Hydrologists regularly utilize data assimilation (DA) techniques to merge remote sensing measurements with physical models in order to characterize hydrologic reservoirs. DA methods generally require an estimate of the uncertainty of the various inputs to the models; in practice, however, the uncertainty of these quantities is often unknown. This paper explores the effects of the unknown uncertainty on the efficiency of a multifrequency, multiscale hydrologic DA scheme for snowpack characterization. Synthetic passive microwave (PM) measurements at 25 km and near-infrared (NIR) measurements at 1 km were assimilated, and both snow water equivalent (SWE) and grain size were estimated at 1 km resolution. It is found that the uncertainty magnitude had a significant effect on the efficiency of both SWE and grain size estimation, but that the uncertainty magnitude had very different effects on these two variables because of the different PM and NIR measurement scales. Secondly, it was found that the uncertainty accuracy had a very important role in this DA scheme and that the filter may degrade the estimate of SWE and grain size if key model inputs are misspecified. Finally, four metrics were used to assess the difference between the PM and NIR measurement innovations and their expected values. It was shown that these metrics could potentially be used in an adaptive filtering scheme to correct misspecified uncertainty. More investigation will be required before the feasibility of such an adaptive filtering scheme is established. These findings have important ramifications for snowpack estimation since it implies that in the context of DA schemes, better use will be made of remote sensing products when better physical characterization of the uncertainty of modeled estimates of snow states is available.