Implications of Representing Snowpack Stratigraphy for the Assimilation of Passive Microwave Satellite Observations

Abstract

Abstract Under certain conditions, passive microwave satellite observations can be used to estimate snow water equivalent (SWE) across large areas, either through direct retrieval or data assimilation. However, the layered character of snowpacks increases the complexities of estimation algorithms. A multilayer model of snowpack stratigraphy that can serve as the forward model of a snow data assimilation system is described and evaluated. The model’s ability to replicate large-scale snowpack layer features is evaluated using observations from the Cold Land Processes Experiment (Colorado, 2002 and 2003) and a 2002 Nome–Barrow snowpit transect [Snow Science Traverse—Alaska Region (SnowSTAR2002)]. The multilayer model linked with a radiative transfer scheme improved the estimation of brightness temperatures both in terms of absolute values and frequency/polarization differences (error reductions ranging from 47% to 72%) relative to a one-layer model with similar, but depth-averaged, physics at the Colorado sites. The two models were also employed along the SnowSTAR2002 transect of snowpit measurements. The general unavailability of meteorological forcings along the transect made the use of coarse-scale reanalysis data necessary to simulate snow properties and microwave radiances. Errors in the precipitation forcings led to overestimation of SWE, and the simulated brightness temperatures from the two models showed large differences, due mostly to the inability of the single-layer model to simulate the observed larger grain sizes. These differences had implications for the estimation of snow depth; assimilation of Special Sensor Microwave Imager (SSM/I) observations into the multilayer model resulted in improved snow depth estimates (RMSEs of 18.1 cm versus 34.1 cm without assimilation), while the single-layer assimilation slightly decreased the estimation skill (RMSEs of 34.1 versus 33.6 cm).