Papers

Bayesian spatio-temporal model for high-resolution short-term forecasting of precipitation fields

SR Johnson, SE Heaps, KJ Wilson… - arXiv preprint arXiv …, 2021 - arxiv.org
Statistics paper stat.ME Suggest

… By opting for a simulation based inference approach we are able to straightforwardly handle both missing and zero (censored) observations by appealing to data augmentation (Tanner …

Cited by Link to paper

BibTeX

@article{2105.03269v1,
Author        = {Stephen Richard Johnson and Sarah Elizabeth Heaps and Kevin James Wilson and Darren James Wilkinson},
Title         = {Bayesian spatio-temporal model for high-resolution short-term
  forecasting of precipitation fields},
Eprint        = {2105.03269v1},
ArchivePrefix = {arXiv},
PrimaryClass  = {stat.ME},
Abstract      = {With extreme weather events becoming more common, the risk posed by surface
water flooding is ever increasing. In this work we propose a model, and
associated Bayesian inference scheme, for generating probabilistic
(high-resolution short-term) forecasts of localised precipitation. The
parametrisation of our underlying hierarchical dynamic spatio-temporal model is
motivated by a forward-time, centred-space finite difference solution to a
collection of stochastic partial differential equations, where the main driving
forces are advection and diffusion. Observations from both weather radar and
ground based rain gauges provide information from which we can learn about the
likely values of the (latent) precipitation field in addition to other unknown
model parameters. Working in the Bayesian paradigm provides a coherent
framework for capturing uncertainty both in the underlying model parameters and
also in our forecasts. Further, appealing to simulation based (MCMC) sampling
yields a straightforward solution to handling zeros, treated as censored
observations, via data augmentation. Both the underlying state and the
observations are of moderately large dimension ($\mathcal{O}(10^4)$ and
$\mathcal{O}(10^3)$ respectively) and this renders standard inference
approaches computationally infeasible. Our solution is to embed the ensemble
Kalman smoother within a Gibbs sampling scheme to facilitate approximate
Bayesian inference in reasonable time. Both the methodology and the
effectiveness of our posterior sampling scheme are demonstrated via simulation
studies and also by a case study of real data from the Urban Observatory
project based in Newcastle upon Tyne, UK.},
Year          = {2021},
Month         = {May},
Url           = {http://arxiv.org/abs/2105.03269v1},
File          = {2105.03269v1.pdf}
}

Share