Assimilating Infrared and Microwave Sounder Observations with Correlated Errors

Satellite observations are operationally assimilated at the National Centers for Environmental Prediction (NCEP) and are a vital part of the data assimilation system. The Gridpoint Statistical Interpolation (GSI) uses prescribed observation errors for infrared and microwave sounders and assumes that the observation errors of different channels are uncorrelated. To compensate for this, observation errors are inflated, however, in order to produce an optimal analysis, errors and error correlations must be accurately defined. The goal of this study is to enhance the specification of these errors in the operational GSI by improving their estimates and by properly accounting for these inter-channel correlations. This presentation details the estimation and conditioning of the correlated error covariance matrices of Infrared Atmospheric Sounding Interferometer (IASI), Atmospheric Infrared Sounder (AIRS), and Advanced Technology Microwave Sounder (ATMS), as well as the impacts of their inclusion in the GSI. The forecast benefits are also assessed after a two month assimilation experiment by verifying against other observations and against ECMWF analyses.