The impact of satellite bias correction on the assimilation of satellite microwave water vapor channels

Satellite observation from microwave sounders are one of the main sources of information on atmospheric temperature and humidity for the Numerical Weather Prediction (NWP) models. Previous studies show that these observations have a very significant impact on the NWP forecasts. However, several factors, such as radiometric and geometric errors in the observations, and errors in radiative transfer calculations due to uncertainty in the spectroscopy databases or land surface emissivity, have limited the full potential of these observations. The aim of the current study is to enhance the assimilation of microwave observations, especially from the microwave water vapor channels, by developing appropriate filters for the surface emissivity and clouds as well as a new bias correction technique for correcting the radiometric errors in the observations. The new bias correction technique has several advantages compared to the variational bias correction, for instance, it does not depend on the accuracy of the radiative transfer calculations or the first guess fields. The NOAA GFS and Gridpoint Statistical Interpolation (GSI) systems are used to demonstrate the new techniques.