Role of Forcing Uncertainty and Model Error Background Characterization in Snow Data Assimilation

Accurate specification of the model error covariances in data assimilation systems is a challenging issue. Ensemble land data assimilation methods rely on stochastic perturbations of input forcing and model prognostic fields for developing representations of input model error covariances. This presentation examines the limitations of using a single forcing dataset for specifying forcing uncertainty inputs for assimilating snow depth retrievals. Using an idealized data assimilation experiment, we demonstrate that the use of an ensemble of forcing inputs provides a better characterization of the model error background, which leads to improved data assimilation results, especially during the snow accumulation and melt time periods. The use of a forcing ensemble is then employed for assimilating snow depth retrievals from the AMSR2 instrument over two domains in the Continental U.S. with different snow evolution characteristics. Over a region near the Great Lakes where the snow evolution tends to be ephemeral, the use of a forcing ensemble provides significant improvements relative to the use of a single forcing dataset. Over the Colorado Headwaters characterized by large snow accumulation, the impact of using the forcing ensemble is less prominent and is largely limited to the snow transition time periods. The results from this study demonstrate that the availability of a better model error background through the forcing ensemble enables the assimilation system in better incorporating the observational information.