Snow on Arctic sea ice plays many roles in feedbacks between sea ice and the global climate. For example, in colder seasons, snow can inhibit sea ice growth by insulating sea ice from the atmosphere, but conversely, the high albedo of snow-covered ice can inhibit sea ice melt. Snow cover also introduces uncertainty into sea ice thickness estimates derived from sea ice altimetry measurements. In-situ observations of snow on Arctic sea ice are infrequent and geographically sparse, due to the logistical challenges of making measurements in such a remote region. Models which produce estimates of snow depth on sea ice can help address this observation gap. This talk will introduce the NASA Eulerian Snow On Sea Ice Model (NESOSIM; Petty et al, 2018), a 2-layer snow depth model which incorporates satellite-derived sea ice drift, passive microwave sea ice concentration, and near-surface winds and snowfall from reanalyses, to produce estimates of snow depth on sea ice. The sensitivity of the NESOSIM model output to the choice of snowfall input to the model will be examined. A calibration of reanalysis snowfall rates to satellite-derived snowfall observations from CloudSat will be presented, and applied to the NESOSIM reanalysis snowfall input. The implications of this calibration for the model parametrization will be discussed.