EGU23-4817, updated on 08 Nov 2023
EGU General Assembly 2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

Algorithmic optimisation of key parameters of OpenIFS

Lauri Tuppi1, Madeleine Ekblom1, Pirkka Ollinaho2, and Heikki Järvinen1
Lauri Tuppi et al.
  • 1University of Helsinki, Institute for Atmospheric and Earth System Research / Physics, Finland (
  • 2Finnish Meteorological Institute, Finland

Numerical weather prediction models contain parameters that are inherently uncertain and cannot be determined exactly. Traditionally, the parameter tuning has been done manually, which can be an extremely labourious task. Tuning the entire model usually requires adjusting a relatively large amount of parameters. In case of manual tuning, the need to balance a number of requirements at the same time can lead the tuning process being a maze of subjective choices. It is, therefore, desirable to have reliable objective approaches for estimation of optimal values and uncertainties of these parameters. In this presentation we present how to optimise 20 key physical parameters having a strong impact on forecast quality. These parameters belong to the Stochastically Perturbed Parameters Scheme in the atmospheric model Open Integrated Forecasting System.

The results show that simultaneous optimisation of O(20) parameters is possible with O(100) algorithm steps using an ensemble of O(20) members, and that the optimised parameters lead to substantial enhancement of predictive skill. The enhanced predictive skill can be attributed to reduced biases in low-level winds and upper-tropospheric humidity in the optimised model. We find that the optimisation process is dependent on the starting values of the parameters that are optimised (starting from better suited values results in a better model). The results also show that the applicability of the tuned parameter values across different model resolutions is somewhat questionable since the model biases seem to be resolution-specific. Moreover, our optimisation algorithm tends to treat the parameter covariances poorly limiting its ability to converge to the global optimum.

How to cite: Tuppi, L., Ekblom, M., Ollinaho, P., and Järvinen, H.: Algorithmic optimisation of key parameters of OpenIFS, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-4817,, 2023.