Constantinescu, Emil M.Chai, TianfengSandu, AdrianCarmichael, Gregory R.2013-06-192013-06-192006-10-01http://hdl.handle.net/10919/19556The task of providing an optimal analysis of the state of the atmosphere requires the development of dynamic data-driven systems that efficiently integrate the observational data and the models. Data assimilation (DA) is the process of adjusting the states or parameters of a model in such a way that its outcome (prediction) is close, in some distance metric, to observed (real) states. It is widely accepted that a key ingredient of successful data assimilation is a realistic estimation of the background error distribution. This paper introduces a new method for estimating the background errors which are modeled using autoregressive processes. The proposed approach is computationally inexpensive and captures the error correlations along the flow lines.application/pdfenIn CopyrightNumerical analysisTechnical reportTR-06-22http://eprints.cs.vt.edu/archive/00000926/01/ar_background.pdf