In this research the effect and importance of horizontal resolution in mesoscale numerical model of MM5 on quality and magnitude of some atmospheric variables has been discussed. A case study was also carried out to verify the results of the findings. The case occurred in an unusually cold winter. This case was activated in the north of Iran during 12-15 October 2004. The main reason for selecting this case is its heavy fall in temperature (10-15 ?c) and precipitation on the southern coast of the Caspian Sea and the northern side of the Alborz Mountain Range. The effect of horizontal resolution of MM5 has been studied by simulating this atmospheric system. The main variables that have been focused on are mean sea level pressure and predicted precipitation by MM5 model . These variables which are achieved from a run of MM5 with 60, 20, and 15 km, are compared with those of synoptical maps and precipitation observatories. The high resolution effect of model has been considered extensively for precipitation values.
The results revealed that the numerical model is able to simulate the mesoscale and synoptic scale characteristics such as precipitation in synoptic scale quite well. By reducing the horizontal grid size to 15-20 km which fell into mesoscale distance, the precipitation could be simulated reasonably . No distinguishing differences exist in changing the horizontal grid size from 20 to 15 km. The effect of land-use in the surface and its coverage are considered. Special attention is paid to the height fluctuations in the surface and also to the slope of the surface because of their intensive effect on precipitation but since the available data of surface topography was in a single resolution, model is run with same topography data for different grid sizes. The different options of model configurations for physical and dynamical conditions have been tested and the present results are based on the best (based on agreement with observation) output of predictions of variable with emphasis on precipitation. There are some differences in some parts and these differences are discussed and evaluated. The main reasons for disagreement of predicted variables and observations are: disagreement in methods of measurements , various instruments of measurements and different managements from different organizations.