Construction of non-diagonal background error covariance matrices for global chemical data assimilation

Abstract

Chemical data assimilation attempts to optimally use noisy observations alongwith imperfect model predictions to produce a better estimate of the chemicalstate of the atmosphere. It is widely accepted that a key ingredient forsuccessful data assimilation is a realistic estimation of the backgrounderror distribution. Particularly important is the specification of thebackground error covariance matrix, which contains information about themagnitude of the background errors and about their correlations. As modelsevolve toward finer resolutions, the use of diagonal background covariancematrices is increasingly inaccurate, as they captures less of the spatialerror correlations. This paper discusses an efficient computational procedurefor constructing non-diagonal background error covariance matrices whichaccount for the spatial correlations of errors. The correlation length scalesare specified by the user; a correct choice of correlation lengths isimportant for a good performance of the data assimilation system. Thebenefits of using the non-diagonal covariance matrices for variational dataassimilation with chemical transport models are illustrated. © 2011 Author(s).