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NASA NTRS2007

NASA's NPOESS Preparatory Project Science Data Segment: A Framework for Measurement-based Earth Science Data Systems

The NPOESS Preparatory Project (NPP) Science Data Segment (SDS) provides a framework for the future of NASA s distributed Earth science data systems. The NPP SDS performs research and data product assessment while using a fully distributed architecture. The components of this architecture are organized around key environmental data disciplines: land, ocean, ozone, atmospheric sounding, and atmospheric composition. The SDS thus establishes a set of concepts and a working prototypes. This paper describes the framework used by the NPP Project as it enabled Measurement-Based Earth Science Data Systems for the assessment of NPP products.

Schwaller, Mathew R.↗
NASA NTRS2007

Online Analysis Enhances Use of NASA Earth Science Data

Giovanni, the Goddard Earth Sciences Data and Information Services Center (GES DISC) Interactive Online Visualization and Analysis Infrastructure, has provided researchers with advanced capabilities to perform data exploration and analysis with observational data from NASA Earth observation satellites. In the past 5-10 years, examining geophysical events and processes with remote-sensing data required a multistep process of data discovery, data acquisition, data management, and ultimately data analysis. Giovanni accelerates this process by enabling basic visualization and analysis directly on the World Wide Web. In the last two years, Giovanni has added new data acquisition functions and expanded analysis options to increase its usefulness to the Earth science research community.

Acker, James G.↗
NASA NTRS2004

Value-added Data Services at the Goddard Earth Sciences Data and Information Services Center

The NASA Goddard Earth Sciences Data and Information Services Center (GES DISC), in addition to serving the Earth Science community as one of the major Distributed Active Archives Centers (DAACs), provides much more than just data. Among the value-added services available to general users are subsetting data spatially and/or by parameter, online analysis (to avoid downloading unnecessarily all the data), and assistance in obtaining data from other centers. Services available to data producers and high-volume users include consulting on building new products with standard formats and metadata and construction of data management systems. A particularly useful service is data processing at the DISC (i.e., close to the input data) with the users algorithm. This can take a number of different forms: as a configuration-managed algorithm within the main processing stream; as a stand-alone program next to the on-line data storage; as build-it-yourself code within the Near-Archive Data Mining (NADM) system; or as an on-the-fly analysis with simple algorithms embedded into the web-based tools. Partnerships between the GES DISC and scientists, both producers and users, allow the scientists to concentrate on science, while the GES DISC handles the data management, e.g., formats, integration, and data processing. The existing data management infrastructure at the GES DISC supports a wide spectrum of options: from simple data support to sophisticated on-line analysis tools, producing economies of scale and rapid time-to-deploy. At the same time, such partnerships allow the GES DISC to serve the user community more efficiently and to better prioritize on-line holdings. Several examples of successful partnerships are described in the presentation.

Leptoukh, Gregory G.↗
NASA NTRS2009

Approach to Managing MeaSURES Data at the GSFC Earth Science Data and Information Services Center (GES DISC)

A major need stated by the NASA Earth science research strategy is to develop long-term, consistent, and calibrated data and products that are valid across multiple missions and satellite sensors. (NASA Solicitation for Making Earth System data records for Use in Research Environments (MEaSUREs) 2006-2010) Selected projects create long term records of a given parameter, called Earth Science Data Records (ESDRs), based on mature algorithms that bring together continuous multi-sensor data. ESDRs, associated algorithms, vetted by the appropriate community, are archived at a NASA affiliated data center for archive, stewardship, and distribution. See http://measures-projects.gsfc.nasa.gov/ for more details. This presentation describes the NASA GSFC Earth Science Data and Information Services Center (GES DISC) approach to managing the MEaSUREs ESDR datasets assigned to GES DISC. (Energy/water cycle related and atmospheric composition ESDRs) GES DISC will utilize its experience to integrate existing and proven reusable data management components to accommodate the new ESDRs. Components include a data archive system (S4PA), a data discovery and access system (Mirador), and various web services for data access. In addition, if determined to be useful to the user community, the Giovanni data exploration tool will be made available to ESDRs. The GES DISC data integration methodology to be used for the MEaSUREs datasets is presented. The goals of this presentation are to share an approach to ESDR integration, and initiate discussions amongst the data centers, data managers and data providers for the purpose of gaining efficiencies in data management for MEaSUREs projects.

Vollmer, Bruce↗
NASA NTRSDate not supplied

Earth Science Data Processing With Nextflow

Earth science data processing tasks present many challenges. These tasks often process large input datasets and require scores of CPU-hours to generate results. All but the simplest tasks will be decomposed into a series of computational or data manipulation steps, also known as a scientific workflow. In order to reduce the burden of orchestrating and running the dependent processing steps, a workflow execution engine is required. This poster describes the lessons learned by the CLARREO Pathfinder (CPF) team while developing multiple scientific workflows and utilizing the open-source Nextflow engine to execute them in a cloud computing environment. The Nextflow engine is designed with the following stated goals: first, the engine does not dictate how individual steps in the task are implemented (i.e. it is language and interface agnostic); second, the engine supports easy configuration and modularity at the workflow level so that others can easily execute our workflows to reproduce results; lastly, the engine eases development by transparently scaling execution from local to remote environments. Nextflow was developed for the bioinformatics domain but is a good fit for other scientific workflows where the overall task is well-described by a dataflow diagram. The CPF team has developed Nextflow pipelines (i.e. scientific workflows) to simulate CLARREO radiance, generate large look-up tables for inter-calibration algorithms, and generate L4 intercalibration data products. These pipelines consume from single-digits to hundreds of thousands of CPU-hours. In the development and evolution of these pipelines we have discovered many design patterns, pitfalls, and solutions to common problems. Our goal is to demonstrate important aspects of how to design, implement, run, and ultimately share Nextflow pipelines in the domain of Earth science.

Aron D Bartle↗
NASA NTRS2015

NASA'S Earth Science Data Stewardship Activities

NASA has been collecting Earth observation data for over 50 years using instruments on board satellites, aircraft and ground-based systems. With the inception of the Earth Observing System (EOS) Program in 1990, NASA established the Earth Science Data and Information System (ESDIS) Project and initiated development of the Earth Observing System Data and Information System (EOSDIS). A set of Distributed Active Archive Centers (DAACs) was established at locations based on science discipline expertise. Today, EOSDIS consists of 12 DAACs and 12 Science Investigator-led Processing Systems (SIPS), processing data from the EOS missions, as well as the Suomi National Polar Orbiting Partnership mission, and other satellite and airborne missions. The DAACs archive and distribute the vast majority of data from NASA’s Earth science missions, with data holdings exceeding 12 petabytes The data held by EOSDIS are available to all users consistent with NASA’s free and open data policy, which has been in effect since 1990. The EOSDIS archives consist of raw instrument data counts (level 0 data), as well as higher level standard products (e.g., geophysical parameters, products mapped to standard spatio-temporal grids, results of Earth system models using multi-instrument observations, and long time series of Earth System Data Records resulting from multiple satellite observations of a given type of phenomenon). EOSDIS data stewardship responsibilities include ensuring that the data and information content are reliable, of high quality, easily accessible, and usable for as long as they are considered to be of value.

metadata↗
NASA NTRS1996

Combined Industry, Space and Earth Science Data Compression Workshop

The sixth annual Space and Earth Science Data Compression Workshop and the third annual Data Compression Industry Workshop were held as a single combined workshop. The workshop was held April 4, 1996 in Snowbird, Utah in conjunction with the 1996 IEEE Data Compression Conference, which was held at the same location March 31 - April 3, 1996. The Space and Earth Science Data Compression sessions seek to explore opportunities for data compression to enhance the collection, analysis, and retrieval of space and earth science data. Of particular interest is data compression research that is integrated into, or has the potential to be integrated into, a particular space or earth science data information system. Preference is given to data compression research that takes into account the scien- tist's data requirements, and the constraints imposed by the data collection, transmission, distribution and archival systems.

Kiely, Aaron B.↗
NASA NTRS2016

Making Connections: Where STEM Learning and Earth Science Data Services Meet

STEM (Science, Technology, Engineering, Mathematics) learning is most effective when students are encouraged to see the connections between science, technology and real world problems. Helping to make these connections has become an increasingly important aspect of Earth Science data research. The Global Hydrology Resource Center (GHRC), one of NASA's 12 EOSDIS (Earth Observing System Data Information System) data centers, has developed a new type of documentation called the micro article to facilitate making connections between data and Earth science research problems.

Micro articles↗
NASA NTRS2016

Earth Science Data Analytics: Preparing for Extracting Knowledge from Information

Data analytics is the process of examining large amounts of data of a variety of types to uncover hidden patterns, unknown correlations and other useful information. Data analytics is a broad term that includes data analysis, as well as an understanding of the cognitive processes an analyst uses to understand problems and explore data in meaningful ways. Analytics also include data extraction, transformation, and reduction, utilizing specific tools, techniques, and methods. Turning to data science, definitions of data science sound very similar to those of data analytics (which leads to a lot of the confusion between the two). But the skills needed for both, co-analyzing large amounts of heterogeneous data, understanding and utilizing relevant tools and techniques, and subject matter expertise, although similar, serve different purposes. Data Analytics takes on a practitioners approach to applying expertise and skills to solve issues and gain subject knowledge. Data Science, is more theoretical (research in itself) in nature, providing strategic actionable insights and new innovative methodologies. Earth Science Data Analytics (ESDA) is the process of examining, preparing, reducing, and analyzing large amounts of spatial (multi-dimensional), temporal, or spectral data using a variety of data types to uncover patterns, correlations and other information, to better understand our Earth. The large variety of datasets (temporal spatial differences, data types, formats, etc.) invite the need for data analytics skills that understand the science domain, and data preparation, reduction, and analysis techniques, from a practitioners point of view. The application of these skills to ESDA is the focus of this presentation. The Earth Science Information Partners (ESIP) Federation Earth Science Data Analytics (ESDA) Cluster was created in recognition of the practical need to facilitate the co-analysis of large amounts of data and information for Earth science. Thus, from a to advance science point of view: On the continuum of ever evolving data management systems, we need to understand and develop ways that allow for the variety of data relationships to be examined, and information to be manipulated, such that knowledge can be enhanced, to facilitate science. Recognizing the importance and potential impacts of the unlimited ways to co-analyze heterogeneous datasets, now and especially in the future, one of the objectives of the ESDA cluster is to facilitate the preparation of individuals to understand and apply needed skills to Earth science data analytics. Pinpointing and communicating the needed skills and expertise is new, and not easy. Information technology is just beginning to provide the tools for advancing the analysis of heterogeneous datasets in a big way, thus, providing opportunity to discover unobvious scientific relationships, previously invisible to the science eye. And it is not easy It takes individuals, or teams of individuals, with just the right combination of skills to understand the data and develop the methods to glean knowledge out of data and information. In addition, whereas definitions of data science and big data are (more or less) available (summarized in Reference 5), Earth science data analytics is virtually ignored in the literature, (barring a few excellent sources).

data analytics↗
NASA NTRS2019

Bringing Analysis Closer to Data: Developing a Visualization Tool for L2 Earth Science Satellite Data

Earth Science satellite missions provide a unique opportunity for scientists to visualize complex and multifaceted observations projected geospatially across maps of the Earth. While visualization tools can help scientists comprehend, analyze, and share data, visualizing Level-2 Earth Sciences data poses its own specific set of challenges. Since the geospatial information in Level-2 data files is stored as independent variables, the plotting process involves matching dimensional information from latitude and longitude with a desired variable. Variables are stored in different ways across various Earth Science data file formats, which complicates the process of extracting data and plotting variables from a given file without requiring extensive user input and prerequisite familiarity with the file type variable structure. In coordination with NASA’s Goddard Earth Sciences Data Information Services Center (GES DISC), the team developed a Level-2 Earth Science data visualization tool that aims to address some of the complexities associated with plotting Level-2 data. This tool offers command-line and user interface support for file and variable selection to accommodate varying use cases and degrees of user familiarity with the structure of a given file. The visualization tool is written in Python 3 and utilizes a modular approach to facilitate continued expansion and reuse. In addressing some common complications involved in plotting Level-2 Earth Sciences data, the tool aims to help to link the process of analysis more directly with data acquisition and visualization, bringing analysis closer to data across levels of processing.

Li, Angela W.↗
NASA NTRS2005

NASA Earth Sciences Data Support System and Services for the Northern Eurasia Earth Science Partnership Initiative

The presentation describes data management of NASA remote sensing data for Northern Eurasia Earth Science Partnership Initiative (NEESPI). Many types of ground and integrative (e.g., satellite, GIs) data will be needed and many models must be applied, adapted or developed for properly understanding the functioning of Northern Eurasia cold and diverse regional system. Mechanisms for obtaining the requisite data sets and models and sharing them among the participating scientists are essential. The proposed project targets integration of remote sensing data from AVHRR, MODIS, and other NASA instruments on board US- satellites (with potential expansion to data from non-US satellites), customized data products from climatology data sets (e.g., ISCCP, ISLSCP) and model data (e.g., NCEPNCAR) into a single, well-architected data management system. It will utilize two existing components developed by the Goddard Earth Sciences Data & Information Services Center (GES DISC) at the NASA Goddard Space Flight Center: (1) online archiving and distribution system, that allows collection, processing and ingest of data from various sources into the online archive, and (2) user-friendly intelligent web-based online visualization and analysis system, also known as Giovanni. The former includes various kinds of data preparation for seamless interoperability between measurements by different instruments. The latter provides convenient access to various geophysical parameters measured in the Northern Eurasia region without any need to learn complicated remote sensing data formats, or retrieve and process large volumes of NASA data. Initial implementation of this data management system will concentrate on atmospheric data and surface data aggregated to coarse resolution to support collaborative environment and climate change studies and modeling, while at later stages, data from NASA and non-NASA satellites at higher resolution will be integrated into the system.

Leptoukh, Gregory↗
NASA NTRS2019

Simplifying NASA Earth Science Data and Information Access Through Natural Language Processing Based Data Analysis and Visualization

NASA Earth science data collected from satellites, model assimilation, airborne missions, and field campaigns, are large, complex and evolving. Such characteristics pose great challenges for end users (e.g., Earth science and applied science users, students, citizen scientists), particularly for those who are unfamiliar with NASA's EOSDIS and thus unable to access and utilize datasets effectively. For example, a novice user may simply ask: what is the total rainfall for a flooding event in my county yesterday? For an experienced user (e.g., algorithm developer), a question can be: how did my rainfall product perform, compared to ground observations, during a flooding event? Nonetheless, with rapid information technology development such as natural language processing, it is possible to develop simplified Web interfaces and back-end processing components to handle such questions and deliver answers in terms of text, data, or graphic results directly to users.In this presentation, we describe the main challenges for end users with different levels of expertise in accessing and utilizing NASA Earth science data. Surveys reveal that most non-professional users normally do not want to download and handle raw data as well as conduct heavy-duty data processing tasks. Often they just want some simple graphics or data for various purposes. To them, simple and intuitive user interfaces are sufficient because complicated ones can be difficult and time-consuming to learn. Professionals also want such interfaces to answer many questions from datasets. One solution is to develop a natural language based search box like Google and the search results can be text, data, graphics and more. Now the challenge is, with natural language processing, can we design a system to process a scientific question typed in by a user? In this presentation, we describe our plan for such a prototype. The workflow is: 1) extract needed information (e.g., variables, spatial and temporal information, processing methods, etc.) from the input, 2) process the data in the backend, and 3) deliver the results (data or graphics) to the user.

Liu, Zhong↗
NASA NTRS2015

The Role and Evolution of NASA's Earth Science Data Systems

One of the three strategic goals of NASA is to Advance understanding of Earth and develop technologies to improve the quality of life on our home planet (NASA strategic plan 2014). NASA's Earth Science Data System (ESDS) Program directly supports this goal. NASA has been launching satellites for civilian Earth observations for over 40 years, and collecting data from various types of instruments. Especially since 1990, with the start of the Earth Observing System (EOS) Program, which was a part of the Mission to Planet Earth, the observations have been significantly more extensive in their volumes, variety and velocity. Frequent, global observations are made in support of Earth system science. An open data policy has been in effect since 1990, with no period of exclusive access and non-discriminatory access to data, free of charge. NASA currently holds nearly 10 petabytes of Earth science data including satellite, air-borne, and ground-based measurements and derived geophysical parameter products in digital form. Millions of users around the world are using NASA data for Earth science research and applications. In 2014, over a billion data files were downloaded by users from NASAs EOS Data and Information System (EOSDIS), a system with 12 Distributed Active Archive Centers (DAACs) across the U. S. As a core component of the ESDS Program, EOSDIS has been operating since 1994, and has been evolving continuously with advances in information technology. The ESDS Program influences as well as benefits from advances in Earth Science Informatics. The presentation will provide an overview of the role and evolution of NASAs ESDS Program.

Remote Sensing↗
NASA NTRS2018

Serving NASA GES DISC Multi-Spatiotemporal Earth Science Data to the GIS community

NASA Earth Science (ES) data is essential to a wide range of GIS research and applications. However, for many GIS users, searching, accessing, using and analyzing NASA ES data can be of a great challenge- ranging from the sheer data volumes, types of science parameters, and to the complexity of data encoding formats. As one of the twelve NASA Science Mission Directorate (SMD) Data Centers, Goddard Earth Sciences (GES) Data and Information Services Center (DISC) archives and distributes petabytes of ES parameters covering atmosphere, land, and ocean fields. Most data are multidimensional and multi-spatiotemporal in nature and are encoded in different science data formats (e.g, HDF, HDF-EOS, netCDF, GRIB, binary), which usually contain multiple variables and different metadata information. By far, GES DISC has been developing a number of services and online tools to help GIS users to easily explore our data products. In this presentation, we will describe our ArcGIS-based data accessing and visualization services and portals, which allow users directly exploring the multi-spatiotemporal ES data in ArcGIS clients without having to pre-download/import the data. The ArcGIS services are also compliant with the Open Geospatial Consortium (OGC) Web Coverage Service (WCS) and Web Map Service (WMS) protocols and can be accessed by any other WCS/WMS clients to get customized GES DISC EO data on-the-fly from such services.

Wei, Jennifer↗
NASA NTRS2014

An Analysis of Earth Science Data Analytics Use Cases

The increase in the number and volume, and sources, of globally available Earth science data measurements and datasets have afforded Earth scientists and applications researchers unprecedented opportunities to study our Earth in ever more sophisticated ways. In fact, the NASA Earth Observing System Data Information System (EOSDIS) archives have doubled from 2007 to 2014, to 9.1 PB (Ramapriyan, 2009; and https:earthdata.nasa.govaboutsystem-- performance). In addition, other US agency, international programs, field experiments, ground stations, and citizen scientists provide a plethora of additional sources for studying Earth. Co--analyzing huge amounts of heterogeneous data to glean out unobvious information is a daunting task. Earth science data analytics (ESDA) is the process of examining large amounts of data of a variety of types to uncover hidden patterns, unknown correlations and other useful information. It can include Data Preparation, Data Reduction, and Data Analysis. Through work associated with the Earth Science Information Partners (ESIP) Federation, a collection of Earth science data analytics use cases have been collected and analyzed for the purpose of extracting the types of Earth science data analytics employed, and requirements for data analytics tools and techniques yet to be implemented, based on use case needs. ESIP generated use case template, ESDA use cases, use case types, and preliminary use case analysis (this is a work in progress) will be presented.

data analytics↗
NASA NTRS2006

NASA Earth Sciences Data Support System and Services for the Northern Eurasia Earth Science Partnership Initiative

The presentation describes the recently awarded ACCESS project to provide data management of NASA remote sensing data for the Northern Eurasia Earth Science Partnership Initiative (NEESPI). The project targets integration of remote sensing data from MODIS, and other NASA instruments on board US-satellites (with potential expansion to data from non-US satellites), customized data products from climatology data sets (e.g., ISCCP, ISLSCP) and model data (e.g., NCEP/NCAR) into a single, well-architected data management system. It will utilize two existing components developed by the Goddard Earth Sciences Data & Information Services Center (GES DISC) at the NASA Goddard Space Flight Center: (1) online archiving and distribution system, that allows collection, processing and ingest of data from various sources into the online archive, and (2) user-friendly intelligent web-based online visualization and analysis system, also known as Giovanni. The former includes various kinds of data preparation for seamless interoperability between measurements by different instruments. The latter provides convenient access to various geophysical parameters measured in the Northern Eurasia region without any need to learn complicated remote sensing data formats, or retrieve and process large volumes of NASA data. Initial implementation of this data management system will concentrate on atmospheric data and surface data aggregated to coarse resolution to support collaborative environment and climate change studies and modeling, while at later stages, data from NASA and non-NASA satellites at higher resolution will be integrated into the system.

Leptoukh, Gregory↗