Space time modeling of precipitation using a hidden Markov model and censored Gaussian distributions

A new hidden Markov model (HMM) for the space-time evolution of daily rainfall is developed which models precipitation within hidden regional weather types by censored power-transformed Gaussian distributions. The latter provide flexible and interpretable multivariate models for the mixed discrete-continuous variables that describe both precipitation, when it occurs, and no precipitation. The model is validated on rainfall data from a small network of stations in New Zealand encompassing a diverse range of orographic effects. It is shown that the proposed model provides a better description of the spatial structure of precipitation than a more conventional HMM commonly used in the literature.