Articles | Volume 26, issue 3
Nonlin. Processes Geophys., 26, 325–338, 2019
https://doi.org/10.5194/npg-26-325-2019
Nonlin. Processes Geophys., 26, 325–338, 2019
https://doi.org/10.5194/npg-26-325-2019

Research article 17 Sep 2019

Research article | 17 Sep 2019

Revising the stochastic iterative ensemble smoother

Patrick Nima Raanes et al.

Related subject area

Subject: Predictability, probabilistic forecasts, data assimilation, inverse problems | Topic: Climate, atmosphere, ocean, hydrology, cryosphere, biosphere
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Revised manuscript accepted for NPG
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Cited articles

Bannister, R. N.: A review of operational methods of variational and ensemble-variational data assimilation, Q. J. Roy. Meteor. Soc., 143, 607–633, 2016. a, b
Bardsley, J. M., Solonen, A., Haario, H., and Laine, M.: Randomize-then-optimize: A method for sampling from posterior distributions in nonlinear inverse problems, SIAM J. Sci. Comput., 36, A1895–A1910, 2014. a
Bocquet, M.: Localization and the iterative ensemble Kalman smoother, Q. J. Roy. Meteor. Soc., 142, 1075–1089, 2016. a
Bocquet, M. and Carrassi, A.: Four-dimensional ensemble variational data assimilation and the unstable subspace, Tellus A, 69, 1304504, https://doi.org/10.1080/16000870.2017.1304504, 2017. a
Bocquet, M. and Sakov, P.: Combining inflation-free and iterative ensemble Kalman filters for strongly nonlinear systems, Nonlin. Processes Geophys., 19, 383–399, https://doi.org/10.5194/npg-19-383-2012, 2012. a, b, c
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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.