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June 2019 Climate inference on daily rainfall across the Australian continent, 1876–2015
Michael Bertolacci, Edward Cripps, Ori Rosen, John W. Lau, Sally Cripps
Ann. Appl. Stat. 13(2): 683-712 (June 2019). DOI: 10.1214/18-AOAS1218


Daily precipitation has an enormous impact on human activity, and the study of how it varies over time and space, and what global indicators influence it, is of paramount importance to Australian agriculture. We analyze over 294 million daily rainfall measurements since 1876, spanning 17,606 sites across continental Australia. The data are not only large but also complex, and the topic would benefit from a common and publicly available statistical framework. We propose a Bayesian hierarchical mixture model that accommodates mixed discrete-continuous data. The observational level describes site-specific temporal and climatic variation via a mixture-of-experts model. At the next level of the hierarchy, spatial variability of the mixture weights’ parameters is modeled by a spatial Gaussian process prior. A parallel and distributed Markov chain Monte Carlo sampler is developed which scales the model to large data sets. We present examples of posterior inference on the mixture weights, monthly intensity levels, daily temporal dependence, offsite prediction of the effects of climate drivers and long-term rainfall trends across the entire continent. Computer code implementing the methods proposed in this paper is available as an R package.


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Michael Bertolacci. Edward Cripps. Ori Rosen. John W. Lau. Sally Cripps. "Climate inference on daily rainfall across the Australian continent, 1876–2015." Ann. Appl. Stat. 13 (2) 683 - 712, June 2019.


Received: 1 April 2018; Revised: 1 October 2018; Published: June 2019
First available in Project Euclid: 17 June 2019

zbMATH: 1423.62157
MathSciNet: MR3963549
Digital Object Identifier: 10.1214/18-AOAS1218

Rights: Copyright © 2019 Institute of Mathematical Statistics


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Vol.13 • No. 2 • June 2019
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