Research on Tehran’s dry and wet periods using second grade Markov chain Model
Subject Areas : Geopoliticپرویز Kerdavani 1 , حسین Mohamadi 2 , مژگان Afshar 3
1 - ندارد
2 - ندارد
3 - ندارد
Keywords: Rain, dry and wet sequences, markov chain mode, Saudi Arabia high
, 
, pressure, Drought, synoptically Analysis,
Abstract :
In order to Statistically and Synoptically analyze and predict the dry and wet periods inTehran during the cold period, the days with 0/1 milimeter rain or more , mehrabadstation was daily chosen and surveyed as wet days during 1985-2003. The secondgrade markov chain model was used For determinig dry and wet periods. At firstfrequancy wet and dry days according to their continuation were classified andfrequancy eash one was studied individually.Tthen the possibility of every sequencewas calculated monthly and a six month of cold ness period.The most frequancy of rainy days 51 days and its least is 25 days a year. Marchwith 188 and October with 81 days rain is the Maximum and the Minimum frequency.1995 and 1996 years have been the driest and wettest years.After determining thesequences , the survey of effective pressure patterns in creating rain during the days 27to 30 of November ,1 to 7 of December 2003 as a longhest period and 10 to 13 ofDecember 1995 as a period of 4 days of wetness during the driest year , the surveyperiod was synoptically analyzed for this reason. It has been necessary to use sea levelpressure maps and 500 hp and also maps of direction and speed of the wind andspessific humidity 700 hp.The comparison of the frequency of predicted sequences with the frequancy ofobserved sequences , shows markov chain model exactness in predicting the dry andwet sequences of Tehran region which have sharp mismathch of rain.In synoptically maps it was specified that the most important source of humidityin Iran have been the Red sea, Arab sea, Adan gulf, which concidence of these wetnesssources with the Persian gulf and Saudi Arabia high pressure has caused moretransition of wetness into Iran and Tehran.