Temperature retrievals from polar-orbiting satellites are clearly beneficial in the Southern Hemisphere and the stratosphere, due to lack of conventional data, but have neutral impact on Northern Hemisphere forecasts. An alternative to retrievals is the direct assimilation of radiance data. The NRL Variational Data Assimilation System (NAVDAS), coupled with the Navy Operational Global Atmospheric Prediction System (NOGAPS) NWP model, constitute a system capable of three-dimensional variational assimilation (3DVar) of radiance data. In particular, the assimilation of microwave radiance data from the Advanced Microwave Sounding Unit (AMSU-A) has shown clear positive impact on 5-day forecasts in both hemispheres. One requirement for successful radiance assimilation is bias correction. Biases are due both to the satellite instrument, and the underlying airmass, resulting from inaccuracies in the fast radiative transfer model that converts NWP fields into simulated radiances. Our approach to airmass bias correction uses multilinear regression of fifteen days of observed minus computed radiances, with selected NWP fields as predictors. Research into hybrid methods, which add the radiances themselves as predictors, is being pursued. Moisture retrievals from AMSU-B can also benefit from bias correction. Preliminary results comparing uncorrected and bias-corrected AMSU-B moisture retrievals are presented. The need for bias correction is universal. Our methodology is robust and general, and should be applicable to current and future satellites.