1School of Earth Sciences, University of Melbourne, Melbourne, Australia
2Departments of Earth System Science and of Computer Science, University of California, Irvine, CA, USA
Received: 07 Jul 2016 – Discussion started: 29 Jul 2016
Abstract. The netCDF-4 format is widely used for large gridded scientific data sets and includes several compression methods: lossy linear scaling and the non-lossy deflate and shuffle algorithms. Many multidimensional geoscientific data sets exhibit considerable variation over one or several spatial dimensions (e.g., vertically) with less variation in the remaining dimensions (e.g., horizontally). On such data sets, linear scaling with a single pair of scale and offset parameters often entails considerable loss of precision. We introduce an alternative compression method called "layer-packing" that simultaneously exploits lossy linear scaling and lossless compression. Layer-packing stores arrays (instead of a scalar pair) of scale and offset parameters. An implementation of this method is compared with lossless compression, storing data at fixed relative precision (bit-grooming) and scalar linear packing in terms of compression ratio, accuracy and speed.
Revised: 28 Oct 2016 – Accepted: 19 Nov 2016 – Published: 27 Jan 2017
When viewed as a trade-off between compression and error, layer-packing yields similar results to bit-grooming (storing between 3 and 4 significant figures). Bit-grooming and layer-packing offer significantly better control of precision than scalar linear packing. Relative performance, in terms of compression and errors, of bit-groomed and layer-packed data were strongly predicted by the entropy of the exponent array, and lossless compression was well predicted by entropy of the original data array. Layer-packed data files must be "unpacked" to be readily usable. The compression and precision characteristics make layer-packing a competitive archive format for many scientific data sets.
Silver, J. D. and Zender, C. S.: The compression–error trade-off for large gridded data sets, Geosci. Model Dev., 10, 413-423, doi:10.5194/gmd-10-413-2017, 2017.