Computer Science Technical Reports
CS at VT

Autoregressive Models of Background Errors for Chemical Data Assimilation

Constantinescu, Emil M and Chai, Tianfeng and Sandu, Adrian and Carmichael, Gregory R (2006) Autoregressive Models of Background Errors for Chemical Data Assimilation. Technical Report TR-06-22, Computer Science, Virginia Tech.

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The 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.

Item Type:Departmental Technical Report
Keywords:background covariance, data assimilation, autoregressive models
Subjects:Computer Science > Numerical Analysis
ID Code:926
Deposited By:Constantinescu, Mr. Emil M.
Deposited On:12 October 2006