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Nonlinear Processes in Geophysics An interactive open-access journal of the European Geosciences Union
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Volume 26, issue 3
Nonlin. Processes Geophys., 26, 325–338, 2019
https://doi.org/10.5194/npg-26-325-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
Nonlin. Processes Geophys., 26, 325–338, 2019
https://doi.org/10.5194/npg-26-325-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 17 Sep 2019

Research article | 17 Sep 2019

Revising the stochastic iterative ensemble smoother

Patrick Nima Raanes1,2, Andreas Størksen Stordal1, and Geir Evensen1,2 Patrick Nima Raanes et al.
  • 1NORCE, Pb. 22 Nygårdstangen, 5838 Bergen, Norway
  • 2Nansen Environmental and Remote Sensing Center, 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 lose 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).

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Short summary
A popular variational ensemble smoother for data assimilation and history matching is simplified. An exact relationship between ensemble linearizations (linear regression) and adjoints (analytic derivatives) is established.
A popular variational ensemble smoother for data assimilation and history matching is...
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