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NASA NTRS2018

Flight Plasma Diagnostics for High-Power, Solar-Electric Deep-Space Spacecraft

NASA's Asteroid Redirect Robotic Mission (ARRM) project plans included a set of plasma and space environment instruments, the Plasma Diagnostic Package (PDP), to fulfill ARRM requirements for technology extensibility to future missions. The PDP objectives were divided into the classes of 1) Plasma thruster dynamics, 2) Solar array-specific environmental effects, 3) Plasma environmental spacecraft effects, and 4) Energetic particle spacecraft environment. A reference design approach and interface requirements for ARRM's PDP was generated by the PDP team at JPL and GRC. The reference design consisted of redundant single-string avionics located on the ARRM spacecraft bus as well as solar array, driving and processing signals from multiple copies of several types of plasma, effects, and environments sensors distributed over the spacecraft and array. The reference design sensor types were derived in part from sensors previously developed for USAF Research Laboratory (AFRL) plasma effects campaigns such as those aboard TacSat-2 in 2007 and AEHF-2 in 2012.

Johnson, Lee↗

NASA NTRS1985

USL NASA/RECON project presentations at the 1985 ACM Computer Science Conference: Abstracts and visuals

This Working Paper Series entry represents the abstracts and visuals associated with presentations delivered by six USL NASA/RECON research team members at the above named conference. The presentations highlight various aspects of NASA contract activities pursued by the participants as they relate to individual research projects. The titles of the six presentations are as follows: (1) The Specification and Design of a Distributed Workstation; (2) An Innovative, Multidisciplinary Educational Program in Interactive Information Storage and Retrieval; (3) Critical Comparative Analysis of the Major Commercial IS and R Systems; (4) Design Criteria for a PC-Based Common User Interface to Remote Information Systems; (5) The Design of an Object-Oriented Graphics Interface; and (6) Knowledge-Based Information Retrieval: Techniques and Applications.

Dominick, Wayne D.↗

NASA NTRS1999

Development of Formation Deployment and Intialization Concepts

NASA's Cross-Cutting Technology Development Program identified formation flying as a key enabler for the next generation Earth and Sciences campaign. It is hoped that this technology will allow a distributed network of autonomous satellites to act collaboratively as a single collective unit paving the way for extensive co-observing campaigns, coordinated multi-point observing programs, improved space-based interferometry, and entirely new approaches to conducting science. APL as a team member with GSFC, funded by the Earth Sciences and Technology Organization (ESTO), investigated formation deployment and initialization concepts which is central to the formation flying concept. This paper presents the analytical approach and preliminary results of the study. The study investigated a simple mission involving the deployment of six micro-satellites, one at a time, from a bus. At the initialization state, the satellites fly in an along-track trajectory separated by nominal spacing. The study entailed the development of a two-body (bus and satellite) relative motion propagator based on Clohessy-Wiltshire (C-W) equations with drag from which the relative motion of the micro-satellites is deduced. This code was used to investigate cluster development characteristics subject to "tip-off' (ejection) conditions. Results indicate that cluster development is very sensitive to the ballistic coefficients of the bus and satellites, and to relative ejection velocity. This information can be used to identify optimum deployment parameters, along with accuracy bounds for a particular mission, and to develop a cluster control strategy minimizing global fuel and cost. A suitable control strategy concept has been identified, however, it needs to be developed further.

Badesha, Surjit S.↗

NASA NTRS2017

Variability and Trends in the Arctic Sea Ice Cover: Results from Different Techniques

Variability and trend studies of sea ice in the Arctic have been conducted using products derived from the same raw passive microwave data but by different groups using different algorithms. This study provides consistency assessment of four of the leading products, namely, Goddard Bootstrap (SB2), Goddard NASA Team (NT1), EUMETSAT Ocean and Sea Ice Satellite Application Facility (OSI-SAF 1.2), and Hadley HadISST 2.2 data in evaluating variability and trends in the Arctic sea ice cover. All four provide generally similar ice patterns but significant disagreements in ice concentration distributions especially in the marginal ice zone and adjacent regions in winter and meltponded areas in summer. The discrepancies are primarily due to different ways the four techniques account for occurrences of new ice and meltponding. However, results show that the different products generally provide consistent and similar representation of the state of the Arctic sea ice cover. Hadley and NT1 data usually provide the highest and lowest monthly ice extents, respectively. The Hadley data also show the lowest trends in ice extent and ice area at negative 3.88 percent decade and negative 4.37 percent decade, respectively, compared to an average of negative 4.36 percent decade and negative 4.57 percent decade for all four. Trend maps also show similar spatial distribution for all four with the largest negative trends occurring at the Kara/Barents Sea and Beaufort Sea regions, where sea ice has been retreating the fastest. The good agreement of the trends especially with updated data provides strong confidence in the quantification of the rate of decline in the Arctic sea ice cover.

Arctic↗

NASA NTRS1993

Using Task Analysis to Understand the Data System Operations Team

The Data System Operations Team (DSOT) currently monitors the Multimission Ground Data System (MGDS) at NASAճ Jet Propulsion Laboratory. The MGDS currently supports five spacecraft and within the next five years it will support ten spacecraft simultaneously. The ground processing element of the MGDS consists of a distributed UNIX-based system of over 40 nodes and 100 processes. The MGDS system provides operators with little or no information about the system's end-to-end processing status or end-to-end configuration. The lack of system visibility has become a critical issue in the daily operation of the MGDS. A task analysis was conducted to determine what kinds of tools were needed to provide DSOT with useful status information and to prioritize the tool development. The analysis provided the formality and structure needed to get the right information exchange between development and operations.

Holder, B. E.↗

NASA NTRS2003

Exploring New Methods of Displaying Bit-Level Quality and Other Flags for MODIS Data

The NASA Distributed Active Archive Center (DAAC) at the National Snow and Ice Data Center (NSIDC) archives and distributes snow and sea ice products derived from the MODerate resolution Imaging Spectroradiometer (MODIS) on board NASA's Terra and Aqua satellites. All MODIS standard products are in the Earth Observing System version of the Hierarchal Data Format (HDF-EOS). The MODIS science team has packed a wealth of information into each HDF-EOS file. In addition to the science data arrays containing the geophysical product, there are often pixel-level Quality Assurance arrays which are important for understanding and interpreting the science data. Currently, researchers are limited in their ability to access and decode information stored as individual bits in many of the MODIS science products. Commercial and public domain utilities give users access, in varying degrees, to the elements inside MODIS HDF-EOS files. However, when attempting to visualize the data, users are confronted with the fact that many of the elements actually represent eight different 1-bit arrays packed into a single byte array. This project addressed the need for researchers to access bit-level information inside MODIS data files. In an previous NASA-funded project (ESDIS Prototype ID 50.0) we developed a visualization tool tailored to polar gridded HDF-EOS data set. This tool,called the Polar researchers to access, geolocate, visualize, and subset data that originate from different sources and have different spatial resolutions but which are placed on a common polar grid. The bit-level visualization function developed under this project was added to PHDIS, resulting in a versatile tool that serves a variety of needs. We call this the EOS Imaging Tool.

Khalsa, Siri Jodha Singh↗

NASA NTRS2014

OMMYDCLD: a New A-train Cloud Product that Co-locates OMI and MODIS Cloud and Radiance Parameters onto the OMI Footprint

Clouds cover approximately 60% of the earth's surface. When obscuring the satellite's field of view (FOV), clouds complicate the retrieval of ozone, trace gases and aerosols from data collected by earth observing satellites. Cloud properties associated with optical thickness, cloud pressure, water phase, drop size distribution (DSD), cloud fraction, vertical and areal extent can also change significantly over short spatio-temporal scales. The radiative transfer models used to retrieve column estimates of atmospheric constituents typically do not account for all these properties and their variations. The OMI science team is preparing to release a new data product, OMMYDCLD, which combines the cloud information from sensors on board two earth observing satellites in the NASA A-Train: Aura/OMI and Aqua/MODIS. OMMYDCLD co-locates high resolution cloud and radiance information from MODIS onto the much larger OMI pixel and combines it with parameters derived from the two other OMI cloud products: OMCLDRR and OMCLDO2. The product includes histograms for MODIS scientific data sets (SDS) provided at 1 km resolution. The statistics of key data fields - such as effective particle radius, cloud optical thickness and cloud water path - are further separated into liquid and ice categories using the optical and IR phase information. OMMYDCLD offers users of OMI data cloud information that will be useful for carrying out OMI calibration work, multi-year studies of cloud vertical structure and in the identification and classification of multi-layer clouds.

OMMYDCLD↗

NASA NTRS2019

Characteristics of Total Column CO2 Retrievals from the OCO Missions: Biases, Information Content and Implications for Flux Inversions

The Orbiting Carbon Observatory-2 and Orbiting Carbon Observatory-3, launched in 2015 and 2019, respectively, are intended to collect and deliver high-resolution observations of CO2 with unprecedented space and time coverage. Observations of CO2 from these remote-sensing missions (also known as XCO2, or column-based average, dry air mole fraction of CO2) are then used by the global carbon cycle community to answer a wide range of science questions, from the distribution and quantification of global and regional CO2 source-sink patterns to quantification of anthropogenic sources at urban scales. Even though we have had the OCO-2 mission flying for a few years now, the retrieval algorithms are continuously evolving and improving to deliver XCO2 retrievals with very high precision and high accuracy (or low biases). In this presentation, we will discuss a simple yet effective quantitative framework that has been developed by the OCO-2 flux team to evaluate the information content of these XCO2 retrievals as soon as they are released, i.e., with lower latency than full-scale flux inversions. This framework serves as a precursor to advanced inverse modeling frameworks and is intended to provide an early but accurate assessment of the signal present in the satellite retrievals, the robustness of that signal, and the ability of these retrievals to resolve patterns in CO2 surface fluxes that cannot be resolved by our current network of surface sites. Specific results will tackle a tiered set of questions that are being addressed using this framework: (a) what are the distribution of retrievals in the different modes of operation and how do they vary in space and time? (b) what is the information that is being given to the inverse modeling frameworks from the space-based data, information above and beyond what is provided by the in-situ data? and (c) how do these factors influence our choices for doing flux inversions with the satellite retrievals? While the primary focus of the results will be on application of this technique to mature OCO-2 retrievals, we will show early results for a couple of months of OCO-3 retrievals. For the time-period that the retrievals from the two missions overlap, we will highlight how this framework allows us to effortlessly put the information from OCO-3 and OCO-2 on an equal footing, thus enabling easy comparison between the two pioneering missions.

Chatterjee, Abhishek↗

NASA NTRS1994

A solar photovoltaic power system for use in Antarctica

A solar photovoltaic power system was designed and built at the NASA Lewis Research Center as part of the NASA/NSF Antarctic Space Analog Program. The system was installed at a remote field camp at Lake Hoare in the Dry Valleys and provided a six-person field team with the power to run personal computers and printers, lab equipment, lightning, and a small microwave oven. The system consists of three silicon photovoltaic sub-arrays delivering 1.5 kW peak power, three lead-acid gel battery modules supplying 2.4 kWh, and electrical distribution system which delivers 120 Vac and 12 Vdc to the user. The system was modularized for each of deployment and operation. Previously the camp has been powered by diesel generators, which have proven to be both noisy and polluting. The NSF, in an effort to reduce their dependence on diesel fuel from both and environmental and cost standpoint is interested in the use of alternate forms of energy, such as solar power. Such a power system will also provide NASA with important data on system level deployment and operation in a remote location by a minimally trained crew, as well as validate initial integration concepts.

Kohout, Lisa L.↗

NASA NTRS2005

Glenn's Telescience Support Center Provided Around-the-Clock Operations Support for Space Experiments on the International Space Station

NASA Glenn Research Center s Telescience Support Center (TSC) allows researchers on Earth to operate experiments onboard the International Space Station (ISS) and the space shuttles. NASA s continuing investment in the required software, systems, and networks provides distributed ISS ground operations that enable payload developers and scientists to monitor and control their experiments from the Glenn TSC. The quality of scientific and engineering data is enhanced while the long-term operational costs of experiments are reduced because principal investigators and engineering teams can operate their payloads from their home institutions.

Malarik, Diane C.↗

NASA NTRS2005

Flight Hardware Fabricated for Combustion Science in Space

OMalley, Terence F.↗

NASA NTRSDate not supplied

The Evolution of NASA’s Land, Atmosphere Near real-time Capability for Earth Observing Systems (LANCE) to Support an Increasingly Diverse Range of User Needs

NASA’s Land, Atmosphere Near Real-Time Capability for Earth Observing Systems (LANCE) supports a host of near real-time (NRT) monitoring applications from air quality to wildfires to flooding to droughts to severe storms. LANCE distributes 40-75 TB of data per week from 13 instruments to a wide range of users in over 200 countries. Most of the data and imagery served through LANCE are available within three hours of satellite overpass. Leveraging the existing NASA infrastructure and science teams has made LANCE unique in its ability to provide data from instruments onboard Earth observing satellites rapidly, accurately, and consistently. LANCE continually ensures these data are findable, readily accessible, and freely available. The evolution of LANCE is ongoing, as it strives to enable researchers and applications users to quickly incorporate the latest satellite data into their work, leading to more timely and accurate understanding of the Earth’s land and atmosphere.

Jenny Hewson↗

NASA NTRSDate not supplied

The Evolution of NASA's Land, Atmosphere Near Real-Time Capability for EOS (LANCE) to Support an Increasingly Diverse Range of User Needs

Jenny Hewson↗

NASA NTRS2008

Cassini Information Management System in Distributed Operations Collaboration and Cassini Science Planning

Launched on October 15, 1997, the Cassini-Huygens spacecraft began its ambitious journey to the Saturnian system with a complex suite of 12 scientific instruments, and another 6 instruments aboard the European Space Agencies Huygens Probe. Over the next 6 1/2 years, Cassini would continue its relatively simplistic cruise phase operations, flying past Venus, Earth, and Jupiter. However, following Saturn Orbit Insertion (SOI), Cassini would become involved in a complex series of tasks that required detailed resource management, distributed operations collaboration, and a data base for capturing science objectives. Collectively, these needs were met through a web-based software tool designed to help with the Cassini uplink process and ultimately used to generate more robust sequences for spacecraft operations. In 2001, in conjunction with the Southwest Research Institute (SwRI) and later Venustar Software and Engineering Inc., the Cassini Information Management System (CIMS) was released which enabled the Cassini spacecraft and science planning teams to perform complex information management and team collaboration between scientists and engineers in 17 countries. Originally tailored to help manage the science planning uplink process, CIMS has been actively evolving since its inception to meet the changing and growing needs of the Cassini uplink team and effectively reduce mission risk through a series of resource management validation algorithms. These algorithms have been implemented in the web-based software tool to identify potential sequence conflicts early in the science planning process. CIMS mitigates these sequence conflicts through identification of timing incongruities, pointing inconsistencies, flight rule violations, data volume issues, and by assisting in Deep Space Network (DSN) coverage analysis. In preparation for extended mission operations, CIMS has also evolved further to assist in the planning and coordination of the dual playback redundancy of highvalue data from targets such as Titan and Enceladus. This paper will outline the critical role that CIMS has played for Cassini in the distributed ops paradigm throughout operations. This paper will also examine the evolution that CIMS has undergone in the face of new science discoveries and fluctuating operational needs. And finally, this paper will conclude with theoretical adaptation of CIMS for other projects and the potential savings in cost and risk reduction that could potentially be tapped into by future missions.

Equils, Douglas J.↗

NASA NTRS2016

A Satellite Data-Driven, Client-Server Decision Support Application for Agricultural Water Resources Management

Water cycle extremes such as droughts and floods present a challenge for water managers and for policy makers responsible for the administration of water supplies in agricultural regions. In addition to the inherent uncertainties associated with forecasting extreme weather events, water planners need to anticipate water demands and water user behavior in a typical circumstances. This requires the use decision support systems capable of simulating agricultural water demand with the latest available data. Unfortunately, managers from local and regional agencies often use different datasets of variable quality, which complicates coordinated action. In previous work we have demonstrated novel methodologies to use satellite-based observational technologies, in conjunction with hydro-economic models and state of the art data assimilation methods, to enable robust regional assessment and prediction of drought impacts on agricultural production, water resources, and land allocation. These methods create an opportunity for new, cost-effective analysis tools to support policy and decision-making over large spatial extents. The methods can be driven with information from existing satellite-derived operational products, such as the Satellite Irrigation Management Support system (SIMS) operational over California, the Cropland Data Layer (CDL), and using a modified light-use efficiency algorithm to retrieve crop yield from the synergistic use of MODIS and Landsat imagery. Here we present an integration of this modeling framework in a client-server architecture based on the Hydra platform. Assimilation and processing of resource intensive remote sensing data, as well as hydrologic and other ancillary information occur on the server side. This information is processed and summarized as attributes in water demand nodes that are part of a vector description of the water distribution network. With this architecture, our decision support system becomes a light weight 'app' that connects to the server to retrieve the latest information regarding water demands, land use, yields and hydrologic information required to run different management scenarios. Furthermore, this architecture ensures all agencies and teams involved in water management use the same, up-to-date information in their simulations.

Agricultural↗

NASA NTRS1993

Using Task Analysis to Understand the Data System Operations Team

The Data System Operations Team (DSOT) currently monitors and controls the Multimission Ground Data System (MGDS) at NASA's Jet Propulsion Laboratory. The MGDS currently supports five spacecraft and within the next five years it will support ten spacecraft simultaneously. The ground processing element of the MGDS consists of a distributed UNIX-based system of over 40 nodes and 100 processes. The MGDS system provides operators with little or no information about the system's end-to-end processing status or end-to-end configuration. The lack of system visibility has become a critical issue in the daily operation of the MGDS. A task analysis was conducted to determine what kinds of tools were needed to provide DSOT with useful status information and to prioritize the tool development...

Holder, Barbara E.↗

NASA NTRS2000

Optimal Reward Functions in Distributed Reinforcement Learning

We consider the design of multi-agent systems so as to optimize an overall world utility function when (1) those systems lack centralized communication and control, and (2) each agents runs a distinct Reinforcement Learning (RL) algorithm. A crucial issue in such design problems is to initialize/update each agent's private utility function, so as to induce best possible world utility. Traditional 'team game' solutions to this problem sidestep this issue and simply assign to each agent the world utility as its private utility function. In previous work we used the 'Collective Intelligence' framework to derive a better choice of private utility functions, one that results in world utility performance up to orders of magnitude superior to that ensuing from use of the team game utility. In this paper we extend these results. We derive the general class of private utility functions that both are easy for the individual agents to learn and that, if learned well, result in high world utility. We demonstrate experimentally that using these new utility functions can result in significantly improved performance over that of our previously proposed utility, over and above that previous utility's superiority to the conventional team game utility.

Wolpert, David H.↗

NASA NTRS2006

Spatial and Temporal Distribution of Clouds as Observed by MODIS Onboard the Terra and Aqua Satellites

The Moderate Resolution Imaging Spectroradiometer (MODIS) was developed by NASA and launched onboard the Terra spacecraft on December 18,1999 and Aqua spacecraft on May 4, 2002. It achieved its final orbit and began Earth observations on February 24,2000 for Terra and June 24,2002 for Aqua. A comprehensive set of remote sensing algorithms for cloud masking and the retrieval of cloud physical and optical properties has been developed by members of the MODIS atmosphere science team. The archived products from these algorithms have applications in climate change studies, climate modeling, numerical weather prediction, and fundamental atmospheric research. In addition to an extensive cloud mask, products include cloud-top properties (temperature, pressure, effective emissivity), cloud thermodynamic phase, cloud optical and microphysical parameters (optical thickness, effective particle radius, water path), as well as derived statistics. Over the last year, extensive improvements and enhancements in the global cloud products have been implemented, and reprocessing of all MODIS data on Terra has commenced since first light in February 2000. In the cloud mask algorithm, the most extensive improvements were in distinguishing clouds at nighttime, including the challenging polar darkness regions of the world. Additional improvements have been made to properly distinguish sunglint from clouds in the tropical ocean regions, and to improve the identification of clouds from snow during daytime in Polar Regions. We will show global monthly mean cloud fraction for both Terra and Aqua, and show how similar the global daytime cloud fraction is from these morning and afternoon orbits, respectively. We will also show the zonal distribution of cloud fraction over land and ocean regions for both Terra and Aqua, and show the time series of global cloud fraction from July 2002 through June 2006.

King, Michael D.↗