عنوان مقاله [English]
نویسندگان [English]چکیده [English]
Because of the role of climate parameters in climate Change and improve the efficiency of climate and rainfall prediction models, the study of these parameters are very important in the world. The study of clouds have special important, because the clouds make connection between the synoptic patters and surface climate conditions and any change in its properties, can start a chain reaction in other climate parameters.
In this study, the linkage between four important cloud properties cloud top temperature and pressure, cloud optical depth and cloud water and ice path and daily precipitation was investigated in Iran. For this purpose, data from synoptic stations and satellite sensor (MODIS) for the entire period (2000 to 2011) was used and the relationship between the cloud properties and precipitation was analyzed by means of linear and non-linear regression and multiple regression models. In the first, few layers of clouds and precipitation data were overlapping in the ArcGIS, and the country was divided into 4 zones. These zones are based on the correlation between precipitation and cloud properties.
The results showed that, cloud-top temperatures explain 20 to 39 percent of rainfall variability in the zones this amount was obtained 20 to 37 percent for cloud top pressure, 21 to 31 for cloud water and ice path and 19 to 31 for cloud optical depth. The results of multiple regression model showed that, the four cloud parameters can explained about 50Cpercent of precipitation variability in zone 3. This amount was obtained about 40 percent in zone 2 and about 30 percent in other zones. The most parts of the precipitation variability in Iran can be explained by the four cloud properties, so these properties can be used as reliable inputs for precipitation prediction models.