EPSRC Centre for Doctoral Training Cloud Computing for Big Data


Matthew Edwards


I have a background in Mathematics and Statistics. I obtained my masters in Statistics from Lancaster University in 2014.

In 2015, I joined the CDT in Cloud Computing for Big Data at Newcastle University. I am currently a fourth year PhD student studying Statistics at Newcastle Univeristy

PhD Title

Stochastic generators for multivariate global spatio-temporal climate data

To understand and quantify the uncertainties in projections and physics of a climate model, we need a collection of climate simulations (an ensemble). Given the high-dimensionality of the input space of a climate model, as well as the complex non-linear relationships between the climate variables, a large ensemble is often required to accurately assess the uncertainties.

If only a small number of climate variables are of interest at a specified spatial and temporal scale, the computational and storage expenses can be significantly reduced by applying a statistical model to the climate simulations. The statistical model would then act as a stochastic generator (SG) able to simulate a large ensemble, given a small training ensemble.

Previous work has focused only on simulating individual climate variables (eg surface temperature, wind speed) independently from a SG. I develop the first SG that achieves a joint simulation for three climate variables. I base this model on a multi-stage spectral approach. This allows for inference of more than 80 million data points for a nonstationary global model. It distributes the inference across many processors and in four stages.

The advantages of jointly simulating climate variables is demonstrated by training the SG on a five member ensemble, from a large ensemble project conducted at the National Center for Atmospheric Research.


Robin Henderson


A Multivariate Global Spatiotemporal Stochastic Generator for Climate EnsemblesEdwards, M. Castruccio, S. Hammerling, D. - Journal of Agricultural, Biological and Environmental Statistics 24, pages 464–483 (2019) - 2019

Marginally parameterized spatio-temporal models and stepwise maximum likelihood estimationEdwards, M. Castruccio, S. Hammerling, D. - Computational Statistics & Data Analysis Volume 151 - November 2020