Articles | Volume 23, issue 2
Nonlin. Processes Geophys., 23, 59–73, 2016
https://doi.org/10.5194/npg-23-59-2016
Nonlin. Processes Geophys., 23, 59–73, 2016
https://doi.org/10.5194/npg-23-59-2016

Research article 11 Mar 2016

Research article | 11 Mar 2016

Hybrid Levenberg–Marquardt and weak-constraint ensemble Kalman smoother method

J. Mandel et al.

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Cited articles

Bell, B.: The Iterated Kalman Smoother as a Gauss-Newton Method, SIAM J. Optim., 4, 626–636, https://doi.org/10.1137/0804035, 1994.
Bergou, E., Gratton, S., and Mandel, J.: On the Convergence of a Non-linear Ensemble Kalman Smoother, arXiv:1411.4608, submitted, 2014.
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.
Bocquet, M. and Sakov, P.: Joint state and parameter estimation with an iterative ensemble Kalman smoother, Nonlin. Processes Geophys., 20, 803–818, https://doi.org/10.5194/npg-20-803-2013, 2013.
Bocquet, M. and Sakov, P.: An iterative ensemble Kalman smoother, Q. J. Roy. Meteor. Soc., 140, 1521–1535, https://doi.org/10.1002/qj.2236, 2014.
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Short summary
A stochastic method, the ensemble Kalman smoother (EnKS), is proposed as a linear solver in four-dimensional variational data assimilation (4DVAR). The method approaches 4DVAR for large ensembles. Regularization provides global convergence, and it is implemented as an additional artificial observation. Since the EnKS is uncoupled from the insides of the 4DVAR, any version of EnKS can be used.