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NASA NTRS2014

MERRA Analytic Services: Meeting the Big Data Challenges of Climate Science Through Cloud-enabled Climate Analytics-as-a-service

Climate science is a Big Data domain that is experiencing unprecedented growth. In our efforts to address the Big Data challenges of climate science, we are moving toward a notion of Climate Analytics-as-a-Service (CAaaS). We focus on analytics, because it is the knowledge gained from our interactions with Big Data that ultimately produce societal benefits. We focus on CAaaS because we believe it provides a useful way of thinking about the problem: a specialization of the concept of business process-as-a-service, which is an evolving extension of IaaS, PaaS, and SaaS enabled by Cloud Computing. Within this framework, Cloud Computing plays an important role; however, we it see it as only one element in a constellation of capabilities that are essential to delivering climate analytics as a service. These elements are essential because in the aggregate they lead to generativity, a capacity for self-assembly that we feel is the key to solving many of the Big Data challenges in this domain. MERRA Analytic Services (MERRAAS) is an example of cloud-enabled CAaaS built on this principle. MERRAAS enables MapReduce analytics over NASAs Modern-Era Retrospective Analysis for Research and Applications (MERRA) data collection. The MERRA reanalysis integrates observational data with numerical models to produce a global temporally and spatially consistent synthesis of 26 key climate variables. It represents a type of data product that is of growing importance to scientists doing climate change research and a wide range of decision support applications. MERRAAS brings together the following generative elements in a full, end-to-end demonstration of CAaaS capabilities: (1) high-performance, data proximal analytics, (2) scalable data management, (3) software appliance virtualization, (4) adaptive analytics, and (5) a domain-harmonized API. The effectiveness of MERRAAS has been demonstrated in several applications. In our experience, Cloud Computing lowers the barriers and risk to organizational change, fosters innovation and experimentation, facilitates technology transfer, and provides the agility required to meet our customers' increasing and changing needs. Cloud Computing is providing a new tier in the data services stack that helps connect earthbound, enterprise-level data and computational resources to new customers and new mobility-driven applications and modes of work. For climate science, Cloud Computing's capacity to engage communities in the construction of new capabilies is perhaps the most important link between Cloud Computing and Big Data.

Data Analytics↗
NASA NTRS2014

Climate Analytics as a Service

Climate science is a big data domain that is experiencing unprecedented growth. In our efforts to address the big data challenges of climate science, we are moving toward a notion of Climate Analytics-as-a-Service (CAaaS). CAaaS combines high-performance computing and data-proximal analytics with scalable data management, cloud computing virtualization, the notion of adaptive analytics, and a domain-harmonized API to improve the accessibility and usability of large collections of climate data. MERRA Analytic Services (MERRA/AS) provides an example of CAaaS. MERRA/AS enables MapReduce analytics over NASA's Modern-Era Retrospective Analysis for Research and Applications (MERRA) data collection. The MERRA reanalysis integrates observational data with numerical models to produce a global temporally and spatially consistent synthesis of key climate variables. The effectiveness of MERRA/AS has been demonstrated in several applications. In our experience, CAaaS is providing the agility required to meet our customers' increasing and changing data management and data analysis needs.

big data↗
NASA NTRSDate not supplied

Enabling Space Biology Knowledge Discovery Through Biospecimen Sharing: The NASA Biological Institutional Scientific Collection and Space Microbial Culture Collection

NASA and international partners have conducted experiments in space to understand the biological impacts and address hazards to health. The resulting basic and applied science is imperative to enabling humanity to venture back to the Moon and then to Mars and beyond. Sending organisms into space is a costly endeavor. All biospecimens not required by spaceflight-relevant Principal Investigators are harvested, preserved, and archived in the NASA Biological Institutional Scientific Collection (NBISC) to maximize the scientific return. The NASA Biological and Physical Sciences (BPS) Division ‘Open Science’ endeavor includes NASA Genelab, the Space Biology Program’s Biospecimen Sharing Program, Physical Sciences Informatics, the Ames Life Sciences Data Archive, and NBISC to integrate extensive data and biospecimen resources from spaceflight and/or ground-based analog experiments. NBISC biospecimens are collected and preserved according to well-established standard operating procedures to maintain scientific quality and are available on-request by the international scientific community. NBISC currently stores over 32,000 biospecimens from Shuttle, International Space Station, and ground-based space analog investigations. Tissue sharing has resulted in at least 33 publications since 2011 and 48 requests since 2016. Many requests for NBISC biospecimen come from first-time investigators who subsequently submit grants as the port-of-entry into the field of space biology. Some NBISC biospecimens have been awarded to NASA Genelab, who then generate various ‘Open Science’ -omics data sets on their platform for bioinformatics. Other NBISC biospecimen awards have led to multiple studies such as fecal microbiome analysis, DNA damage analysis using single-cell DNA sequencing, enzymatic-pathway identification involved in spaceflight muscle atrophy, and characterization of ocular morphological changes. Of note, NBISC has expanded to include a new Space Microbial Culture Collection (SMCC) for the collection, identification, documentation, long-term preservation, and distribution of space-related microbial isolates.

biospecimens↗
NASA NTRS1992

The Spectral Image Processing System (SIPS): Software for integrated analysis of AVIRIS data

The Spectral Image Processing System (SIPS) is a software package developed by the Center for the Study of Earth from Space (CSES) at the University of Colorado, Boulder, in response to a perceived need to provide integrated tools for analysis of imaging spectrometer data both spectrally and spatially. SIPS was specifically designed to deal with data from the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) and the High Resolution Imaging Spectrometer (HIRIS), but was tested with other datasets including the Geophysical and Environmental Research Imaging Spectrometer (GERIS), GEOSCAN images, and Landsat TM. SIPS was developed using the 'Interactive Data Language' (IDL). It takes advantage of high speed disk access and fast processors running under the UNIX operating system to provide rapid analysis of entire imaging spectrometer datasets. SIPS allows analysis of single or multiple imaging spectrometer data segments at full spatial and spectral resolution. It also allows visualization and interactive analysis of image cubes derived from quantitative analysis procedures such as absorption band characterization and spectral unmixing. SIPS consists of three modules: SIPS Utilities, SIPS_View, and SIPS Analysis. SIPS version 1.1 is described below.

Kruse, F. A.↗
DOE OSTI2025

Radiative impact of record-breaking wildfires from integrated ground-based data

The radiative effects of wildfires have been traditionally estimated by models using radiative transfer calculations. Assessment of model-predicted radiative effects commonly involves information on observation-based aerosol optical properties. However, lack or incompleteness of this information for dense plumes generated by intense wildfires reduces substantially the applicability of this assessment. Here we introduce a novel method that provides additional observational constraints for such assessments using widely available ground-based measurements of shortwave and spectrally resolved irradiances and aerosol optical depth (AOD) in the visible and near-infrared spectral ranges. We apply our method to quantify the radiative impact of the record-breaking wildfires that occurred in the Western US in September 2020. For our quantification we use integrated ground-based data collected at the Atmospheric Measurements Laboratory in Richland, Washington, USA with a location frequently downwind of wildfires in the Western US. We demonstrate that remarkably dense plumes generated by these wildfires strongly reduced the solar surface irradiance (up to 70% or 450 Wm -2 for total shortwave flux) and almost completely masked the sun from view due to extremely large AOD (above 10 at 500 nm wavelength). We also demonstrate that the plume-induced radiative impact is comparable in magnitude with those produced by a violent volcano eruption occurred in the Western US in 1980 and continental cumuli.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2002

ISAIA: Interoperable Systems for Archival Information Access

The ISAIA project was originally proposed in 1999 as a successor to the informal AstroBrowse project. AstroBrowse, which provided a data location service for astronomical archives and catalogs, was a first step toward data system integration and interoperability. The goals of ISAIA were ambitious: '...To develop an interdisciplinary data location and integration service for space science. Building upon existing data services and communications protocols, this service will allow users to transparently query hundreds or thousands of WWW-based resources (catalogs, data, computational resources, bibliographic references, etc.) from a single interface. The service will collect responses from various resources and integrate them in a seamless fashion for display and manipulation by the user.' Funding was approved only for a one-year pilot study, a decision that in retrospect was wise given the rapid changes in information technology in the past few years and the emergence of the Virtual Observatory initiatives in the US and worldwide. Indeed, the ISAIA pilot study was influential in shaping the science goals, system design, metadata standards, and technology choices for the virtual observatory. The ISAIA pilot project also helped to cement working relationships among the NASA data centers, US ground-based observatories, and international data centers. The ISAIA project was formed as a collaborative effort between thirteen institutions that provided data to astronomers, space physicists, and planetary scientists. Among the fruits we ultimately hoped would come from this project would be a central site on the Web that any space scientist could use to efficiently locate existing data relevant to a particular scientific question. Furthermore, we hoped that the needed technology would be general enough to allow smaller, more-focused community within space science could use the same technologies and standards to provide more specialized services. A major challenge to searching for data across a broad community is that information that describe some data products are either not relevant to other data or not applicable in the same way. Some previous metadata standard development efforts (e.g., in the earth science and library communities) have produced standards that are very large and difficult to support. To address this problem, we studied how a standard may be divided into separable pieces. Data providers that wish to participate in interoperable searches can support only those parts of the standard that are relevant to them. We prototyped a top-level metadata standard that was small and applicable to all space science data.

Hanisch, Robert J.↗
NASA NTRS1991

TADPLOT program, version 2.0: User's guide

The TADPLOT Program, Version 2.0 is described. The TADPLOT program is a software package coordinated by a single, easy-to-use interface, enabling the researcher to access several standard file formats, selectively collect specific subsets of data, and create full-featured publication and viewgraph quality plots. The user-interface was designed to be independent from any file format, yet provide capabilities to accommodate highly specialized data queries. Integrated with an applications software network, data can be assessed, collected, and viewed quickly and easily. Since the commands are data independent, subsequent modifications to the file format will be transparent, while additional file formats can be integrated with minimal impact on the user-interface. The graphical capabilities are independent of the method of data collection; thus, the data specification and subsequent plotting can be modified and upgraded as separate functional components. The graphics kernel selected adheres to the full functional specifications of the CORE standard. Both interface and postprocessing capabilities are fully integrated into TADPLOT.

Hammond, Dana P.↗
DOE OSTI2025

Deliverable 6.7-Final Technical Report: Development Summary and Evaluation of the Solar Uncertainty Integrator (SUNI) Software

The Data Quality and Uncertainty Integration Project was a three-year effort to address stakeholder needs for assessing solar radiation resource data quality based on existing tools for estimating radiometer measurement uncertainties and assessing post-measurement data quality. The annual research objectives for the project addressed a logical progression of effort needed to achieve the ultimate project goal of developing the Solar Uncertainty Integrator (SUNI) software. This final technical report summarizes the development process for achieving these key research objectives and addresses the outreach and code development efforts in the final year of the project to develop a new solar irradiance data uncertainty integration software package.

14 SOLAR ENERGY↗
NASA NTRS1994

Mission Operations Centers (MOCs): Integrating key spacecraft ground data system components

In an environment characterized by decreasing budgets, limited system development time, and user needs for increased capabilities, the Mission Operations Division (MOD) at the National Aeronautics and Space Administration Goddard Space Flight Center initiated a new, cost-effective concept in developing its spacecraft ground data systems: the Mission Operations Center (MOC). In the MOC approach, key components are integrated into a comprehensive and cohesive spacecraft planning, monitoring, command, and control system with a single, state-of-the-art graphical user interface. The MOD is currently implementing MOC's, which feature a common, reusable, and extendable system architecture, to support the X-Ray Timing Explorer (XTE), Tropical Rainfall Measuring Mission (TRMM), and Advanced Composition Explorer (ACE) missions. As a result of the MOC approach, mission operations are integrated, and users can, with a single system, perform real-time health and safety monitoring, real-time command and control, real-time attitude processing, real-time and predictive graphical spacecraft monitoring, trend analysis, mission planning and scheduling, command generation and management, network scheduling, guide star selection, and (using an expert system) spacecraft monitoring and fault isolation. The MOD is also implementing its test and training simulators under the new MOC management structure. This paper describes the MOC concept, the management approaches used in developing MOC systems, the technologies employed and the development process improvement initiatives applied in implementing MOC systems, and the expected benefits to both the user and the mission project in using the MOC approach.

Harbaugh, Randy↗
DOE OSTI2017

Availability of Neutronics Benchmarks in the ICSBEP and IRPhEP Handbooks for Computational Tools Testing

In the past several decades, numerous experiments have been performed worldwide to support reactor operations, measurements, design, and nuclear safety. Those experiments represent an extensive international investment in infrastructure, expertise, and cost, representing significantly valuable resources of data supporting past, current, and future research activities. Those valuable assets represent the basis for recording, development, and validation of our nuclear methods and integral nuclear data [1]. The loss of these experimental data, which has occurred all too much in the recent years, is tragic. The high cost to repeat many of these measurements can be prohibitive, if not impossible, to surmount. Two international projects were developed, and are under the direction of the Organisation for Co-operation and Development Nuclear Energy Agency (OECD NEA) to address the challenges of not just data preservation, but evaluation of the data to determine its merit for modern and future use. The International Criticality Safety Benchmark Evaluation Project (ICSBEP) was established to identify and verify comprehensive critical benchmark data sets; evaluate the data, including quantification of biases and uncertainties; compile the data and calculations in a standardized format; and formally document the effort into a single source of verified benchmark data [2]. Similarly, the International Reactor Physics Experiment Evaluation Project (IRPhEP) was established to preserve integral reactor physics experimental data, including separate or special effects data for nuclear energy and technology applications [3]. Annually, contributors from around the world continue to collaborate in the evaluation and review of select benchmark experiments for preservation and dissemination. The extensively peer-reviewed integral benchmark data can then be utilized to support nuclear design and safety analysts to validate the analytical tools, methods, and data needed for next-generation reactor design, safety analysis requirements, and all other front- and back-end activities contributing to the overall nuclear fuel cycle where quality neutronics calculations are paramount.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2019

Visualization and Analytics of Distribution Systems with Deep Penetration of Distributed Energy Resources (VADER)

In its current state, the distribution system is incapable of handling small to moderate amounts of photovoltaic (PV) penetration. This is because it was initially designed for handling passive loads which, at the level of a substation, have low variability and are forecastable with high accuracy. It has been an open loop system with little monitoring and control. With the addition of PV energy sources, the overall scenario will change dramatically due to (1) two way power flow on network; and (2) high aggregate variability. Additionally, changes on the consumption side lead to a number of smart loads, Electric Vehicles (EVs), and Demand Response. These fundamental changes in the characteristics of the generation and consumption of power will lead to a number of practical engineering problems which must be overcome to allow increased penetration of Distributed PV. Solving the unique engineering challenges which arise at moderate levels of PV penetration requires closed loop integration of data from (1) PV sources; (2) customer load data from smart meters; (3) EV charging data; and (4) local and line mounted precision instruments. These data are not traditionally used by utilities in operations since they are ”non-SCADA” and the current grid does not require such levels of control. To integrate this data and provide real time intelligence from these non-SCADA data, we created the Visualization and Analytics of Distribution Systems with Deep Penetration of Distributed Energy Resources (VADER) platform. VADER is a collection of analytics enabled by integration of massive and heterogeneous data streams for granular real-time monitoring, visualization and control of Distributed Energy Resources (DER) in distribution networks. VADER analytics enable utilities to have greater visibility into distributed energy resources. We built several batch- and stream-analytics in VADER which help operators better understand the impact of distributed energy resources on the grid.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

PV Degradation Modeling: Applying Geospatial Workflows with "PVDeg"

Accurate degradation modeling is essential for predicting photovoltaic (PV) module performance, estimating longevity and informing design decisions. With degradation rates varying significantly by location, geospatial analysis is critical for PV and broader applications, such as agrivoltaics, weathering and environmental data analysis. This work presents PVDeg, an open-source tool designed for geospatial degradation analysis. PVDeg integrates meteorological data from global sources, including the National Solar Radiation Database (NSRDB) and Photovoltaic Geographical Information System (PVGIS), with degradation models. The toolkit enables users to customize geospatial workflows by integrating weather data, material parameters, and user-defined Python functions. It facilitates accelerated downloads of NSRDB and PVGIS datasets and optimizes geospatial point selection to preserve data density in regions of interest. Additionally, PVDeg provides a local database for storage and spatial queries, supporting large-scale analyses without the need for high-performance computing (HPC) resources. PVDeg provides a foundational workflow that extends its utility beyond PV applications, enabling researchers to analyze geospatial processes across discipline.

14 SOLAR ENERGY↗
NASA NTRS1979

Assessing urbanized area expansion through the integration of Landsat and conventional data /79052/

An image base information system (IBIS) is utilized to integrate Landsat and census data for the purpose of mapping urban land and updating urbanized-area outer lines. IBIS is a subset of the video image communication and retrieval digital image processing system developed at JPL. IBIS is used to analyze three urban areas: Orlando, Florida; Seattle, Washington; and Boston, Massachusetts. In all three applications the primary objective is to map the expansion of urban land cover in the urban fringe. Pertinent tabular reports are produced.

Davis, J. B.↗
DOE OSTI2017

Additions to the ICSBEP and IRPHEP handbooks since NCSD 2013

Innumerable experiments have been performed worldwide to support criticality safety activities. Those experiments represent significant investments in cost, expertise, and infrastructure; as such, they represent valuable assets as the basis for recording, development, and validation of methods and nuclear data vital for modern and future criticality safety needs. Preservation and evaluation of these experimental data as benchmarks serves to provide for the best interest of the nuclear criticality safety community. High-quality integral benchmark experiments have always been a priority. There is ever growing interest in developing integral benchmark data to increasingly quantify and reduce calculational uncertainties to meet the demands of future criticality safety needs supporting next generation reactors and advanced fuel cycle concepts. The importance of drawing upon existing benchmark data is becoming more apparent because of dwindling availability of critical facilities worldwide and the high cost of performing new experiments. Integral benchmark data from the International Handbook of Evaluated Criticality Safety Benchmark Experiments (ICSBEP Handbook) and the International Handbook of Reactor Physics Benchmark Experiments (IRPhEP Handbook) are widely utilized. Significant benchmark data have been added to those two handbooks since the last Nuclear Criticality Safety Division Topical Meeting in Wilmington, North Carolina (September-October 2013). This paper highlights those additions. The 2016 edition of the ICSBEP Handbook now includes data for 570 evaluations containing benchmark specifications for 4913 critical, subcritical, or near-critical configurations, representing contributions from over 20 countries. There are a total of seven criticality-alarm-placement/shielding evaluations containing a total of 45 benchmark configurations, and eight fundamental physics benchmark evaluations containing a total of 215 measurements relevant to criticality safety applications. The IRPhEP Handbook now includes data from 151 experimental series (representing 50 reactor facilities) and also represents contributions from over 20 countries. Of the 151 benchmarks, five are draft contributions. Both the ICSBEP and IRPhEP are collaborative efforts that involve numerous scientists, engineers, and administrative support personnel worldwide. Continued contribution to these handbooks is expected to further provide the high-quality benchmark data necessary to compliment criticality safety activities going forward. (authors)

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2017

Additions to the ICSBEP AND IRPHEP Handbooks Since NCSD 2013

Innumerable experiments have been performed worldwide to support criticality safety activities. Those experiments represent significant investments in cost, expertise, and infrastructure; as such, they represent valuable assets as the basis for recording, development, and validation of methods and nuclear data vital for modern and future criticality safety needs. Preservation and evaluation of these experimental data as benchmarks serves to provide for the best interest of the nuclear criticality safety community. High-quality integral benchmark experiments have always been a priority. There is ever growing interest in developing integral benchmark data to increasingly quantify and reduce calculational uncertainties to meet the demands of future criticality safety needs supporting next generation reactors and advanced fuel cycle concepts. The importance of drawing upon existing benchmark data is becoming more apparent be-cause of dwindling availability of critical facilities worldwide and the high cost of per-forming new experiments. Integral benchmark data from the International Handbook of Evaluated Criticality Safety Benchmark Experiments (ICSBEP Handbook) and the International Handbook of Reactor Physics Benchmark Experiments (IRPhEP Handbook) are widely utilized. Significant benchmark data have been added to those two handbooks since the last Nuclear Criticality Safety Division Topical Meeting in Wilmingt on, North Carolina (September-October 2013). This paper highlights those additions. The 2016 edition of the ICSBEP Handbook now includes data for 570 evaluations containing benchmark specifications for 4,913 critical, subcritical, or near-critical configurations, representing contributions from over 20 countries. There are a total of 7 criticality-alarm-placement/shielding evaluations containing a total of 45 benchmark configurations, and 8 fundamental physics benchmark evaluations containing a total of 215 measurements relevant to criticality safety applications. The IRPhEP Handbook now includes data from 151 experimental series (representing 50 reactor facilities) and also represents contributions from over 20 countries. Of the 151 benchmarks, five are draft contributions. Both the ICSBEP and IRPhEP are collaborative efforts that involve numerous scientists, engineers, and administrative support personnel worldwide. Continued contribution to these handbooks is expected to further provide the high-quality benchmark data necessary to compliment criticality safety activities going forward.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2017

The medical science DMZ: a network design pattern for data-intensive medical science

Abstract Objective We describe a detailed solution for maintaining high-capacity, data-intensive network flows (eg, 10, 40, 100 Gbps+) in a scientific, medical context while still adhering to security and privacy laws and regulations. Materials and Methods High-end networking, packet-filter firewalls, network intrusion-detection systems. Results We describe a “Medical Science DMZ” concept as an option for secure, high-volume transport of large, sensitive datasets between research institutions over national research networks, and give 3 detailed descriptions of implemented Medical Science DMZs. Discussion The exponentially increasing amounts of “omics” data, high-quality imaging, and other rapidly growing clinical datasets have resulted in the rise of biomedical research “Big Data.” The storage, analysis, and network resources required to process these data and integrate them into patient diagnoses and treatments have grown to scales that strain the capabilities of academic health centers. Some data are not generated locally and cannot be sustained locally, and shared data repositories such as those provided by the National Library of Medicine, the National Cancer Institute, and international partners such as the European Bioinformatics Institute are rapidly growing. The ability to store and compute using these data must therefore be addressed by a combination of local, national, and industry resources that exchange large datasets. Maintaining data-intensive flows that comply with the Health Insurance Portability and Accountability Act (HIPAA) and other regulations presents a new challenge for biomedical research. We describe a strategy that marries performance and security by borrowing from and redefining the concept of a Science DMZ, a framework that is used in physical sciences and engineering research to manage high-capacity data flows. Conclusion By implementing a Medical Science DMZ architecture, biomedical researchers can leverage the scale provided by high-performance computer and cloud storage facilities and national high-speed research networks while preserving privacy and meeting regulatory requirements.

Peisert, Sean↗
DOE OSTI2022

Uncovering Where Compensating Errors Could Hide in ENDF/B-VIII.0

Unconstrained physics spaces between two or more nuclear data observables in a library occur when their values can be simultaneously adjusted without violating the uncertainties in either differential information or simulations of relevant integral experiments. Differential data are often too imprecise to fully bound all nuclear data observables of interest for application simulations. Integral data are simulated with combinations of nuclear data so that an error in one observable may be hidden by a counterbalancing error in another. In this manner compensating errors may lurk within nuclear data libraries and these errors have the potential to undermine the predictive power of neutron transport simulations, particularly in situations where there is no conclusive validation experiment that resembles the application of interest. The EUCLID project (Experiments Underpinned by Computational Learning for Improvements in Nuclear Data) developed a preliminary workflow to identify these unconstrained physics spaces by bringing together results from a large collection of integral experiments with their simulated counter-parts as well as differential information that have a one-to-one correspondence to nuclear data. This wealth of information is processed by machine learning tools for subsequent refinement by human experts. Here, we show how the EUCLID work-flow is executed by applying it first to 239 Pu and then to 9 Be nuclear data in ENDF/B-VIII.0.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗