A comparison of three stochastic multi-site precipitation occurrence generators

This paper presents a comparison of three multi-site stochastic weather generators for simulation of point rainfall occurrences at a network of 30 raingauge stations around Sydney, Australia. The approaches considered include a parametric hidden Markov model (HMM), a multi-site stochastic precipitation generation model (proposed by [Wilks, D.S., 1998. Multi-site generalization of a daily stochastic precipitation model, J. Hydrol. 210, 178–191.]) and a non-parametric K-nearest neighbour (KNN) model. The HMM generates the precipitation distribution conditional on a discrete weather state representing certain identified spatial rainfall distribution patterns. The spatial dependence is maintained by assumption of a common weather state across all stations while the temporal dependence is simulated by assuming the weather state to be Markovian in nature. The Wilks model preserves serial dependence through the assumption of an order one Markov dependence at each location. The spatial dependence is simulated by prescribing a dependence pattern on the uniform random variates used to generate the rainfall occurrence at each location from the associated conditional probability distribution. The K-nearest neighbour approach simulates spatial dependence by simultaneously generating precipitation occurrence at all locations. Temporal persistence is simulated through Markovian assumptions on the rainfall occurrence process.