Key points are not available for this paper at this time.
A variational analysis method to detect and correct displacement and amplification errors in short-range forecasts of a data assimilation system is developed and tested. Collectively these errors are termed distortion errors. The method uses a variational approach to solve a nonlinear least squares estimation problem with side constraints to determine the distortion that alters an a priori background field to best fit the available observations. In this study, the data are Special Sensing Microwave/Imager (SSM/I) retrievals of integrated water vapor and the a priori background fields are analyses of the European Centre for Medium-Range Weather Forecasts (ECMWF). In practice the background fields would be operational 6-h forecasts. The necessary algorithms and methodologies were developed, implemented, and tested on a sufficient number of cases to demonstrate the utility of the method. Cases were selected that have noticeable features in the SSM/I vertically integrated water vapor fields. In all cases studied, the SSM/I data, together with the distortion representation of error, produces significant changes to the ECMWF analyses, reducing the variance of the difference between the analysis and SSM/I data by 45%–86%. Further work is suggested to examine impacts on objective analyses and subsequent numerical forecasts.
Hoffman et al. (Thu,) studied this question.