Statistics Seminar: Prof. Sally Cripps -- A spatio-temporal mixture model for Australian daily rainfall, 1876--2015 Modeling daily rainfall over the Australian continent

Abstract: 

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. The topic is complex and would 
benefit from a common and publicly available statistical framework that scales to 
large data sets. We propose a general Bayesian spatio-temporal mixture model 
accommodating mixed discrete-continuous data. Our analysis uses over 294 million 
daily rainfall measurements since 1876, spanning 17,606 rainfall measurement sites. 
The size of the data calls for a parsimonious yet flexible model as well as 
computationally efficient methods for performing the statistical inference. 
Parsimony is achieved by encoding spatial, temporal and climatic variation entirely 
within a mixture model whose mixing weights depend on covariates. Computational 
efficiency is achieved by constructing a Markov chain Monte Carlo sampler that 
runs in parallel in a distributed computing framework. We present examples of 
posterior inference on short-term daily component classification, monthly intensity 
levels, 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.