Bias correction and quality control algorithms for satellite radiance data are adjusted for better simulation of the strong Arctic Major Sudden Stratospheric Warming (SSW) event that occurred in late January 2009. A series of five-day hindcast experiments is performed with cycling data assimilation using NOGAPS (Navy Operational Global Atmospheric Prediction System) / NAVDAS-AR (Naval Research Laboratory Atmospheric Variational Data Assimilation System – Accelerated Representer, a 4DVar system). The quality control check that limits the size of the innovation (observation minus background) is optimally determined to reduce rejection of useful radiance data. It is found that when the innovation limits for selected high-peaking channels of AMSU-A and SSMI/S sensors are properly calibrated, both the analysis and forecast of the SSW event can be improved. The effect of calibrated bias correction is discussed in terms of the anomaly correlation (AC) of the geopotential height, which depends strongly on the quality of the analysis as well as that of the forecast. The impact of the SSW on the surface weather is also investigated in view of the Arctic Oscillation (AO) Index calculated using 1000-hPa geopotential height. The AO Index values better match those obtained using verification data when the revised innovation limits are used, suggesting an improved coupling between the middle and lower atmospheric processes represented in the model.