Joint state and parameter estimation with an iterative ensemble Kalman smoother

@inproceedings{Bocquet2013JointSA,
  title={Joint state and parameter estimation with an iterative ensemble Kalman smoother},
  author={Marc Bocquet and Pavel Sakov},
  year={2013}
}
Both ensemble filtering and variational data assimilation methods have proven useful in the joint estimation of state variables and parameters of geophysical models. Yet, their respective benefits and drawbacks in this task are distinct. An ensemble variational method, known as the iterative ensemble Kalman smoother (IEnKS) has recently been introduced. It is based on an adjoint model-free variational, but flow-dependent, scheme. As such, the IEnKS is a candidate tool for joint state and… CONTINUE READING
Highly Cited
This paper has 27 citations. REVIEW CITATIONS

References

Publications referenced by this paper.
Showing 1-10 of 46 references

An iterative ensemble Kalman smoother

  • M. Bocquet, P. Sakov
  • Q. J. R. Meteorol. Soc.,
  • 2014

Joint state and parameter estimation with an iterative ensemble Kalman smoother

  • M. IBocquet, P. Sakov
  • Nonlin. Processes Geophys.,
  • 2013

Tests of different flavours of EnKF on a simple model

  • N. Bowler, J. Flowerdew, S. Pring
  • Q . J . R . Meteorol . Soc .
  • 2013

Combining inflation-free and iterative ensemble Kalman filters for strongly nonlinear systems

  • M. IBocquet, P. Sakov
  • Nonlin. Processes Geophys
  • 2012

Combining inflationfree and iterative ensemble Kalman filters for strongly nonlinear systems , Nonlin

  • M. Bocquet, P. Sakov
  • J . Roy . Meteor . Soc .
  • 2012
3 Excerpts

Ensemble Randomized Maximum Likelihood Method as an Iterative Ensemble Smoother

  • Y. Chen, D. S. Oliver
  • Math . Geosci .
  • 2012

Gaussian anamorphosis extension of the DEnKF for combined state parameter estimation : application to a 1 D ocean ecosystem model

  • E. Simon, L. Bertino
  • J . Mar . Syst .
  • 2012

Similar Papers

Loading similar papers…