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NASA NTRS2018

The GMAO Hybrid Ensemble-Variational Atmospheric Data Assimilation System: Version 2.0

This document describes the implementation and usage of the Goddard Earth Observing System (GEOS) Hybrid Ensemble-Variational Atmospheric Data Assimilation System (Hybrid EVADAS). Its aim is to provide comprehensive guidance to users of GEOS ADAS interested in experimenting with its hybrid functionalities. The document is also aimed at providing a short summary of the state-of-science in this release of the hybrid system. As explained here, the ensemble data assimilation system (EnADAS) mechanism added to GEOS ADAS to enable hybrid data assimilation applications has been introduced to the pre-existing machinery of GEOS in the most non-intrusive possible way. Only very minor changes have been made to the original scripts controlling GEOS ADAS with the objective of facilitating its usage by both researchers and the GMAO's near-real-time Forward Processing applications. In a hybrid scenario two data assimilation systems run concurrently in a two-way feedback mode such that: the ensemble provides background ensemble perturbations required by the ADAS deterministic (typically high resolution) hybrid analysis; and the deterministic ADAS provides analysis information for recentering of the EnADAS analyses and information necessary to ensure that observation bias correction procedures are consistent between both the deterministic ADAS and the EnADAS. The nonintrusive approach to introducing hybrid capability to GEOS ADAS means, in particular, that previously existing features continue to be available. Thus, not only is this upgraded version of GEOS ADAS capable of supporting new applications such as Hybrid 3D-Var, 3D-EnVar, 4D-EnVar and Hybrid 4D-EnVar, it remains possible to use GEOS ADAS in its traditional 3D-Var mode which has been used in both MERRA and MERRA-2. Furthermore, as described in this document, GEOS ADAS also supports a configuration for exercising a purely ensemble-based assimilation strategy which can be fully decoupled from its variational component. We should point out that Release 1.0 of this document was made available to GMAO in mid-2013, when we introduced Hybrid 3D-Var capability to GEOS ADAS. This initial version of the documentation included a considerably different state-of-science introductory section but many of the same detailed description of the mechanisms of GEOS EnADAS. We are glad to report that a few of the desirable Future Works listed in Release 1.0 have now been added to the present version of GEOS EnADAS. These include the ability to exercise an Ensemble Prediction System that uses the ensemble analyses of GEOS EnADAS and (a very early, but functional version of) a tool to support Ensemble Forecast Sensitivity and Observation Impact applications.

Todling, Ricardo↗

DOE OSTI2015

A low cost network of spectrometer radiation detectors based on the ArduSiPM a compact transportable Software/Hardware Data Acquisition system with Arduino DUE

The necessity to use Photo Multipliers (PM) as light detector limited in the past the use of crystals in radiation handled device preferring the Geiger approach. The Silicon Photomultipliers (SiPMs) are very small and cheap, solid photon detectors with good dynamic range and single photon detection capability, they are usable to supersede cumbersome and difficult to use Photo Multipliers (PM). A SiPM can be coupled with a scintillator crystal to build efficient, small and solid radiation detector. A cost effective and easily replicable Hardware software module for SiPM detector readout is made using the ArduSiPM solution. The ArduSiPM is an easily battery operable handled device using an Arduino DUE (an open Software/Hardware board) as processor board and a piggy-back custom designed board (ArduSiPM Shield), the Shield contains all the blocks features to monitor, set and acquire the SiPM using internet network. (authors)

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

NASA NTRSDate not supplied

New developments in space radiation research at NASA: Annotating data using a novel radiation biology ontology

Like many interdisciplinary sciences, data producers and consumers in the field of radiation biology often use a wide variety of terminology to describe their experiments and data. Furthermore, space systems and technologies are rapidly evolving, and a shared understanding and common terminology for these is also lacking. The efficiency of research organizations can be enhanced by standardizing metadata through the use of knowledge resources like ontologies. Employing a sophisticated model such as a formal ontology to standardize metadata enables automated data acquisition processes and supports more complete, accurate meta-analysis through more efficient and complete data discovery and retrieval, particularly when using multiple data sources. Thus, we developed the Radiation Biology Ontology (RBO) in order to improved radiation biology metadata uniformity and transparency. We used open-source software (the Ontology Development Kit, Protégé and WebProtégé) and worked within the OBO Foundry framework, which includes a set of ontology development principles and practices for ontology consistency, uniformity, and accountability. The RBO has now been incorporated into two radiation research data repositories, NASA’s GeneLab omics database (https://genelab.nasa.gov), and the European Commission STORE database (https://www.storedb.org/). Continuous build integration tools allowed our international RBO collaboration to be more efficient and focus its efforts on semantic model design. Currently, the RBO contains over 300 annotated classes and individuals specific to the study of radiation on biological systems, as well as imports of many additional classes from other OBO Foundry ontologies that relate to and/or provide context for these RBO entities. We publish the RBO through the OBO Foundry, so that it is available for browsing, download, and querying through NCBI Bioportal web site and application programming interface. The NASA Ames Life Science Data Archive (ALSDA) is also in the process of adopting use of the RBO, taking NASA one step closer to a knowledge-based system for space biology data. It is our hope that the global communities of radiation research Investigators, data curators and data analysts can similarly leverage the RBO and will contribute to its further development.

radiation↗

DOE OSTI2021

Hypernetwork Science: From Multidimensional Networks to Computational Topology

As data structures and mathematical objects used for complex systems modeling, hypergraphs sit nicely poised between on the one hand the world of network models, and on the other that of higher-order mathematical abstractions from algebra, lattice theory, and topology. They are able to represent complex systems interactions more faithfully than graphs and networks, while also being some of the simplest classes of systems representing topological structures as collections of multidimensional objects connected in a particular pattern. In this paper we discuss the role of (undirected) hypergraphs in the science of complex networks, and provide a mathematical overview of the core concepts needed for hypernetwork modeling, including duality and the relationship to bicolored graphs, quantitative adjacency and incidence, the nature of walks in hypergraphs, and available topological relationships and properties. We close with a brief discussion of two example applications: biomedical databases for disease analysis, and domain-name system (DNS) analysis of cyber data.

Joslyn, Cliff A.↗

NASA NTRS1994

Earth Observing System (EOS) Communication (Ecom) Modeling, Analysis, and Testbed (EMAT) activiy

This paper describes the Earth Observing System (EOS) Communication (Ecom) Modeling, Analysis, and Testbed (EMAT) activity performed by Code 540 in support of the Ecom project. Ecom is the ground-to-ground data transport system for operational EOS traffic. The National Aeronautic and Space Administration (NASA) Communications (Nascom) Division, Code 540, is responsible for implementing Ecom. Ecom interfaces with various systems to transport EOS forward link commands, return link telemetry, and science payload data. To understand the complexities surrounding the design and implementation of Ecom, it is necessary that sufficient testbedding, modeling, and analysis be conducted prior to the design phase. These activities, when grouped, are referred to as the EMAT activity. This paper describes work accomplished to date in each of the three major EMAT activities: modeling, analysis, and testbedding.

Desai, Vishal↗

NASA NTRS1983

An end-to-end data system for the Gamma-Ray Observatory

A data system, which includes parts in the orbiting Gamma-Ray Observatory and in its associated ground system, has been designed to rapidly deliver autonomous, packeted data to the science users. Data autonomy means all of the data, including auxiliary data, necessary for processing is included in the data packet that leaves the spacecraft. The data packets leaving the spacecraft remain unopened until they reach the user. Handling the data on a packet rather than a byte level allows simpler and generic software. The data goes through the system more quickly. This in turn reduces cost.

Hrastar, J.↗

NASA NTRS2007

Mars Reconnaissance Orbiter Mission: Systems Engineering Challenges on the Mars Reconnaissance Orbiter Mission

MRO project is a system of systems requiring system engineering team to architect, design, integrate, test, and operate these systems at each level of the project. The challenge of system engineering mission objectives into a single mission architecture that can be integrated tested, launched, and operated. Systems engineering must translate high-level requirements into integrated mission design. Systems engineering challenges were overcome utilizing a combination by creative designs built into MRO's flight and ground systems: a) Design of sophisticated spacecraft targeting and data management capabilities b) Establishment of a strong operations team organization; c) Implementation of robust operational processes; and d) Development of strategic ground tools. The MRO system has met the challenge of its driving requirements: a) MRO began its two-year primary science phase on November 7, 2006, and by July 2007, met it minimum requirement to collect 15 Tbits of data after only eight months of operations. Currently we have collected 22 Tbits. b) Based on current performance, mission data return could return 70 Tbits of data by the end of the primary science phase in 2008.

systems engineering↗

NASA NTRS2004

Lunar and Planetary Science XXXV: Outer Solar System

The session "Outer Solar System" included the following reports:New Data About Seasonal Variations of the North-South Asymmetry of Polarized Light of Jupiter; Appearance of Second Harmonic in the Jupiter Spectrum; Dynamics of Confined Liquid Mass, Spreading on Planet Surface; "Cassini" will Discover 116 New Satellites of Saturn!; Jupiter's Light Reflection Law;and Internal Structure Modelling of Europa.

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DOE OSTI2025

Privacy Preservation from High-Performance Computing to Autonomous Science [Industrial and Governmental Activities]

High-Performance Computing (HPC) and Leadership-Class Supercomputing are driving forces behind scientific advancements, enabling researchers to tackle complex challenges in physics, chemistry, biology, and engineering. These systems power vast simulations and data analyses, fueling discoveries in fields ranging from materials science to climate modeling. However, their use often involves processing sensitive data—such as proprietary industry simulations, biomedical records, and national security computations—posing significant privacy concerns. In conclusion, this issue is amplified in collaborative environments like Department of Energy (DOE) user facilities, where HPC resources are shared across institutions to foster innovation.

Kotevska, Olivera [Oak Ridge National Laboratory (↗

NASA NTRS2012

Landsat Data Continuity Mission (LDCM) Flight Dynamics System (FDS)

The Landsat Data Continuity Mission (LDCM) will be launched in January 2013 to continue the legacy of Landsat land imagery collection that has been on-going for the past 40 years. While the overall mission and science goals are designed to produce the SAME data over the years, the ground systems designed to support the mission objectives have evolved immensely. The LDCM Flight Dynamics System (FDS) currently being tested and deployed for operations is highly automated and well integrated with the other ground system elements. The FDS encompasses the full suite of flight dynamics functional areas, including orbit and attitude determination and prediction, orbit and attitude maneuver planning and execution, and planning product generation. The integration of the orbit, attitude, maneuver, and products functions allows a very smooth flow for daily operations support with minimal input needed from the operator. The system also provides a valuable real-time component that monitors the on-board orbit and attitude during every ground contact and will autonomously alert the Flight Operations Team (FOT) personnel when any violations are found. This paper provides an overview of the LDCM Flight Dynamics System and a detailed description of how it is used to support space operations. For the first time on a Goddard Space Flight Center (GSFC)-managed mission, the ground attitude and orbits systems are fully integrated into a cohesive package. The executive engine of the FDS permits three levels of automation: low, medium, and high. The high-level, which will be the standard mode for LDCM, represents nearly lights-out operations. The paper provides an in-depth look at these processes within the FDS in support of LDCM in all mission phases.

Good, Susan M.↗

NASA NTRS1990

Improving a data-acquisition software system with abstract data type components

Abstract data types and object-oriented design are active research areas in computer science and software engineering. Much of the interest is aimed at new software development. Abstract data type packages developed for a discontinued software project were used to improve a real-time data-acquisition system under maintenance. The result saved effort and contributed to a significant improvement in the performance, maintainability, and reliability of the Goldstone Solar System Radar Data Acquisition System.

Howard, S. D.↗

DOE OSTI2014

Atmospheric Research Measurement Climate Research Facility - Atmospheric System Research High-Resolution Modeling Workshop, May 19-20, 2014

The Atmospheric Radiation Measurement (ARM) Climate Research Facility is a scientific user facility within the U.S. Department of Energy (DOE) Climate and Environmental Sciences Division (CESD) of the Office of Biological and Environmental Research (BER). ARM is an observation facility whose purpose is to provide ground-based observations of the atmosphere to support climate research and to support the improvement of global climate models. ARM is closely affiliated with the Atmospheric System Research (ASR) program, which uses ARM data to address key atmospheric science issues and improve the parameterization of physical processes in climate models. A common methodology for using ARM data to improve climate models has been to carry out multi-model evaluation projects, typically in conjunction with field campaigns in which intensive radiosonde launches support the development of model forcing data sets. These exercises have yielded important insights. However, in recent years, there has been a growing interest in supporting high-resolution model simulations on a more routine basis. An important aspect of ARM sites is the continuous collection of high-resolution data over long periods of time. More routine model simulations would take better advantage of these long-term data sets. Nevertheless, accomplishing this will require careful attention to the requirements for running and evaluating these models together with a research program that maximizes the benefit of the model output.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2017

S5: Information Technology for Science Missions

NASA Missions and Programs create a wealth of science data and information that are essential to understanding our earth, our solar system and the universe. Advancements in information technology will allow many people within and beyond the Agency to more effectively analyze and apply these data and information to create knowledge. The desired end result is to see that NASA data and science information are used to generate the maximum possible impact to the nation: to advance scientific knowledge and technological capabilities, to inspire and motivate the nation's students and teachers, and to engage and educate the public.

SBIR↗

NASA NTRS1991

Applications of the massively parallel machine, the MasPar MP-1, to Earth sciences

The computational workload of upcoming NASA science missions, especially the ground data processing for the Earth Observing System, is projected to be quite large (in the 50 to 100 gigaFLOPS range) and corespondingly very expensive to perform using conventional supercomputer systems. High performance, general purpose massively parallel computer systems such as the MasPar MP-1 are being investigated by NASA as a more cost effective alternative. Massively parallel systems are targeted for accelerated development and maturation by NASA's upcoming five-year High Performance Computing and Communications Program. A summary of the broad range of applications currently running on the MP-1 at NASA/Goddard are presented in this paper along with descriptions of the parallel algorithmic techniques employed in five applications that have bearing on Earth sciences.

Fischer, James R.↗

NASA NTRS1989

Data system interprobability

Software was developed and tested which allows an end-user to build a book data object in an interactive fashion. The electronic book model was developed for the Library front-end of the DAVID (Distributed Access View Integrated Database) system. It is a complex data object which is capable of holding data of different types and formats. After the task, the plan was to assess the Book model and investigate the possibility of it serving as a knowledge base for a expert system front-end to the DAVID system. For the rest of the fellowship period, data system interoperability was examined. Specifically, a plan was prepared titled Implementations of the Land Analysis System (LAS) to Meet UNEP/GRID Requirements. This plan is aimed at reaching the goal of building one virtual global change information system which will link existing and future data systems allowing earth and space scientists to learn about, locate, access, and correlate data of interest regardless of its physical location. The plan proposed the construction of a master directory (MD) for the European data systems that is equivalent in structure to NASA's MD. The MD is to contain brief, high-level information about existing Earth and space science data sets sufficient for a user to know where data of interest reside and how to go about getting additional information. The plan also outlined the tasks that would be involved to electronically connect the MD system to the various discipline data systems (e.g., LAS) in order to achieve general interroperabilty, that is, the abilty to perform cross-systems searches and exchange of and access to distributed information.

Wakim, Nagi T.↗

DOE OSTI2025

Using feature importance as an exploratory data analysis tool on Earth system models

Abstract. Machine learning (ML) models are commonly used to generate predictions, but these models can also support the discovery of new science. Generating accurate predictions necessitates that a model captures the structure of the underlying data. If the structure is properly extracted, ML could be a useful exploratory and evidential tool. In this paper, we present a case study that demonstrates the use of ML for exploratory data analysis (EDA) in the climate space. We apply the ML explainability method of spatiotemporal zeroed feature importance (stZFI) to understand how climate-variable associations evolve over space and time. Our analyses focus on data from ensembles of Earth system models (ESMs) which provide data on different climate states and conditions. We elect to work with ESM ensembles since they allow us to compare feature importance across alternative scenarios not available with observed data. The ensembles also account for natural variability so that we can distinguish between signal and noise due to natural climate variability when computing feature importance. The use of perturbed initial condition ensembles introduces variability mimicking the natural variability in the atmosphere; thus the signals emerging using feature importance (FI) can be evaluated against the natural variability in the climate system. For our analyses, we consider the 1991 volcanic eruption of Mount Pinatubo, which was a large stratospheric aerosol injection. We explore the climate pathway associated with the eruption from aerosols to radiation to temperature at both the near-surface and stratospheric levels. In addition to applying the method to data generated from two different ESMs, we apply stZFI to reanalysis data to compare the associations identified by stZFI. We show how stZFI tracks the importance of aerosol optical depth over time on forecasting temperatures. This case study illustrates usefulness of an ML tool (stZFI) for EDA on a well-studied climate exemplar.

Ries, Daniel (ORCID:0000000250294647)↗

NASA NTRS2015

GeneLab: A Systems Biology Platform for Spaceflight Omics Data

NASA's mission includes expanding our understanding of biological systems to improve life on Earth and to enable long-duration human exploration of space. Resources to support large numbers of spaceflight investigations are limited. NASA's GeneLab project is maximizing the science output from these experiments by: (1) developing a unique public bioinformatics database that includes space bioscience relevant "omics" data (genomics, transcriptomics, proteomics, and metabolomics) and experimental metadata; (2) partnering with NASA-funded flight experiments through bio-sample sharing or sample augmentation to expedite omics data input to the GeneLab database; and (3) developing community-driven reference flight experiments. The first database, GeneLab Data System Version 1.0, went online in April 2015. V1.0 contains numerous flight datasets and has search and download capabilities. Version 2.0 will be released in 2016 and will link to analytic tools. In 2015 Genelab partnered with two Biological Research in Canisters experiments (BBRIC-19 and BRIC-20) which examine responses of Arabidopsis thaliana to spaceflight. GeneLab also partnered with Rodent Research-1 (RR1), the maiden flight to test the newly developed rodent habitat. GeneLab developed protocols for maxiumum yield of RNA, DNA and protein from precious RR-1 tissues harvested and preserved during the SpaceX-4 mission, as well as from tissues from mice that were frozen intact during spaceflight and later dissected. GeneLab is establishing partnerships with at least three planned flights for 2016. Organism-specific nationwide Science Definition Teams (SDTs) will define future GeneLab dedicated missions and ensure the broader scientific impact of the GeneLab missions. GeneLab ensures prompt release and open access to all high-throughput omics data from spaceflight and ground-based simulations of microgravity and radiation. Overall, GeneLab will facilitate the generation and query of parallel multi-omics data, and deep curation of metadata for integrative analysis, allowing researchers to uncover cellular networks as observed in systems biology platforms. Consequently, the scientific community will have access to a more complete picture of functional and regulatory networks responsive to the spaceflight environment.. Analysis of GeneLab data will contribute fundamental knowledge of how the space environment affects biological systems, and enable emerging terrestrial benefits resulting from mitigation strategies to prevent effects observed during exposure to space. As a result, open access to the data will foster new hypothesis-driven research for future spaceflight studies spanning basic science to translational science.

proteomics↗

NASA NTRS1988

Data base development and research and editorial support

The Life Sciences Bibliographic Data Base was created in 1981 and subsequently expanded. A systematic, professional system was developed to collect, organize, and disseminate information about scientific publications resulting from research. The data base consists of bibliographic information and hard copies of all research papers published by Life Sciences-supported investigators. Technical improvements were instituted in the database. To minimize costs, take advantage of advances in personal computer technology, and achieve maximum flexibility and control, the data base was transferred from the JSC computer to personal computers at George Washington University (GWU). GWU also performed a range of related activities such as conducting in-depth searches on a variety of subjects, retrieving scientific literature, preparing presentations, summarizing research progress, answering correspondence requiring reference support, and providing writing and editorial support.

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