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

@inproceedings{Leutbecher2017StochasticRO,
  title={Stochastic representations of model uncertainties at ECMWF: state of the art and future vision},
  author={Martin Leutbecher and Sarah-Jane Lock and Pirkka Ollinaho and Simon T. K. Lang and Gianpaolo Balsamo and Peter K. Bechtold and Massimo Bonavita and Hannah M Christensen and Michail Diamantakis and Emanuel Dutra and Stephen J. English and Michael Aaron Fisher and Richard M. Forbes and Jacqueline Clare Goddard and Thomas Haiden and Robin J. Hogan and Stephan Juricke and Heather Lawrence and Dave MacLeod and Linus Magnusson and Sylvie Malardel and S'ebastien Massart and Irina Sandu and Piotr K. Smolarkiewicz and Aneesh C. Subramanian and Frederic Pol. Vitart and Nils P. Wedi and Antje Weisheimer},
  year={2017}
}
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 paper. The coming years are likely to see a… CONTINUE READING
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