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Markov chain models for pre‐monsoon season thunderstorms over Pune
Author(s) -
Kulkarni M. K.,
Kandalgaonkar S. S.,
Tinmaker M. I. R.,
Nath Asha
Publication year - 2002
Publication title -
international journal of climatology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.58
H-Index - 166
eISSN - 1097-0088
pISSN - 0899-8418
DOI - 10.1002/joc.782
Subject(s) - thunderstorm , markov chain , akaike information criterion , climatology , monsoon , probabilistic logic , meteorology , environmental science , statistics , mathematics , geography , geology
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 (1970–80). 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. Copyright © 2002 Royal Meteorological Society.