Stochastic simulation of non-stationary meteorological time-series daily precipitation indicators, maximum and minimum air temperature simulation using latent and transformed Gaussian processes

In this paper a stochastic parametric simulation model that provides daily values for precipitation indicators, maximum and minimum temperature at a single site on a yearlong time-interval is presented. The model is constructed on the assumption that these weather elements are non-stationary random processes and their one-dimensional distributions vary from day to day. A latent Gaussian process and its threshold transformation are used for simulation of precipitation indicators. Parameters of the model (parameters of one-dimensional distributions, auto-and cross-correlation functions) are chosen for each location on the basis of real data from a weather station situated in this location. Several examples of model applications are given. It is shown that simulated data may be used for estimation of probability of extreme weather events occurrence (e.g. sharp temperature drops, extended periods of high temperature and precipitation absence).