عنوان مقاله [English]
Marcov’s Chain precipitation is one of the useful methods to recognize and predict climate phenomena. Marcov’s Chain is a model through which the current system status depends on previous states. By using Marcov’s Chain, the probability of the occurrence of return periods in climate phenomena such as precipitation can be calculated. In the present study we have investigated about the application of Marcov’s Chain using the daily precipitation statistics of climate stations in Mazandaran province (since its commencement to 2015) regarding the frequency and permanence of precipitation and dry days. The statistics related to precipitation days were arranged based on the counting matrix of changes in occurrence states of dry and humid days (days without precipitation and days with precipitation). The data were calculated based on maximum status change index. After calculating probability matrix, it was assessed and analyzed with frequent stable powers and daily return periods. Also, return periods for two to five days of precipitation for dry and rainy days were assessed and a single probability of the occurrence of these periods were studied based on stable matrix models. Results showed that based on stable matrix return periods range a domain of 3 to 10 days for the probability of precipitation days and 1 to 3 days for the probability of dry days. For example, the probability of precipitation with two days permanence has been estimated to be between 0.18 and 0.19 and for precipitation with a permanence of 3 days, it was estimated to be between 0.01 and 0.05. Therefore, it was observed that in Mazandaran province, the precipitation has had an incongruent distribution.