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One of the critical issues of EOSDIS is data subsetting. Often users require data from only a selected region of interest. This paper outlines a few examples of subsetting that have been applied to existing satellite data for earth radiation budget studies.
We analyzed noble gases from 18 samples of weathering products ("iddingsite") from the Lafayette meteorite. Potassium-argon ages of 12 samples range from near zero to 670 +/- 91 Ma. These ages confirm the martian origin of the iddingsite, but it is not clear whether any or all of the ages represent iddingsite formation as opposed to later alteration or incorporation of martian atmospheric Ar-40. In any case, because iddingsite formation requires liquid water, this data requires the presence of liquid water near the surface of Mars at least as recently as 1300 Ma ago, and probably as recently as 650 Ma ago. Krypton and Xe analysis of a single 34 microg sample indicates the presence of fractionated martian atmosphere within the iddingsite. This also confirms the martian origin of the iddingsite. The mechanism of incorporation could either be through interaction with liquid water during iddingsite formation or a result of shock implantation of adsorbed atmospheric gas.
A Mathcad computer program largely automates the design and analysis of the restraint layer (the primary load-bearing layer) of an inflatable vessel that consists of one or more sections having cylindrical, toroidal, and/or spherical shape(s). A restraint layer typically comprises webbing in the form of multiple straps. The design task includes choosing indexing locations along the straps, computing the load at every location in each strap, computing the resulting stretch at each location, and computing the amount of undersizing required of each strap so that, once the vessel is inflated and the straps thus stretched, the vessel can be expected to assume the desired shape. Prior to the development of this program, the design task was performed by use of a difficult-to-use spreadsheet program that required manual addition of rows and columns depending on the numbers of strap rows and columns of a given design. In contrast, this program is completely parametric and includes logic that automatically adds or deletes rows and columns as needed. With minimal input from the user, this program automatically computes indexing locations, strap lengths, undersizing requirements, and all design data required to produce detailed drawings and assembly procedures. It also generates textual comments that help the user understand the calculations.
The Carbon Dioxide Observational Platform System (CO-OPS) is a near-space, geostationary, multi-user, unmanned microwave powered monitoring platform system. This systems engineering feasibility study addressed identified existing requirements such as: carbon dioxide observational data requirements, communications requirements, and eye-in-the-sky requirements of other groups like the Defense Department, the Forestry Service, and the Coast Guard. In addition, potential applications in: earth system science, space system sciences, and test and verification (satellite sensors and data management techniques) were considered. The eleven month effort is summarized. Past work and methods of gathering the required observational data were assessed and rough-order-of magnitude cost estimates have shown the CO-OPS system to be most cost effective (less than $30 million within a 10 year lifetime). It was also concluded that there are no technical, schedule, or obstacles that would prevent achieving the objectives of the total 5-year CO-OPS program.
The compilation of Scientific Data Requirements (SDRs) were based on discussions with a representative cross section of the scientific community and a selected survey of the extensive literature dealing with the measurement of CO2-induced climatic changes. This approach resulted in a baseline set of SDRs to determine what could be accomplished with space-based sensors. Twenty-three SDRs emerged as the basis for the investigation of space systems.
Distributed memory machines do not provide hardware support for a global address space. Thus programmers are forced to partition the data across the memories of the architecture and use explicit message passing to communicate data between processors. The compiler support required to allow programmers to express their algorithms using a global name-space is examined. A general method is presented for analysis of a high level source program and along with its translation to a set of independently executing tasks communicating via messages. If the compiler has enough information, this translation can be carried out at compile-time. Otherwise run-time code is generated to implement the required data movement. The analysis required in both situations is described and the performance of the generated code on the Intel iPSC/2 is presented.
Distributed memory machines do not provide hardware support for a global address space. Thus programmers are forced to partition the data across the memories of the architecture and use explicit message passing to communicate data between processors. The compiler support required to allow programmers to express their algorithms using a global name-space is examined. A general method is presented for analysis of a high level source program and its translation into a set of independently executing tasks communicating via messages. If the compiler has enough information, this translation can be carried out at compile time. Otherwise, run-time code is generated to implement the required data movement. The analysis required in both situations is described and the performance of the generated code on the Intel iPSC/2 is presented.
The data growth experienced in the recent past has been of staggering proportions. Over the past 10 years, tape data storage density (with the same form factor) has increased according to Moore's law, doubling every 18 months. However, during the same period, data transfer speeds have only increased at a rate of about 1.3 times every 18 months, and thus have fallen behind data density growth rates by a factor of at least 3. Coupled with data media density growth, data storage requirements have gone up significantly. According to a recent Computer Technology Review article (March 1998) the total storage at a typical Fortune 1000 site is projected to escalate from just 10 TB in 1997 to 1 PB by the year 2000. In the next 5 years, a typical large database system for U.S. government agencies is expected to accept 5 TB per day, maintain 300 TB on-line (within 15 seconds to 1 minute access time), and archive from 15 to 100 PB. Additionally, data intensive programs such as NASA's Earth Observation System (EOS) and the intelligence data archival systems at the Rome Air Development Center, and scientific laboratories such as Thomas Jefferson National Accelerator Facility will have enormously large scientific databases with very large storage requirements.
The state-of-art satellite observations of atmospheric aerosols over the last two decades from NASA's MODIS instruments have been extensively utilized in climate change and air quality research and applications. The operational algorithms now produce level 2 aerosol data at varying spatial resolutions (1, 3, and 10 km) and level 3 data at 1 degree. The local and global applications have been benefited from the coarse resolution gridded data sets (i.e., level 3, 1 degree), as it is easier to use since data volume is low and, several online and offline tools are readily available to access and analyze the data with minimal computing resources. At the same time, researchers who require data at much finer spatial scales have to go through a challenging process of obtaining, processing, and analyzing larger volumes of data sets that require high-end computing resources and coding skills. Therefore, we have created a high spatial resolution (HRG, 0.1x0.1 degree) daily and monthly aerosol optical depth (AOD) product by combining two MODIS operational algorithms, namely Deep Blue (DB) and Dark Target (DT). The new HRG AODs meets the accuracy requirements of level 2 AOD data and provide either the same or more spatial coverage on daily and monthly scales. The data sets are provided in daily and monthly files through open Ftp server with python scripts to read and map the data. The reduced data volume with an easy to use format and tools to access the data will encourage more users to utilize the data for research and applications.
Results of a survey of earth resources user applications and their data requirements, earth resources multispectral scanner sensor technology, and preprocessing algorithms for correcting the sensor outputs and for data bulk reduction are presented along with a candidate data format. Computational requirements required to implement the data analysis algorithms are included along with a review of computer architectures and organizations. Computer architectures capable of handling the algorithm computational requirements are suggested and the environmental effects of an on-board processor discussed. By relating performance parameters to the system requirements of each of the user requirements the feasibility of on-board processing is determined for each user. A tradeoff analysis is performed to determine the sensitivity of results to each of the system parameters. Significant results and conclusions are discussed, and recommendations are presented.
The Geostationary Earth Observatories (GEO's) are the space-based element of NASA's Mission to Planet Earth program which provide the excellent temporal resolution data required for a thorough understanding of earth processes and their role in global climate change. This paper discusses the scientific rationale, required instrumentation, observatory configuration, and data system of the GEO program.
The Astronomy and Physics Division at NASA Headquarters has an active and vibrant program in Laboratory Astrophysics. The objective of the program is to provide the spectroscopic data required by observers to analyze data from NASA space astronomy missions. The program also supports theoretical investigations to provide those spectroscopic parameters that cannot be obtained in the laboratory; simulate space environment to understand formation of certain molecules, dust grains and ices; and production of critically compiled databases of spectroscopic parameters. NASA annually solicits proposals, and utilizes the peer review process to select meritorious investigations for funding. As the mission of NASA evolves, new missions are launched, and old ones are terminated, the Laboratory Astrophysics program needs to evolve accordingly. Consequently, it is advantageous for NASA and the astronomical community to periodically conduct a dialog to assess the status of the program. This Workshop provides a forum for producers and users of laboratory data to get together and understand each others needs and limitations. A multi-wavelength approach enables a cross fertilization of ideas across wavelength bands.
Giovanni, the Goddard Earth Sciences Data and Information Services Center (GES DISC) Interactive Online Visualization and Analysis Infrastructure, has provided researchers with advanced capabilities to perform data exploration and analysis with observational data from NASA Earth observation satellites. In the past 5-10 years, examining geophysical events and processes with remote-sensing data required a multistep process of data discovery, data acquisition, data management, and ultimately data analysis. Giovanni accelerates this process by enabling basic visualization and analysis directly on the World Wide Web. In the last two years, Giovanni has added new data acquisition functions and expanded analysis options to increase its usefulness to the Earth science research community.
In order to explore Titan, a moon of Saturn, airships must be able to traverse the atmosphere autonomously. To achieve this, an accurate model and accurate control of the vehicle must be developed so that it is understood how the airship will react to specific sets of control inputs. This paper explains how longitudinal aircraft stability derivatives can be used with airship parameters to create a linear model of the airship solely by combining geometric and aerodynamic airship data. This method does not require system identification of the vehicle. All of the required data can be derived from computational fluid dynamics and wind tunnel testing. This alternate method of developing dynamic airship models will reduce time and cost. Results are compared to other stable airship dynamic models to validate the methods. Future work will address a lateral airship model using the same methods.
Studying the solar system and especially the Sun relies on the data gathered daily from space missions. These missions are data-intensive and compressing this data to make them efficiently transferable to the ground station is a twofold decision to make. Stronger compression methods, by distorting the data, can increase data throughput at the cost of accuracy which could affect scientific analysis of the data. On the other hand, preserving subtle details in the compressed data requires a high amount of data to be transferred, reducing the desired gains from compression. In this work, we propose a neural network-based lossy compression method to be used in NASA’s data-intensive imagery missions. We chose NASA’s Solar Dynamics Observatory (SDO) mission which transmits 1.4 terabytes of data each day as a proof of concept for the proposed algorithm. In this work, we propose an adversarially trained neural network, equipped with local and non-local attention modules to capture both the local and global structure of the image resulting in a better trade-off in rate-distortion (RD) compared to conventional hand-engineered codecs. The RD variational autoencoder used in this work is jointly trained with a channel-dependent entropy model as a shared prior between the analysis and synthesis transforms to make the entropy coding of the latent code more effective. We also studied how optimizing perceptual losses could help our neural compressor to preserve high-frequency details of the data in the reconstructed compressed image. Our neural image compression algorithm outperforms currently-in-use and state-of-the-art codecs such as JPEG and JPEG-2000 in terms of the RD performance when compressing extreme-ultraviolet (EUV) data. As a proof of concept for use of this algorithm in SDO data analysis, we have performed coronal hole (CH) detection using our compressed images, and generated consistent segmentations, even at a compression rate of ∼ 0.1 bits per pixel (compared to 8 bits per pixel on the original data) using EUV data from SDO.
The polar format approach to SAR image formation requires data to be interpolated from a warped grid onto a Cartesian lattice. In general, this requires that data be interpolated between varying sampling rates. In this paper, frequency-domain optimality criteria for polar format interpolators are defined and justified, and an approach to designing the corresponding digital filters is described.
The requirements for a data management system in support of technical or scientific applications and possible courses of action were reviewed. Specific requirements were evolved while working towards higher level integration impacting all phases of the current design process and through examination of commercially marketed systems and related data base research. Arguments are proposed for varied approaches in implementing data base systems ranging from no action necessary to immediate procurement of an existing data base management system.