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Techniques for Exploring Cluster Compressed Geospatial-Temporal Satellite Datasets

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dc.contributor.advisor Carr, Daniel B.
dc.contributor.author Ashley, John M.
dc.creator Ashley, John M. en_US
dc.date.accessioned 2014-08-28T03:14:40Z
dc.date.available 2014-08-28T03:14:40Z
dc.date.issued 2013-08 en_US
dc.identifier.uri https://hdl.handle.net/1920/8779
dc.description.abstract NASA satellite data products are part of the recent big data explosion. An example of this are the individual physically referenced and processed footprints of data from the AIRS satellite (L2 Data Product), Each 2.3 MB data file covers a 6 minute period. Daily data volumes are 0.552GB/day and the collection of data products now spans over a decade. This research addressed NASA's L3Q Data Products. NASA has developed the L3Q Entropy Constrained Vector Quantization (ECVQ) cluster compressed dataset to provide a compact representation of the detailed data that retains much of the original multi-variate, altitudinally indexed information content summarized to a 5o x 5o Earth grid cell over a period of one month. The monthly summary files are- roughly 5.5MB in size, so the compression factor is about 3000 to 1. These multivariate L3Q monthly summaries differ from the NASA's L3 products which contain univariate statistics (means and standard deviations) for 1 x 1 degree earth grid cells.
dc.format.extent 185 pages en_US
dc.language.iso en en_US
dc.rights Copyright 2013 John M. Ashley en_US
dc.subject Computer science en_US
dc.subject Atmospheric sciences en_US
dc.subject Statistics en_US
dc.subject Climate Change en_US
dc.subject Data Compression en_US
dc.subject Data Reduction en_US
dc.subject Quantization en_US
dc.subject Remote Sensing en_US
dc.subject Visualization en_US
dc.title Techniques for Exploring Cluster Compressed Geospatial-Temporal Satellite Datasets en_US
dc.type Dissertation en
thesis.degree.level Doctoral en
thesis.degree.discipline Computational Sciences and Informatics en
thesis.degree.grantor George Mason University en


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