Stochastic representations of model uncertainties at ECMWF: state of the art and future vision. (26th September 2017)

• Record Type: Journal Article
• Authors: Leutbecher, Martin; Lock, Sarah‐Jane; Ollinaho, Pirkka; Lang, Simon T. K.; Balsamo, Gianpaolo; Bechtold, Peter; Bonavita, Massimo; Christensen, Hannah M.; Diamantakis, Michail; Dutra, Emanuel; English, Stephen; Fisher, Michael; Forbes, Richard M.; Goddard, Jacqueline; Haiden, Thomas; Hogan, Robin J.; Juricke, Stephan; Lawrence, Heather; MacLeod, Dave; Magnusson, Linus; Malardel, Sylvie; Massart, Sebastien; Sandu, Irina; Smolarkiewicz, Piotr K.; Subramanian, Aneesh; Vitart, Frédéric; Wedi, Nils; Weisheimer, Antje
• Is Part Of: Quarterly journal of the Royal Meteorological Society. Volume 143:Number 707(2017)
• Page Start: 2315
• Page End: 2339
• Publication Date: 2017-09-26
• Subjects: ensemble forecasts -- ensemble data assimilation -- weak‐constraint 4D‐Var -- numerical weather prediction -- dynamical core -- Earth system model -- model uncertainty -- stochastic parametrization; Meteorology -- Periodicals; 551.5
• DOI: 10.1002/qj.3094
• Languages: English
• ISSNs: 0035-9009

Abstract

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. Abstract : Parametrization schemes generate tendencies that adjust the model state variables to account for physical processes. Pictured here is the ensemble mean of the net temperature tendencies (K) in the mid‐troposphere from the physics schemes for the first 3 h of an ensemble forecast. What is the uncertainty associated with such tendencies? And how should we represent the uncertainty in our ensemble forecasting systems? This article reviews methods for stochastic representations of model uncertainty for assimilation and forecasting.