Markov chain models for pre‐monsoon season thunderstorms over Pune

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 test...

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Bibliographic Details
Published in:International journal of climatology 2002-09, Vol.22 (11), p.1415-1420
Main Authors: Kulkarni, M. K., Kandalgaonkar, S. S., Tinmaker, M. I. R., Nath, Asha
Format: Article
Language:English
Subjects:
Akaike's information criterion
Earth, ocean, space
Exact sciences and technology
External geophysics
Markov chain model
Meteorology
non‐thunderstorm
probabilistic distribution
thunderstorm
Weather analysis and prediction
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Summary: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.
ISSN:0899-8418
1097-0088
DOI:10.1002/joc.782