Search NASA⌕ Search

SEARCH · Search NASA

Results for “data formats”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 595 records · Page 33
All sourcesNASA NTRSDOE OSTI
NASA NTRS1994

MODIS Technical Report Series. Volume 4: MODIS data access user's guide: Scan cube format

The software described in this document provides I/O functions to be used with Moderate Resolution Spectroradiometer (MODIS) level 1 and 2 data, and could be easily extended to other data sources. This data is in a scan cube data format: a 3-dimensional ragged array containing multiple bands which have resolutions ranging from 250 to 1000 meters. The complexity of the data structure is handled internally by the library. The I/O calls allow the user to access any pixel in any band through 'C' structure syntax. The high MODIS data volume (approaching half a terabyte per day) has been a driving factor in the library design. To avoid recopying data for user access, all I/O is performed through dynamic 'C' pointer manipulation. This manual contains background material on MODIS, several coding examples of library usage, in-depth discussions of each function, reference 'man' type pages, and several appendices with details of the included files used to customize a user's data product for use with the library.

Kalb, Virginia L.↗
NASA NTRS2004

Modified polar format algorithm for processing spaceborne SAR data

This paper describes a modified polar format algorithm for SAR image-formation processing. the algorithm focuses raw data assuming a spherical reference surface (ground surface), and, unlike standard polar format algorithms, it assumes spherical rather than planar signal wavefronts.

polar format processing↗
NASA NTRS1990

Climate data system supports FIRE

The NASA Climate Data System (NCDS) at Goddard Space Flight Center is serving as the FIRE Central Archive, providing a centralized data holding and data cataloging service for the FIRE project. NCDS members are carrying out their responsibilities by holding all reduced observations and data analysis products submitted by individual principal investigators in the agreed upon format, by holding all satellite data sets required for FIRE, by providing copies of any of these data sets to FIRE investigators, and by producing and updating a catalog with information about the FIRE holdings. FIRE researchers were requested to provide their reduced data sets in the Standard Data Format (SDF) to the FIRE Central Archive. This standard format is proving to be of value. An improved SDF document is now available. The document provides an example from an actual FIRE SDF data set and clearly states the guidelines for formatting data in SDF. NCDS has received SDF tapes from a number of investigators. These tapes were analyzed and comments provided to the producers. One product which is now available is William J. Syrett's sodar data product from the Stratocumulus Intensive Field Observation. Sample plots from all SDF tapes submitted to the archive will be available to FSET members. Related cloud products are also available through NCDS. Entries describing the FIRE data sets are being provided for the NCDS on-line catalog. Detailed information for the Extended Time Observations is available in the general FIRE catalog entry. Separate catalog entries are being written for the Cirrus Intensive Field Observation (IFO) and for the Marine Stratocumulus IFO. Short descriptions of each FIRE data set will be installed into the NCDS Summary Catalog.

Olsen, Lola M.↗
NASA NTRS1972

Synchronous meteorological satellite system description document, volume 4

The command and data acquisition (CDA) station for the Synchronous Meteorological Satellite (SMS) program, located on the Eastern shore of Virginia, is described in terms of facility layout, ground support instrumentation, and capabilities. Data systems and equipment are described in detailed block diagrams. The major subsystems are identified and information on equipment specifications, frequency ranges, and data processing format is given. Automatic picture transmission, data utilization stations, data collection platforms, and the various communication links from the CDA station to the SMS spacecraft are discussed.

Lahzun, J.↗
NASA NTRS1978

System design package for solar heating and cooling site data acquisition subsystem

The Site Data Acquisition Subsystem (SDAS) designed to collect data from sensors located on residential or commercial buildings using a solar heating and/or cooling system is described. It takes the data obtained from sensors located on the solar system, processes the data into suitable format, stores the data for a period of time, and provides the capability for either telephone retrieval by the central data processing system or manual retrieval of the data for transfer to a central site. The SDAS is also designed so that it will not degrade the operation of the solar heating/cooling system which it is monitoring.

Source record↗
NASA NTRS1981

Computer-implemented remote sensing techniques for measuring coastal productivity and nutrient transport systems

An automatic technique has been developed to measure marsh plant production by inference from a species classification derived from Landsat MSS data. A separate computer technique has been developed to calculate the transport path length of detritus and nutrients from their point of origin in the marsh to the shoreline from Landsat data. A nutrient availability indicator, the ratio of production to transport path length, was derived for each marsh-identified Landsat cell. The use of a data base compatible with the Landsat format facilitated data handling and computations.

Butera, M. K.↗
NASA NTRS1981

Generic functional requirements for a NASA general-purpose data base management system

Generic functional requirements for a general-purpose, multi-mission data base management system (DBMS) for application to remotely sensed scientific data bases are detailed. The motivation for utilizing DBMS technology in this environment is explained. The major requirements include: (1) a DBMS for scientific observational data; (2) a multi-mission capability; (3) user-friendly; (4) extensive and integrated information about data; (5) robust languages for defining data structures and formats; (6) scientific data types and structures; (7) flexible physical access mechanisms; (8) ways of representing spatial relationships; (9) a high level nonprocedural interactive query and data manipulation language; (10) data base maintenance utilities; (11) high rate input/output and large data volume storage; and adaptability to a distributed data base and/or data base machine configuration. Detailed functions are specified in a top-down hierarchic fashion. Implementation, performance, and support requirements are also given.

Lohman, G. M.↗
NASA NTRS1984

Documentation for the machine readable version of the Yale Catalogue of the Positions and Proper Motions of Stars between Declinations -60 deg and -70 deg (Fallon 1983)

The machine-readable, character-coded version of the catalog, as it is currently being distributed from the Astronomical Data Center(ADC), is described. The format and data provided in the magnetic tape version differ somewhat from those of the published catalog, which was also produced from a tape prepared at the ADC. The primary catalog data are positions and proper motions (equinox 1950.0) for 14597 stars.

Roman, N. G.↗
NASA NTRS1988

Bibliography On Multiprocessors And Distributed Processing

Multiprocessor and Distributed Processing Bibliography package consists of large machine-readable bibliographic data base, which in addition to usual keyword searches, used for producing citations, indexes, and cross-references. Data base contains UNIX(R) "refer" -formatted ASCII data and implemented on any computer running under UNIX(R) operating system. Easily convertible to other operating systems. Requires approximately one megabyte of secondary storage. Bibliography compiled in 1985.

Miya, Eugene N.↗
NASA NTRS1995

Cryptography Would Reveal Alterations In Photographs

Public-key decryption method proposed to guarantee authenticity of photographic images represented in form of digital files. In method, digital camera generates original data from image in standard public format; also produces coded signature to verify standard-format image data. Scheme also helps protect against other forms of lying, such as attaching false captions.

Friedman, Gary L.↗
NASA NTRS2000

BOREAS RSS-17 Stem, Soil, and Air Temperature Data

The BOREAS RSS-17 team collected several data sets in support of its research in monitoring and analyzing environmental and phenological states using radar data. This data set consists of tree bole and soil temperature measurements from various BOREAS flux tower sites. Temperatures were measured with thermistors implanted in the hydroconductive tissue of the trunks of several trees at each site and at various depths in the soil. Data were stored on a data logger at intervals of either 1 or 2 hours. The majority of the data were acquired between early 1994 and early 1995. The primary product of this data set is the diurnal stem temperature measurements acquired for selected trees at five BOREAS tower sites. The data are provided in tabular ASCII format. The data files are available on a CD-ROM (see document number 20010000884) or from the Oak Ridge National Laboratory (ORNL) Distributed Active Archive Center (DAAC).

Zimmerman, Reiner↗
NASA NTRS2000

BOREAS RSS-8 BIOME-BGC SSA Simulation of Annual Water and Carbon Fluxes

The BOREAS RSS-8 team performed research to evaluate the effect of seasonal weather and landcover heterogeneity on boreal forest regional water and carbon fluxes using a process-level ecosystem model, BIOME-BGC, coupled with remote sensing-derived parameter maps of key state variables. This data set contains derived maps of landcover type and crown and stem biomass as model inputs to determine annual evapotranspiration, gross primary production, autotrophic respiration, and net primary productivity within the BOREAS SSA-MSA, at a 30-m spatial resolution. Model runs were conducted over a 3-year period from 1994-1996; images are provided for each of those years. The data are stored in binary image format. The data files are available on a CD-ROM (see document number 20010000884), or from the Oak Ridge National Laboratory (ORNL) Distributed Active Archive Center (DAAC).

Hall, Forrest G.↗
NASA NTRS2002

MODIS Data from the GES DISC DAAC: Moderate-Resolution Imaging Spectroradiometer (MODIS)

The Goddard Earth Sciences (GES) Distributed Active Archive Center (DAAC) is responsible for the distribution of the Level 1 data, and the higher levels of all Ocean and Atmosphere products (Land products are distributed through the Land Processes (LP) DAAC DAAC, and the Snow and Ice products are distributed though the National Snow and Ice Data Center (NSIDC) DAAC). Ocean products include sea surface temperature (SST), concentrations of chlorophyll, pigment and coccolithophores, fluorescence, absorptions, and primary productivity. Atmosphere products include aerosols, atmospheric water vapor, clouds and cloud masks, and atmospheric profiles from 20 layers. While most MODIS data products are archived in the Hierarchical Data Format-Earth Observing System (HDF-EOS 2.7) format, the ocean binned products and primary productivity products (Level 4) are in the native HDF4 format. MODIS Level 1 and 2 data are of the Swath type and are packaged in files representing five minutes of Files for Level 3 and 4 are global products at daily, weekly, monthly or yearly resolutions. Apart from the ocean binned and Level 4 products, these are in Grid type, and the maps are in the Cylindrical Equidistant projection with rectangular grid. Terra viewing (scenes of approximately 2000 by 2330 km). MODIS data have several levels of maturity. Most products are released with a provisional level of maturity and only announced as validated after rigorous testing by the MODIS Science Teams. MODIS/Terra Level 1, and all MODIS/Terra 11 micron SST products are announced as validated. At the time of this publication, the MODIS Data Support Team (MDST) is working with the Ocean Science Team toward announcing the validated status of the remainder of MODIS/Terra Ocean products. MODIS/Aqua Level 1 and cloud mask products are released with provisional maturity.

Source record↗
NASA NTRS2006

Processing TES Level-2 Data

TES Level 2 Subsystem is a set of computer programs that performs functions complementary to those of the program summarized in the immediately preceding article. TES Level-2 data pertain to retrieved species (or temperature) profiles, and errors thereof. Geolocation, quality, and other data (e.g., surface characteristics for nadir observations) are also included. The subsystem processes gridded meteorological information and extracts parameters that can be interpolated to the appropriate latitude, longitude, and pressure level based on the date and time. Radiances are simulated using the aforementioned meteorological information for initial guesses, and spectroscopic-parameter tables are generated. At each step of the retrieval, a nonlinear-least-squares- solving routine is run over multiple iterations, retrieving a subset of atmospheric constituents, and error analysis is performed. Scientific TES Level-2 data products are written in a format known as Hierarchical Data Format Earth Observing System 5 (HDF-EOS 5) for public distribution.

Poosti, Sassaneh↗
NASA NTRS2011

Online Time Series Analysis of Land Products over Asia Monsoon Region via Giovanni

Time series analysis is critical to the study of land cover/land use changes and climate. Time series studies at local-to-regional scales require higher spatial resolution, such as 1km or less, data. MODIS land products of 250m to 1km resolution enable such studies. However, such MODIS land data files are distributed in 10ox10o tiles, due to large data volumes. Conducting a time series study requires downloading all tiles that include the study area for the time period of interest, and mosaicking the tiles spatially. This can be an extremely time-consuming process. In support of the Monsoon Asia Integrated Regional Study (MAIRS) program, NASA GES DISC (Goddard Earth Sciences Data and Information Services Center) has processed MODIS land products at 1 km resolution over the Asia monsoon region (0o-60oN, 60o-150oE) with a common data structure and format. The processed data have been integrated into the Giovanni system (Goddard Interactive Online Visualization ANd aNalysis Infrastructure) that enables users to explore, analyze, and download data over an area and time period of interest easily. Currently, the following regional MODIS land products are available in Giovanni: 8-day 1km land surface temperature and active fire, monthly 1km vegetation index, and yearly 0.05o, 500m land cover types. More data will be added in the near future. By combining atmospheric and oceanic data products in the Giovanni system, it is possible to do further analyses of environmental and climate changes associated with the land, ocean, and atmosphere. This presentation demonstrates exploring land products in the Giovanni system with sample case scenarios.

Shen, Suhung↗
NASA NTRS2015

Observing System Simulations for Small Satellite Formations Estimating Bidirectional Reflectance

The bidirectional reflectance distribution function (BRDF) gives the reflectance of a target as a function of illumination geometry and viewing geometry, hence carries information about the anisotropy of the surface. BRDF is needed in remote sensing for the correction of view and illumination angle effects (for example in image standardization and mosaicing), for deriving albedo, for land cover classification, for cloud detection, for atmospheric correction, and other applications. However, current spaceborne instruments provide sparse angular sampling of BRDF and airborne instruments are limited in the spatial and temporal coverage. To fill the gaps in angular coverage within spatial, spectral and temporal requirements, we propose a new measurement technique: Use of small satellites in formation flight, each satellite with a VNIR (visible and near infrared) imaging spectrometer, to make multi-spectral, near-simultaneous measurements of every ground spot in the swath at multiple angles. This paper describes an observing system simulation experiment (OSSE) to evaluate the proposed concept and select the optimal formation architecture that minimizes BRDF uncertainties. The variables of the OSSE are identified; number of satellites, measurement spread in the view zenith and relative azimuth with respect to solar plane, solar zenith angle, BRDF models and wavelength of reflection. Analyzing the sensitivity of BRDF estimation errors to the variables allow simplification of the OSSE, to enable its use to rapidly evaluate formation architectures. A 6-satellite formation is shown to produce lower BRDF estimation errors, purely in terms of angular sampling as evaluated by the OSSE, than a single spacecraft with 9 forward-aft sensors. We demonstrate the ability to use OSSEs to design small satellite formations as complements to flagship mission data. The formations can fill angular sampling gaps and enable better BRDF products than currently possible.

small satellite↗
NASA NTRS2016

Earth Science Data Analytics: Preparing for Extracting Knowledge from Information

Data analytics is the process of examining large amounts of data of a variety of types to uncover hidden patterns, unknown correlations and other useful information. Data analytics is a broad term that includes data analysis, as well as an understanding of the cognitive processes an analyst uses to understand problems and explore data in meaningful ways. Analytics also include data extraction, transformation, and reduction, utilizing specific tools, techniques, and methods. Turning to data science, definitions of data science sound very similar to those of data analytics (which leads to a lot of the confusion between the two). But the skills needed for both, co-analyzing large amounts of heterogeneous data, understanding and utilizing relevant tools and techniques, and subject matter expertise, although similar, serve different purposes. Data Analytics takes on a practitioners approach to applying expertise and skills to solve issues and gain subject knowledge. Data Science, is more theoretical (research in itself) in nature, providing strategic actionable insights and new innovative methodologies. Earth Science Data Analytics (ESDA) is the process of examining, preparing, reducing, and analyzing large amounts of spatial (multi-dimensional), temporal, or spectral data using a variety of data types to uncover patterns, correlations and other information, to better understand our Earth. The large variety of datasets (temporal spatial differences, data types, formats, etc.) invite the need for data analytics skills that understand the science domain, and data preparation, reduction, and analysis techniques, from a practitioners point of view. The application of these skills to ESDA is the focus of this presentation. The Earth Science Information Partners (ESIP) Federation Earth Science Data Analytics (ESDA) Cluster was created in recognition of the practical need to facilitate the co-analysis of large amounts of data and information for Earth science. Thus, from a to advance science point of view: On the continuum of ever evolving data management systems, we need to understand and develop ways that allow for the variety of data relationships to be examined, and information to be manipulated, such that knowledge can be enhanced, to facilitate science. Recognizing the importance and potential impacts of the unlimited ways to co-analyze heterogeneous datasets, now and especially in the future, one of the objectives of the ESDA cluster is to facilitate the preparation of individuals to understand and apply needed skills to Earth science data analytics. Pinpointing and communicating the needed skills and expertise is new, and not easy. Information technology is just beginning to provide the tools for advancing the analysis of heterogeneous datasets in a big way, thus, providing opportunity to discover unobvious scientific relationships, previously invisible to the science eye. And it is not easy It takes individuals, or teams of individuals, with just the right combination of skills to understand the data and develop the methods to glean knowledge out of data and information. In addition, whereas definitions of data science and big data are (more or less) available (summarized in Reference 5), Earth science data analytics is virtually ignored in the literature, (barring a few excellent sources).

data analytics↗
NASA NTRSDate not supplied

Open-Source Science-led Development of the Atmosphere Observing System (AOS) Mission Science Data System (SDS)

The Earth System Observatory (ESO) Atmosphere Observing System (AOS) mission will provide space-based and suborbital observations of collocated cloud, dynamic, precipitation and aerosol processing leading to improved weather, air quality, and climate predictions. The AOS Science Data System (SDS) will be a system of systems developed within the Cloud to manage the research and operational processing of AOS mission orbital and suborbital sensors and curate these data for reprocessing (e.g., in near real-time or by collection) and transfer them to a NASA Distributed Active Archive Center (DAAC) for long-term storage. Further, AOS SDS will follow guidelines provided by NASA Earth Science Data Systems (ESDS) program including standard conventions for data file formats, naming, and metadata to improve data interoperability, interpretability, usability, discovery, provenance, and spatiotemporal representativeness. The AOS mission follows NASA’s lead in making a commitment to Open-Source Science (OSS) including the sharing of data, software, and knowledge in an open and timely manner. Each of the AOS SDS system components will be developed with open-source concepts including components of SDS itself as well as AOS mission algorithms. Further, the AOS SDS assumes the role to lead and facilitate OSS activities for the AOS mission. This presentation describes the framework of the AOS SDS and its integral part in facilitating OSS within the AOS mission.

David Giles↗