Spring Rainfall Prediction of Khorasan-e Razavi Province, Based upon Climatic Large Scale Signals by Using Artificial Neural Network

The aim of this research is investigating the relations between climatic large scale signals and spring rainfall of Khorasan-e Razavi province. In this research, we have analyzed 38 years of rainfall data in khorasan-e Razavi province, located in northeastern Iran. We attempted to train Artificial Neural Network based upon climatic large scale signals with 38 years of rainfall data. For performance evaluation, network predicted outputs were compared with the actual rainfall data. At the outset of this study, the relationships between synoptically pattern variations, including Sea Level Pressure (SLP), Sea Surface Temperature (SST), Sea Level Pressure Gradient (?SLP), Sea Surface Temperature Difference (?SST), Air Temperature at 700 hPa, Thickness between 500 and 1000 hPa level, Relative Humidity at 300 hPa and Precipitable water are investigated. In the second step, model was calibrated from 1970 to 1997. Finally, rainfall prediction is performed from 1998 to 2007. Simulation results reveal that Artificial Neural Network is promising and efficient. Root mean square was obtained 2.5 millimeters.