Abstract: The laws and characteristics of precipitation in Anhui Province were analyzed and simulated using the Hidden Markov Model (HMM) to verify its applicability in regional precipitation. HMM with four implicit states was employed to fit the daily precipitation data sequence of many years in six major cities in the province. Bayesian Information Criterion was adopted to determine the implicit state quantity, the Baum-Welch algorithm to train and obtain the optimal model parameters, and the Viterbi algorithm to determine the optimal sequence of the model states. The above methods were adopted to simulate the precipitation in the summer of 1960-2009 in six cities of Anhui Province. The first 4-decade was for model training and analyzing, and the later 1 decade for model validation and evaluation. Results showed that HMM is of high practicability by better simulating rainfall characteristics.

Key words: hidden Markov model, implicit state, precipitation characteristics, Bayesian information criterion, Baum-Welch algorithm, Viterbi algorithm