a prediction of maximum monthly precipitation recorded at ilam meteorological station based on persian gulf and red sea surface temperature through recordings data mining method

a prediction of maximum monthly rainfall is indispensable for management of agriculture and for management of water resources. previous studies have demonstrated that see surface temperatures affect rainfall in their surrounding regions. in this study the relationship between sea surface temperatures of persian gulf and that of red sea, and maximum monthly rainfalls recorded at illam meteorological station and also the possibility of using these temperature data for a prediction of rainfall were investigated. a forty five year data set of monthly temperatures of the above seas surfaces along with monthly rainfall records at illam station were employed for the purpose. the index confidences of the relationship between each see surface temperature at persian gulf and red sea surfaces and maximum monthly rainfall were calculated as more than 60 percent. this indicates the high correlation between these two sets of data. the results finally reveald that, maximum monthly rainfalls can be predicted using the above two sea surface temperatures with an index confidence of 66.8 percent and with one month lag times.