Download the paper:
Abstract:
Eight years (2002-2010) of AMSR-E snow water equivalent (SWE) retrievals and MODIS snow cover fraction (SCF) observations are assimilated separately or jointly into the Noah land surface model over a domain in Northern Colorado. A multi-scale ensemble Kalman filter (EnKF) is used, supplemented with a rule-based update. The satellite data are either left unscaled
or are scaled for anomaly assimilation. The results are validated against in
situ observations at 14 high-elevation SNOTEL sites with typically deep snow
and at 4 lower-elevation COOP sites.
Assimilation of coarse-scale AMSR-E SWE and fine-scale MODIS SCF observations both result in realistic spatial SWE patterns. At COOP sites with
shallow snowpacks, AMSR-E SWE and MODIS SCF data assimilation are
beneficial separately, and joint SWE and SCF assimilation yields significantly
improved RMSE and correlation values for scaled and unscaled data assimilation. In areas of deep snow where the SNOTEL sites are located, however,
AMSR-E retrievals are typically biased low and assimilation without prior
scaling leads to degraded SWE estimates. Anomaly SWE assimilation could
not improve the interannual SWE variations in the assimilation results because the AMSR-E retrievals lack realistic interannual variability in deep snowpacks. SCF assimilation has only a marginal impact at the SNOTEL locations because these sites experience extended periods of near-complete snow
cover. Across all sites, SCF assimilation improves the timing of the onset of
the snow season but without a net improvement of SWE amounts.