Journal cover Journal topic
Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
Journal topic

Journal metrics

Journal metrics

  • IF value: 5.154 IF 5.154
  • IF 5-year value: 5.697 IF 5-year
    5.697
  • CiteScore value: 5.56 CiteScore
    5.56
  • SNIP value: 1.761 SNIP 1.761
  • IPP value: 5.30 IPP 5.30
  • SJR value: 3.164 SJR 3.164
  • Scimago H <br class='hide-on-tablet hide-on-mobile'>index value: 59 Scimago H
    index 59
  • h5-index value: 49 h5-index 49
Volume 10, issue 1
Geosci. Model Dev., 10, 413-423, 2017
https://doi.org/10.5194/gmd-10-413-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
Geosci. Model Dev., 10, 413-423, 2017
https://doi.org/10.5194/gmd-10-413-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.

Development and technical paper 27 Jan 2017

Development and technical paper | 27 Jan 2017

The compression–error trade-off for large gridded data sets

Jeremy D. Silver and Charles S. Zender
Viewed  
Total article views: 1,591 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
1,121 341 129 1,591 182 123 131
  • HTML: 1,121
  • PDF: 341
  • XML: 129
  • Total: 1,591
  • Supplement: 182
  • BibTeX: 123
  • EndNote: 131
Views and downloads (calculated since 29 Jul 2016)
Cumulative views and downloads (calculated since 29 Jul 2016)
Cited  
Saved (final revised paper)  
No saved metrics found.
Saved (discussion paper)  
No saved metrics found.
Discussed (final revised paper)  
No discussed metrics found.
Discussed (discussion paper)  
No discussed metrics found.
Latest update: 26 Jun 2019
Publications Copernicus
Download
Short summary
Many modern scientific research projects generate large amounts of data. Storage space is valuable and may be limited; hence compression is vital. We tested different compression methods for large gridded data sets, assessing the space savings and the amount of precision lost. We found a general trade-off between precision and compression, with compression well-predicted by the entropy of the data set. A method introduced here proved to be a competitive archive format for gridded numerical data.
Many modern scientific research projects generate large amounts of data. Storage space is...
Citation