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At least 307 records · Page 17

Report from the Passive Microwave Data Set Management Workshop

Passive microwave data sets are some of the most important data sets in the Earth Observing System Data and Information System (EOSDIS), providing data as far back as the early 1970s. The widespread use of passive microwave (PM) radiometer data has led to their collection and distribution over the years at several different Earth science data centers. The user community is often confused by this proliferation and the uneven spread of information about the data sets. In response to this situation, a Passive Microwave Data Set Management Workshop was held 17 ]19 May 2011 at the Global Hydrology Resource Center, sponsored by the NASA Earth Science Data and Information System (ESDIS) Project. The workshop attendees reviewed all primary (Level 1 ]3) PM data sets from NASA and non ]NASA sensors held by NASA Distributed Active Archive Centers (DAACs), as well as high ]value data sets from other NASA ]funded organizations. This report provides the key findings and recommendations from the workshop as well as detailed tabluations of the datasets considered.

Armstrong, Ed↗

Moisture Forecast Bias Correction in GEOS DAS

Data assimilation methods rely on numerous assumptions about the errors involved in measuring and forecasting atmospheric fields. One of the more disturbing of these is that short-term model forecasts are assumed to be unbiased. In case of atmospheric moisture, for example, observational evidence shows that the systematic component of errors in forecasts and analyses is often of the same order of magnitude as the random component. we have implemented a sequential algorithm for estimating forecast moisture bias from rawinsonde data in the Goddard Earth Observing System Data Assimilation System (GEOS DAS). The algorithm is designed to remove the systematic component of analysis errors and can be easily incorporated in an existing statistical data assimilation system. We will present results of initial experiments that show a significant reduction of bias in the GEOS DAS moisture analyses.

Dee, D.↗

Enabling Cloud Services and Enhanced Data Discovery With Earthdata-Varinfo

NASA’s Earth Observing System Data and Information System (EOSDIS) contains thousands of Earth science datasets from satellites, models, and field campaigns. Each of these collections can contain hundreds of variables that describe each measurement within the dataset, therefore an automated method for generating UMM-Var records is necessary. The Unified Metadata Model for Variables (UMM-Var) provides a framework for variable metadata records in NASA’s Common Metadata Repository (CMR). The Python tool, earthdata-varinfo, was developed to solve this problem of automating the curation of UMM-Var records. Given either a collection DMR file or a netCDF-4 file, earthdata-varinfo can scrape variable metadata and return a CMR compliant UMM-Var record. Earthdata-varinfo can generate thousands of UMM-Var records in a matter of seconds, thus enabling subsetting capabilities and enhancing data discovery.

Eni Awowale↗

Application of Bayesian Classification to Content-Based Data Management

The high volume of Earth Observing System data has proven to be challenging to manage for data centers and users alike. At the Goddard Earth Sciences Distributed Active Archive Center (GES DAAC), about 1 TB of new data are archived each day. Distribution to users is also about 1 TB/day. A substantial portion of this distribution is MODIS calibrated radiance data, which has a wide variety of uses. However, much of the data is not useful for a particular user's needs: for example, ocean color users typically need oceanic pixels that are free of cloud and sun-glint. The GES DAAC is using a simple Bayesian classification scheme to rapidly classify each pixel in the scene in order to support several experimental content-based data services for near-real-time MODIS calibrated radiance products (from Direct Readout stations). Content-based subsetting would allow distribution of, say, only clear pixels to the user if desired. Content-based subscriptions would distribute data to users only when they fit the user's usability criteria in their area of interest within the scene. Content-based cache management would retain more useful data on disk for easy online access. The classification may even be exploited in an automated quality assessment of the geolocation product. Though initially to be demonstrated at the GES DAAC, these techniques have applicability in other resource-limited environments, such as spaceborne data systems.

Lynnes, Christopher↗

BIOME: A scientific data archive search-and-order system using browser-aware, dynamic pages

The Oak Ridge National Laboratory's (ORNL) Distributed Active Archive Center (DAAC) is a data archive and distribution center for the National Air and Space Administration's (NASA) Earth Observing System Data and Information System (EOSDIS). Both the Earth Observing System (EOS) and EOSDIS are components of NASA's contribution to the US Global Change Research Program through its Mission to Planet Earth Program. The ORNL DAAC provides access to data used in ecological and environmental research such as global change, global warming, and terrestrial ecology. Because of its large and diverse data holdings, the challenge for the ORNL DAAC is to help users find data of interest from the hundreds of thousands of files available at the DAAC without overwhelming them. Therefore, the ORNL DAAC has developed the Biogeochemical Information Ordering Management Environment (BIOME), a customized search and order system for the World Wide Web (WWW). BIOME is a public system located at http://www-eosdis. ornl.gov/BIOME/biome.html.

CLIMATIC CHANGE↗

(abstract) Satellite Physical Oceanography Data Available From an EOSDIS Archive

The Physical Oceanography Distributed Active Archive Center (PO.DAAC) at the Jet Propulsion Laboratory archives and distributes data as part of the Earth Observing System Data and Information System (EOSDIS). Products available from JPL are largely satellite derived and include sea-surface height, surface-wind speed and vectors, integrated water vapor, atmospheric liquid water, sea-surface temperature, heat flux, and in-situ data as it pertains to satellite data. Much of the data is global and spans fourteen years.There is email access, a WWW site, product catalogs, and FTP capabilities. Data is free of charge.

oceanography Earth Observing System oceans data cl↗

Web Coverage Service Challenges for NASA's Earth Science Data

In an effort to ensure that data in NASA's Earth Observing System Data and Information System (EOSDIS) is available to a wide variety of users through the tools of their choice, NASA continues to focus on exposing data and services using standards based protocols. Specifically, this work has focused recently on the Web Coverage Service (WCS). Experience has been gained in data delivery via GetCoverage requests, starting out with WCS v1.1.1. The pros and cons of both the version itself and different implementation approaches will be shared during this session. Additionally, due to limitations with WCS v1.1.1 ability to work with NASA's Earth science data, this session will also discuss the benefit of migrating to WCS 2.0.1 with EO-x to enrich this capability to meet a wide range of anticipated user's needs This will enable subsetting and various types of data transformations to be performed on a variety of EOS data sets.

Web Coverage Services↗

Flood Analysis of the Thomas Flood in California Using NASA Data in a GIS

In December 2017, the Thomas Fire burned areas in Ventura and Santa Barbara Counties in California, consuming 281,893 acres (440 square miles), and becoming the largest wildfire in California history. On January 8, 2018, a winter storm made landfall in Santa Barbara County, releasing more than 5 inches of rain onto the same scarred area, causing San Ysidro and Montecito Creeks to overflow and leading to devastating flash flooding and debris flows by January 9, 2018. Several areas that were not typically prone to flooding were at risk due to the higher than usual flows in the nearby streams and creeks. The unprecedented flooding killed at least 20 people and left several others missing. NASA's Earth Observing System Data Information System (EOSDIS) manages Earth Observation satellites and the Distributed Active Archive Centers (DAACs), where the data is stored and processed. This poster presents the use of satellite data from NASA's inventory that have the potential for use in identification and analysis of forest fire risk and subsequent after-effects within a geographic information system (GIS).

science↗

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

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 also home to the data archive for the GPM predecessor, the Tropical Rainfall Measuring Mission (TRMM). Over the past 17 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: -Level-1 GPM Microwave Imager (GMI) and partner radiometer products, DPR products -Level-2 Goddard Profiling Algorithm (GPROF) GMI and partner products, DPR products -Level-3 daily and monthly products, DPR products -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.gov/gpm). 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. The United User Interface (UUI) is the next step in the evolution of the GES DISC web site. It attempts to provide seamless access to data, information and services through a single interface without sending the user to different applications or URLs (e.g., search, access, subset, Giovanni, documents).

precipitation↗

Policy issues and data communications for NASA earth observation missions until 1985

The series of LANDSAT sensors with the highest potential data rates of the missions were examined. An examination of LANDSAT imagery uses shows that relatively few require transmission of the full resolution data on a repetitive quasi real time basis. Accuracy of global crop size forecasting can possibly be improved through information derived from LANDSAT imagery. A current forecasting experiment uses the imagery for crop area estimation only, yield being derived from other data sources.

Corte, A. B.↗

The Distribution of Carbon Monoxide in the GOCART Model

Carbon monoxide (CO) is an important trace gas because it is a significant source of tropospheric Ozone (O3) as well as a major sink for atmospheric hydroxyl radical (OH). The distribution of CO is set by a balance between the emissions, transport, and chemical processes in the atmosphere. The Georgia Tech/Goddard Global Ozone Chemistry Aerosol Radiation and Transport (GOCART) model is used to simulate the atmospheric distribution of CO. The GOCART model is driven by the assimilated meteorological data from the Goddard Earth Observing System Data Assimilation System (GEOS DAS) in an off-line mode. We study the distribution of CO on three time scales: (1) day to day fluctuation produced by the synoptic waves; (2) seasonal changes due to the annual cycle of CO sources and sinks; and (3) interannual variability induced by dynamics. Comparison of model results with ground based and remote sensing measurements will also be presented.

Fan, Xiaobiao↗

Federated Giovanni: A Distributed Web Service for Analysis and Visualization of Remote Sensing Data

The Geospatial Interactive Online Visualization and Analysis Interface (Giovanni) is a popular tool for users of the Goddard Earth Sciences Data and Information Services Center (GES DISC) and has been in use for over a decade. It provides a wide variety of algorithms and visualizations to explore large remote sensing datasets without having to download the data and without having to write readers and visualizers for it. Giovanni is now being extended to enable its capabilities at other data centers within the Earth Observing System Data and Information System (EOSDIS). This Federated Giovanni will allow four other data centers to add and maintain their data within Giovanni on behalf of their user community. Those data centers are the Physical Oceanography Distributed Active Archive Center (PO.DAAC), MODIS Adaptive Processing System (MODAPS), Ocean Biology Processing Group (OBPG), and Land Processes Distributed Active Archive Center (LP DAAC). Three tiers are supported: Tier 1 (GES DISC-hosted) gives the remote data center a data management interface to add and maintain data, which are provided through the Giovanni instance at the GES DISC. Tier 2 packages Giovanni up as a virtual machine for distribution to and deployment by the other data centers. Data variables are shared among data centers by sharing documents from the Solr database that underpins Giovanni's data management capabilities. However, each data center maintains their own instance of Giovanni, exposing the variables of most interest to their user community. Tier 3 is a Shared Source model, in which the data centers cooperate to extend the infrastructure by contributing source code.

Giovanni↗

Opportunities for Accelerating Research in the Cloud

As the data holdings of the Earth Observation System Data and Information System expand over the next several years, the typical data analysis process of downloading data to local compute resources will become increasingly inefficient. However, cloud computing promises to mitigate that by allowing the user to process close to the data. These improvements will be obtained via a variety of mechanisms: 1 - improving the ability of data transformation services to reduce the data prior to analysis; 2 - providing cloud-native analysis capabilities for common analysis functions; and 3 - providing the ability to work directly with data in Web Object Storage.

data system↗

Evolving NASA's Data and Information Systems for Earth Science

The Earth Observing System Data and Information System (EOSDIS) project at GSFC (Goddard Space Flight Center) maintains and operates a data and information system for NASA's Science Mission Directorate (SMD) and its Earth Science Division (ESD) to support multidisciplinary research in Earth science and public data access.

Ramachandran, Rahul↗

Maximum ADPE Approach for a High Rate CCSDS Return Link Processing System

The earth observing system data and operations system (EDOS) multi-mission data processing and distribution system for the earth observing system is considered. The EDOS was based on the Consultative Committee for Space Data Systems (CCSDS) protocols. The development included the challenge of developing and demonstrating a 150 Mbps CCSDS return link processing capability for the support of the first EDOS delivery. The approach used general-purpose automated data processing equipment (ADPE) and minimized the use of customized hardware. The way in which the system was developed is described. The principle design decisions and the performance benchmark results are presented.

Krimchansky, Alexander↗

An Overview of the Earth Observing System MODIS Instrument Performance, Data Systems Performance, and Data Products Status and Availability

The Moderate Resolution Imaging Spectroradiometer (MODIS) on the Earth Observing System (EOS) Terra Mission began to produce data in February 2000. Now, approximately 2 years from that time, the instrument is operating well. All subsystems of the instrument are performing as expected, the signal-to-noise (S/N) performance meets or exceeds specifications, band-to-band registration meets specifications, geodetic registration of observations is nearing 50 meters (one sigma) and the spectral bands are located where they were intended to be pre-launch and attendant gains and offsets are stable to date. The data systems have performed well and are producing a wide variety of data products useful for scientific and applications studies in relatively consistent fashion extending from November 2000 to the present. Within the approximately 40 MODIS data products, several are new and represent powerful and exciting capabilities. The remainder of the MODIS products exceed or, at a minimum, match the capabilities of products from heritage sensors such as, for example, the Advanced Very High Resolution Radiometer (AVHRR). Efforts are underway to provide data sets for the greater Earth science community and to improve access to these products at the various Distributed Active Archive Centers (DAAC's) or through Direct Broadcast (DB) stations. The MODIS instrument on the EOS Aqua mission should also be expected to be in orbit and functioning in the Spring of 2002.

Salomonson, Vincent V.↗

Evolution of Web Services in EOSDIS: Search and Order Metadata Registry (ECHO)

During 2005 through 2008, NASA defined and implemented a major evolutionary change in it Earth Observing system Data and Information System (EOSDIS) to modernize its capabilities. This implementation was based on a vision for 2015 developed during 2005. The EOSDIS 2015 Vision emphasizes increased end-to-end data system efficiency and operability; increased data usability; improved support for end users; and decreased operations costs. One key feature of the Evolution plan was achieving higher operational maturity (ingest, reconciliation, search and order, performance, error handling) for the NASA s Earth Observing System Clearinghouse (ECHO). The ECHO system is an operational metadata registry through which the scientific community can easily discover and exchange NASA's Earth science data and services. ECHO contains metadata for 2,726 data collections comprising over 87 million individual data granules and 34 million browse images, consisting of NASA s EOSDIS Data Centers and the United States Geological Survey's Landsat Project holdings. ECHO is a middleware component based on a Service Oriented Architecture (SOA). The system is comprised of a set of infrastructure services that enable the fundamental SOA functions: publish, discover, and access Earth science resources. It also provides additional services such as user management, data access control, and order management. The ECHO system has a data registry and a services registry. The data registry enables organizations to publish EOS and other Earth-science related data holdings to a common metadata model. These holdings are described through metadata in terms of datasets (types of data) and granules (specific data items of those types). ECHO also supports browse images, which provide a visual representation of the data. The published metadata can be mapped to and from existing standards (e.g., FGDC, ISO 19115). With ECHO, users can find the metadata stored in the data registry and then access the data either directly online or through a brokered order to the data archive organization. ECHO stores metadata from a variety of science disciplines and domains, including Climate Variability and Change, Carbon Cycle and Ecosystems, Earth Surface and Interior, Atmospheric Composition, Weather, and Water and Energy Cycle. ECHO also has a services registry for community-developed search services and data services. ECHO provides a platform for the publication, discovery, understanding and access to NASA s Earth Observation resources (data, service and clients). In their native state, these data, service and client resources are not necessarily targeted for use beyond their original mission. However, with the proper interoperability mechanisms, users of these resources can expand their value, by accessing, combining and applying them in unforeseen ways.

Mitchell, Andrew↗

NASA Archives and Data Stewardship in the Cloud with Cumulus

NASA's Earth Observing System Data and Information System (EOSDIS) houses nearly 30PB (petabytes) of critical Earth Science data and with upcoming missions is expected to balloon to between 200PBs-300PBs over the next seven years. The magnitude of data collected makes it infeasible to download data and process it locally, forcing us to re-think how we store and work with earth science data. NASA has looked to the cloud to address this, building its open source Cumulus software to manage the ingest of diverse data in a wide variety of formats into the cloud and provide services to manage and access the data. In this talk, we will describe how Cumulus provides common features needed to manage a cloud archive in the realm of ingest, data stewardship, and cost-controlled distribution of science data to users and services.

NASA↗