Annual rainfall forecasting based on synoptic patterns of tele-connection using statistical models

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Abstract

The research show that global climate changes and atmospheric general circulation are affected by large scale phenomena that occurred in the sea surface. These large scale phenomena are often named "climate large scale signals". These signals are calculated based on criteria such as sea Level Pressure (SLP), Sea Surface Temperature (SST) and so on. A method for weather forecasting is a special approach based on statistical modeling. In this study, data of 37 rainfall stations were used to model the relation between precipitation and Sea Level Pressure (SLP), Sea Surface Temperature (SST), Sea Level Pressure gradient (?SLP) and the difference between sea surface temperature and air temperature at 1000 HP. The results show that statistical modeling can successfully predict the amount of annual rainfall. The mean root square error for stepwise model were obtained 49 millimeters.

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