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Big Earth Data Initiative (BEDI) The Big Earth Data Initiative (BEDI) invests in standardizing and optimizing the collection, management and delivery of U.S. Government's civil Earth observation data to improve discovery, access use, and understanding of Earth observations by the broader user community. Complete and consistent standard metadata helps address all three goals.
NASA's Earth Science Data and Information Systems (ESDIS) have undergone a significant evolution, particularly with the introduction of the Big Earth Data Initiative (BEDI) and the Open-Source Science Initiative (OSSI). In this talk, I will provide an overview of NASA's Earth Science Data and Information systems, highlighting key components such as EOSDIS, ESDIS, and ESDS. Moving forward, I will delve into the BEDI initiative, discussing its objectives, key players, and lessons learned. The second part of the talk will cover the OSSI initiative, exploring its objectives, strategy, and innovative solutions. Throughout the presentation, I will provide insights into the requests, strategies, and solutions behind both BEDI and OSSI. By the end, you will gain a comprehensive understanding of how NASA's Earth Science Data Systems have evolved over the years and witness the organization's commitment to advancing an open-source and collaborative approach to data science. Join me for an enlightening exploration into the future of Earth science data and the pivotal role played by NASA in shaping this transformative landscape.
The goal of NASA's effort for BEDI is to improve the usability, discoverability, and accessibility of Earth Observation data in support of societal benefit areas. Accomplishments: In support of BEDI goals, datasets have been entered into Common Metadata Repository(CMR), made available via the Open-source Project for a Network Data Access Protocol (OPeNDAP), have a Digital Object Identifier (DOI) registered for the dataset, and to support fast visualization many layers have been added in to the Global Imagery Browse Services (GIBS).
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The National Aeronautics and Space Administration (NASA) acquires and distributes an abundance of Earth science data on a daily basis to a diverse user community worldwide. The NASA Big Earth Data Initiative (BEDI) is an effort to make the acquired science data more discoverable, accessible, and usable. This presentation will provide a brief introduction to the Earth Observing System Data and Information System (EOSDIS) project and the nature of advances that have been made by BEDI to other Federal Users.
President Obama's Big Data Research and Development Initiative seeks to improve our ability to acquire knowledge and discover insights into large and complex collections of digital data. The Big Earth Data Initiative (BEDI) Invests in standardizing and optimizing the collection, management and delivery of U.S. Government's civil Earth observation data.
ESIP Community members create and manage a large collection of environmental datasets that span multiple decades, the entire globe, and many parts of the solar system. Metadata are critical for discovering, accessing, using and understanding these data effectively and ESIP community members have successfully created large collections of metadata describing these data. As part of the White House Big Earth Data Initiative (BEDI), ESDIS has developed a suite of tools for evaluating these metadata in native dialects with respect to recommendations from many organizations. We will describe those tools and demonstrate evolving techniques for sharing results with data providers.
At American Geophysical Union(AGU) 2016 Fall Meeting, Goddard Earth Sciences Data Information Services Center (GES DISC) unveiled a novel way to access data: Datalist. Currently, datalist is a collection of predefined data variables from one or more archived datasets, curated by our subject matter expert (SME). Our science support team has curated a predefined Hurricane Datalist and received very positive feedback from the user community. Datalist uses the same architecture our new website uses and have the same look and feel as other datasets on our web site. and also provides a one-stop shopping for data, metadata, citation, documentation, visualization and other available services. Since the last AGU Meeting, we have further developed a few new datalists corresponding to the Big Earth Data Initiative (BEDI) Societal Benefit Areas and A-Train data. We now have four datalists: Hurricane, Wind Energy, Greenhouse Gas and A-Train. We have also started working with our User Working Group members to create their favorite datalists and working with other DAAC to explore the possibility to include their products in our datalists that may also lead to a future of potential federated (cross-DAAC) datalists. Since our datalist prototype effort was a success, we are planning to make datalist operational. It's extremely important to have a common metadata model to support datalist, this will also be the foundation of federated datalist. We mapped our datalist metadata model to the unpublished UMM(Universal Metadata Model)-Var (Variable) (June version) and found that the UMM-var together with UMM-C (Collection) and possible UMM-S (Service) will meet our basic requirements. For example: Dataset shortname, and version are already specified in UMM-C, variable name, long name, units, dimensions are all specified in UMM-Var. UMM-Var also facilitates Science Keywords to allow tagging at variable level and Characteristics for optional variable characteristics. Measurements is useful for grouping of the variables and Set is promising to define datalist. And finally, the UMM-Service model to specify the available services for the variable will be very beneficial. In summary, UMM-Var, UMM-C and UMM-S are the basis of federated datalist and the development and deployment of datalist will contribute to the evolution of the UMM.
The advent of low earth orbit (LEO) commercial communication satellites provides an opportunity to dramatically reduce Telemetry, Tracking and Control (TT&C) costs of launch vehicles, Unpiloted Aerial Vehicles (UAVs), Research Balloons and spacecraft by reducing or eliminating ground infrastructure. Personnel from the Goddard Space Flight Center's Wallops Flight Facility (GSFC\WFF) have successfully used commercial Geostationary Earth Orbit (GEO) and Big LEO communications satellites for Long Duration Balloon Flight TT&C. The Flight Modem is a GSFC\WFF Advanced Range Technology initiative (ARTI) designed to streamline TT&C capability in the user community of these scientific data gathering platforms at low cost. Making use of existing LEO satellites and adapting and ruggedized commercially available components; two-way, over the horizon communications may be established with these vehicles at great savings due to reduced infrastructure. Initially planned as a means for permitting GPS data for tracking and recovery of sounding rocket and balloon payloads, expectations are that the bandwidth can soon be expanded to allow more comprehensive data transfer. The system architecture which integrates antennas, GPS receiver, commercial satellite packet data modem and a single board computer with custom software is described and technical challenges are discussed along with the plan for their resolution. A three-phase testing and development plan is outlined and the current results are reported. Results and status of ongoing flight tests on aircraft and sounding rockets are reported. Future applications on these platforms and the potential for satellite support are discussed along with an analysis of cost effectiveness of this method vs. other tracking and data transmission schemes.
Severe natural phenomena such as hurricane, volcano, blizzard, flood and drought have the potential to cause immeasurable property damages, great socioeconomic impact, and tragic loss of human life. From searching to assessing the Big, i.e., massive and heterogeneous scientific data (particularly, satellite and model products) in order to investigate those natural hazards, it has, however, become a daunting task for Earth scientists and applications researchers, especially during recent decades. The NASA Goddard Earth Sciences Data and Information Service Center (GES DISC) has served Big Earth science data, and the pertinent valuable information and services to the aforementioned users of diverse communities for years. In order to help and guide our users to online readily (i.e., with a minimum effort) acquire their requested data from our enormous resource at GES DISC for studying their targeted hazard event, we have thus initiated a Bundle Data approach in 2014, first targeting the hurricane event topic. We have recently worked on new topics such as volcano and blizzard. The bundle data of a specific hazard event is basically a sophisticated integrated data package consisting of a series of proper datasets containing a group of relevant (knowledge--based) data variables readily accessible to users via a system-prearranged table linking those data variables to the proper datasets (URLs). This online approach has been developed by utilizing a few existing data services such as Mirador as search engine; Giovanni for visualization; and OPeNDAP for data access, etc. The online Data Cookbook site at GES DISC is the current host for the bundle data. We are now also planning on developing an Automated Virtual Collection Framework that shall eventually accommodate the bundle data, as well as further improve our management in Big Data.
The Research Data Alliance (RDA) enables data to be shared across barriers through focused working groups and interest groups, formed of experts from around the world - from academia, industry and government. Its Big Data Analytics (BDA) interest groups seeks to develop community based recommendations on feasible data analytics approaches to address scientific community needs of utilizing large quantities of data. BDA seeks to analyze different scientific domain applications (e.g. earth science use cases) and their potential use of various big data analytics techniques. These techniques reach from hardware deployment models up to various different algorithms (e.g. machine learning algorithms such as support vector machines for classification). A systematic classification of feasible combinations of analysis algorithms, analytical tools, data and resource characteristics and scientific queries will be covered in these recommendations. This contribution will outline initial parts of such a classification and recommendations in the specific context of the field of Earth Sciences. Given lessons learned and experiences are based on a survey of use cases and also providing insights in a few use cases in detail.
Collect Earth Online (CEO) is a free and open online implementation of the FAO Collect Earth system for collaboratively collecting environmental data through the visual interpretation of Earth observation imagery. The primary collection mechanism in CEO is human interpretation of land surface characteristics in imagery served via Web Map Services (WMS). However, interpreters may not have enough contextual information to classify samples by only viewing the imagery served via WMS, be they high resolution or otherwise. To assist in the interpretation and collection processes in CEO, SERVIR, a joint NASA-USAID initiative that brings Earth observations to improve environmental decision making in developing countries, developed the GeoDash system, an embedded and critical component of CEO. GeoDash leverages Google Earth Engine (GEE) by allowing users to set up custom browser-based widgets that pull from GEE's massive public data catalog. These widgets can be quick looks of other satellite imagery, time series graphs of environmental variables, and statistics panels of the same. Users can customize widgets with any of GEE's image collections, such as the historical Landsat collection with data available since the 1970s, select date ranges, image stretch parameters, graph characteristics, and create custom layouts, all on-the-fly to support plot interpretation in CEO. This presentation focuses on the implementation and potential applications, including the back-end links to GEE and the user interface with custom widget building. GeoDash takes large data volumes and condenses them into meaningful, relevant information for interpreters. While designed initially with national and global forest resource assessments in mind, the system will complement disaster assessments, agriculture management, project monitoring and evaluation, and more.
NASA Earth Exchange (NEX) is a data, supercomputing and knowledge collaboratory that houses NASA satellite, climate and ancillary data where a focused community can come together to address large-scale challenges in Earth sciences. As NEX has been growing into a petabyte-size platform for analysis, experiments and data production, it has been increasingly important to enable users to easily retrace their steps, identify what datasets were produced by which process chains, and give them ability to readily reproduce their results. This can be a tedious and difficult task even for a small project, but is almost impossible on large processing pipelines. We have developed an initial reproducibility and knowledge capture solution for the NEX, however, if users want to move the code to another system, whether it is their home institution cluster, laptop or the cloud, they have to find, build and install all the required dependencies that would run their code. This can be a very tedious and tricky process and is a big impediment to moving code to data and reproducibility outside the original system. The NEX team has tried to assist users who wanted to move their code into OpenNEX on Amazon cloud by creating custom virtual machines with all the software and dependencies installed, but this, while solving some of the issues, creates a new bottleneck that requires the NEX team to be involved with any new request, updates to virtual machines and general maintenance support. In this presentation, we will describe a solution that integrates NEX and Docker to bridge the gap in code-to-data migration. The core of the solution is saemi-automatic conversion of science codes, tools and services that are already tracked and described in the NEX provenance system, to Docker - an open-source Linux container software. Docker is available on most computer platforms, easy to install and capable of seamlessly creating and/or executing any application packaged in the appropriate format. We believe this is an important step towards seamless process deployment in heterogeneous environments that will enhance community access to NASA data and tools in a scalable way, promote software reuse, and improve reproducibility of scientific results.
In this paper, a preliminary evaluation of removing used nuclear fuel (UNF) from 12 shutdown sites was conducted. Shutdown sites are defined as those commercial nuclear power reactor sites where the nuclear power reactors have been shut down and the site has been decommissioned or is undergoing decommissioning. The shutdown sites were Maine Yankee, Yankee Rowe, Connecticut Yankee, Humboldt Bay, Big Rock Point, Rancho Seco, Trojan, La Crosse, Zion, Crystal River, Kewaunee, and San Onofre. The evaluation was divided into four components: • characterization of the UNF and greater-than-Class C low-level radioactive waste (GTCC waste) inventory • a description of the on-site infrastructure and conditions relevant to transportation activities • an evaluation of the near-site transportation infrastructure and experience relevant to shipping transportation casks containing UNF from the shutdown sites, including gaps in information • an evaluation of the actions necessary to prepare for and remove UNF and GTCC waste from the shutdown sites. The primary sources for the inventory of UNF and GTCC waste are the DOE RW-859 used nuclear fuel inventory database, industry sources such as StoreFUEL and SpentFUEL, and government sources such as the NRC. The primary sources for the information on the site conditions and near-site transportation infrastructure and experience include site visits to the Maine Yankee, Yankee Rowe, Connecticut Yankee, Humboldt Bay, Big Rock Point, Rancho Seco, Trojan, La Crosse, and Zion sites; information provided by managers at the shutdown sites; Facility Interface Data Sheets compiled for DOE in 2005; Services Planning Documents prepared for DOE in 1993 and 1994; industry publications such as Radwaste Solutions; and Google Earth. State and Regional Group representatives, a Tribal representative, and a Federal Railroad Administration representative participated in six of the shutdown site visits. All sites were found to have at least one off-site transportation mode option for removing their UNF and GTCC waste, and some sites have two options. Large component removals during reactor decommissioning provided an important source of information in determining transportation mode options. Especially important in conducting the evaluation were site visits, through which information was obtained that would not have been available otherwise. Extensive photographs taken during the site visits proved to be particularly useful in documenting the current conditions at or near the sites. Additional conclusions from this evaluation include: • Although there are common aspects, each site has some unique features and/or conditions. • The 12 sites use designs from 4 different suppliers, including 9 different (horizontal and vertical) storage systems that would require 8 different transportation cask designs. • Although some regulatory actions will be required, all UNF at the initial 9 shutdown sites (Maine Yankee, Yankee Rowe, Connecticut Yankee, Humboldt Bay, Big Rock Point, Rancho Seco, Trojan, La Crosse, and Zion) is in licensed systems that can be transported, including a small amount of high-burnup fuel. • Each site indicated that 2-3 years of advanced time is required for preparations prior to pick-up. • Most sites have multiple transportation options, e.g., rail, barge, or heavy haul truck, as well as constraints and preferences. It is expected that additional site visits will be conducted to update the information used in the evaluation. Additional sites may be added to the evaluation if further site closures are announced.
This report presents a preliminary evaluation of removing used nuclear fuel (UNF) from 12 shutdown nuclear power plant sites. At these shutdown sites the nuclear power reactors have been permanently shut down and the sites have been decommissioned or are undergoing decommissioning. The shutdown sites are Maine Yankee, Yankee Rowe, Connecticut Yankee, Humboldt Bay, Big Rock Point, Rancho Seco, Trojan, La Crosse, Zion, Crystal River, Kewaunee, and San Onofre. The evaluation was divided into four components: characterization of the UNF and greater-than-Class C low-level radioactive waste (GTCC waste) inventory; a description of the on-site infrastructure and conditions relevant to transportation of UNF and GTCC waste; an evaluation of the near-site transportation infrastructure and experience relevant to shipping transportation casks containing UNF and GTCC waste, including identification of gaps in information; and, an evaluation of the actions necessary to prepare for and remove UNF and GTCC waste. The primary sources for the inventory of UNF and GTCC waste are the U.S. Department of Energy (DOE) RW-859 used nuclear fuel inventory database, industry sources such as StoreFUEL and SpentFUEL, and government sources such as the U.S. Nuclear Regulatory Commission. The primary sources for information on the conditions of site and near-site transportation infrastructure and experience included observations and information collected during visits to the Maine Yankee, Yankee Rowe, Connecticut Yankee, Humboldt Bay, Big Rock Point, Rancho Seco, Trojan, La Crosse, and Zion sites; information provided by managers at the shutdown sites; Facility Interface Data Sheets compiled for DOE in 2005; Services Planning Documents prepared for DOE in 1993 and 1994; industry publications such as Radwaste Solutions; and Google Earth. State and Regional Group representatives, a Tribal representative, and a Federal Railroad Administration representative participated in six of the shutdown site visits. Every site was found to have at least one off-site transportation mode option for removing its UNF and GTCC waste; some have multiple options. Experience removing large components during reactor decommissioning provided an important source of information used to identify the transportation mode options for the sites. Especially important in conducting the evaluation were site visits, through which information was obtained that would not have been available otherwise. Extensive photographs taken during the site visits proved to be particularly useful in documenting the current conditions at or near the sites. Additional conclusions from this evaluation include: The 12 shutdown sites use designs from 4 different suppliers involving 9 different (horizontal and vertical) dry storage systems that would require the use of 8 different transportation cask designs to remove the UNF and GTCC waste from the shutdown sites; Although there are common aspects, each site has some unique features and/or conditions; Although some regulatory actions will be required, all UNF at the initial 9 shutdown sites (Maine Yankee, Yankee Rowe, Connecticut Yankee, Humboldt Bay, Big Rock Point, Rancho Seco, Trojan, La Crosse, and Zion) is in licensed systems that can be transported, including a small amount of high-burnup fuel; Each site indicated that 2-3 years of advance time would be required for its preparations before shipments could begin; Most sites have more than one transportation option, e.g., rail, barge, or heavy haul truck, as well as constraints and preferences. It is expected that additional site visits will be conducted to add to the information presented in the evaluation.
Focal Area(s): (2)Predictive modeling using AI techniques and AI-derived model components; use of AI and other tools to design a prediction system comprising of a hierarchy of models. (3) Insight gleaned from complex data (both observed and simulated) using AI, big data analytics, and other advanced methods, including explainable AI and physics- or knowledge- guided AI. Science Challenge: The Energy Exascale Earth System Model (E3SM) is a fully coupled, state-of-the-science Earth system model that uses code optimized for DOE's advanced computers to address the most critical scientific questions facing our nation and society (Golaz et al., 2019). The E3SM Land model (ELM) is designed to understand how the changes in terrestrial land surfaces will interact with other Earth system components and has been used to understand hydrologic cycles, biogeophysics, and ecosystem dynamics. In spite of great successes, the ELM has several known issues that restrain rapid improvements. For example, the ELM uses equilibrium models to simulate dynamic land-climate interactions and it requires long model spin-up time to identify suitable initial conditions for transient simulations. The ELM lacks built-in uncertainty mechanisms that can improve the robustness of model predictions. The ELM is a holistic, deterministic model system with a rigid design, and in many situations, it is hard to modify the ELM system to incorporate new theory/hypothesis and new data across scales to address emerging science problems (such as predicting the impacts of water cycle extremes). In addition, The ELM is technically optimized for traditional CPU-centric computers and it cannot fully utilize the current and incoming leadership computers for model simulations and uncertainty quantification (UQ). The success of artificial intelligence (AI) has inspired scientists to use AI models to discover intrinsic features from simulation data (Chattopadhyay et al., 2020) and observational data (Reichstein et al., 2019) to gain further process understanding of Earth science problems. However, autonomous AI model training through deep learning usually requires a huge amount of annotated data. To overcome the limitations from the data and computing resources, knowledge-guided AI models are necessary where human-knowledge is ingested in model construction (Banino et al., 2018) and training process (Silver et al., 2016) for efficient learning. Herein, we present a new way that leverages the process understanding from the ELM to guide AI model development for the ELM enhancement and UQ. We hope this study can inspire further Earth and environmental system model developments and transformations.
A preliminary evaluation of removing spent nuclear fuel (SNF) from 13 shutdown nuclear power plant sites was performed. At these shutdown sites the nuclear power reactors have been permanently shut down and the sites have been decommissioned or are undergoing decommissioning. The shutdown sites were Maine Yankee, Yankee Rowe, Connecticut Yankee, Humboldt Bay, Big Rock Point, Rancho Seco, Trojan, La Crosse, Zion, Crystal River, Kewaunee, San Onofre, and Vermont Yankee. The evaluation was divided into four components: Characterization of the SNF and greater-than-Class C low-level radioactive waste (GTCC waste) inventory A description of the on-site infrastructure at the shutdown sites An evaluation of the near-site transportation infrastructure and transportation experience at the shutdown sites An evaluation of the actions necessary to prepare for and remove SNF and GTCC waste. The primary sources for the inventory of SNF and GTCC waste were the U.S. Department of Energy (DOE) spent nuclear fuel inventory database, industry publications such as StoreFUEL, and government sources such as the U.S. Nuclear Regulatory Commission. The primary sources for information on the conditions of on-site infrastructure and near-site transportation infrastructure and experience included information collected during site visits, information provided by managers at the shutdown sites, Facility Interface Data Sheets compiled for DOE in 2005, Services Planning Documents prepared for DOE in 1993 and 1994, industry publications such as Radwaste Solutions, and Google Earth. State staff, State Regional Group representatives, a Tribal representative, and a Federal Railroad Administration representative have participated in nine of the shutdown site visits. Every shutdown site was found to have at least one off-site transportation mode option for removing its SNF and GTCC waste; some have multiple options. Experience removing large components during reactor decommissioning provided an important source of information used to identify the transportation mode options for the sites. Especially important in conducting the evaluation were site visits, through which information was obtained that would not have been available otherwise. Extensive photographs taken during the site visits proved to be particularly useful in documenting the current conditions at or near the sites. Additional conclusions from this evaluation include: The 13 shutdown sites use designs from 4 different suppliers involving 11 different (horizontal and vertical) dry storage systems that would require the use of 9 different transportation cask designs to remove the SNF and GTCC waste from the shutdown sites. Although some changes to transportation certificates of compliance will be required, the SNF at the initial 9 shutdown sites (Maine Yankee, Yankee Rowe, Connecticut Yankee, Humboldt Bay, Big Rock Point, Rancho Seco, Trojan, La Crosse, and Zion) is in dual purpose dry storage canisters that can be transported, including a small amount of high-burnup fuel. Most sites indicated that 2-3 years of advance time would be required for its preparations before shipments could begin. Some sites could be ready in less time. As additional sites such as Fort Calhoun, Clinton, Quad Cities, Pilgrim, Oyster Creek, and Diablo Canyon shut down, these sites will be included in updates to the evaluation.
A preliminary evaluation of removing spent nuclear fuel (SNF) from 14 shutdown nuclear power plant sites was performed. At these shutdown sites the nuclear power reactors have been permanently shut down and the sites have been decommissioned or are undergoing decommissioning. The shutdown sites were Maine Yankee, Yankee Rowe, Connecticut Yankee, Humboldt Bay, Big Rock Point, Rancho Seco, Trojan, La Crosse, Zion, Crystal River, Kewaunee, San Onofre, Vermont Yankee, and Fort Calhoun. The evaluation was divided into four components: (1) characterization of the SNF and greater-than-Class C low-level radioactive waste (GTCC waste) inventory, (2) a description of the on-site infrastructure at the shutdown sites, (3) an evaluation of the near-site transportation infrastructure and transportation experience at the shutdown sites, and (4) an evaluation of the actions necessary to prepare for and remove SNF and GTCC waste. The primary sources for the inventory of SNF and GTCC waste were the U.S. Department of Energy (DOE) spent nuclear fuel inventory database, industry publications such as StoreFUEL, and government sources such as the U.S. Nuclear Regulatory Commission. The primary sources for information on the conditions of on-site infrastructure and near-site transportation infrastructure and experience included information collected during site visits, information provided by managers at the shutdown sites, Facility Interface Data Sheets compiled for DOE in 2005, Services Planning Documents prepared for DOE in 1993 and 1994, industry publications such as Radwaste Solutions, and Google Earth. State representatives, State Regional Group representatives, Tribal representatives, and a Federal Railroad Administration representatives have participated in eleven of the shutdown site visits. Every shutdown site was found to have at least one off-site transportation mode option for removing its SNF and GTCC waste; some have multiple options. Experience removing large components during reactor decommissioning provided an important source of information used to identify the transportation mode options for the sites. Especially important in conducting the evaluation were site visits, through which information was obtained that would not have been available otherwise. Extensive photographs taken during the site visits proved to be particularly useful in documenting the current conditions at or near the sites. Additional conclusions from this evaluation include: The 14 shutdown sites use designs from 4 different suppliers involving 11 different (horizontal and vertical) dry storage systems that would require the use of 9 different transportation cask designs to remove the SNF and GTCC waste from the shutdown sites. Although some changes to transportation certificates of compliance will be required, the SNF at the initial 9 shutdown sites (Maine Yankee, Yankee Rowe, Connecticut Yankee, Humboldt Bay, Big Rock Point, Rancho Seco, Trojan, La Crosse, and Zion) is in dual purpose dry storage canisters that can be transported, including a small amount of high-burnup fuel. Most sites indicated that 2-3 years of advance time would be required for its preparations before shipments could begin. Some sites could be ready in less time. As additional sites such as Palisades, Pilgrim, Three Mile Island, Oyster Creek, Indian Point, and Diablo Canyon shut down, these sites will be included in updates to the evaluation.