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.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 352-357 |
| Number of pages | 6 |
| Journal | Journal of Agricultural, Biological, and Environmental Statistics |
| Volume | 28 |
| Issue number | 2 |
| DOIs | |
| State | Published - Jun 2023 |
Keywords
- Climate data compression
- Deep neural network
- Machine learning
- Variational autoencoders
ASJC Scopus subject areas
- Statistics and Probability
- General Environmental Science
- Agricultural and Biological Sciences (miscellaneous)
- General Agricultural and Biological Sciences
- Statistics, Probability and Uncertainty
- Applied Mathematics
Fingerprint
Dive into the research topics of 'Discussion of “Saving Storage in Climate Ensembles: A Model-Based Stochastic Approach”'. Together they form a unique fingerprint.Cite this
- APA
- Standard
- Harvard
- Vancouver
- Author
- BIBTEX
- RIS