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https://doi.org/10.5194/npg-2021-35
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/npg-2021-35
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
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Status: this preprint has been withdrawn by the authors.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
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This preprint has been withdrawn.
How to cite. Bach, E. and Ghil, M.: Brief communication: An innovation-based estimation method for model error covariance in Kalman filters, Nonlin. Processes Geophys. Discuss. [preprint], https://doi.org/10.5194/npg-2021-35, 2021.
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Interactive discussion
Status: closed
Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor
| : Report abuse
- RC1: 'Comment on npg-2021-35', Anonymous Referee #1, 08 Dec 2021
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RC2: 'Comment on npg-2021-35', Anonymous Referee #2, 09 Dec 2021
The comment was uploaded in the form of a supplement: https://npg.copernicus.org/preprints/npg-2021-35/npg-2021-35-RC2-supplement.pdf
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Geosciences Department and Laboratoire de Météorologie Dynamique (CNRS and IPSL), École Normale Supérieure and PSL University, Paris, France
Michael Ghil
Geosciences Department and Laboratoire de Météorologie Dynamique (CNRS and IPSL), École Normale Supérieure and PSL University, Paris, France
Department of Atmospheric and Oceanic Science, University of California at Los Angeles, Los Angeles, United States
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.
Data assimilation (DA) is the process of combining model forecasts with observations in order to...