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Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
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Volume 7, issue 5 | Copyright
Geosci. Model Dev., 7, 1889-1900, 2014
https://doi.org/10.5194/gmd-7-1889-2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.

Development and technical paper 02 Sep 2014

Development and technical paper | 02 Sep 2014

Optimization of NWP model closure parameters using total energy norm of forecast error as a target

P. Ollinaho1,2, H. Järvinen2, P. Bauer3, M. Laine1, P. Bechtold3, J. Susiluoto1,2, and H. Haario4 P. Ollinaho et al.
  • 1Finnish Meteorological Institute, Helsinki, Finland
  • 2University of Helsinki, Department of Physics, Helsinki, Finland
  • 3European Centre for Medium-Range Weather Forecasts, Reading, UK
  • 4Lappeenranta University of Technology, Lappeenranta, Finland

Abstract. We explore the use of dry total energy norm in improving numerical weather prediction (NWP) model forecast skill. The Ensemble Prediction and Parameter Estimation System (EPPES) is utilized to estimate ECHAM5 atmospheric GCM (global circulation models) closure parameters related to clouds and precipitation. The target criterion in the optimization is the dry total energy norm of 3-day forecast error with respect to the ECMWF (European Centre for Medium-Range Weather Forecasts) operational analyses. The results are summarized as follows: (i) forecast error growth in terms of energy norm is slower in the optimized than in the default model up to day 10 forecasts (and beyond), (ii) headline forecast skill scores are improved in the training sample as well as in independent samples, (iii) the decrease of the forecast error energy norm at day three is mainly because of smaller kinetic energy error in the tropics, and (iv) this impact is spread into midlatitudes at longer ranges and appears as a smaller forecast error of potential energy. The interpretation of these results is that the parameter optimization has reduced the model error so that the forecasts remain longer in the vicinity of the analyzed state.

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