The work focuses on retrieving sea ice parameters using reanalysis, climatological and remote sensing data. A numerical sea ice model was implemented with a data assimilation scheme on a high performance computer. The model input includes atmospheric reanalysis and ocean climatological data. The assimilation of data acquired from satellite microwave radiometer improves model accuracy. The advantage of the model is the possibility to forecast ice parameters such as concentration, thickness, draft, ridging etc. on a high resolution scale. The modeled ice parameters can be used for risk analysis for offshore infrastructure and ship navigation in the ice covered regions. The results can also be used in regional climate studies by coupling with ocean-atmospheric models. The model was extensively tested and evaluated with satellite data and field measurements. The simulated ice draft results demonstrated a good agreement with the measurements from upward looking sonar (ULS) deployed on the Makkovik Bank (in the Labrador Sea). For example, the standard deviation (STD) of level ice draft is less than 5.0 cm and the bias is less than 0.2 cm for March-April of 2009. The simulated ice thickness was also compared with the thickness derived from Soil Moisture Ocean Salinity - Microwave Imaging Radiometer using Aperture Synthesis (SMOS-MIRAS) (). The results show that the estimated thickness from the model is within the uncertainty limits of the SMOS product.