Daily spatiotemporal precipitation simulation using latent and transformed Gaussian processes

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A Transdisciplinary Review of Deep Learning Research and Its Relevance for Water Resources Scientists (2017, background): “In hydrology, researchers have employed GPR to make predictions for streamflow (A. Y. Sun et al., 2014; J. Yang et al., 2018), precipitation (Kleiber et al., 2012), soil moisture (Andugula et al., 2017), etc. Challenges facing GPR include computational efficiency, expressive power, the use of…”
Coupled stochastic weather generation using spatial and generalized linear models (2015, methodology): “Kleiber et al. (2012) used an exponential covariance function to model spatial correlation, for example.”
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.”
A bivariate mixed distribution with a heavy‐tailed component and its application to single‐site daily rainfall simulation (2013, background): “[2] Daily rainfall is a major input to drive many models of hydrologic, agricultural, ecological, and other environmental systems [Mehrotra et al., 2012; Kleiber et al., 2011].”