AOPB systems manual - Program description. Orbital tracking data analysis program
Orbital tracking program for analysis of data from C-band and S-band radar
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Mark 3 interactive data analysis system
The interactive data analysis system, a major subset of the total Mark 3 very long baseline interferometry (VLBI) software system is described. The system consists of two major and a number of small programs. These programs provide for the scientific analysis of the observed values of delay and delay rate generated by the VLBI data reduction programs and product the geophysical and astrometric parameters which are among the ultimate products of VLBI. The two major programs are CALC and SOLVE. CALC generates the theoretical values of VLBI delay rate as well as partial derivatives based on apriori values of the geophysical and astronometric parameters. SOLVE is a least squares parameters estimation program which yields the geophysical and astrometric parameters using the observed values by the data processing system and theoretical values and partial derivatives provided by CALC. SOLVE is a highly interactive program in which the user selects the exact form of the recovered parameters and the data to be accepted into the solution.
Ryan, J. W.↗
Real Data and Rapid Results: Ocean Color Data Analysis with Giovanni (GES DISC Interactive Online Visualization and ANalysis Infrastructure)
The NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) has taken a major step addressing the challenge of using archived Earth Observing System (EOS) data for regional or global studies by developing an infrastructure with a World Wide Web interface which allows online, interactive, data analysis: the GES DISC Interactive Online Visualization and ANalysis Infrastructure, or "Giovanni." Giovanni provides a data analysis environment that is largely independent of underlying data file format. The Ocean Color Time-Series Project has created an initial implementation of Giovanni using monthly Standard Mapped Image (SMI) data products from the Sea-viewing Wide Field-of-view Sensor (SeaWiFS) mission. Giovanni users select geophysical parameters, and the geographical region and time period of interest. The system rapidly generates a graphical or ASCII numerical data output. Currently available output options are: Area plot (averaged or accumulated over any available data period for any rectangular area); Time plot (time series averaged over any rectangular area); Hovmeller plots (image view of any longitude-time and latitude-time cross sections); ASCII output for all plot types; and area plot animations. Future plans include correlation plots, output formats compatible with Geographical Information Systems (GIs), and higher temporal resolution data. The Ocean Color Time-Series Project will produce sensor-independent ocean color data beginning with the Coastal Zone Color Scanner (CZCS) mission and extending through SeaWiFS and Moderate Resolution Imaging Spectroradiometer (MODIS) data sets, and will enable incorporation of Visible/lnfrared Imaging Radiometer Suite (VIIRS) data, which will be added to Giovanni. The first phase of Giovanni will also include tutorials demonstrating the use of Giovanni and collaborative assistance in the development of research projects using the SeaWiFS and Ocean Color Time-Series Project data in the online Laboratory for Ocean Color Users (LOCUS). The synergy of Giovanni with high-quality ocean color data provides users with the ability to investigate a variety of important oceanic phenomena, such as coastal primary productivity related to pelagic fisheries, seasonal patterns and interannual variability, interdependence of atmospheric dust aerosols and harmful algal blooms, and the potential effects of climate change on oceanic productivity.
Acker, J. G.↗
Earth resources data analysis system
The Earth Resources Laboratory Data Analysis System provides a valuable research tool in the evaluation of multispectral, radiometric, photographic, and passive microwave imagery data. The system has been in operational use for several months and has performed quite well. Emphasis on versatility of both the hardware and software design facilitates system expansion to many specialized data analysis tasks. The many independent software handlers provide building blocks for major processing programs.
Source record↗
Geotail MCA plasma wave data analysis
NASA Grant NAG 5-2346 supports the data analysis effort at The University of Iowa for the GEOTAIL Multi-Channel Analyzer (MCA) which is a part of the GEOTAIL Plasma Wave Instrument (PWI). At the beginning of this reporting period we had just begun to receive our GEOTAIL Sirius data on CD-ROMs. Much programming effort went into adapting and refining the data analysis programs to include the CD-ROM inputs. Programs were also developed to display the high-frequency-resolution PWI Sweep Frequency Analyzer (SFA) data and to include in all the various plot products the electron cyclotron frequency derived from the magnitude of the magnetic field extracted from the GEOTAIL Magnetic Field (MGF) data included in the GEOTAIL Sirius data. We also developed programs to use the MGF data residing in the Institute of Space and Astronautical Science (ISAS) GEOTAIL Scientific Data Base (SDB). Our programmers also developed programs and provided technical support for the GEOTAIL data analysis efforts of Co-lnvestigator William W. L. Taylor at Nichols Research Corporation (NRC). At the end of this report we have included brief summaries of the NRC effort and the progress being made.
Anderson, Roger R.↗
High-Performance Data Analysis Tools for Sun-Earth Connection Missions
The data analysis tool of choice for many Sun-Earth Connection missions is the Interactive Data Language (IDL) by ITT VIS. The increasing amount of data produced by these missions and the increasing complexity of image processing algorithms requires access to higher computing power. Parallel computing is a cost-effective way to increase the speed of computation, but algorithms oftentimes have to be modified to take advantage of parallel systems. Enhancing IDL to work on clusters gives scientists access to increased performance in a familiar programming environment. The goal of this project was to enable IDL applications to benefit from both computing clusters as well as graphics processing units (GPUs) for accelerating data analysis tasks. The tool suite developed in this project enables scientists now to solve demanding data analysis problems in IDL that previously required specialized software, and it allows them to be solved orders of magnitude faster than on conventional PCs. The tool suite consists of three components: (1) TaskDL, a software tool that simplifies the creation and management of task farms, collections of tasks that can be processed independently and require only small amounts of data communication; (2) mpiDL, a tool that allows IDL developers to use the Message Passing Interface (MPI) inside IDL for problems that require large amounts of data to be exchanged among multiple processors; and (3) GPULib, a tool that simplifies the use of GPUs as mathematical coprocessors from within IDL. mpiDL is unique in its support for the full MPI standard and its support of a broad range of MPI implementations. GPULib is unique in enabling users to take advantage of an inexpensive piece of hardware, possibly already installed in their computer, and achieve orders of magnitude faster execution time for numerically complex algorithms. TaskDL enables the simple setup and management of task farms on compute clusters. The products developed in this project have the potential to interact, so one can build a cluster of PCs, each equipped with a GPU, and use mpiDL to communicate between the nodes and GPULib to accelerate the computations on each node.
Messmer, Peter↗
Multi-seasonal data analysis and some extensions for environmental monitoring
Multispectral data analysis was incorporated with multiseasonal data analysis based on a spectral radiance data-set. Concepts include comparing data types, exploring relationships between periodicity of LANDSAT and the seasonal sense of the Orientals, and derivation of a method to register the acquired data. Examples include a quality investigation of a paddy field by seasonal LANDSAT data, a progress check of the field in harvest season, and a detailed survey of the vegetational environment by summer and winter LANDSAT data.
Tanaka, S.↗
Proceedings of the Airborne Imaging Spectrometer Data Analysis Workshop
The Airborne Imaging Spectrometer (AIS) Data Analysis Workshop was held at the Jet Propulsion Laboratory on April 8 to 10, 1985. It was attended by 92 people who heard reports on 30 investigations currently under way using AIS data that have been collected over the past two years. Written summaries of 27 of the presentations are in these Proceedings. Many of the results presented at the Workshop are preliminary because most investigators have been working with this fundamentally new type of data for only a relatively short time. Nevertheless, several conclusions can be drawn from the Workshop presentations concerning the value of imaging spectrometry to Earth remote sensing. First, work with AIS has shown that direct identification of minerals through high spectral resolution imaging is a reality for a wide range of materials and geological settings. Second, there are strong indications that high spectral resolution remote sensing will enhance the ability to map vegetation species. There are also good indications that imaging spectrometry will be useful for biochemical studies of vegetation. Finally, there are a number of new data analysis techniques under development which should lead to more efficient and complete information extraction from imaging spectrometer data. The results of the Workshop indicate that as experience is gained with this new class of data, and as new analysis methodologies are developed and applied, the value of imaging spectrometry should increase.
Vane, G.↗
Post-mission data analysis of Surveyor mission chemical data
Prime data from chemical analysis experiments, of Surveyor 5, 6, and 7 are critically examined and analyzed. This and associated laboratory work has given final chemical composition results for the lunar regolith at three locations. The conclusions made on the basis of the preliminary examinations of the data are confirmed and extended.
Turkevich, A.↗
Earth Science Data Analysis in the Era of Big Data
Anyone with even a cursory interest in information technology cannot help but recognize that "Big Data" is one of the most fashionable catchphrases of late. From accurate voice and facial recognition, language translation, and airfare prediction and comparison, to monitoring the real-time spread of flu, Big Data techniques have been applied to many seemingly intractable problems with spectacular successes. They appear to be a rewarding way to approach many currently unsolved problems. Few fields of research can claim a longer history with problems involving voluminous data than Earth science. The problems we are facing today with our Earth's future are more complex and carry potentially graver consequences than the examples given above. How has our climate changed? Beside natural variations, what is causing these changes? What are the processes involved and through what mechanisms are these connected? How will they impact life as we know it? In attempts to answer these questions, we have resorted to observations and numerical simulations with ever-finer resolutions, which continue to feed the "data deluge." Plausibly, many Earth scientists are wondering: How will Big Data technologies benefit Earth science research? As an example from the global water cycle, one subdomain among many in Earth science, how would these technologies accelerate the analysis of decades of global precipitation to ascertain the changes in its characteristics, to validate these changes in predictive climate models, and to infer the implications of these changes to ecosystems, economies, and public health? Earth science researchers need a viable way to harness the power of Big Data technologies to analyze large volumes and varieties of data with velocity and veracity. Beyond providing speedy data analysis capabilities, Big Data technologies can also play a crucial, albeit indirect, role in boosting scientific productivity by facilitating effective collaboration within an analysis environment. To illustrate the effects of combining a Big Data technology with an effective means of collaboration, we relate the (fictitious) experience of an early-career Earth science researcher a few years beyond the present, interlaced and contrasted with reminiscences of its recent past (i.e., the present).
Kuo, K.-S.↗
Low-cost data analysis systems for processing multispectral scanner data
The basic hardware and software requirements are described for four low cost analysis systems for computer generated land use maps. The data analysis systems consist of an image display system, a small digital computer, and an output recording device. Software is described together with some of the display and recording devices, and typical costs are cited. Computer requirements are given, and two approaches are described for converting black-white film and electrostatic printer output to inexpensive color output products. Examples of output products are shown.
Whitely, S. L.↗
Integrating HPC, AI, and Workflows for Scientific Data Analysis: Report from Dagstuhl Seminar 23352
The Dagstuhl Seminar 23352, titled “Integrating HPC, AI, and Workflows for Scientific Data Analysis,” held from August 27 to September 1, 2023, was a significant event focusing on the synergy between High-Performance Computing (HPC), Artificial Intelligence (AI), and scientific workflow technologies. The seminar recognized that modern Big Data analysis in science rests on three pillars: workflow technologies for reproducibility and steering, AI and Machine Learning (ML) for versatile analysis, and HPC for handling large data sets. These elements, while crucial, have traditionally been researched separately, leading to gaps in their integration. The seminar aimed to bridge these gaps, acknowledging the challenges and opportunities at the intersection of these technologies. The event highlighted the complex interplay between HPC, workflows, and ML, noting how ML has increasingly been integrated into scientific workflows, thereby enhancing resource demands and bringing new requirements to HPC architectures, like support for GPUs and iterative computations. The seminar also addressed the challenges in adapting HPC for large-scale ML tasks, including in areas like deep learning, and the need for workflow systems to evolve to leverage ML in data analysis fully. Moreover, the seminar explored how ML could optimize scientific workflow systems and HPC operations, such as through improved scheduling and fault tolerance. A key focus was on identifying prestigious use cases of ML in HPC and understanding their unique, unmet requirements. The stochastic nature of ML and its impact on the reproducibility of data analysis on HPC systems was also a topic of discussion.
97 MATHEMATICS AND COMPUTING↗
Jupiter Systems Data Analysis Program Galileo Multi-Spectral Analysis of the Galilean Satellites
Progress was made on this project at the University of Colorado, particularly concerning analysis of data of the galilean moons Io and Europa. The goal of the Io portion of this study is to incorporate Near Infrared Mapping Spectrometer (NIMS) measured sulfur dioxide (SO2) frost amounts into models used with Ultraviolet spectrometer (UVS) spectra, in order to better constrain SO2 gas amounts determined by the UVS. The overall goal of this portion of the study is to better understand the thickness and distribution of Io's SO2 atmosphere. The goal of the analysis of the Europa data is to better understand the source of the UV absorption feature centered near 280 rim which has been noted in disk-integrated spectra primarily on the trailing hemisphere. The NIMS data indicate asymmetric water ice bands on Europa, particularly over the trailing hemisphere, and especially concentrated in the visibly dark regions associated with chaotic terrain and lines. The UPS data, the first-ever disk-resolved UV spectra of Europa, shown that the UV absorber is likely concentrated in regions where the NIMS data show asymmetric water ice bands. The material that produces both spectral features is likely the same, and we use data from both wavelength regions to better understand this material, and whether it is endogenically or exogenically produced. This work is still in progress at JPL.
Hendrix, Amanda↗
Ocean Optics Protocols for Satellite Ocean Color Sensor Validation, Revision 4, Volume IV: Inherent Optical Properties: Instruments, Characterizations, Field Measurements and Data Analysis Protocols
This document stipulates protocols for measuring bio-optical and radiometric data for the Sensor Intercomparision and Merger for Biological and Interdisciplinary Oceanic Studies (SIMBIOS) Project activities and algorithm development. The document is organized into 6 separate volumes as Ocean Optics Protocols for Satellite Ocean Color Sensor Validation, Revision 4. Volume I: Introduction, Background, and Conventions; Volume II: Instrument Specifications, Characterization and Calibration; Volume III: Radiometric Measurements and Data Analysis Methods; Volume IV: Inherent Optical Properties: Instruments, Characterization, Field Measurements and Data Analysis Protocols; Volume V: Biogeochemical and Bio-Optical Measurements and Data Analysis Methods; Volume VI: Special Topics in Ocean Optics Protocols and Appendices. The earlier version of Ocean Optics Protocols for Satellite Ocean Color Sensor Validation, Revision 3 is entirely superseded by the six volumes of Revision 4 listed above.
Mueller, J. L.↗
A brief description of an Earth Resources Technology Satellite (ERTS) computer data analysis and management program
A data analysis and management procedure currently being used at Marshall Space Flight Center to analyze ERTS digital data is described. The objective is to acquaint potential users with the various computer programs that are available for analysis of multispectral digital imagery and to show how these programs are used in the overall data management plan. The report contains a brief description of each computer routine, and references are provided for obtaining more detailed information.
Jayroe, R. R., Jr.↗
Structural Dynamics and Data Analysis
This project consists of two parts, the first will be the post-flight analysis of data from a Delta IV launch vehicle, and the second will be a Finite Element Analysis of a CubeSat. Shock and vibration data was collected on WGS-5 (Wideband Global SATCOM- 5) which was launched on a Delta IV launch vehicle. Using CAM (CAlculation with Matrices) software, the data is to be plotted into Time History, Shock Response Spectrum, and SPL (Sound Pressure Level) curves. In this format the data is to be reviewed and compared to flight instrumentation data from previous flights of the same launch vehicle. This is done to ensure the current mission environments, such as shock, random vibration, and acoustics, are not out of family with existing flight experience. In family means the peaks on the SRS curve for WGS-5 are similar to the peaks from the previous flights and there are no major outliers. The curves from the data will then be compiled into a useful format so that is can be peer reviewed then presented before an engineering review board if required. Also, the reviewed data will be uploaded to the Engineering Review Board Information System (ERBIS) to archive. The second part of this project is conducting Finite Element Analysis of a CubeSat. In 2010, Merritt Island High School partnered with NASA to design, build and launch a CubeSat. The team is now called StangSat in honor of their mascot, the mustang. Over the past few years, the StangSat team has built a satellite and has now been manifested for flight on a SpaceX Falcon 9 launch in 2014. To prepare for the final launch, a test flight was conducted in Mojave, California. StangSat was launched on a Prospector 18D, a high altitude rocket made by Garvey Spacecraft Corporation, along with their sister satellite CP9 built by California Polytechnic University. However, StangSat was damaged during an off nominal landing and this project will give beneficial insights into what loads the CubeSat experienced during the crash. During the year, the MIHS students generated a SolidWorks (CAD software) geometry model of StangSat. This model will be imported into FEMAP (Finite Element Analysis (FEA) Software) and a finite element model wiiJ be created to predict the loads encountered during the crash of this rocket. This analysis will require learning how to import CAD models into the FEM, mesh and add constraints and concentrated masses to represent components inside the CubeSat frame, such as circuit boards, batteries and accelerometers. During the analysis the loads will be varied, in effort to duplicate the damage to the CubeSat. Results will then be peer reviewed and documented.
Luthman, Briana L.↗
Viking radio science data analysis and synthesis
Viking radio data analysis and synthesis was used for the following: (1) Solar System Model and Data Set; (2) Rotation of Mars; and (3) Solar System Constants and Tests of Relativity.
Shapiro, I. I.↗