Markov chain models for pre‐monsoon season thunderstorms over Pune

Abstract

The probabilistic distribution of the thunderstorm phenomenon during the pre-monsoon season (1 March to 18 June) over Pune, a tropical Indian station, has been examined with the help of Markov chain models using daily thunderstorm data for a period of 11 years . The data have also been tested using Akaike's information criterion. This test has clearly indicated that the first-order Markov chain model is the best fit model for thunderstorm forecasting, which has described the appropriate period (8 days) of occurrence of thunderstorm phenomenon over Pune. Further, the steady-state probabilities and mean recurrence time of thunderstorm days and non-thunderstorm days have also been calculated for the first-and second-order Markov chain models. These computations have revealed that the observed and theoretical values of steady-state probabilities are realistically matched.

Markov chain model to study the occurrence of pre-monsoon thunderstorms over Bhubaneswar, India

“…Before applying MC model on a dichotomous dataset, one has to examine whether this data is following the Markovian property by the serial independence test (Wilks, 2006). But earlier study on MC analysis for pre-monsoon thunderstorm (Dasgupta and De, 2001;Kulkarni et al, 2002) did not discuss this issue. In this present study, for each time series, the following hypotheses are framed: null hypothesis H 0 : the time series X t f g is characterized by serial independence i.e.…”

“…The AICs are presented in Table 1. Following Kulkarni et al (2002), the n-step probabilities are now obtained by FOTSMC for the years 2000, 2001, 2004 and 2006 and are presented in Tables 2-5. These n-step probabilities are the elements of a matrix of the type P n , where P is the one-step transition matrix.…”

“…Earlier, Dasgupta and De (2001) implemented Markov chain for forecasting the premonsoon thunderstorm over Kolkata, a station of eastern India. Kulkarni et al (2002) carried out a Markov chain analysis for pre-monsoon thunderstorms over Pune, a station belonging to the western India.…”

Optimal stochastic multi‐states first‐order Markov chain parameters for synthesizing daily rainfall data using multi‐objective differential evolution in Thailand

“…In its first order models it employs conditional probability to describe the process x ( t ) at the present time t using only the outcome at previous time t − 1. A higher‐order Markov Chain model, corresponding to the number of preceding days (Chapman, ), could also be formulated (Kulkarni et al , ). SMFOMC may then be considered as a simple two‐state for a dry day (no rain) and a wet day.…”