Snow processes are the most important among the components of the land surface schemes due to their role in both energy and water budget. Snow cover fraction (SFC) is one of the most important variables in snow parameterization because of the greatest spatial and temporal fluctuation among the components of the land surface schemes and its impact on the surface albedo, and thus on the radiative balance. The Noah-MP land surface scheme coupled with Weather Research and Forecasting (WRF) regional climate model, version 3.4, parameterizes SFC through the hyperbolic tangent relationship between snow depth, snow density, and snow melting factor (the model’s default equals one). Evaluation of the SFC of the model and calibration of the snow melting factor was done using daily MODIS images (MOD10A1), version 6; however, MODIS images are not available at most of the snowfall time periods because of the cloud coverage. For this reason, we study to see whether the calibration of snow melting factor has a significant impact on the WRF snow output, including SFC, snow depth, and minimum temperature, over the lands with different characteristics covers. The WRF/Noah-MP model runs with two separated 5 km domains in the western and northern part of Iran during several periods of heavy snow in the winter of 2013 and 2014. Based on the DEM and land use map, the study area is categorized into five areas, including forests, rangelands, low lands, and mountainous regions with high and low slopes. The calibrated snow melting factor equaled the model’s default snow melting factor only for forests (m=1); however, in other areas, the calibrated snow melting factor was different from the default value (m=0.5). Due to its positive efficiency coefficient, the WRF/Noah-MP model with the model’s default snow melting factor (WRF/Noah-MP) is successful in estimating the SFC in most areas, except for rangelands (-0.28) and mountainous areas with high slopes (-0.02). It has the highest performance
