Markov chain-mixed exponential model for daily rainfall in Hong Kong - Xu, YuChen (2023)
In this study, we applied a stochastic rainfall model which is capable in generating synthetic daily rainfall sequences that exhibit similar characteristics to observed data, thereby assessing the amount of rainfall over a specific period. The model utilized for this purpose is the Markov Chain Mixing Index (MCME). This model integrates both rainfall occurrence, represented by a first-order two-state Markov chain, and rainfall distribution, described by a mixture index distribution. The feasibility of the MCME model was evaluated using daily rainfall data collected from 15 stations in Hong Kong over a 20-year record period (2003-2022). The evaluation revealed that the proposed MCME model adequately captures both the occurrence and quantity of rainfall across all stations. Various statistical analysis were implemented to analyze the rainfall data. In conclusion, the validation results indicate that while the model effectively describes the characteristics of rainfall and able to simulate the rainfall based on the parameters estimated.