Markov chain models for pre-monsoon thunderstorm in Calcutta, India

Abstract

Markov chain models of first, second and third order have been used to provide a probabilistic description of the occurrence of the thunderstorm phenomenon in the pre-monsoon season (March-May) in Calcutta (India). The analysis is based on the relevant data for the 10-year period 1985-94. Use of Akaike’s information criterion (AIC) reveals that the Markov chain of the third order is perhaps the model that describes best the phenomenon. This finding is also supported by actual verification of the fact that the Markov chain of third order simulates the observed data best. Besides, the time independent behaviour of the chain has also been studied with the help of the steady state probabilities. The theoretical values of the mean recurrence time for thunderstorm and fair weather days hence obtained are in close agreement with the observed data.