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.
This is a preview of subscription content, log in via an institution to check access.
References
Behrens G, Beucler T, Gentine P, Iglesias-Suarez F, Pritchard M, Eyring V (2022) Non-linear dimensionality reduction with a variational encoder decoder to understand convective processes in climate models. J Adv Model Earth Syst 14(8):e2022MS003130
Cartwright L, Zammit-Mangion A, Deutscher NM (2021) Emulation of greenhouse-gas sensitivities using variational autoencoders. arXiv preprint arXiv:2112.12524
Chung J, Kastner K, Dinh L, Goel K, Courville AC, Bengio Y (2015) A recurrent latent variable model for sequential data. Adv Neural Inf Process Syst, 28
Girin L, Leglaive S, Bie X, Diard J, Hueber T, Alameda-Pineda X (2020) Dynamical variational autoencoders: a comprehensive review. arXiv preprint arXiv:2008.12595
Huang H, Castruccio S, Baker AH, Genton MG (2023) Saving storage in climate ensembles: a model-based stochastic approach. J Agric Biol Environ Stat. https://doi.org/10.1007/s13253-022-00518-x
Kingma DP, Ba J (2014) Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980
Kingma DP, Welling M (2014) Auto-encoding variational bayes. In: International conference on learning representations
Krinitskiy MA, Zyulyaeva YA, Gulev SK (2019) Clustering of polar vortex states using convolutional autoencoders. CEUR Workshop Proceedings 2426:52–61
LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553):436–444
Neal RM, Hinton GE (1998) A view of the EM algorithm that justifies incremental, sparse, and other variants. In: Learning in graphical models, Springer, pp 355–368
Rasp S, Pritchard MS, Gentine P (2018) Deep learning to represent subgrid processes in climate models. Proc Natl Acad Sci 115(39):9684–9689
Saenz JA, Lubbers N, Urban NM (2018) Dimensionality-reduction of climate data using deep autoencoders. arXiv preprint arXiv:1809.00027
Saha A, Basu S, Datta A (2021) Random forests for spatially dependent data. J Am Stat Assoc 118(541):665–683. https://doi.org/10.1080/01621459.2021.19 50003
Sigrist F (2020) Gaussian process boosting. arXiv preprint arXiv:2004.02653
Wang J, Yang Y, Mao J, Huang Z, Huang C, Xu W (2016) Cnn-rnn: a unified framework for multi-label image classification. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2285–2294
Wikle CK, Zammit-Mangion A (2022) Statistical deep learning for spatial and spatio-temporal data. arXiv preprint arXiv:2206.02218
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
This article is a commentary for https://doi.org/10.1007/s13253-022-00518-x.
Rights and permissions
About this article
Cite this article
Datta, A. Discussion of “Saving Storage in Climate Ensembles: A Model-Based Stochastic Approach”. JABES 28, 352–357 (2023). https://doi.org/10.1007/s13253-023-00539-0
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s13253-023-00539-0