Stochastic representations of model uncertainties at ECMWF: state of the art and future vision
January 1, 2017·,,,,,,,,,,,,,,,,,,,,,,,,,,,·
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Martin Leutbecher
Sarah-Jane Lock
Pirkka Ollinaho
Simon T. K. Lang
Gianpaolo Balsamo
Peter Bechtold
Massimo Bonavita
Hannah M. Christensen
Michail Diamantakis
Emanuel Dutra
Stephen English
Michael Fisher
Richard M. Forbes
Jacqueline Goddard
Thomas Haiden
Robin J. Hogan
Stephan Juricke
Heather Lawrence
Dave MacLeod
Linus Magnusson
Sylvie Malardel
Sebastien Massart
Irina Sandu
Piotr K. Smolarkiewicz
Aneesh Subramanian
Frederic Vitart
Nils Wedi
Antje Weisheimer
Abstract
Members in ensemble forecasts differ due to the representations of initial uncertainties and model uncertainties. The inclusion of stochastic schemes to represent model uncertainties has improved the probabilistic skill of the ECMWF ensemble by increasing reliability and reducing the error of the ensemble mean. Recent progress, challenges and future directions regarding stochastic representations of model uncertainties at ECMWF are described in this article. The coming years are likely to see a further increase in the use of ensemble methods in forecasts and assimilation. This will put increasing demands on the methods used to perturb the forecast model. An area that is receiving greater attention than 5-10 years ago is the physical consistency of the perturbations. Other areas where future efforts will be directed are the expansion of uncertainty representations to the dynamical core and other components of the Earth system, as well as the overall computational efficiency of representing model uncertainty.
Type
Publication
QUARTERLY JOURNAL OF THE ROYAL METEOROLOGICAL SOCIETY