Journal cover Journal topic
Nonlinear Processes in Geophysics An interactive open-access journal of the European Geosciences Union
Journal topic

Journal metrics

Journal metrics

  • IF value: 1.699 IF 1.699
  • IF 5-year value: 1.559 IF 5-year
    1.559
  • CiteScore value: 1.61 CiteScore
    1.61
  • SNIP value: 0.884 SNIP 0.884
  • IPP value: 1.49 IPP 1.49
  • SJR value: 0.648 SJR 0.648
  • Scimago H <br class='hide-on-tablet hide-on-mobile'>index value: 52 Scimago H
    index 52
  • h5-index value: 21 h5-index 21
NPG | Articles | Volume 25, issue 4
Nonlin. Processes Geophys., 25, 765–807, 2018
https://doi.org/10.5194/npg-25-765-2018
© 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
https://doi.org/10.5194/npg-25-765-2018
© 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
Related authors  
On the numerical integration of the Lorenz-96 model, with scalar additive noise, for benchmark twin experiments
Colin Grudzien, Marc Bocquet, and Alberto Carrassi
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2019-258,https://doi.org/10.5194/gmd-2019-258, 2019
Manuscript under review for GMD
Short summary
Diagnosing spatial error structures in CO2 mole fractions and XCO2 column mole fractions from atmospheric transport
Thomas Lauvaux, Liza I. Díaz-Isaac, Marc Bocquet, and Nicolas Bousserez
Atmos. Chem. Phys., 19, 12007–12024, https://doi.org/10.5194/acp-19-12007-2019,https://doi.org/10.5194/acp-19-12007-2019, 2019
Short summary
Data assimilation as a learning tool to infer ordinary differential equation representations of dynamical models
Marc Bocquet, Julien Brajard, Alberto Carrassi, and Laurent Bertino
Nonlin. Processes Geophys., 26, 143–162, https://doi.org/10.5194/npg-26-143-2019,https://doi.org/10.5194/npg-26-143-2019, 2019
Short summary
Combining data assimilation and machine learning to emulate a dynamical model from sparse and noisy observations: a case study with the Lorenz 96 model
Julien Brajard, Alberto Carrassi, Marc Bocquet, and Laurent Bertino
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2019-136,https://doi.org/10.5194/gmd-2019-136, 2019
Revised manuscript not accepted
Short summary
Calibration of a multi-physics ensemble for estimating the uncertainty of a greenhouse gas atmospheric transport model
Liza I. Díaz-Isaac, Thomas Lauvaux, Marc Bocquet, and Kenneth J. Davis
Atmos. Chem. Phys., 19, 5695–5718, https://doi.org/10.5194/acp-19-5695-2019,https://doi.org/10.5194/acp-19-5695-2019, 2019
Short summary
Related subject area  
Subject: Predictability, Data Assimilation | Topic: Climate, Atmosphere, Ocean, Hydrology, Cryosphere, Biosphere
Generalization properties of feed-forward neural networks trained on Lorenz systems
Sebastian Scher and Gabriele Messori
Nonlin. Processes Geophys., 26, 381–399, https://doi.org/10.5194/npg-26-381-2019,https://doi.org/10.5194/npg-26-381-2019, 2019
Short summary
Order of operation for multi-stage post-processing of ensemble wind forecast trajectories
Nina Schuhen
Nonlin. Processes Geophys. Discuss., https://doi.org/10.5194/npg-2019-55,https://doi.org/10.5194/npg-2019-55, 2019
Revised manuscript accepted for NPG
Short summary
Seasonal statistical-dynamical prediction of the North Atlantic Oscillation by probabilistic post-processing
André Düsterhus
Nonlin. Processes Geophys. Discuss., https://doi.org/10.5194/npg-2019-50,https://doi.org/10.5194/npg-2019-50, 2019
Revised manuscript accepted for NPG
Short summary
Application of local attractor dimension to reduced space strongly coupled data assimilation for chaotic multiscale systems
Courtney Quinn, Terence J. O'Kane, and Vassili Kitsios
Nonlin. Processes Geophys. Discuss., https://doi.org/10.5194/npg-2019-51,https://doi.org/10.5194/npg-2019-51, 2019
Revised manuscript accepted for NPG
Short summary
Revising the stochastic iterative ensemble smoother
Patrick Nima Raanes, Andreas Størksen Stordal, and Geir Evensen
Nonlin. Processes Geophys., 26, 325–338, https://doi.org/10.5194/npg-26-325-2019,https://doi.org/10.5194/npg-26-325-2019, 2019
Short summary
Cited articles  
Acevedo, W., de Wiljes, J., and Reich, S.: Second-order accurate ensemble transform particle filters, SIAM J. Sci. Comput., 39, A1834–A1850, https://doi.org/10.1137/16M1095184, 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, https://doi.org/10.1002/qj.2370, 2015. a, b, c
Anderson, J. L.: A Method for Producing and Evaluating Probabilistic Forecasts from Ensemble Model Integrations, J. Climate, 9, 1518–1530, https://doi.org/10.1175/1520-0442(1996)009<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, https://doi.org/10.5194/npg-20-329-2013, 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, https://doi.org/10.1109/78.978374, 2002. a
Publications Copernicus
Download
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...
Citation