Estimation Probability of Daily precipitation by using Markov Chain Models in Different Climates of Iran

The Markov chain used for statistical analysis events that are not independent and are related to their previous events. In this study, events possibility of serial dry and wet days were analyzed by Markov Chain method for 15 synoptic stations with different climates of coldest arid climates to its moderate humid in Iran. For this purpose was used daily precipitation data (1978-2009) for Kerman, Mashhad, Shiraz and Bandar Abbas stations, (1978-2008) for Tabriz, Khorramabad, Isfahan, Tehran, Zahedan, Ahwaz, Ardabil, Gorgan stations, (1978-2007) for Zanjan station, (1987-2008) for Yasouj stations and (2000-2008) for Sari station. These data are arranged according to frequency matrix of rainy days and without rainfall, then probability matrix was calculated by maximum likelihood method. The respectively produced the Probability contour maps during the dry monsoon. In the study stations, results showed that lack of precipitation probability has variable, 0.811- 0.909 in the arid climates, 0.685- 0.84 in the semi-arid climates and 0.695- 0.728 in the moderate and humid climates.