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Estimation of Model Error Variances During Data AssimilationData assimilation is all about understanding the error characteristics of the data and models that are used in the assimilation process. Reliable error estimates are needed to implement observational quality control, bias correction of observations and model fields, and intelligent data selection. Meaningful covariance specifications are obviously required for the analysis as well, since the impact of any single observation strongly depends on the assumed structure of the background errors. Operational atmospheric data assimilation systems still rely primarily on climatological background error covariances. To obtain error estimates that reflect both the character of the flow and the current state of the observing system, it is necessary to solve three problems: (1) how to account for the short-term evolution of errors in the initial conditions; (2) how to estimate the additional component of error caused by model defects; and (3) how to compute the error reduction in the analysis due to observational information. Various approaches are now available that provide approximate solutions to the first and third of these problems. However, the useful accuracy of these solutions very much depends on the size and character of the model errors and the ability to account for them. Model errors represent the real-world forcing of the error evolution in a data assimilation system. Clearly, meaningful model error estimates and/or statistics must be based on information external to the model itself. The most obvious information source is observational, and since the volume of available geophysical data is growing rapidly, there is some hope that a purely statistical approach to model error estimation can be viable. This requires that the observation errors themselves are well understood and quantifiable. We will discuss some of these challenges and present a new sequential scheme for estimating model error variances from observations in the context of an atmospheric data assimilation system.
Document ID
20030111840
Acquisition Source
Goddard Space Flight Center
Document Type
Conference Paper
Authors
Dee, Dick
(Science Applications International Corp.)
Date Acquired
August 21, 2013
Publication Date
January 1, 2003
Subject Category
Meteorology And Climatology
Report/Patent Number
EAE03-A-02659
Meeting Information
Meeting: EGS-AGU-EUG Joint Assembly Meeting
Location: Nice
Country: France
Start Date: April 6, 2003
End Date: April 11, 2003
Sponsors: American Geophysical Union, European Union of Geosciences, European Geophysical Society
Distribution Limits
Public
Copyright
Work of the US Gov. Public Use Permitted.

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