Articles | Volume 25, issue 3
https://doi.org/10.5194/npg-25-633-2018
© Author(s) 2018. 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-25-633-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
04 Sep 2018
Research article |
| 04 Sep 2018
| 04 Sep 2018
Chaotic dynamics and the role of covariance inflation for reduced rank Kalman filters with model error
Nansen Environmental and Remote Sensing Center, Bergen, Norway
Alberto Carrassi
Nansen Environmental and Remote Sensing Center, Bergen, Norway
Marc Bocquet
CEREA, joint laboratory École des Ponts ParisTech and EDF R&D, Université Paris-Est,
Champs-sur-Marne, France
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- Dynamical effects of inflation in ensemble‐based data assimilation under the presence of model error G. Scheffler et al. 10.1002/qj.4307
- Asymptotic Forecast Uncertainty and the Unstable Subspace in the Presence of Additive Model Error C. Grudzien et al. 10.1137/17M114073X
- Inference of stochastic parametrizations for model error treatment using nested ensemble Kalman filters G. Scheffler et al. 10.1002/qj.3542
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Latest update: 22 Nov 2024
Short summary
Using the framework Lyapunov vectors, we analyze the asymptotic properties of ensemble based Kalman filters and how these are influenced by dynamical chaos, especially in the context of random model errors and small ensemble sizes. Particularly, we show a novel derivation of the evolution of forecast uncertainty for ensemble-based Kalman filters with weakly-nonlinear error growth, and discuss its impact for filter design in geophysical models.
Using the framework Lyapunov vectors, we analyze the asymptotic properties of ensemble based...
