Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 14, issue 10
Articles | Volume 14, issue 10
https://doi.org/10.5194/gmd-14-5957-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-14-5957-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 14, issue 10
Model description paper
 | 
04 Oct 2021
Model description paper |  | 04 Oct 2021

SymPKF (v1.0): a symbolic and computational toolbox for the design of parametric Kalman filter dynamics

Olivier Pannekoucke and Philippe Arbogast

Viewed

Total article views: 2,934 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
2,133 706 95 2,934 82 107
  • HTML: 2,133
  • PDF: 706
  • XML: 95
  • Total: 2,934
  • BibTeX: 82
  • EndNote: 107
Views and downloads (calculated since 23 Apr 2021)
Cumulative views and downloads (calculated since 23 Apr 2021)

Viewed (geographical distribution)

Total article views: 2,934 (including HTML, PDF, and XML) Thereof 2,785 with geography defined and 149 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 06 Nov 2025
Download
Short summary
This contributes to research on uncertainty prediction, which is important either for determining the weather today or estimating the risk in prediction. The problem is that uncertainty prediction is numerically very expensive. An alternative has been proposed wherein uncertainty is presented in a simplified form with only the dynamics of certain parameters required. This tool allows for the determination of the symbolic equations of these parameter dynamics and their numerical computation.
Read more
Share