Autoregressive Models of Background Errors for Chemical Data Assimilation

TR Number
TR-06-22
Date
2006-10-01
Journal Title
Journal ISSN
Volume Title
Publisher
Department of Computer Science, Virginia Polytechnic Institute & State University
Abstract

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.

Description
Keywords
Numerical analysis
Citation