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 2
Nonlin. Processes Geophys., 25, 387–412, 2018
https://doi.org/10.5194/npg-25-387-2018
© 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, 387–412, 2018
https://doi.org/10.5194/npg-25-387-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 28 May 2018

Research article | 28 May 2018

Exploring the Lyapunov instability properties of high-dimensional atmospheric and climate models

Lesley De Cruz et al.
Viewed  
Total article views: 1,785 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
1,181 520 84 1,785 247 77 90
  • HTML: 1,181
  • PDF: 520
  • XML: 84
  • Total: 1,785
  • Supplement: 247
  • BibTeX: 77
  • EndNote: 90
Views and downloads (calculated since 03 Jan 2018)
Cumulative views and downloads (calculated since 03 Jan 2018)
Viewed (geographical distribution)  
Total article views: 1,578 (including HTML, PDF, and XML) Thereof 1,556 with geography defined and 22 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Cited  
Saved (final revised paper)  
No saved metrics found.
Saved (discussion paper)  
No saved metrics found.
Discussed (final revised paper)  
No discussed metrics found.
Discussed (discussion paper)  
No discussed metrics found.
Latest update: 10 Dec 2019
Publications Copernicus
Download
Short summary
The predictability of weather models is limited largely by the initial state error growth or decay rates. We have computed these rates for PUMA, a global model for the atmosphere, and MAOOAM, a more simplified, coupled model which includes the ocean. MAOOAM has processes at distinct timescales, whereas PUMA surprisingly does not. We propose a new programme to compute the natural directions along the flow that correspond to the growth or decay rates, to learn which components play a role.
The predictability of weather models is limited largely by the initial state error growth or...
Citation