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NPG | Articles | Volume 25, issue 4
Nonlin. Processes Geophys., 25, 765–807, 2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Special issue: Numerical modeling, predictability and data assimilation in...

Nonlin. Processes Geophys., 25, 765–807, 2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Review article 12 Nov 2018

Review article | 12 Nov 2018

Review article: Comparison of local particle filters and new implementations

Alban Farchi and Marc Bocquet

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Cited articles

Acevedo, W., de Wiljes, J., and Reich, S.: Second-order accurate ensemble transform particle filters, SIAM J. Sci. Comput., 39, A1834–A1850,, 2017. a
Ades, M. and van Leeuwen, P. J.: The equivalent-weights particle filter in a high-dimensional system, Q. J. Roy. Meteor. Soc., 141, 484–503,, 2015. a, b, c
Anderson, J. L.: A Method for Producing and Evaluating Probabilistic Forecasts from Ensemble Model Integrations, J. Climate, 9, 1518–1530,<1518:AMFPAE>2.0.CO;2, 1996. a
Apte, A. and Jones, C. K. R. T.: The impact of nonlinearity in Lagrangian data assimilation, Nonlin. Processes Geophys., 20, 329–341,, 2013. a
Arulampalam, M. S., Maskell, S., Gordon, N., and Clapp, T.: A tutorial on particle filters for online nonlinear non-Gaussian Bayesian Tracking, IEEE T. Signal Proces., 50, 174–188,, 2002. a
Publications Copernicus
Short summary
Data assimilation looks for an optimal way to learn from observations of a dynamical system to improve the quality of its predictions. The goal is to filter out the noise (both observation and model noise) to retrieve the true signal. Among all possible methods, particle filters are promising; the method is fast and elegant, and it allows for a Bayesian analysis. In this review paper, we discuss implementation techniques for (local) particle filters in high-dimensional systems.
Data assimilation looks for an optimal way to learn from observations of a dynamical system to...