Satellite Bias Correction in the Regional Model ALADIN/CZ: Comparison of Different VarBC Approaches

Abstract

Abstract The bias correction of satellite radiances is an essential component of data assimilation system in numerical weather prediction (NWP). The variational bias correction (VarBC) scheme is widely used by global NWP centers, but there are still open questions regarding its use in limited-area models (LAMs). We present a study of key VarBC aspects in the limited-area 3D-Var system using the state-of-the-art NWP system ALADIN. Two basic VarBC applications are tested, specifically adopting bias coefficients from the global model ARPEGE and cycling bias coefficients independently in the LAM ALADIN (VarBC-LAM). The latter application is studied using daily update of bias coefficients with regards to static and dynamic settings of the VarBC stiffness. Extensive testing shows that the VarBC-LAM methods outperform the use of global coefficients from ARPEGE providing the better quality of the model first guess (3-h forecast), in the assimilation cycle with the largest normalized impact of 2%–3% for temperature and wind components in the midtroposphere. Compared to the global coefficients, there was little forecast impact between 24 and 48 h from using the VarBC-LAM coefficients. The various VarBC-LAM methods were comparable, but the CAM method may be most useful when an unexpected bias shows up.