Quantifying Uncertainty in Modeling Snow Microwave Radiance for a Mountain Snowpack at the Point-Scale, Including Stratigraphic Effects

Abstract

Merging microwave radiances and modeled estimates of snowpack states in a data assimilation scheme is a potential method for snowpack characterization. A radiance assimilation scheme for snow requires a land surface model (LSM) coupled to a radiative transfer model (RTM). In this paper, we explore the degree of model fidelity required in order for radiance assimilation to yield benefits for snowpack characterization. Specifically, we characterize the uncertainty of Microwave Emission Model for Layered Snowpacks (MEMLS) radiance predictions by quantifying model accuracy and sensitivity to the following: (1) the LSM snowpack layering scheme and (2) the properties of the snow layers, including melt-refreeze ice layers. MEMLS was consistent with the measured brightness temperatures at 18.7 and 36.5 GHz with a bias (mean absolute error) of 0.1 K (3.1 K) for the vertical polarization and 3.4 K (9.3 K) for the horizontal polarization. An error in the predictions at horizontal polarization is due to uncertainty in ice-layer properties. It was found that in order for predicted brightness temperatures from the coupled LSM and RTM to be adequate for radiance assimilation purposes, the following must be satisfied: (1) the LSM snowpack layering scheme must accurately represent the stratigraphic snowpack layers; (2) dynamics of melt-refreeze ice layers must be modeled explicitly, and the predicted density of melt-refreeze layers must be accurate within ; and (3) the MEMLS correlation length must be predicted within 0.016 mm, or effective optical grain diameter must be predicted within 0.045 mm. Recommendations for future field measurements are made.