P13  Assimilation of Cloud-affected Infrared Satellite Radiances

AulignŽ, Thomas and Hongli Wang, National Center for Atmospheric Research

The assimilation of cloud-and-rain-contaminated satellite radiances is the next frontier for improving the model short-term prediction skills. Infrared radiances are particularly difficult to handle because of the data volume, the cost of the radiative transfer model and mostly the non-linearities in the observations operator. Typical model background errors translate into huge observation-minus-background differences in radiance space, which are not linearly related to the model state. We will present various techniques to pre-process the first-guess from a high-resolution WRF model run in order to improve the fit to the observations. A variational analysis is performed with an augmented control variable to include cloud parameters. An incremental approach is followed to handle the non-linearities in the observation operator and a robust l1-l2 (Huber) norm used to account for non-Gaussian error distributions. The analysis manages to fit the observations closely and adjust the cloud variables in a reasonable manner.