Evaluating a Physically-Based Cloud Model for 6-hour Rainfall Prediction Using Meteorological Data and Meteoasat Images in the Basin of Urmia Lake

Rainfall is considered as one of the most important inputs of the hydrological systems that its study and measurement in several different conditions such as the prediction of atmospheric condition, designing the hydraulic structures, estimation and modeling of flood is necessary. The purpose of this study was to estimate the amount of 6 hours of rainfall in Urmia Lake basin usinga physical cloud model. The inputs of this model include the high temperature of the cloud which is estimated from the infrared band ofMeteosat. the pressure, temperature and dew point temperature from meteorological stations in time scale is six hours . The calibration of the model was conducted by using the observed data of 16 synoptic stations in the basin of Urmia Lake during the statistical period of 2005 to 2011 for six rain events. For comaring the estimated amount of precipitatin by the registered model and amounts in ground stations , the statistical criteria of mean error(ME), mean absolute error(MAE), root mean square error(RMSE) and absolute bias(abias) have been used. The mean of each error criteria has obtained 0.86, 1.61, 2.39 and 0.67 mm respectively. Small amount of error criteria for physical cloud model represents the relatively high performance of this model for 6 hours rainfall estimation in Urmia lake basin .