determination of dew point temperature based on simultaneous multivariate models and vector time series considering heterogeneity in meteorological stations in eastern iran

In this research, meteorological parameters of eleven stations were monitored to build a suitable model for predicting dew point values. considering the importance of dew point temperature in forecasting frosts or rainfall and its other applications, the prediction of this parameter is of particular importance. the stations investigated in this research are: bam, birjand, chabahar, iranshahr, kerman, mashhad, sabzevar, tabas, torbat heydarieh, zabul, and zahedan, all of which are classified in dry climate. first, the correlation of different weather parameters with dew point was checked and then three parameters of average, maximum and minimum temperature were entered into the model as input with the highest correlation with dew point. carma and var models were used. then, the stability of the remaining series was calculated for both models and these series were developed using the garch model. the result of the development of the models was the modeling of the dew point in eleven meteorological stations with the developed carma-garch and var-garch models. our findings show that the var-garch model has superior performance in both training and testing stages and is selected as the best model of this research. one of the important reasons for the superiority of this model is its higher memory in processing time series. the definitive result is the improvement of both primary models if the remaining series of the model are developed using the garch model, which increases the accuracy of both models in the test phase by 6 to 30 percent.