Snow is an important part of the global water cycle and an important source of freshwater. Snowmelt causes floods in river runoff and leads to natural disasters. Therefore, modeling snow depth changes is necessary because of the limited interpolation methods of station measurements. This study aimed to investigate the ability of the WRF model to estimate snow depth using different initial and boundary conditions of GFS (with 0.5-degree spatial resolution) and ERA5 (with 0.25-degree spatial resolution). The study area is in the northern region of Tehran; heavy snowfall occurred on 13 and 14 February 2017. Observations of the synoptic stations in the Tehran area were used to compare the snow depth output under different initial and boundary conditions. The results show that the model underestimated the snow depth using both boundary conditions, and the model's simulation results with ERA5 are more consistent with the synoptic station's data.