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NASA NTRSDate not supplied

TRUST, Trustworthiness and EOSDIS

In recent years there has been considerable attention by the international scientific research and applications community to ensure high quality of data and information management. The terms FAIR (Findable, Accessible, Interoperable, Reusable) data, TRUST (Transparency, Responsibility, User Community, Sustainability, and Technology) principles, and CARE (Collective Benefit, Authority to Control, Responsibility, and Ethics) principles have come into vogue during the last decade. NASA has been managing data and information for over 60 years. NASA’s Earth Observing System Data and Information System (EOSDIS) has been in operation for over 25 years, managing most of NASA’s Earth science data. Trustworthiness is a goal that NASA has always strived to achieve or exceed, because it: enables the success of any NASA science mission; inspires general science research and applications; justifies the cost of operations; contributes to the value of NASA’s Open Data Policy; and influences the long term, historical view for the data collection. Given the recent growth of interest in TRUST principles, it is useful to assess and show how NASA’s attention to trustworthiness maps into those principles. This presentation addresses shows how the various steps that have been taken by the Earth Science Data and Information System (ESDIS) Project in the implementation and evolution of EOSDIS map into the TRUST principles.

Remote Sensing↗

NASA NTRS2017

Building A Cloud Based Distributed Active Data Archive Center

NASA's Earth Science Data System (ESDS) Program facilitates the implementation of NASA's Earth Science strategic plan, which is committed to the full and open sharing of Earth science data obtained from NASA instruments to all users. The Earth Science Data information System (ESDIS) project manages the Earth Observing System Data and Information System (EOSDIS). Data within EOSDIS are held at Distributed Active Archive Centers (DAACs). One of the key responsibilities of the ESDS Program is to continuously evolve the entire data and information system to maximize returns on the collected NASA data.

Earth Science Informatics↗

NASA NTRS1999

Parallelization of the Physical-Space Statistical Analysis System (PSAS)

Atmospheric data assimilation is a method of combining observations with model forecasts to produce a more accurate description of the atmosphere than the observations or forecast alone can provide. Data assimilation plays an increasingly important role in the study of climate and atmospheric chemistry. The NASA Data Assimilation Office (DAO) has developed the Goddard Earth Observing System Data Assimilation System (GEOS DAS) to create assimilated datasets. The core computational components of the GEOS DAS include the GEOS General Circulation Model (GCM) and the Physical-space Statistical Analysis System (PSAS). The need for timely validation of scientific enhancements to the data assimilation system poses computational demands that are best met by distributed parallel software. PSAS is implemented in Fortran 90 using object-based design principles. The analysis portions of the code solve two equations. The first of these is the "innovation" equation, which is solved on the unstructured observation grid using a preconditioned conjugate gradient (CG) method. The "analysis" equation is a transformation from the observation grid back to a structured grid, and is solved by a direct matrix-vector multiplication. Use of a factored-operator formulation reduces the computational complexity of both the CG solver and the matrix-vector multiplication, rendering the matrix-vector multiplications as a successive product of operators on a vector. Sparsity is introduced to these operators by partitioning the observations using an icosahedral decomposition scheme. PSAS builds a large (approx. 128MB) run-time database of parameters used in the calculation of these operators. Implementing a message passing parallel computing paradigm into an existing yet developing computational system as complex as PSAS is nontrivial. One of the technical challenges is balancing the requirements for computational reproducibility with the need for high performance. The problem of computational reproducibility is well known in the parallel computing community. It is a requirement that the parallel code perform calculations in a fashion that will yield identical results on different configurations of processing elements on the same platform. In some cases this problem can be solved by sacrificing performance. Meeting this requirement and still achieving high performance is very difficult. Topics to be discussed include: current PSAS design and parallelization strategy; reproducibility issues; load balance vs. database memory demands, possible solutions to these problems.

Larson, J. W.↗

NASA NTRS1993

Accessing Earth science data from the EOS data and information system

An overview of the Earth Observing System Data and Information System (EOSDIS) is presented, concentrating on the users' interactions with the system and highlighting those features that are driven by the unique requirements of the Global Change Research Program and the supported science community. However, a basic premise of the EOSDIS is that the system must evolve to meet changes in user needs and to incorporate advances in data system technology. Therefore, the development process which is being used to accommodate these changes and some of the potential areas of change are also addressed.

Mcdonald, Kenneth R.↗

NASA NTRS2010

A Quality Screening Service for Remote Sensing Data

NASA provides a wide variety of Earth-observing satellite data products to a diverse community. These data are annotated with quality information in a variety of ways, with the result that many users struggle to understand how to properly account for quality when dealing with satellite data. To address this issue, a Data Quality Screening Service (DQSS) is being implemented for a number of datasets. The DQSS will enable users to obtain data files in which low-quality pixels have been filtered out, based either on quality criteria recommended by the science team or on the user s particular quality criteria. The objective is to increase proper utilization of this critical quality data in science data analysis of satellite data products.

Lynnes, Christopher↗

NASA NTRS2018

How NASA is Building a Petabyte Scale Geospatial Archive in the Cloud

NASA's Earth Observing System Data and Information System (EOSDIS) is working towards a vision of a cloud-based, highly-flexible, ingest, archive, management, and distribution system for its ever-growing and evolving data holdings. This free and open source system, Cumulus, is emerging from its prototyping stages and is poised to make a huge impact on how NASA manages and disseminates its Earth science data. This talk outlines the motivation for this work, present the achievements and hurdles of the past 18 months and charts a course for the future expansion of Cumulus. We explore not just the technical, but also the socio-technical challenges that we face in evolving a system of this magnitude into the cloud. The NASA EOSDIS archive is currently at nearly 30 PBs and will grow to over 300PBs in the coming years. We've presented progress on this effort at AWS re:Invent and the American Geophysical Union (AGU) Fall Meeting in 2017 and hope to have the opportunity to share with FOSS4G attendees information on the availability of the open sourced software and how NASA intends on making its Earth Observing Geospatial data available for free to the public in the cloud.

NASA Archive↗

NASA NTRS2004

New Earth Science Data and Access Methods

NASA's Earth Science Enterprise, working with its domestic and international partners, provides scientific data and analysis to improve life here on Earth. NASA provides science data products that cover a wide range of physical, geophysical, biochemical and other parameters, as well as services for interdisciplinary Earth science studies. Management and distribution of these products is administered through the Earth Observing System Data and Information System (EOSDIS) Distributed Active Archive Centers (DAACs), which all hold data within a different Earth science discipline. This paper will highlight selected EOS datasets and will focus on how these observations contribute to the improvement of essential services such as weather forecasting, climate prediction, air quality, and agricultural efficiency. Emphasis will be placed on new data products derived from instruments on board Terra, Aqua and ICESat as well as new regional data products and field campaigns. A variety of data tools and services are available to the user community. This paper will introduce primary and specialized DAAC-specific methods for finding, ordering and using these data products. Special sections will focus on orienting users unfamiliar with DAAC resources, HDF-EOS formatted data and the use of desktop research and application tools.

Moses, John F.↗

DOE OSTI2026

MOSAIC-CONUS: A Multimodal, Multi-Temporally Paired Dataset for Earth Sciences

Earth embeddings—vector representations of geographic locations indexed in space and time—are emerging as a unifying interface for geospatial AI. However, their quality depends not only on model design, but on how multimodal Earth observation (EO) data are spatially indexed, temporally aligned, and cross-modally associated during pretraining. We introduce MOSAIC-CONUS (Multimodal Observations with Spatially Aligned Imagery, Urban Points of Interest, In-Situ Measurements and Text Captions), a large-scale EO dataset over the contiguous United States, organized around 250,000 stratified point indices that serve as stable spatial keys across seven modalities: active radar, passive optical imagery, lidar-derived elevation, land cover, functional context, hydrometeorological measurements, and textual summaries. Unlike existing EO datasets, MOSAIC-CONUS introduces four contributions not jointly addressed in prior work: 1. an open-source, large-scale multimodal EO corpus structured around point-indexed data designed to support Earth embedding learning; 2. explicit radar-optical pairing tables spanning twelve temporal alignment regimes, formalizing cross-sensor alignment as a controllable variable for analyzing how temporal mismatch across modalities influences learned embeddings quality; 3. a benchmark suite spanning cross-modal retrieval, annual nightlights regression, and basin-held-out streamflow prediction, positioning MOSAIC-CONUS as a benchmark-ready resource for multimodal AI systems; and 4. a language-based embedding layer through co-registered textual summaries, enabling Earth embeddings to function as a queryable interface for agentic AI systems. The dataset and pairing protocols are publicly released.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2018

Importance and Incorporation of User Feedback in Data Stewardship

Since August 1994, The National Aeronautics and Space Administration's (NASA's) Earth Observation System Data and Information System (EOSDIS) has been serving a global community of over 3 million users with Earth science data in a variety of disciplines. During the entire life of EOSDIS, various mechanisms for user feedback have been extremely important and valuable to its evolution and proven performance. Some inputs from user groups have resulted in fundamental changes in the architecture, design and operations of EOSDIS, while others have provided ideas for incremental changes. The purpose of this paper is to share this experience and the benefits that have resulted from the user feedback.In early to mid-1990s, the EOSDIS Advisory Panel (a.k.a. Data Panel) provided significant inputs for the architecture and design of EOSDIS resulting in NASA's establishment of a set of Distributed Active Archive Centers (DAACs) and development of a "working prototype with operating elements" called Version 0 EOSDIS, which went into operation in August 1994. The Data Panel also participated in many of the requirement and design reviews and influenced the design of EOSDIS through their comments.In 1995, the U.S. National Research Council's Committee on Global Change Research conducted a review of the U.S. Global Change Research Program and NASA's Mission to Planet Earth/EOS, including the plans for EOSDIS. One of this committee's recommendations was that the "Responsibility for product generation and publication and for user services should be transferred to a federation of partners selected through a competitive process open to all". In response, NASA initiated an experiment with a "self-governing" federation called the Working Prototype ESIP (WP-ESIP) Federation. This federation, with support from NASA, NOAA and USGS, has now grown into the Earth Science Information Partners (ESIP) with over 140 member organizations.The EOSDIS DAACs' User Working Groups (UWGs) represent broad user communities served by the respective DAACs. As regular users of the DAACs and experts in their scientific disciplines, the UWG members provide valuable inputs for planning and prioritizing the services as well as addition of new datasets for the benefit of the community.

Research Integrity↗

NASA NTRS2005

Development and Testing of Data Mining Algorithms for Earth Observation

The new algorithms developed under this project included a principled procedure for classification of objects, events or circumstances according to a target variable when a very large number of potential predictor variables is available but the number of cases that can be used for training a classifier is relatively small. These "high dimensional" problems require finding a minimal set of variables -called the Markov Blanket-- sufficient for predicting the value of the target variable. An algorithm, the Markov Blanket Fan Search, was developed, implemented and tested on both simulated and real data in conjunction with a graphical model classifier, which was also implemented. Another algorithm developed and implemented in TETRAD IV for time series elaborated on work by C. Granger and N. Swanson, which in turn exploited some of our earlier work. The algorithms in question learn a linear time series model from data. Given such a time series, the simultaneous residual covariances, after factoring out time dependencies, may provide information about causal processes that occur more rapidly than the time series representation allow, so called simultaneous or contemporaneous causal processes. Working with A. Monetta, a graduate student from Italy, we produced the correct statistics for estimating the contemporaneous causal structure from time series data using the TETRAD IV suite of algorithms. Two economists, David Bessler and Kevin Hoover, have independently published applications using TETRAD style algorithms to the same purpose. These implementations and algorithmic developments were separately used in two kinds of studies of climate data: Short time series of geographically proximate climate variables predicting agricultural effects in California, and longer duration climate measurements of temperature teleconnections.

Glymour, Clark↗

NASA NTRS1985

Patchy reconnection and magnetic ropes in astrophysical plasmas

The formation of flux ropes in the magnetosphere of Venus due to velocity shear is examined in order to infer the properties of flux ropes in the earth magnetosphere. Observational data obtained by the International Sun-Earth Explorer (ISEE) spacecraft for the geometry of directed flows in the earth magnetosheath are compared with magnetometer measurements carried out on board Pioneer Venus Orbiter, and the results are discussed in detail. It is shown that the velocity shear is the dominant element shaping the magnetic structure of the magnetosheaths.

Russell, C. T.↗

NASA NTRS1992

EOS Data and Information System (EOSDIS)

In the past decade, science and technology have reached levels that permit assessments of global environmental change. Scientific success in understanding global environmental change depends on integration and management of numerous data sources. The Global Change Data and Information System (GCDIS) must provide for the management of data, information dissemination, and technology transfer. The Earth Observing System Data and Information System (EOSDIS) is NASA's portion of this global change information system.

Source record↗

NASA NTRS1995

Data catalog for JPL Physical Oceanography Distributed Active Archive Center (PO.DAAC)

The Physical Oceanography Distributed Active Archive Center (PO.DAAC) archive at the Jet Propulsion Laboratory contains satellite data sets and ancillary in-situ data for the ocean sciences and global-change research to facilitate multidisciplinary use of satellite ocean data. Geophysical parameters available from the archive include sea-surface height, surface-wind vector, surface-wind speed, surface-wind stress vector, sea-surface temperature, atmospheric liquid water, integrated water vapor, phytoplankton pigment concentration, heat flux, and in-situ data. PO.DAAC is an element of the Earth Observing System Data and Information System and is the United States distribution site for TOPEX/POSEIDON data and metadata.

Digby, Susan↗

NASA NTRS2018

Toward a Common Earth Data Publication Framework

Data publication is an essential activity for all data archives. Each of NASA's twelve Distributed Active Archive Centers (DAACs) have established publication workflows which account for the heterogeneous suite of missions, instruments, data providers, and datasets managed within the Earth Observation System Data and Information System (EOSDIS) program. Some aspects of data publication vary across DAACs: workflows range from manual to automatic, terms used to describe publication elements differ, and systems used to publish and manage data vary. Despite these differences, the DAAC data publication processes are generally the same: obtain the data and related information from data providers, describe the data with metadata and documentation, and release the data for access by the user community. In order to improve consistency and reduce the time required to publish data, we have developed a cross-DAAC initiative called the Common Earthdata Publication Framework (Earthdata Pub). Earthdata Pub seeks to: standardize communications and interactions with data providers; identify and standardize common workflows and steps in the data publication process; and design/implement a front-end system with features that include a common web interface, email & status tracking, and common application programming interfaces (APIs) to communicate with various DAAC-specific software components (services and applications) on the back-end. We will present the latest updates on this effort's progress and future plans.

data publication↗

NASA NTRS2018

NASA ESDIS Employs the Latest Generation Web Analytics Tool

Under the auspices of NASA's Earth Observing System Data and Information System (EOSDIS), The Earth Science Data and Information System (ESDIS) Project distributes science data to a wide community of users through its Distributed Active Archive Centers (DAACs). Much of this data is made available via interactive websites - which involve links, downloads, on page interaction, and referrals to additional online pages. To meet the earth science data and information needs of many data users representing numerous different science domains, over 80 specialized websites are supported by multiple distributed organizations under the ESDIS umbrella.

Web Metrics↗

NASA NTRSDate not supplied

Simplifying Analysis of Hierarchical HDF5 and NetCDF4 Files with Xarray-Datatree

NASA’s Earth Observing System Data and Information System (EOSDIS) contains thousands of Earth science datasets from satellites, models, and field campaigns. EOSDIS data are stored in formats that are well supported by the Earth Science community. These formats include the Hierarchical Data Format (HDF), with derivative flavors such as HDF-5 and the Network Common Data Format (NetCDF-4). The HDF specification allows for a directory-like hierarchy within a single file, known as "groups". Observational data and associated metadata within a single file can be distributed amongst multiple internal groups, which can also be nested to multiple levels. Working with datasets that have a group hierarchical structure can be difficult because of the nested structure of groups. Widely used packages, such as xarray, have data models that do not accommodate the hierarchical structure within HDF files, requiring users to traverse the file and open different HDF groups as separate, unrelated objects. Xarray-datatree is a Python package developed to solve the difficulty of traversing HDFs with a hierarchical group structure by creating a tree-like hierarchical data structure in xarray. The tree-like structure allows each group to be accessed once a DataTree object is instantiated. The migration of xarray-datatree into the xarray core library will reduce barriers to accessing Earth science data by eliminating the need to understand and traverse the specific hierarchy of a grouped HDF file.

Eni Awowale↗

NASA NTRS1995

Development and analysis of SCR requirements tables for system scenarios

We describe the use of scenarios to develop and refine requirement tables for parts of the Earth Observing System Data and Information System (EOSDIS). The National Aeronautics and Space Administration (NASA) is developing EOSDIS as part of its Mission-To-Planet-Earth (MTPE) project to accept instrument/platform observation requests from end-user scientists, schedule and perform requested observations of the Earth from space, collect and process the observed data, and distribute data to scientists and archives. Current requirements for the system are managed with tools that allow developers to trace the relationships between requirements and other development artifacts, including other requirements. In addition, the user community (e.g., earth and atmospheric scientists), in conjunction with NASA, has generated scenarios describing the actions of EOSDIS subsystems in response to user requests and other system activities. As part of a research effort in verification and validation techniques, this paper describes our efforts to develop requirements tables from these scenarios for the EOSDIS Core System (ECS). The tables specify event-driven mode transitions based on techniques developed by the Naval Research Lab's (NRL) Software Cost Reduction (SCR) project. The SCR approach has proven effective in specifying requirements for large systems in an unambiguous, terse format that enhance identification of incomplete and inconsistent requirements. We describe development of SCR tables from user scenarios and identify the strengths and weaknesses of our approach in contrast to the requirements tracing approach. We also evaluate the capabilities of both approach to respond to the volatility of requirements in large, complex systems.

Callahan, John R.↗

NASA NTRS1995

Aircraft scanner data availability via the version 0 Information Management System

As part of the Earth Observing System Data and Information System (EOSDIS) development, NASA and other government agencies have developed an operational prototype of the Information Management System (IMS). The IMS provides access to the data archived at the Distributed Active Archive Centers (DAAC's) that allows users to search through metadata describing the (image) data. Criteria based on sensor name or type, date and time, and geographic location are used to search the archive. Graphical representations of coverage and browse images are available to further refine a user's selection. previously, the EROS Data Center (EDC) DAAC had identified the Advanced SOlid-state Array Spectrometer (ASAS), Airborne Visible and infrared Imaging Spectrometer (AVIRIS), NS-001, and Thermal Infrared Multispectral Scanner (TIMS) as precursor data sets similar to those the DAAC will handle in the Earth Observing System era. Currently, the EDC DAAC staff, in cooperation with NASA, has transcribed TIMS, NS-001, and Thematic Mapper Simulation (TMS) data from Ames Research Center and also TIMS data from Stennis Space Center. During the transcription process, the IMS metadata and browse images were created to populate the inventory at the EDC DAAC. These data sets are now available in the IMS and may be requested from the any of the DAAC's via the IMS.

Mah, G. R.↗