Evaluation of SHArP weather generator for simulation of air temperature data in several climates of Iran

Temperature is a key variable in climate and agriclutiral studies especially crop models, water requirement estimation and climate change. Despite of ease of measurement and large number of recording stations, data gaps in remote areas and the need for downscaling the grided climate model output has led to development of weather generators. In this study, the skill of Stochastic Harmonic Autoregressive Parametric (SHArP)SHArP weather generator in simulation of the daily maximum and minimum air temperature on a daily scale in 4 weather stations was evaluated. For this purpose, maximum and minimum temperature data as well as CNRM CMIP5 climate model projections were used in four synoptic stations of Kerman, Ahvaz, Karaj and Tabriz during the period of 2000-2015. The results of Pearson correlation coefficient showed that there is a significant correlation between observed data (0.78 to 0.93) and climatic model outputs.Comparing the observed and simulated temperature data generated by the SHArP model showed a good agreement and significant correlation which confirms the skill of this generator. The correlation coefficient in the studied stations varies between 0.80 to 0.95. The highest value of this coefficient belonged to the maximum temperature. The SHArp model also less simulates the temperature. In general, the findings of this study revealed that the SHArP model is capable to generate temperature data and can be used for filling the gaps.