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NASA NTRS2000

A Study of Cloud Radiative Forcing and Feedback

The main objective of the grant proposal was to participate in the CERES (Cloud and Earth's Radiant Energy System) Satellite experiment and perform interdisciplinary investigation of NASA's Earth Observing System (EOS). During the grant period, massive amounts of scientific data from diverse platforms have been accessed, processed and archived for continuing use; several software packages have been developed for integration of different data streams for performing scientific evaluation; extensive validation studies planned have been completed culminating in the development of important algorithms that are being used presently in the operational production of data from the CERES. Contributions to the inter-disciplinary science investigations have been significantly more than originally envisioned. The results of these studies have appeared in several refereed journals and conference proceedings. They are listed at the end of this report.

Ramanathan, Veerabhadran↗

NASA NTRS2015

Python-Based Scientific Analysis and Visualization of Precipitation Systems at NASA Marshall Space Flight Center

At NASA Marshall Space Flight Center (MSFC), Python is used several different ways to analyze and visualize precipitating weather systems. A number of different Python‐based software packages have been developed, which are available to the larger scientific community. The approach in all these packages is to utilize pre‐existing Python modules as well as to be object‐oriented and scalable. The first package that will be described and demonstrated is the Python Advanced Microwave Precipitation Radiometer (AMPR) Data Toolkit, or PyAMPR for short. PyAMPR reads geolocated brightness temperature data from any flight of the AMPR airborne instrument over its 25‐year history into a common data structure suitable for user‐defined analyses. It features rapid, simplified (i.e., one line of code) production of quick‐look imagery, including Google Earth overlays, swath plots of individual channels, and strip charts showing multiple channels at once. These plotting routines are also capable of significant customization for detailed, publication‐ready figures. Deconvolution of the polarization‐varying channels to static horizontally and vertically polarized scenes is also available. Examples will be given of PyAMPR's contribution toward real‐time AMPR data display during the Integrated Precipitation and Hydrology Experiment (IPHEx), which took place in the Carolinas during May‐June 2014. The second software package is the Marshall Multi‐Radar/Multi‐Sensor (MRMS) Mosaic Python Toolkit, or MMM‐Py for short. MMM‐Py was designed to read, analyze, and display three‐dimensional national mosaicked reflectivity data produced by the NOAA National Severe Storms Laboratory (NSSL). MMM‐Py can read MRMS mosaics from either their unique binary format or their converted NetCDF format. It can also read and properly interpret the current mosaic design (4 regional tiles) as well as mosaics produced prior to late July 2013 (8 tiles). MMM‐Py can easily stitch multiple tiles together to provide a larger regional or national picture of precipitating weather systems. Composites, horizontal and vertical crosssections, and combinations thereof are easily displayed using as little as one line of code. MMM‐Py can also write to the native MRMS binary format, and sub‐sectioning of tiles (or multiple stitched tiles) is anticipated to be in place by the time of this meeting. Thus, MMM‐Py also can be used to power the creation of custom mosaics for targeted regional studies. Overlays of other data (e.g., lightning observations) are easily accomplished. Demonstrations of MMM‐Py, including the creation of animations, will be shown. Finally, Marshall has done significant work to interface Python‐based analysis routines with the U.S. Department of Energy's Py‐ART software package for radar data ingest, processing, and analysis. One example of this is the Python Turbulence Detection Algorithm (PyTDA), an MSFC‐based implementation of the National Center for Atmospheric Research (NCAR) Turbulence Detection Algorithm (NTDA) for the purposes of convective‐scale analysis, situational awareness, and forensic meteorology. PyTDA exploits Py‐ART's radar data ingest routines and data model to rapidly produce aviation‐relevant turbulence estimates from Doppler radar data. Work toward processing speed optimization and better integration within the Py‐ART framework will be highlighted. Python‐based analysis within the Py‐ART framework is also being done for new research related to intercomparison of ground‐based radar data with satellite estimates of ocean winds, as well as research on the electrification of pyrocumulus clouds.

Lang, Timothy J.↗

NASA NTRS2001

Code Parallelization with CAPO: A User Manual

A software tool has been developed to assist the parallelization of scientific codes. This tool, CAPO, extends an existing parallelization toolkit, CAPTools developed at the University of Greenwich, to generate OpenMP parallel codes for shared memory architectures. This is an interactive toolkit to transform a serial Fortran application code to an equivalent parallel version of the software - in a small fraction of the time normally required for a manual parallelization. We first discuss the way in which loop types are categorized and how efficient OpenMP directives can be defined and inserted into the existing code using the in-depth interprocedural analysis. The use of the toolkit on a number of application codes ranging from benchmark to real-world application codes is presented. This will demonstrate the great potential of using the toolkit to quickly parallelize serial programs as well as the good performance achievable on a large number of toolkit to quickly parallelize serial programs as well as the good performance achievable on a large number of processors. The second part of the document gives references to the parameters and the graphic user interface implemented in the toolkit. Finally a set of tutorials is included for hands-on experiences with this toolkit.

Jin, Hao-Qiang↗

NASA NTRS1992

Issues in knowledge representation to support maintainability: A case study in scientific data preparation

Scientific data preparation is the process of extracting usable scientific data from raw instrument data. This task involves noise detection (and subsequent noise classification and flagging or removal), extracting data from compressed forms, and construction of derivative or aggregate data (e.g. spectral densities or running averages). A software system called PIPE provides intelligent assistance to users developing scientific data preparation plans using a programming language called Master Plumber. PIPE provides this assistance capability by using a process description to create a dependency model of the scientific data preparation plan. This dependency model can then be used to verify syntactic and semantic constraints on processing steps to perform limited plan validation. PIPE also provides capabilities for using this model to assist in debugging faulty data preparation plans. In this case, the process model is used to focus the developer's attention upon those processing steps and data elements that were used in computing the faulty output values. Finally, the dependency model of a plan can be used to perform plan optimization and runtime estimation. These capabilities allow scientists to spend less time developing data preparation procedures and more time on scientific analysis tasks. Because the scientific data processing modules (called fittings) evolve to match scientists' needs, issues regarding maintainability are of prime importance in PIPE. This paper describes the PIPE system and describes how issues in maintainability affected the knowledge representation used in PIPE to capture knowledge about the behavior of fittings.

Chien, Steve↗

NASA NTRS1993

Intelligent assistance in scientific data preparation

Scientific data preparation is the process of extracting usable scientific data from raw instrument data. This task involves noise detection (and subsequent noise classification and flagging or removal), extracting data from compressed forms, and construction of derivative or aggregate data (e.g. spectral densities or running averages). A software system called PIPE provides intelligent assistance to users developing scientific data preparation plans using a programming language called Master Plumber. PIPE provides this assistance capability by using a process description to create a dependency model of the scientific data preparation plan. This dependency model can then be used to verify syntactic and semantic constraints on processing steps to perform limited plan validation. PIPE also provides capabilities for using this model to assist in debugging faulty data preparation plans. In this case, the process model is used to focus the developer's attention upon those processing steps and data elements that were used in computing the faulty output values. Finally, the dependency model of a plan can be used to perform plan optimization and run time estimation. These capabilities allow scientists to spend less time developing data preparation procedures and more time on scientific analysis tasks.

Chien, Steve↗

NASA NTRS1992

High performance compression of science data

In the future, NASA expects to gather over a tera-byte per day of data requiring space for levels of archival storage. Data compression will be a key component in systems that store this data (e.g., optical disk and tape) as well as in communications systems (both between space and Earth and between scientific locations on Earth). We propose to develop algorithms that can be a basis for software and hardware systems that compress a wide variety of scientific data with different criteria for fidelity/bandwidth tradeoffs. The algorithmic approaches we consider are specially targeted for parallel computation where data rates of over 1 billion bits per second are achievable with current technology.

Storer, James A.↗

NASA NTRS1993

High performance compression of science data

Storer, James A.↗

NASA NTRS2000

Scientific Data Analysis and Software Support: Geodynamics

The support on this contract centers on development of data analysis strategies, geodynamic models, and software codes to study four-dimensional geodynamic and oceanographic processes, as well as studies and mission support for near-Earth and interplanetary satellite missions. SRE had a subcontract to maintain the optical laboratory for the LTP, where instruments such as MOLA and GLAS are developed. NVI performed work on a Raytheon laser altimetry task through a subcontract, providing data analysis and final data production for distribution to users. HBG had a subcontract for specialized digital topography analysis and map generation. Over the course of this contract, Raytheon ITSS staff have supported over 60 individual tasks. Some tasks have remained in place during this entire interval whereas others have been completed and were of shorter duration. Over the course of events, task numbers were changed to reflect changes in the character of the work or new funding sources. The description presented below will detail the technical accomplishments that have been achieved according to their science and technology areas. What will be shown is a brief overview of the progress that has been made in each of these investigative and software development areas. Raytheon ITSS staff members have received many awards for their work on this contract, including GSFC Group Achievement Awards for TOPEX Precision Orbit Determination and the Joint Gravity Model One Team. NASA JPL gave the TOPEX/POSEIDON team a medal commemorating the completion of the primary mission and a Certificate of Appreciation. Raytheon ITSS has also received a Certificate of Appreciation from GSFC for its extensive support of the Shuttle Laser Altimeter Experiment.

Klosko, Steven↗

NASA NTRS2001

Component Verification and Certification in NASA Missions

Software development for NASA missions is a particularly challenging task. Missions are extremely ambitious scientifically, have very strict time frames, and must be accomplished with a maximum degree of reliability. Verification technologies must therefore be pushed far beyond their current capabilities. Moreover, reuse and adaptation of software architectures and components must be incorporated in software development within and across missions. This paper discusses NASA applications that we are currently investigating from these perspectives.

Giannakopoulou, Dimitra↗

NASA NTRS2012

Frameworks Coordinate Scientific Data Management

Jet Propulsion Laboratory computer scientists developed a unique software framework to help NASA manage its massive amounts of science data. Through a partnership with the Apache Software Foundation of Forest Hill, Maryland, the technology is now available as an open-source solution and is in use by cancer researchers and pediatric hospitals.

Source record↗

NASA NTRSDate not supplied

Artemis Internal Science Team Update: Data and Software Development

Artemis will reestablish human presence on the Moon and lead to a new era of scientific discovery and exploration. Led by the National Aeronautics and Space Administration (NASA), Artemis includes a collaboration of space agencies and companies from around the world. In support of Artemis, a cross-disciplinary effort of science, engineering, mission operations, and human factors personnel is developing the best methods, facilities, and field locations to test hardware, train astronauts, and evaluate modern concepts of operations. This abstract, as part of the Artemis Internal Science Team (AIST), provides an update to the science-relevant data and software developments and integrated testing efforts that have occurred in 2022.

M. J. Miller↗

NASA NTRS2001

A Component-based Programming Model for Composite, Distributed Applications

The nature of scientific programming is evolving to larger, composite applications that are composed of smaller element applications. These composite applications are more frequently being targeted for distributed, heterogeneous networks of computers. They are most likely programmed by a group of developers. Software component technology and computational frameworks are being proposed and developed to meet the programming requirements of these new applications. Historically, programming systems have had a hard time being accepted by the scientific programming community. In this paper, a programming model is outlined that attempts to organize the software component concepts and fundamental programming entities into programming abstractions that will be better understood by the application developers. The programming model is designed to support computational frameworks that manage many of the tedious programming details, but also that allow sufficient programmer control to design an accurate, high-performance application.

Eidson, Thomas M.↗

NASA NTRS1992

Developing satellite ground control software through graphical models

This paper discusses a program of investigation into software development as graphical modeling. The goal of this work is a more efficient development and maintenance process for the ground-based software that controls unmanned scientific satellites launched by NASA. The main hypothesis of the program is that modeling of the spacecraft and its subsystems, and reasoning about such models, can--and should--form the key activities of software development; by using such models as inputs, the generation of code to perform various functions (such as simulation and diagnostics of spacecraft components) can be automated. Moreover, we contend that automation can provide significant support for reasoning about the software system at the diagram level.

Bailin, Sidney↗

NASA NTRS2013

Software Engineering Tools for Scientific Models

Software tools were constructed to address issues the NASA Fortran development community faces, and they were tested on real models currently in use at NASA. These proof-of-concept tools address the High-End Computing Program and the Modeling, Analysis, and Prediction Program. Two examples are the NASA Goddard Earth Observing System Model, Version 5 (GEOS-5) atmospheric model in Cell Fortran on the Cell Broadband Engine, and the Goddard Institute for Space Studies (GISS) coupled atmosphere- ocean model called ModelE, written in fixed format Fortran.

Abrams, Marc↗

NASA NTRS2003

Lessons Learned From Developing A Streaming Data Framework for Scientific Analysis

We describe the development and usage of a streaming data analysis software framework. The framework is used for three different applications: Earth science hyper-spectral imaging analysis, Electromyograph pattern detection, and Electroencephalogram state determination. In each application the framework was used to answer a series of science questions which evolved with each subsequent answer. This evolution is summarized in the form of lessons learned.

Wheeler. Kevin R.↗

NASA NTRS2016

Automation of Flight Software Regression Testing

NASA is developing the Space Launch System (SLS) to be a heavy lift launch vehicle supporting human and scientific exploration beyond earth orbit. SLS will have a common core stage, an upper stage, and different permutations of boosters and fairings to perform various crewed or cargo missions. Marshall Space Flight Center (MSFC) is writing the Flight Software (FSW) that will operate the SLS launch vehicle. The FSW is developed in an incremental manner based on "Agile" software techniques. As the FSW is incrementally developed, testing the functionality of the code needs to be performed continually to ensure that the integrity of the software is maintained. Manually testing the functionality on an ever-growing set of requirements and features is not an efficient solution and therefore needs to be done automatically to ensure testing is comprehensive. To support test automation, a framework for a regression test harness has been developed and used on SLS FSW. The test harness provides a modular design approach that can compile or read in the required information specified by the developer of the test. The modularity provides independence between groups of tests and the ability to add and remove tests without disturbing others. This provides the SLS FSW team a time saving feature that is essential to meeting SLS Program technical and programmatic requirements. During development of SLS FSW, this technique has proved to be a useful tool to ensure all requirements have been tested, and that desired functionality is maintained, as changes occur. It also provides a mechanism for developers to check functionality of the code that they have developed. With this system, automation of regression testing is accomplished through a scheduling tool and/or commit hooks. Key advantages of this test harness capability includes execution support for multiple independent test cases, the ability for developers to specify precisely what they are testing and how, the ability to add automation, and the ability of the harness and cases to be executed continually. This test concept is an approach that can be adapted to support other projects.

Tashakkor, Scott B.↗

NASA NTRS1999

The Kalman Filter and High Performance Computing at NASA's Data Assimilation Office (DAO)

Atmospheric data assimilation is a method of combining actual observations with model simulations to produce a more accurate description of the earth system than the observations alone provide. The output of data assimilation, sometimes called "the analysis", are accurate regular, gridded datasets of observed and unobserved variables. This is used not only for weather forecasting but is becoming increasingly important for climate research. For example, these datasets may be used to assess retrospectively energy budgets or the effects of trace gases such as ozone. This allows researchers to understand processes driving weather and climate, which have important scientific and policy implications. The primary goal of the NASA's Data Assimilation Office (DAO) is to provide datasets for climate research and to support NASA satellite and aircraft missions. This presentation will: (1) describe ongoing work on the advanced Kalman/Lagrangian filter parallel algorithm for the assimilation of trace gases in the stratosphere; and (2) discuss the Kalman filter in relation to other presentations from the DAO on Four Dimensional Data Assimilation at this meeting. Although the designation "Kalman filter" is often used to describe the overarching work, the series of talks will show that the scientific software and the kind of parallelization techniques that are being developed at the DAO are very different depending on the type of problem being considered, the extent to which the problem is mission critical, and the degree of Software Engineering that has to be applied.

Lyster, Peter M.↗

NASA NTRS2002

Long Term Manipulations of Intact Microbial Mat Communities in a Greenhouse Collaboratory: Simulating Earth's Present and Past Field Environments

Photosynthetic microbial mat communities were obtained from marine hypersaline saltern ponds, maintained in a greenhouse facility, and examined for the effects of salinity variations. Because these microbial mats are considered to be useful analogs of equivalent ancient marine communities, they offer insights about evolutionary events during the greater than 3 billion year time interval wherein mats co-evolved with Earth's geosphere and atmosphere. Although photosynthetic mats can be highly dynamic and exhibit extremely high activity, the mats in the present study have been maintained for more than one year with relatively minor changes. The major groups of microorganisms, as assayed using microscopic, genetic, and biomarker methodologies, are essentially the same as those in the original field samples. Field and greenhouse mats were similar with respect to rates of exchange of oxygen and dissolved inorganic carbon across the mat-water interface, both during the day and at night. Field and greenhouse mats exhibited similar rates of efflux of methane and hydrogen. Manipulations of salinity in the water overlying the mats produced changes in the community that strongly resemble those observed in the field. A collaboratory testbed and an array of automated features are being developed to support remote scientific experimentation with the assistance of intelligent software agents. This facility will permit teams of investigators to explore ancient environmental conditions that are rare or absent today but might have influenced the early evolution of these photosynthetic ecosystems.

Bebout, Brad↗