Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 13, issue 11
Articles | Volume 13, issue 11
https://doi.org/10.5194/gmd-13-5799-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-13-5799-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 13, issue 11
Development and technical paper
 | 
26 Nov 2020
Development and technical paper |  | 26 Nov 2020

Necessary conditions for algorithmic tuning of weather prediction models using OpenIFS as an example

Lauri Tuppi, Pirkka Ollinaho, Madeleine Ekblom, Vladimir Shemyakin, and Heikki Järvinen

Viewed

Total article views: 3,469 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
2,904 497 68 3,469 67 65
  • HTML: 2,904
  • PDF: 497
  • XML: 68
  • Total: 3,469
  • BibTeX: 67
  • EndNote: 65
Views and downloads (calculated since 17 Jun 2020)
Cumulative views and downloads (calculated since 17 Jun 2020)

Viewed (geographical distribution)

Total article views: 3,469 (including HTML, PDF, and XML) Thereof 3,076 with geography defined and 393 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 29 Jun 2024
Download
Short summary
This paper presents general guidelines on how to utilise computer algorithms efficiently in order to tune weather models so that they would produce better forecasts. The main conclusions are that the computer algorithms work most efficiently with a suitable cost function, certain forecast length and ensemble size. We expect that our results will facilitate the use of algorithmic methods in the tuning of weather models.
Read more