards.Achieving consistent compliance for datasets is particularly challenging with the diverse community of missions and scientists providing data to EOSDIS. To address this, EOSDIS's constituent Distributed Active Archive Centers work with data producers to utilize the applicable standards and conventions. In addition, a guidebook is under development to provide comprehensive and understandable guidance on how to construct compliant, and thus more reusable, data products.

Hampapuram (Rama) Ramapriyan↗

NASA NTRS2000

STS-99 / Endeavour SRTM Science Briefing and Applications from JSC

The primary objective of the STS-99 mission was to complete high resolution mapping of large sections of the Earth's surface using the Shuttle Radar Topography Mission (SRTM), a specially modified radar system. This radar system produced unrivaled 3-D images of the Earth's Surface. This videotape shows a science press briefing. The panel members are Michael Kobrick, the SRTM Project Scientist at JPL; Thomas Henning, SRTM Program Manager at the National Imagery and Mapping Agency; Diane Evans, the Director of the Earth Sciences Program at NASA; and Marian Werner, XSAR Project Manager for the DLR, Deutschen Zentrum fur Luft- und Raumfahrt, Germany's National Aerospace Research Center. Michael Kobrick explained the mechanics of interferometric measurements of the Earth. He explained and demonstrated with a scale model the deployable mast's use. He also explained the importance of the attitude and orbit determination avionics. A brief animated video showing how four beams would give a 225 km wide swath of the Earth topography was viewed. Thomas Henning discussed some of the usage of the digital terrain elevation data for flood relief planning, cell phone station placement, military planning for command and control centers, and flight simulation. He explained that public access to the most precise data would be limited. Diane Evans described data usage in flood prediction, earthquake fault identification and archeology. Marian Werner described the German and Italian input to the project. The questions from the press concerned the time to process this data, and the reasons for the limited access to the more precise data.

Source record↗

NASA NTRS1994

Data assimilation and model evaluation experiment datasets

The Institute for Naval Oceanography, in cooperation with Naval Research Laboratories and universities, executed the Data Assimilation and Model Evaluation Experiment (DAMEE) for the Gulf Stream region during fiscal years 1991-1993. Enormous effort has gone into the preparation of several high-quality and consistent datasets for model initialization and verification. This paper describes the preparation process, the temporal and spatial scopes, the contents, the structure, etc., of these datasets. The goal of DAMEE and the need of data for the four phases of experiment are briefly stated. The preparation of DAMEE datasets consisted of a series of processes: (1) collection of observational data; (2) analysis and interpretation; (3) interpolation using the Optimum Thermal Interpolation System package; (4) quality control and re-analysis; and (5) data archiving and software documentation. The data products from these processes included a time series of 3D fields of temperature and salinity, 2D fields of surface dynamic height and mixed-layer depth, analysis of the Gulf Stream and rings system, and bathythermograph profiles. To date, these are the most detailed and high-quality data for mesoscale ocean modeling, data assimilation, and forecasting research. Feedback from ocean modeling groups who tested this data was incorporated into its refinement. Suggestions for DAMEE data usages include (1) ocean modeling and data assimilation studies, (2) diagnosis and theoretical studies, and (3) comparisons with locally detailed observations.

Lai, Chung-Cheng A.↗

NASA NTRS2017

Use of Schema on Read in Earth Science Data Archives

Traditionally, NASA Earth Science data archives have file-based storage using proprietary data file formats, such as HDF and HDF-EOS, which are optimized to support fast and efficient storage of spaceborne and model data as they are generated. The use of file-based storage essentially imposes an indexing strategy based on data dimensions. In most cases, NASA Earth Science data uses time as the primary index, leading to poor performance in accessing data in spatial dimensions. For example, producing a time series for a single spatial grid cell involves accessing a large number of data files. With exponential growth in data volume due to the ever-increasing spatial and temporal resolution of the data, using file-based archives poses significant performance and cost barriers to data discovery and access. Storing and disseminating data in proprietary data formats imposes an additional access barrier for users outside the mainstream research community. At the NASA Goddard Earth Sciences Data Information Services Center (GES DISC), we have evaluated applying the schema-on-read principle to data access and distribution. We used Apache Parquet to store geospatial data, and have exposed data through Amazon Web Services (AWS) Athena, AWS Simple Storage Service (S3), and Apache Spark. Using the schema-on-read approach allows customization of indexing spatially or temporally to suit the data access pattern. The storage of data in open formats such as Apache Parquet has widespread support in popular programming languages. A wide range of solutions for handling big data lowers the access barrier for all users. This presentation will discuss formats used for data storage, frameworks with This presentation will discuss formats used for data storage, frameworks with support for schema-on-read used for data access, and common use cases covering data usage patterns seen in a geospatial data archive.

cloud applications↗

NASA NTRS1982

The reusable Space Shuttle Main Engine prepares for long life

Attention is given to Space Shuttle Main Engine (SSME) life extension and logistical systems that support launch and ground operations. The operations and maintenance tasks involved in this aspect of SSME development include turnaround requirements, ground support equipment, and control documentation. A discussion is presented on the spare parts provisioning system, which emphasizes the source of decision-making data and encompasses the hardware tracking system, usage data from ground testing and launch operations, and current and projected life limits. The life extension test program leads the launch program, offering comfortable life limit margins. Planned overhauls for the extension of useful hardware life beyond the 55-mission requirement, a turnabout improvement program, and a continuous training program contribute to launch cost reductions, reliable operations, and long life.

Klatt, F. P., Jr.↗

NASA NTRS1986

Rdesign: A data dictionary with relational database design capabilities in Ada

Data Dictionary is defined to be the set of all data attributes, which describe data objects in terms of their intrinsic attributes, such as name, type, size, format and definition. It is recognized as the data base for the Information Resource Management, to facilitate understanding and communication about the relationship between systems applications and systems data usage and to help assist in achieving data independence by permitting systems applications to access data knowledge of the location or storage characteristics of the data in the system. A research and development effort to use Ada has produced a data dictionary with data base design capabilities. This project supports data specification and analysis and offers a choice of the relational, network, and hierarchical model for logical data based design. It provides a highly integrated set of analysis and design transformation tools which range from templates for data element definition, spreadsheet for defining functional dependencies, normalization, to logical design generator.

Lekkos, Anthony A.↗

NASA NTRS2016

The EOSDIS Products Usability for Disaster Response

The focus of the study is to categorize both NRT and standard data products based on applicability to the SDR-defined disaster types. This will identify which datasets from current NASA satellite missions instruments are best suited for disaster response. The distribution metrics of the products that have been used for studying various selected disasters that have occurred over last 5 years will be analyzed that include volume, number of files, number of users, user domains, user country, etc. This data usage analysis will provide information to the data centers staff that can help them develop the functionality and allocate the resources needed for enhanced access and timely availability of the data products that are critical for the time-sensitive analyses.

LANCE↗

NASA NTRS2005

Goddard Atmospheric Composition Data Center: Aura Data and Services in One Place

The Goddard Atmospheric Composition Data and Information Services Center (AC-DISC) is a portal to the Atmospheric Composition specific, user driven, multi-sensor, on-line, easy access archive and distribution system employing data analysis and visualization, data mining, and other user requested techniques for the better science data usage. It provides convenient access to Atmospheric Composition data and information from various remote-sensing missions, from TOMS, UARS, MODIS, and AIRS, to the most recent data from Aura OMI, MLS, HIRDLS (once these datasets are released to the public), as well as Atmospheric Composition datasets residing at other remote archive site.

Leptoukh, G.↗

NASA NTRS2013

GHRSST-14 DAS-TAG Report

The DAS-TAG provides the informatics and data management expertise in emerging information technologies for the GHRSST community. It provides expertise in data and metadata formats and standards, fosters improvements for GHRSST data curation, experiments with new data processing paradigms, and evaluates services and tools for data usage. It provides a forum for producer and distributor data management issues and coordination.

data processing↗

NASA NTRS1987

Performability modeling based on real data: A casestudy

Described is a measurement-based performability model based on error and resource usage data collected on a multiprocessor system. A method for identifying the model structure is introduced and the resulting model is validated against real data. Model development from the collection of raw data to the estimation of the expected reward is described. Both normal and error behavior of the system are characterized. The measured data show that the holding times in key operational and error states are not simple exponentials and that a semi-Markov process is necessary to model the system behavior. A reward function, based on the service rate and the error rate in each state, is then defined in order to estimate the performability of the system and to depict the cost of different types of errors.

Hsueh, M. C.↗

NASA NTRS1988

Performability modeling based on real data: A case study

Described is a measurement-based performability model based on error and resource usage data collected on a multiprocessor system. A method for identifying the model structure is introduced and the resulting model is validated against real data. Model development from the collection of raw data to the estimation of the expected reward is described. Both normal and error behavior of the system are characterized. The measured data show that the holding times in key operational and error states are not simple exponentials and that a semi-Markov process is necessary to model system behavior. A reward function, based on the service rate and the error rate in each state, is then defined in order to estimate the performability of the system and to depict the cost of apparent types of errors.

Hsueh, M. C.↗

NASA NTRS2019

Learning from GES DISC's MLS and OMI Data Users: Metrics Matter

It has been over 15 years since Aura research satellite launched in 2004 to observe the Earth's ozone layer, air quality, and climate from four different instruments - the High Resolution Dynamics Limb Sounder (HIRDLS), the Microwave Limb Sounder (MLS), the Ozone Monitoring Instrument (OMI), and the Tropospheric Emission Spectrometer (TES). Observations from the Aura mission have established a concrete understanding of the changing chemistry of our atmosphere.The NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) is the official archive and distribution center for the HIRDLS, MLS, and OMI instruments. This presentation will report metrics of data usage and services on these instruments. Key to GES DISC's mission to provide better data support is gaining a better understanding of our users' needs and behaviors as they discover, access and utilize these data. We will summarize the users' needs from these instruments based on user inquiry information collected over the Aura mission lifetime and present findings from this ensemble metrics.

Metrics↗

NASA NTRS1991

Status of the FAA flight loads monitoring program

In support of the Federal Aviation Administration Aging Aircraft Research Program, the Agency has established a Flight Loads Data Collection Program for Commercial Aircraft. The objectives of this Program are to: - Review existing data collected by other sources including but not limited to U.S., Foreign, Military, etc. - Collect current operational usage data from both large and small transport aircraft - Develop criteria for future generations of transports. This paper presents the status of the various programs which are completed, underway or planned. The FAA will be collecting, storing, and analyzing the data which characterize typical commercial transport operations. The airframe manufacturers will handle the task of calculating the loads and stresses.

Aging Aircraft↗

NASA NTRS2002

The D3 Middleware Architecture

DARWIN is a NASA developed, Internet-based system for enabling aerospace researchers to securely and remotely access and collaborate on the analysis of aerospace vehicle design data, primarily the results of wind-tunnel testing and numeric (e.g., computational fluid-dynamics) model executions. DARWIN captures, stores and indexes data; manages derived knowledge (such as visualizations across multiple datasets); and provides an environment for designers to collaborate in the analysis of test results. DARWIN is an interesting application because it supports high-volumes of data. integrates multiple modalities of data display (e.g., images and data visualizations), and provides non-trivial access control mechanisms. DARWIN enables collaboration by allowing not only sharing visualizations of data, but also commentary about and views of data. Here we provide an overview of the architecture of D3, the third generation of DARWIN. Earlier versions of DARWIN were characterized by browser-based interfaces and a hodge-podge of server technologies: CGI scripts, applets, PERL, and so forth. But browsers proved difficult to control, and a proliferation of computational mechanisms proved inefficient and difficult to maintain. D3 substitutes a pure-Java approach for that medley: A Java client communicates (though RMI over HTTPS) with a Java-based application server. Code on the server accesses information from JDBC databases, distributed LDAP security services, and a collaborative information system. D3 is a three tier-architecture, but unlike 'E-commerce' applications, the data usage pattern suggests different strategies than traditional Enterprise Java Beans - we need to move volumes of related data together, considerable processing happens on the client, and the 'business logic' on the server-side is primarily data integration and collaboration. With D3, we are extending DARWIN to handle other data domains and to be a distributed system, where a single login allows a user transparent access to test results from multiple servers and authority domains.

Walton, Joan↗

NASA NTRS2015

Global Precipitation Measurement (GPM) Mission Products and Services at the NASA Goddard Earth Sciences (GES) Data and Information Services Center (DISC)

On February 27, 2014, the NASA Global Precipitation Measurement (GPM) mission was launched to provide the next-generation global observations of rain and snow (http:pmm.nasa.govGPM). The GPM mission consists of an international network of satellites in which a GPM Core Observatory satellite carries both active and passive microwave instruments to measure precipitation and serve as a reference standard, to unify precipitation measurements from a constellation of other research and operational satellites. The NASA Goddard Earth Sciences (GES) Data and Information Services Center (DISC) hosts and distributes GPM data within the NASA Earth Observation System Data Information System (EOSDIS). The GES DISC is home to the data archive for the GPM predecessor, the Tropical Rainfall Measuring Mission (TRMM). Over the past 16 years, the GES DISC has served the scientific as well as other communities with TRMM data and user-friendly services. During the GPM era, the GES DISC will continue to provide user-friendly data services and customer support to users around the world. GPM products currently and to-be available include the following: 1. Level-1 GPM Microwave Imager (GMI) and partner radiometer products. 2. Goddard Profiling Algorithm (GPROF) GMI and partner products. 3. Integrated Multi-satellitE Retrievals for GPM (IMERG) products. (early, late, and final)A dedicated Web portal (including user guides, etc.) has been developed for GPM data (http:disc.sci.gsfc.nasa.govgpm). Data services that are currently and to-be available include Google-like Mirador (http:mirador.gsfc.nasa.gov) for data search and access; data access through various Web services (e.g., OPeNDAP, GDS, WMS, WCS); conversion into various formats (e.g., netCDF, HDF, KML (for Google Earth), ASCII); exploration, visualization, and statistical online analysis through Giovanni (http:giovanni.gsfc.nasa.gov); generation of value-added products; parameter and spatial subsetting; time aggregation; regridding; data version control and provenance; documentation; science support for proper data usage, FAQ, help desk; monitoring services (e.g. Current Conditions) for applications.In this presentation, we will present GPM data products and services with examples.

NASA GES DISC↗

NASA NTRSDate not supplied

A Regional Perspective on Global NWP from North America and Recent Developments in the NASA GEOS System

Satellite data have played an important role in improving model forecast skills. This presentation will give a perspective of data usages of vital satellites on global NWP and show some examples of using existing satellite observations in the GEOS data assimilation system at NASA GMAO. The efforts to utilize emerging satellite data and to prepare for the upcoming new instruments NASA supports will be presented as well.

Yanqiu Zhu↗