期刊名称：Tellus A: Dynamic Meteorology and Oceanography
电子版ISSN：1600-0870
出版年度：2019
卷号：71
期号：1
页码：1-23
DOI：10.1080/16000870.2019.1615168
摘要：The relevance of climatological background error statistics for mesoscale data assimilation has been
investigated with regard to basic assumptions and also with regard to the ensemble generation techniques
that are applied to derive the statistics. It is found that background error statistics derived by simulation
through Ensemble Data Assimilation are more realistic than the corresponding statistics derived by
downscaling from larger scale ensemble data. In case perturbation of observations is used to inject a spread
into the ensemble, and the ensemble is integrated over a few hours only, it was found that the derived
structure functions may be contaminated by the geometry of the observing network. The effects of the
assumptions of stationarity, homogeneity and isotropy, that are generally applied in the generation of
background error statistics, and the implications of the background error covariance model have also been
illustrated. Spatial covariances derived under these assumptions were contrasted against spatial covariances
obtained by ensemble averaging only, preserving the signals from forecast errors of the day. This indicates
that it is likely to be favourable to apply data assimilation with ensemble background error statistics
obtained from ensemble averaging, like in ensemble Kalman filters or in hybrids between variational and
ensemble data assimilation techniques.
关键词：Mesoscale data assimilation ; background error covariance model ; ensemble prediction system ; climatological balance relationships ; homogeneity and isotropy
