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15 A E 0+! 0 BC (?D % ) "i/ ( ) ' )FX # ; '7 ( K QF.(39) 'K+. 9 9,! 7G >X 45 7 K #.)YX 4."P J" E" "."( 0 P Y ' " E"%& O".(393) V;"" '? Ahrens, C. D. (0). Meteorology today: an introduction to weather, climate, and the environment. Cengage Learning. Blunden, J., & Arndt, D. S. (06). State of the Climate in 05. Bulletin of the American Meteorological Society, 97(8). Chattopadhyay, S., Acharya, N., Chattopadhyay, G., Prasad, S. K., & Mohanty, U. C. (0). Markov chain model to study the occurrence of pre-monsoon thunderstorms over Bhubaneswar, India. Comptes Rendus Geoscience, 344(0), Dasgupta, S., & De, U. K. (00). Markov chain models for pre-monsoon thunderstorm in Calcutta, India. Gaál, L., Molnar, P., & Szolgay, J. (04, May). Spatial analysis of intense thunderstorms in Switzerland and temporal trends in their occurrence. In EGU General Assembly Conference Abstracts (Vol. 6, p. 36). Kulkarni, M. K., Kandalgaonkar, S. S., Tinmaker, M. I. R., & Nath, A. (00). Markov chain models for pre monsoon season thunderstorms over Pune. International journal of climatology, (), Mohee, F. M., & Miller, C. (00). Climatology of thunderstorms for North Dakota, Journal of Applied Meteorology and Climatology, 49(9), Moon, S. E., Ryoo, S. B., & Kwon, J. G. (994). A Markov chain model for daily precipitation occurrence in South Korea. International journal of climatology, 4(9), Pinto, O., Pinto, I. R. C. A., & Ferro, M. A. S. (03). A study of the long term variability of thunderstorm days in southeast Brazil. Journal of Geophysical Research: Atmospheres, 8(), Sonnadara, U. (06). Spatial and temporal variations of thunderstorm activities over Sri Lanka. Theoretical and Applied Climatology, 4(3-4), Wilks, D. S. (006). Statistical methods in the atmospheric sciences (second edition). Academic Press, USA.

16 Journal of Natural Environmental Hazards, Vol.07, Issue 8, Winter Estimating the occurrence probability of Spring Thunderstorms using Markov chain, Case Study: Tabriz Mohammadreza Azizzadeh, Khadijeh Javan*, Ali Akbar Rasouli 3, Elnaz Ostadi 4 Received: Accepted: Abstract Thunderstorms are one of the climatic phenomena that cause numerous damages in different parts of the world, due to the accompaniment with thunder, high winds, hail and heavy precipitation. In this study, the probability of thunderstorm days in Tabriz in the spring is analyzed using probabilistic rules and Markov chain model. For this purpose, the daily data related to thunderstorms (codes 95 to 99) was used for 65 years (95-05). At first, the daily data is classified into the normal days (code 0) and thunderstorm days (code ). Then the frequency matrix is formed and the probability matrix is created accordingly based on maximum likelihood method. The Markov chain properties such as empirical probability and equilibrium probability, Frequency of occurrence, mean time periods and weather cycle were investigated. Finally, Occurrence Probability and return period of these spells were determined. The results show that the shortest weather cycle is in May, which has the highest frequency of thunderstorms. Conversely, the longest weather cycle is in April, which has the lowest frequency of thunderstorms. Also, in 65 years period, the one-day and two-day stormy sequences have the highest frequency. And For longer sequences, the frequency of thunderstorm days is reduced. The return period of one-day and twoday stormy spells is.5 and 5 days. Regression relation between the observed and estimated values of n-step periods of thunderstorm days shows that the considered accuracy and reliability for all months is more than 99%. Keywords: Thunderstorms, Markov chain, Probability matrix, Return period, Tabriz. - Department of Geography, Payam Noor University, Tehran, Iran. *- Assistant Professor of Climatology, Department of Geography, Urmia University, Urmia, Iran kh.javan@urmia.ac.ir 3 - Professor of Climatology, Faculty of Geography and planning, Tabriz University, Tabriz, Iran 4 - PhD Student of Climatology, Faculty of Geography and planning, Tabriz University, Tabriz, Iran