Journal cover Journal topic
Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
Geosci. Model Dev., 9, 1921-1935, 2016
https://doi.org/10.5194/gmd-9-1921-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
Development and technical paper
25 May 2016
Evaluation of the Plant–Craig stochastic convection scheme (v2.0) in the ensemble forecasting system MOGREPS-R (24 km) based on the Unified Model (v7.3)
Richard J. Keane1,2, Robert S. Plant3, and Warren J. Tennant4 1Deutscher Wetterdienst, Frankfurter Strasse 135, 63067 Offenbach, Germany
2Meteorologisches Institut, Ludwig-Maximilians-Universität München, Munich, Germany
3Department of Meteorology, University of Reading, Reading, UK
4Met Office, FitzRoy Road, Exeter, EX1 3PB, UK
Abstract. The Plant–Craig stochastic convection parameterization (version 2.0) is implemented in the Met Office Regional Ensemble Prediction System (MOGREPS-R) and is assessed in comparison with the standard convection scheme with a simple stochastic scheme only, from random parameter variation. A set of 34 ensemble forecasts, each with 24 members, is considered, over the month of July 2009. Deterministic and probabilistic measures of the precipitation forecasts are assessed. The Plant–Craig parameterization is found to improve probabilistic forecast measures, particularly the results for lower precipitation thresholds. The impact on deterministic forecasts at the grid scale is neutral, although the Plant–Craig scheme does deliver improvements when forecasts are made over larger areas. The improvements found are greater in conditions of relatively weak synoptic forcing, for which convective precipitation is likely to be less predictable.

Citation: Keane, R. J., Plant, R. S., and Tennant, W. J.: Evaluation of the Plant–Craig stochastic convection scheme (v2.0) in the ensemble forecasting system MOGREPS-R (24 km) based on the Unified Model (v7.3), Geosci. Model Dev., 9, 1921-1935, https://doi.org/10.5194/gmd-9-1921-2016, 2016.
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Short summary
A widely studied stochastic deep convection scheme is evaluated over an extended forecasting period for the first time. It is found to significantly improve the probabilistic forecast for weakly forced cases – which tend to be less predictable – and to be comparable to a well-tuned reference scheme for strongly forced cases. A newly developed verification metric is applied to provide evidence that the improved probabilistic forecast is in large part due to the stochasticity of the scheme.
A widely studied stochastic deep convection scheme is evaluated over an extended forecasting...
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