EGU2020-4824, updated on 08 Nov 2023
EGU General Assembly 2020
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

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, Helsinki, Finland
  • 2Finnish Meteorological Institute, Helsinki, Finland
  • 3School of Engineering Science, Lappeenranta University of Technology, Lappeenranta, Finland

Algorithmic model tuning is a promising approach to yield the best possible performance of multiscale multi-phase atmospheric models once the model structure is fixed. We are curious about to what degree one can trust the algorithmic tuning process. We approach the problem by studying the convergence of this process in a semi-realistic case. Let us denote M(x0;θd) as the default model, where x0 and θd are the initial state and default model parameter vectors, respectively. A necessary condition for an algorithmic tuning process to converge in a fully-realistic case is that the default model is recovered if the tuning process is initialised with perturbed model parameters θ and the default model forecasts are used as pseudo-observations. In this paper we study the circumstances where this condition is valid by carrying out a large set of convergence tests using two different tuning methods and the OpenIFS model. These tests are interpreted as guidelines for algorithmic model tuning applications.

The results of this study can be used as recipe for maximising efficiency of algorithmic tuning. In the convergence tests, maximised efficiency was reached with using ensemble initial conditions, cost function that covers entire model domain, short forecast length and medium-sized ensembles.

How to cite: Tuppi, L., Ollinaho, P., Ekblom, M., Shemyakin, V., and Järvinen, H.: Necessary conditions for algorithmic tuning of weather prediction models using OpenIFS as an example, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-4824,, 2020.


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