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

Silver, Jeremy D.; Zender, Charles S.

doi:https://doi.org/10.5194/gmd-10-413-2017

Articles | Volume 10, issue 1

https://doi.org/10.5194/gmd-10-413-2017

© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.

https://doi.org/10.5194/gmd-10-413-2017

© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.

Articles | Volume 10, issue 1

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

Supplement

https://doi.org/10.5194/gmd-10-413-2017-supplement

Data sets

Sample CAM SE model output C. S. Zender https://doi.org/10.4225/49/576ca64db2a14

Sample output of the mineral Dust Entrainment And Deposition (DEAD) model C. S. Zender https://doi.org/10.4225/49/576c95f254b67

Sample MERRA analysis C. S. Zender https://doi.org/10.4225/49/576c934f73be7

Sample output from the Weather, Research and Forecasting model J. D. Silver https://doi.org/10.4225/49/576c900f7d289

Sample MOZART model output J. D. Silver https://doi.org/10.4225/49/576c8ba706ac9

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.