Assessment of Teleconnection Patterns Affecting Rainfall and Temperature (Case Study: Mashhad Synoptic Station)

Rainfall and mean temperature are the most important meteorological parameters in determining distribution of other meteorological variables and also in climatic classifications. In addition, they have undeniable effects on human, natural and especially agricultural activities. Therefore knowing the value, variation and prediction of these parameters could result in precise management in different sectors. This research was done for the assessment of teleconnection indices, which affect the two mentioned parameters in Mashhad during the period of 1956-2010. The stepwise regression model with limited input was used for training and also evaluation data to derive monthly and seasonal prediction models. The statistical results showed that these models have the suitable performance to predict the rainfall and temperature parameters. In general, this performance was more appropriate for monthly models than seasonal ones, and also for temperature than rainfall.