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Discussion of “Saving Storage in Climate Ensembles: A Model-Based Stochastic Approach”

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  • Abhirup Datta

    (Johns Hopkins University)

Abstract

Huang et al (J Agric Biol Environ Stat, 2023, https://doi.org/10.1007/s13253-022-00518-x ) a suite of statistical models for storage-efficient climate model emulation. In this discussion, I review and explore possibility of using machine learning methods, in particular, deep neural network (DNN)-based variational autoencoders (VAE) for the same task of spatio-temporal climate data compression. I discuss the pros and cons of the statistical and the machine learning paradigms.

Suggested Citation

  • Abhirup Datta, 2023. "Discussion of “Saving Storage in Climate Ensembles: A Model-Based Stochastic Approach”," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 28(2), pages 352-357, June.
    • Handle: RePEc:spr:jagbes:v:28:y:2023:i:2:d:10.1007_s13253-023-00539-0
    DOI: 10.1007/s13253-023-00539-0
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    1. Huang Huang & Stefano Castruccio & Allison H. Baker & Marc G. Genton, 2023. "Saving Storage in Climate Ensembles: A Model-Based Stochastic Approach," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 28(2), pages 324-344, June.
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