Abstract
Abstract. Chemical data assimilation attempts to optimally use noisy observations along with imperfect model predictions to produce a better estimate of the chemical state of the atmosphere. It is widely accepted that a key ingredient for successful data assimilation is a realistic estimation of the background error distribution. Particularly important is the specification of the background error covariance matrix, which contains information about the magnitude of the background errors and about their correlations. As models evolve toward finer resolutions, the use of diagonal background covariance matrices is increasingly inaccurate, as they captures less of the spatial error correlations. This paper discusses an efficient computational procedure for constructing non-diagonal background error covariance matrices which account for the spatial correlations of errors. The correlation length scales are specified by the user; a correct choice of correlation lengths is important for a good performance of the data assimilation system. The benefits of using the non-diagonal covariance matrices for variational data assimilation with chemical transport models are illustrated.
Highlights
Chemical data assimilation attempts to optimally use noisy observations along with imperfect model predictions to produce a better estimate of the chemical state of the atmosphere
A non-diagonal background error covariance matrix allows the information from local observations to spread out in space to contribute to corrections of state variables in neighboring locations; it allows observations of certain components of the state vector to contribute to corrections of other components
The results indicate that 3D-Var is sensitive to the correlation length used in the construction of the background error covariance matrix
Summary
The close integration of observational data is recognized as essential in weather/climate analysis and forecast activities. A popular approach to approximate the background covariance matrix is the NMC method (Parrish and Derber, 1992), in which the differences between several forecasts verifying at the same time are used to approximate the background error This method has been successfully applied to chemical data assimilation (Chai etal., 2006). We propose here a computationally efficient approach for constructing (background) error covariances that account for spatial correlations in both horizontal and vertical directions, and assess its impact on the assimilation of tropospheric ozone profiles from TES. 4. Section 5 presents assimilation results of TES ozone profiles with the global chemical transport model GEOS-Chem, and illustrates the benefits of nondiagonal covariances in both three and four dimensional variational data assimilation settings. They are typically assumed to have a normal distribution with mean zero and covariance Ri,
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