Abstract
A methodology for assimilating ground-based and remotely sensed snow data within a snow-evolution modeling system (SnowModel) is presented. The data assimilation scheme (SnowAssim) is consistent with optimal interpolation approaches in which the differences between the observed and modeled snow values are used to constrain modeled outputs. The calculated corrections are applied retroactively to create improved fields prior to the assimilated observations. Thus, one of the values of this scheme is the improved simulation of snow-related distributions throughout the entire snow season, even when observations are only available late in the accumulation and/or ablation periods. Because of this, the technique is particularly applicable to reanalysis applications. The methodology includes the ability to stratify the assimilation into regions where either the observations and/or model has unique error properties, such as the differences between forested and nonforested snow environments. The methodologies are introduced using synthetic data and a simple simulation domain. In addition, the model is applied over NASA’s Cold Land Processes Experiment (CLPX), Rabbit Ears Pass, Colorado, observation domain. Simulations using the data assimilation scheme were found to improve the modeled snow water equivalent (SWE) distributions, and simulated SWE displayed considerably more realistic spatial heterogeneity than that provided by the observations alone.
Corresponding author address: Dr. Glen E. Liston, Cooperative Institute for Research in the Atmosphere, Colorado State University, Fort Collins, CO 80523-1375. Email: liston@cira.colostate.edu
This article included in the The Cold Land Processes Experiment (CLPX) special collection.
