Space–time modelling of precipitation by using a hidden Markov model and censored Gaussian distributions

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Daily spatiotemporal precipitation simulation using latent and transformed Gaussian processes (2012, methodology): “…of approaches to spatial-temporal modeling of precipitation, including hidden Markov models for occurrence [Hughes and Guttorp, 1999] and intensity [Ailliot et al., 2009; Charles et al., 1999], resampling based on nearest neighbors [Apipattanavis et al., 2007; Buishand and Brandsma, 2001;…”
A spatiotemporal precipitation generator based on a censored latent Gaussian field (2015, methodology): “Ailliot et al. [2009] used a censored, power-transformed Gaussian distribution for daily rainfall, while Kleiber et al. [2012] developed a multiscale extension of the chain-dependent model.”
Modeling of daily precipitation at multiple locations using a mixture of distributions to characterize the extremes (2009, background): “Ba´rdossy and Plate [1992], Hutchinson [1995], and Ailliot et al. [2009] implemented this approach to model the rainfall process at multiple sites.”
Disaggregating daily precipitations into hourly values with a transformed censored latent Gaussian process (2015, unknown): “Another type of approach (see e.g. Allcroft and Glasbey 2003; Thompson et al. 2007; Ailliot et al. 2009, Kleiber et al. 2013) is to deﬁne a latent Gaussian variable for which dry conditions correspond to censored values below a given threshold.”