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

Abhirup Datta ()
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Abhirup Datta: Johns Hopkins University

Journal of Agricultural, Biological and Environmental Statistics, 2023, vol. 28, issue 2, No 10, 352-357

Abstract: 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.

Keywords: Machine learning; Deep neural network; Variational autoencoders; Climate data compression (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s13253-023-00539-0

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