A finite-element sea ice model (FESIM) is applied in a data assimilation study with the singular evolutive interpolated Kalman (SEIK) filter. The model has been configured for a regional Arctic domain and is forced with a combination of daily NCEP reanalysis data for 2-m air temperature and 10-m winds with monthly mean humidities from the ECMWF reanalysis and climatological fields for precipitation and cloudiness. We assimilate 3-day mean ice drift fields derived from passive microwave satellite data. Based on multivariate covariances (which describe the statistical relationship between anomalies in different model fields), the sea ice drift data assimilation produces not only direct modifications of the ice drift but also updates for sea ice concentration and thickness, which in turn yield sustainable corrections of ice drift. We use observed buoy trajectories as an independent data set to validate the analyzed sea ice drift field. A good agreement between modeled and observed tracks is achieved already in the reference simulation. Application of the SEIK filter with satellite-derived drift fields further improves the agreement. Spatial and temporal variability of ice thickness increases due to the assimilation procedure; a comparison to thickness data from a submarine-based upward looking sonar indicates that the thickness distribution becomes more realistic. Validation with regard to satellite data shows that the velocity data assimilation has only a small effect on ice concentration, but a general improvement of the ice concentration within the pack is still evident. Copyright 2009 by the American Geophysical Union.