The role of model dynamics in ensemble Kalman filter performance for chaotic systems
The ensemble Kalman filter (EnKF) is susceptible to losing track of observations, or ‘diverging’, when applied to large chaotic systems such as atmospheric and ocean models. Past studies have demonstrated the adverse impact of sampling error during the filter’s update step. We examine how system dyn...
| Main Authors: | , , , , , , |
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| Other Authors: | |
| Format: | Article |
| Language: | en_US |
| Published: |
Co-Action Publishing
2014
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| Online Access: | http://hdl.handle.net/1721.1/89042 https://orcid.org/0000-0002-8362-4761 |