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.

引用

页码：352 / 357

页数：6

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