Impact of non-linearities on an incremental 4D-VAR data assimilation method in a high resolution numerical ocean model

Pierre-Antoine Bouttier 1, 2 Eric Blayo 1 Jacques Verron 2
1 MOISE - Modelling, Observations, Identification for Environmental Sciences
Inria Grenoble - Rhône-Alpes, LJK - Laboratoire Jean Kuntzmann, INPG - Institut National Polytechnique de Grenoble
2 LEGI - Laboratoire des Écoulements Géophysiques et Industriels [Grenoble]
Abstract : A current stake for numerical ocean models is to adequately represent meso- and small-scale activity, in order to simulate its crucial role in the general ocean circulation and energy budget. It is therefore also a challenge for data assimilation (DA) methods to control these scales. However this small-scale activity is strongly linked to the nonlinear character of the flow, whereas DA methods are generally much less efficient in such contexts than in (almost) linear ones. The purpose of this poster is to address this problem specifically, by exploring the behaviour of an incremental 4D-VAR DA method in a nonlinear ocean model. A series of experiments assimilating simulated altimeter data in an idealized Gulfstream-like configuration of the NEMO ocean model at increasing resolutions (which is a proxy for increasing nonlinearity) are analyzed. We present in particular results characterizing scales and structures of the analysis error along the assimilation process, as well as tentative links with small scale activity. Moreover we investigate some strategies for DA in such nonlinear contexts, with the aim of reducing this analysis error.
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Conference papers
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https://hal.inria.fr/hal-00763932
Contributor : Eric Blayo <>
Submitted on : Tuesday, December 11, 2012 - 9:34:12 PM
Last modification on : Thursday, January 3, 2019 - 4:30:09 PM

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Pierre-Antoine Bouttier, Eric Blayo, Jacques Verron. Impact of non-linearities on an incremental 4D-VAR data assimilation method in a high resolution numerical ocean model. Ocean Sciences Meeting, American Geophysical Union, Feb 2012, Salt Lake City, United States. ⟨hal-00763932⟩

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