Accuracy Assessment of Weather Assimilators of CLIMGEN, LARS-WG and Weather Man in Assimilation of three Different Climatic Parameters of three Different Climate (Gorgan, Gonbad and Mashhad)

In recent years, artificial models of weather generator parameters have extensively been applied in hydrological and ecological systems and also in the studies of climatic impact potential on agricultural ecosystems throughout the world. These models have been developed as a statistical technique for generating daily weather data when long-term data are not available. The purpose of this investigation was to evaluate the performance of three models of CLIMGEN, LARS-WG and Weather Man to predict at fine-scale and at meteorological stations’ scale for climatic variables including maximum temperature, minimum temperature, precipitation and solar radiation for the years of 2000-20009 in three regions of Gorgan, Gonbad and Mashhad. Firstly, daily weather data of each station from 1975-1999 for Gorgan and Gonbad and 1961-1999 for Mashhad were implemented in the models and the daily data for years 2000-2009 were obtained. For assessment of the mentioned models, the comparison of statistical indicators such as Root Mean Squared Error (RMSE), Model Efficiency (EF) and Determination Coefficient (R2) were used. The results obtained from comparing the monthly average of produced data during a ten-years period by models showed that temperature variable has been predicted better than other parameters by the three models. LARS-WG in Gorgan and Mashhad and CLIMGEN in Gonbad performed better to simulate the minimum temperature. CLIMGEN in Mediterranean climate of Gorgan and semi-arid climate of Gonbad and Weather Man in dry climate of Mashhad have simulated the maximum temperature better than the other models. Solar radiation variable by LARS-WG model in Mashad and Gonbad regions and by CLIMGEN model in Gorgan has been predicted by a more effective performance. Despite the differences among the outputs of the models, it seems that they can be applied in crop modeling and in the issues of climatic changes.