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

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

doi:https://doi.org/10.5194/gmd-13-5799-2020

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

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Final revised paper (published on 26 Nov 2020)
Preprint (discussion started on 17 Jun 2020)

Interactive discussion

Status: closed

AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment

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- Supplement

RC1: 'Review "Necessary conditions for algorithmic tuning of weather prediction models using OpenIFS as an example"', Peter Düben, 29 Jul 2020
RC2: 'Review of "Necessary conditions for algorithmic tuning of weather prediction models using OpenIFS as an example"', Joakim Kjellsson, 13 Sep 2020
AC1: 'Final authors' responses to review comments', Lauri Tuppi, 10 Oct 2020

Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision

AR by Lauri Tuppi on behalf of the Authors (10 Oct 2020) Manuscript

ED: Publish as is (15 Oct 2020) by Simone Marras

AR by Lauri Tuppi on behalf of the Authors (16 Oct 2020)

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.