Preprints
https://doi.org/10.5194/npg-2021-35
https://doi.org/10.5194/npg-2021-35
12 Nov 2021
 | 12 Nov 2021
Status: this preprint has been withdrawn by the authors.

Brief communication: An innovation-based estimation method for model error covariance in Kalman filters

Eviatar Bach and Michael Ghil

Abstract. We present a simple innovation-based model error covariance estimation method for Kalman filters. The method is based on Berry and Sauer (2013) and the simplification results from assuming known observation error covariance. We carry out experiments with a prescribed model error covariance using a Lorenz (1996) model and ensemble Kalman filter. The prescribed error covariance matrix is recovered with high accuracy.

This preprint has been withdrawn.

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Eviatar Bach and Michael Ghil

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Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
Eviatar Bach and Michael Ghil
Eviatar Bach and Michael Ghil

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This preprint has been withdrawn.

Short summary
Data assimilation (DA) is the process of combining model forecasts with observations in order to provide an optimal estimate of the system state. When models are imperfect, the uncertainty in the forecasts may be underestimated, requiring inflation of the corresponding error covariance. Here, we present a simple method for estimating the magnitude and structure of the model error covariance matrix. We demonstrate the efficacy of this method with idealized experiments.