A latent Gaussian Markov random field model for spatio- temporal rainfall disaggregation
نویسندگان
چکیده
Rainfall data are often collected at coarser spatial scales than required for input into hydrology and agricultural models. We therefore describe a spatio-temporal 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 (GMRF). Gibbs sampling is then used to efficiently generate realisations of rainfall at the fine scale. Results compare favourably with previous, less elegant methods.
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