Application of Neural Networks to the Simulation of the Heat Island over Athens, Greece, Using Synoptic Types as a Predictor

Abstract

The effect of the synoptic-scale atmospheric circulation on the urban heat island phenomenon over Athens, Greece, was investigated and quantified for a period of 2 yr, employing a neural network approach. A neural network model was appropriately designed and tested for the estimation of the heat island intensity at 23 stations during the examined period. The day-by-day synoptic-scale atmospheric circulation in the lower troposphere for the same period was classified into eight statistically distinct categories. The neural network model employed as an input the corresponding synoptic categories in conjunction with four meteorological parameters that are closely related to the urban heat island. It was found that the synoptic-scale circulation is a predominant input parameter, affecting considerably the heat island intensity. Also, it was demonstrated that the high pressure ridge mostly favors the heat island phenomenon and categories characterized by intense northerly component winds are responsible for its nonappearance or termination.