On the Markov chain models for monsoonal rainfall occurrence in different zones of West Bengal
Abstract: The probabilistic distribution of pattern of rainfall during the monsoonal season (June-September) over different regions of West Bengal (India) has been reviewed with the help of Markov chain models of various orders. The analysis is based on relevant data of 25 years (1970-1995) in the ten meteorological stations spread over the state. The determination of the proper order that best describes the precipitation over the region is carried out using Akaike’s Information Criteria. The test clearly indicates that first order Markov chain model is the best /one for rainfall forecasting. It is found that an appropriate period (2-4 days) of occurrence of rainfall phenomenon over the various stations. Moreover, the steady-state probabilities and mean occurrence time of precipitation days and dry days have also been calculated for first- and second-order Markov chain models. The computation reveals that the observed and theoretical values of steady-state probabilities are realistically matched
Markov chain model, Akaike’s Information criteria, rainfall probability, Stationary probability, Mean recurrence time, Dry and Wet spell.
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