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

被引：0
|
作者：
Datta, Abhirup [1 ]
机构：
[1] Johns Hopkins Univ, Dept Biostat, Baltimore, MD 21218 USA
来源：
JOURNAL OF AGRICULTURAL BIOLOGICAL AND ENVIRONMENTAL STATISTICS | 2023年 / 28卷 / 02期
关键词：
Machine learning; Deep neural network; Variational autoencoders; Climate data compression;
D O I：
10.1007/s13253-023-00539-0
中图分类号：
Q [生物科学];
学科分类号：
07 ; 0710 ; 09 ;
摘要：
Huang et al (J Agric Biol Environ Stat, 2023, https://doi.org/10.1007/s13253-022-00 518-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.
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页码：352 / 357
页数：6
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