期刊名称：Tellus A: Dynamic Meteorology and Oceanography
电子版ISSN：1600-0870
出版年度：1998
卷号：50
期号：1
页码：42-57
DOI：10.3402/tellusa.v50i1.14511
摘要：Identifying a viable strategy for specifying the background error covariance remains an importantproblem in meteorological data assimilation. From a formal point of view the number ofindependent parameters needed for this is n2/2 where n is the dimension of the model statespace. In most analysis systems used in operational mode at the present time, the error covarianceis modeled using assumptions about homogeneity and isotropy, and the resulting backgrounderror covariance matrix thus does not depend on the state of the atmosphere. In thispaper, we propose a simple and inexpensive method for specifying univariate background errorcorrelations in terms of the background field itself. We illustrate the positive impact of theimplementation of this model by a simple example in which we reconstruct a total ozone fieldfrom a sparse set of observations. We finally discuss the generalization of the basic idea involvedto univariate correlations for general meteorological models.
