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| Titel |
Parameter variations in prediction skill optimization at ECMWF |
| VerfasserIn |
P. Ollinaho, P. Bechtold, M. Leutbecher, M. Laine, A. Solonen, H. Haario, H. Järvinen |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1023-5809
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| Digitales Dokument |
URL |
| Erschienen |
In: Nonlinear Processes in Geophysics ; 20, no. 6 ; Nr. 20, no. 6 (2013-11-22), S.1001-1010 |
| Datensatznummer |
250086074
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| Publikation (Nr.) |
 copernicus.org/npg-20-1001-2013.pdf |
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| Zusammenfassung |
| Algorithmic numerical weather prediction (NWP) skill optimization has been
tested using the Integrated Forecasting System (IFS) of the European Centre
for Medium-Range Weather Forecasts (ECMWF). We report the results of initial
experimentation using importance sampling based on model parameter estimation
methodology targeted for ensemble prediction systems, called the ensemble
prediction and parameter estimation system (EPPES). The same methodology was
earlier proven to be a viable concept in low-order ordinary differential
equation systems, and in large-scale atmospheric general circulation models
(ECHAM5). Here we show that prediction skill optimization is possible even in
the context of a system that is (i) of very high dimensionality, and (ii)
carefully tuned to very high skill. We concentrate on four closure parameters
related to the parameterizations of sub-grid scale physical processes of
convection and formation of convective precipitation. We launch standard
ensembles of medium-range predictions such that each member uses different
values of the four parameters, and make sequential statistical inferences
about the parameter values. Our target criterion is the squared forecast
error of the 500 hPa geopotential height at day three and day ten. The EPPES
methodology is able to converge towards closure parameter values that
optimize the target criterion. Therefore, we conclude that estimation and
cost function-based tuning of low-dimensional static model parameters is
possible despite the very high dimensional state space, as well as the
presence of stochastic noise due to initial state and physical tendency
perturbations. The remaining question before EPPES can be considered as a
generally applicable tool in model development is the correct formulation of
the target criterion. The one used here is, in our view, very selective.
Considering the multi-faceted question of improving forecast model
performance, a more general target criterion should be developed. This is a
topic of ongoing research. |
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