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Massive Dataset Analysis for NASA's Atmospheric Infrared Sounder

Summary: NASA's Atmospheric Infrared Sounder (AIRS) produces massive quantities of valuable data on atmospheric temperature, cloud structure and water vapor distribution. The data set is so large and complex that useful information is not accessible without data reduction, which is challenging because of the way the data are staged and stored. The authors reduce the AIRS data in a way that preserves valuable distributional data across subsets. This article, a followup to two earlier articles proposing a reduction methodology, describes the operationalization of the proposed algorithm and describes the insights uncovered by analysis of the AIRS data.

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  • Topics: Data Quality
  • Keywords: Data analysis, Climate change, Data compression, Data reduction, Remote sensing, Quantization, National Aeronautics and Space Administration (NASA)
  • Author: Braverman, Amy J.; Fetzer, Eric J.; Kahn, Brian H.; Manning, Evan M.; Oliphant, Robert B.; Teixeira, Joao P.
  • Journal: Technometrics