IN11B:
Compressing Scientific Data: Reducing Storage While Preserving Information Posters

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Session ID#: 22360

Session Description:
Due to rapid technological progress our ability to generate increasingly larger data sets from high resolution numerical models is outpacing our ability to store, manage and effectively access these vast volumes of data. Similar statements can be made with regard to observational data captured by a variety of advanced instruments. One potential solution to this Big Data dilemma is the use of compression. Lossless compression offers perfect reconstruction, but provides only limited compaction when confronted with floating point data. Lossy compression, however, is able to achieve substantial reduction, but by its very definition is unable to exactly reproduce original values. The challenge then becomes addressing the question: how much information loss may be tolerated without affecting the interpretation of results, and how can this best be achieved? This session will address a variety of scientific data compression topics including: novel technologies, applications, and methods for evaluation.
Primary Convener:  John Clyne, NCAR, Boulder, United States
Conveners:  Dorit Hammerling, National Center for Atmospheric Research, Institute for Mathematics Applied to Geosciences, Boulder, CO, United States and Allison H Baker, National Center for Atmospheric Research, Boulder, United States
Chairs:  John Clyne, NCAR, Boulder, United States, Allison H Baker, National Center for Atmospheric Research, Boulder, United States and Dorit Hammerling, National Center for Atmospheric Research, Institute for Mathematics Applied to Geosciences, Boulder, CO, United States
OSPA Liaison:  Dorit Hammerling, National Center for Atmospheric Research, Institute for Mathematics Applied to Geosciences, Boulder, CO, United States

Cross-Listed:
  • A - Atmospheric Sciences
  • DI - Study of the Earth's Deep Interior
  • GC - Global Environmental Change
  • OS - Ocean Sciences
Index Terms:

0525 Data management [COMPUTATIONAL GEOPHYSICS]
0545 Modeling [COMPUTATIONAL GEOPHYSICS]
0550 Model verification and validation [COMPUTATIONAL GEOPHYSICS]
0599 General or miscellaneous [COMPUTATIONAL GEOPHYSICS]

Abstracts Submitted to this Session:

Anti-aliasing filters for deriving high-accuracy DEMs from TLS data: A case study from Freeport, Texas (246512)
Lin Xiong, University of Houston, Houston, TX, United States, Guoquan Wang, University of Houston, Department of Earth and Atmospheric Sciences, Houston, TX, United States and Paul Wessel, University of Hawaii at Manoa, Honolulu, HI, United States
Statistical Compression of Wind Speed Data (258123)
Felipe Tagle1, Stefano Castruccio1, Paola Crippa2 and Marc Genton3, (1)University of Notre Dame, Applied and Computational Mathematics and Statistics, Notre Dame, IN, United States, (2)University of Notre Dame, Department of Civil and Environmental Engineering and Earth Sciences, Notre Dame, IN, United States, (3)King Abdullah University of Science and Technology, Thuwal, Saudi Arabia
Lossy compression for Animated Web Visualisation (272980)
Jacob Tomlinson1, Rachel Prudden2, Niall Robinson1 and Alberto Arribas1, (1)Met Office, Informatics Lab, Exeter, United Kingdom, (2)Met Office, Informatics lab, Exeter, United Kingdom

See more of: Earth and Space Science Informatics