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A latent Gaussian Markov random‐field model for spatiotemporal rainfall disaggregation
Author(s) -
Allcroft David J.,
Glasbey Chris A.
Publication year - 2003
Publication title -
journal of the royal statistical society: series c (applied statistics)
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.205
H-Index - 72
eISSN - 1467-9876
pISSN - 0035-9254
DOI - 10.1111/1467-9876.00419
Subject(s) - scale (ratio) , gaussian , random field , markov chain , gaussian random field , gibbs sampling , gaussian process , sampling (signal processing) , mathematics , statistics , computer science , statistical physics , environmental science , geography , cartography , bayesian probability , physics , quantum mechanics , filter (signal processing) , computer vision
Summary. Rainfall data are often collected at coarser spatial scales than required for input into hydrology and agricultural models. We therefore describe a spatiotemporal model which allows multiple imputation of rainfall at fine spatial resolutions, with a realistic dependence structure in both space and time and with the total rainfall at the coarse scale consistent with that observed. The method involves the transformation of the fine scale rainfall to a thresholded Gaussian process which we model as a Gaussian Markov random field. Gibbs sampling is then used to generate realizations of rainfall efficiently at the fine scale. Results compare favourably with previous, less elegant methods.