Articles | Volume 14, issue 2
Geosci. Model Dev., 14, 875–887, 2021
https://doi.org/10.5194/gmd-14-875-2021
Geosci. Model Dev., 14, 875–887, 2021
https://doi.org/10.5194/gmd-14-875-2021

Development and technical paper 11 Feb 2021

Development and technical paper | 11 Feb 2021

Lossy compression of Earth system model data based on a hierarchical tensor with Adaptive-HGFDR (v1.0)

Zhaoyuan Yu et al.

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Cited articles

Andrew, P., Joseph, N., Noah, Feldman., Allison, H. B., Alexander, P., and Dorit, M. H.: A statistical analysis of lossily compressed climate model data, Comput. Geosci., 145, 104599, https://doi.org/10.1016/j.cageo.2020.104599, 2020. 
Baker, A. H., Xu, H., Dennis, J. M., Levy, M. N., Nychka, D., Mickelson, S. A., 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, Vancouver, Canada, 23–27 June 2014. 
Baker, A. H., Hammerling, D. M., Mickelson, S. A., Xu, H., Stolpe, M. B., Naveau, P., Sanderson, B., Ebert-Uphoff, I., Samarasinghe, S., De Simone, F., Carbone, F., Gencarelli, C. N., Dennis, J. M., Kay, J. E., and Lindstrom, P.: Evaluating lossy data compression on climate simulation data within a large ensemble, Geosci. Model Dev., 9, 4381–4403, https://doi.org/10.5194/gmd-9-4381-2016, 2016. 
Bengua, J. A., Phien, H. N., Tuan, H. D., and Do, M. N.: Matrix product state for higher-order tensor compression and classification, IEEE Trans. Signal Process., 65, 4019–4030, https://doi.org/10.1109/TSP.2017.2703882, 2016. 
Cai, J. Y., Chen, X., and Lu, P.: Non-negative weighted #csps: an effective complexity dichotomy, Comput. Sci., 6, 45–54, https://doi.org/10.1109/CCC.2011.32, 2012. 
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Short summary
Few lossy compression methods consider both the global and local multidimensional coupling correlations, which could lead to information loss in data compression. Here we develop an adaptive lossy compression method, Adaptive-HGFDR, to capture both the global and local variation of multidimensional coupling correlations and improve approximation accuracy. The method can achieve good compression performances for most flux variables with significant spatiotemporal heterogeneity.