Conditional probability, dry and wet periods weather forecasts recovery course using Markov chain model (Case study: Khuzestan province, Iran)

The aim of this study is the spatial analysis of rainfall in wet and dry months of the year in south-west Iran especially khouzestan. To this end, the model of Markov chain is used. In Markov chain, it is assumed that the climate has only two states: dry or wet. For instance, rainfall has only two states: it rains (1) or it will not rain (0).There is not a third state. The statistical years are not the same in this research and change from 10 to 50 years. Twenty stations are selected in Khuzestan and the surrounding provinces (synoptic and climatology).The three months of the summer are not included in calculations because the precipitation was zero or near zero. As the result of that, the reoccurrence of dry periods is generally longer than that of the wet ones. In the other words, dry periods last a long time in south-west Iran. Regarding area, long dry periods cover a wide area in the southwest while the area covered by short-term periods is less. When the length of time of periods increases to possibly 8 days, the changes are reversed and the longer time the probability of 8-day aridness takes, the more area it covers and the shorter time it takes, the less area it covers.