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Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
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https://doi.org/10.5194/gmd-2020-56
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-2020-56
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: development and technical paper 17 Jun 2020

Submitted as: development and technical paper | 17 Jun 2020

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This preprint is currently under review for the journal GMD.

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

Lauri Tuppi1, Pirkka Ollinaho2, Madeleine Ekblom1, Vladimir Shemyakin3, and Heikki Järvinen1 Lauri Tuppi et al.
  • 1Institute for Atmospheric and Earth System Research / Physics, University of Helsinki, Finland
  • 2Finnish Meteorological Institute, Helsinki, Finland
  • 3School of Engineering Science, Lappeenranta University of Technology, Lappeenranta, Finland

Abstract. Algorithmic model tuning is a promising approach to yield the best possible forecast performance of multi-scale multi-phase atmospheric models once the model structure is fixed. The problem is to what degree we can trust algorithmic model tuning. We approach the problem by studying the convergence of this process in a semi-realistic case. Let M (x, θ) denote the time evolution model, where x and θ are the initial state and the default model parameter vectors, respectively. A necessary condition for an algorithmic tuning process to converge is that θ is recovered when the tuning process is initialised with perturbed model parameters θ and the default model forecasts are used as pseudo-observations. The aim here is to gauge which conditions are sufficient in semi-realistic test setting to obtain reliable results, and thus building confidence on the tuning in fully-realistic cases. A large set of convergence tests is carried in semi-realistic cases applying two different ensemble-based parameter estimation methods and the OpenIFS model. The results are interpreted as general guidance for algorithmic model tuning, which we successfully tested in a more demanding case of simultaneous estimation of eight OpenIFS model parameters.

Lauri Tuppi et al.

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Lauri Tuppi et al.

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Latest update: 22 Sep 2020
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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 certain forecast length and ensemble size. We expect that our results will facilitate the use of algorithmic methods in tuning of weather models.
This paper presents general guidelines on how to utilise computer algorithms efficiently in...
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