Probability analysis of the frequency and persistence of precipitation days in Shiraz by using Markov chain model

probability analysises are methodes useful for understanding and predicting phenomena uch as precipitation. One f these methodes is the Markov chain. Markov chain model is particular state of models hat the current state of a ystem is depends on previouses state. This methode calculates return period of ccurrence climate phenomena such as recipitation. Hence، in the present study daily precipitation data for 58 years (1956 - 013) synoptic station in Shiraz re used، the frequency and persistence of precipitation days are studied by using the Markov chain model. Above statistic data are based on the transition probabilities matrix of the wet and the dry days (days without precipitation and precipitation days) are sorted، then changing of the situation matrix is calculated based on the maximum likelihood method. Late matrix with repeated power، reliable and daily rainfall return period are analyzed and evaluated. In the following، return periods for rainfall days of 2 to 5 days and return periods for dry days of 1، are evaluated. Then return period continuation of precipitation days 2 to 5 days for twelve months of the year is calculated. The results show that the probability of precipitation (wet days) per day is %0. 1167 and the probability of lack precipitation (dry days) is %0. 8833 percent. It is obvious that the most occurrence of days with rainfall، especially during the winter، are the January and the February. For example، the return period of 2 consecutive precipitation days in January was estimated with 5 days. Hence، observed that rainfall distribution is heterogeneity in Shiraz. In other words، rainfall is not uniform and concentrated in Shiraz.