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Nonlinear Processes in Geophysics An interactive open-access journal of the European Geosciences Union
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Discussion papers
https://doi.org/10.5194/npg-2019-10
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/npg-2019-10
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: research article 15 Mar 2019

Submitted as: research article | 15 Mar 2019

Review status
This discussion paper is a preprint. A revision of this manuscript was accepted for the journal Nonlinear Processes in Geophysics (NPG) and is expected to appear here in due course.

Revising the stochastic iterative ensemble smoother

Patrick N. Raanes1,2, Andreas S. Stordal1, and Geir Evensen1,2 Patrick N. Raanes et al.
  • 1NORCE, Pb. 22 Nygårdstangen, 5838 Bergen, Norway
  • 2NERSC, Thormøhlens gate 47, 5006 Bergen, Norway

Abstract. Ensemble randomized maximum likelihood (EnRML) is an iterative (stochastic) ensemble smoother, used for large and nonlinear inverse problems, such as history matching and data assimilation. Its current formulation is overly complicated and has issues with computational costs, noise, and covariance localization, even causing some practitioners to omit crucial prior information. This paper resolves these difficulties and streamlines the algorithm, without changing its output. These simplifications are achieved through the careful treatment of the linearizations and subspaces. For example, it is shown (a) how ensemble linearizations relate to average sensitivity, and (b) that the ensemble does not loose rank during updates. The paper also draws significantly on the theory of the (deterministic) iterative ensemble Kalman smoother (IEnKS). Comparative benchmarks are obtained with the Lorenz-96 model with these two smoothers and the ensemble smoother using multiple data assimilation (ES-MDA).

Patrick N. Raanes et al.
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AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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Interactive discussion
Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
Printer-friendly Version - Printer-friendly version Supplement - Supplement
Patrick N. Raanes et al.
Model code and software

Data Assimilation with Python: a Package for Experimental Research (DAPPER) P. N. Raanes, C. Grudzien, and 14tondeu https://doi.org/10.5281/zenodo.2029296

Patrick N. Raanes et al.
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Latest update: 15 Sep 2019
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Short summary
A popular (variational ensemble smoother) method of data assimilation is simplified. An exact relationship between ensemble linearizations (linear regression) and adjoints (analytic derivatives) is established.
A popular (variational ensemble smoother) method of data assimilation is simplified. An exact...
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