Stochastic representations of model uncertainties at ECMWF: state of the art and future vision

January 1, 2017·
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
· 0 min read
DOI
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
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