Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 13, issue 4
Articles | Volume 13, issue 4
https://doi.org/10.5194/gmd-13-1903-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-13-1903-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 13, issue 4
Development and technical paper
 | 
16 Apr 2020
Development and technical paper |  | 16 Apr 2020

On the numerical integration of the Lorenz-96 model, with scalar additive noise, for benchmark twin experiments

Colin Grudzien, Marc Bocquet, and Alberto Carrassi

Viewed

Total article views: 3,115 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
2,123 909 83 3,115 89 86
  • HTML: 2,123
  • PDF: 909
  • XML: 83
  • Total: 3,115
  • BibTeX: 89
  • EndNote: 86
Views and downloads (calculated since 24 Oct 2019)
Cumulative views and downloads (calculated since 24 Oct 2019)

Viewed (geographical distribution)

Total article views: 3,115 (including HTML, PDF, and XML) Thereof 2,828 with geography defined and 287 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 23 Apr 2025
Download
Short summary
All scales of a dynamical physical process cannot be resolved accurately in a multiscale, geophysical model. The behavior of unresolved scales of motion are often parametrized by a random process to emulate their effects on the dynamically resolved variables, and this results in a random–dynamical model. We study how the choice of a numerical discretization of such a system affects the model forecast and estimation statistics, when the random–dynamical model is unbiased in its parametrization.
Read more
Share