Articles | Volume 9, issue 12
Geosci. Model Dev., 9, 4381–4403, 2016
https://doi.org/10.5194/gmd-9-4381-2016
Geosci. Model Dev., 9, 4381–4403, 2016
https://doi.org/10.5194/gmd-9-4381-2016
Development and technical paper
07 Dec 2016
Development and technical paper | 07 Dec 2016

Evaluating lossy data compression on climate simulation data within a large ensemble

Allison H. Baker et al.

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Cited articles

Ana, F. and de Haan, L.: On the block maxima method in extreme value theory, Ann. Stat., 43, 276–298, 2015.
Baker, A., Xu, H., Dennis, J., Levy, M., Nychka, D., Mickelson, S., Edwards, J., Vertenstein, M., and Wegener, A.: A Methodology for Evaluating the Impact of Data Compression on Climate Simulation Data, in: Proceedings of the 23rd International Symposium on High-performance Parallel and Distributed Computing, HPDC '14, 23–27 June 2014, Vancouver, Canada, 203–214, 2014.
Baker, A. H., Hammerling, D. M., Levy, M. N., Xu, H., Dennis, J. M., Eaton, B. E., Edwards, J., Hannay, C., Mickelson, S. A., Neale, R. B., Nychka, D., Shollenberger, J., Tribbia, J., Vertenstein, M., and Williamson, D.: A new ensemble-based consistency test for the Community Earth System Model (pyCECT v1.0), Geosci. Model Dev., 8, 2829–2840, https://doi.org/10.5194/gmd-8-2829-2015, 2015.
Beirlant, J., Goegebeur, Y., Segers, J., and Teugels, J.: Statistics of Extremes: Theory and Applications, Wiley Series in Probability and Statistics, Hoboken, USA, 2004.
Bicer, T., Yin, J., Chiu, D., Agrawal, G., and Schuchardt, K.: Integrating online compression to accelerate large-scale data analytics applications. IEEE International Symposium on Parallel and Distributed Processing (IPDPS), 20–24 May 2013, Boston, Massachusetts, USA, 1205–1216, https://doi.org/10.1109/IPDPS.2013.81, 2013.
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Short summary
We apply lossy data compression to output from the Community Earth System Model Large Ensemble Community Project. We challenge climate scientists to examine features of the data relevant to their interests and identify which of the ensemble members have been compressed, and we perform direct comparisons on features critical to climate science. We find that applying lossy data compression to climate model data effectively reduces data volumes with minimal effect on scientific results.