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: 1.558 IF 1.558
  • IF 5-year value: 1.475 IF 5-year
    1.475
  • CiteScore value: 2.8 CiteScore
    2.8
  • SNIP value: 0.921 SNIP 0.921
  • IPP value: 1.56 IPP 1.56
  • SJR value: 0.571 SJR 0.571
  • Scimago H <br class='hide-on-tablet hide-on-mobile'>index value: 55 Scimago H
    index 55
  • h5-index value: 22 h5-index 22
Volume 25, issue 3
Nonlin. Processes Geophys., 25, 633–648, 2018
https://doi.org/10.5194/npg-25-633-2018
© 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, 633–648, 2018
https://doi.org/10.5194/npg-25-633-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 04 Sep 2018

Research article | 04 Sep 2018

Chaotic dynamics and the role of covariance inflation for reduced rank Kalman filters with model error

Colin Grudzien et al.

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., 13, 1903–1924, https://doi.org/10.5194/gmd-13-1903-2020,https://doi.org/10.5194/gmd-13-1903-2020, 2020
Short summary

Related subject area

Subject: Predictability, Data Assimilation | Topic: Climate, Atmosphere, Ocean, Hydrology, Cryosphere, Biosphere
From research to applications – examples of operational ensemble post-processing in France using machine learning
Maxime Taillardat and Olivier Mestre
Nonlin. Processes Geophys., 27, 329–347, https://doi.org/10.5194/npg-27-329-2020,https://doi.org/10.5194/npg-27-329-2020, 2020
Short summary
Correcting for model changes in statistical postprocessing – an approach based on response theory
Jonathan Demaeyer and Stéphane Vannitsem
Nonlin. Processes Geophys., 27, 307–327, https://doi.org/10.5194/npg-27-307-2020,https://doi.org/10.5194/npg-27-307-2020, 2020
Short summary
Brief communication: Residence time of energy in the atmosphere
Carlos Osácar, Manuel Membrado, and Amalio Fernández-Pacheco
Nonlin. Processes Geophys., 27, 235–237, https://doi.org/10.5194/npg-27-235-2020,https://doi.org/10.5194/npg-27-235-2020, 2020
Short summary
Simulating model uncertainty of subgrid-scale processes by sampling model errors at convective scales
Michiel Van Ginderachter, Daan Degrauwe, Stéphane Vannitsem, and Piet Termonia
Nonlin. Processes Geophys., 27, 187–207, https://doi.org/10.5194/npg-27-187-2020,https://doi.org/10.5194/npg-27-187-2020, 2020
Short summary
Data-driven versus self-similar parameterizations for stochastic advection by Lie transport and location uncertainty
Valentin Resseguier, Wei Pan, and Baylor Fox-Kemper
Nonlin. Processes Geophys., 27, 209–234, https://doi.org/10.5194/npg-27-209-2020,https://doi.org/10.5194/npg-27-209-2020, 2020
Short summary

Cited articles

Barreira, L. and Pesin, Y.: Lyapunov Exponents and Smooth Ergodic Theory, Student Mathematical Library, American Mathematical Society, 38–40, 2002.
Benettin, G., Galgani, L., Giorgilli, A., and Strelcyn, J.: Lyapunov characteristic exponents for smooth dynamical systems and for Hamiltonian systems; a method for computing all of them. Part 1: Theory, Meccanica, 15, 9–20, 1980.
Bocquet, M.: Ensemble Kalman filtering without the intrinsic need for inflation, Nonlin. Processes Geophys., 18, 735–750, https://doi.org/10.5194/npg-18-735-2011, 2011.
Bocquet, M. and Carrassi, A.: Four-dimensional ensemble variational data assimilation and the unstable subspace, Tellus A, 69, 1304 504, 2017.
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.
Publications Copernicus
Download
Short summary
Using the framework Lyapunov vectors, we analyze the asymptotic properties of ensemble based Kalman filters and how these are influenced by dynamical chaos, especially in the context of random model errors and small ensemble sizes. Particularly, we show a novel derivation of the evolution of forecast uncertainty for ensemble-based Kalman filters with weakly-nonlinear error growth, and discuss its impact for filter design in geophysical models.
Using the framework Lyapunov vectors, we analyze the asymptotic properties of ensemble based...
Citation