Editorial Type:
Article
Article Type:
Research Article

Impact of Stochastic Physics in a Convection-Permitting Ensemble

François Bouttier CNRM-GAME, CNRS, and Météo-France, Toulouse, France

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Benoît Vié CNRM-GAME, CNRS, and Météo-France, Toulouse, France

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Olivier Nuissier CNRM-GAME, CNRS, and Météo-France, Toulouse, France

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Laure Raynaud CNRM-GAME, CNRS, and Météo-France, Toulouse, France

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Print Publication:
01 Nov 2012
DOI:
https://doi.org/10.1175/MWR-D-12-00031.1
Page(s):
3706–3721
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Abstract

A stochastic physics scheme is tested in the Application of Research to Operations at Mesoscale (AROME) short-range convection-permitting ensemble prediction system. It is an adaptation of ECMWF’s stochastic perturbation of physics tendencies (SPPT) scheme. The probabilistic performance of the AROME model ensemble is found to be significantly improved, when verified against observations over two 2-week periods. The main improvement lies in the ensemble reliability and the spread–skill consistency. Probabilistic scores for several weather parameters are improved. The tendency perturbations have zero mean, but the stochastic perturbations have systematic effects on the model output, which explains much of the score improvement. Ensemble spread is an increasing function of the SPPT space and time correlations. A case study reveals that stochastic physics do not simply increase ensemble spread, they also tend to smooth out high-spread areas over wider geographical areas. Although the ensemble design lacks surface perturbations, there is a significant end impact of SPPT on low-level fields through physical interactions in the atmospheric model.

Corresponding author address: François Bouttier, CNRM-GAME, CNRS, and Météo-France, 42 Av Coriolis, F31057, Toulouse, France. E-mail: francois.bouttier@meteo.fr

Abstract

A stochastic physics scheme is tested in the Application of Research to Operations at Mesoscale (AROME) short-range convection-permitting ensemble prediction system. It is an adaptation of ECMWF’s stochastic perturbation of physics tendencies (SPPT) scheme. The probabilistic performance of the AROME model ensemble is found to be significantly improved, when verified against observations over two 2-week periods. The main improvement lies in the ensemble reliability and the spread–skill consistency. Probabilistic scores for several weather parameters are improved. The tendency perturbations have zero mean, but the stochastic perturbations have systematic effects on the model output, which explains much of the score improvement. Ensemble spread is an increasing function of the SPPT space and time correlations. A case study reveals that stochastic physics do not simply increase ensemble spread, they also tend to smooth out high-spread areas over wider geographical areas. Although the ensemble design lacks surface perturbations, there is a significant end impact of SPPT on low-level fields through physical interactions in the atmospheric model.

Corresponding author address: François Bouttier, CNRM-GAME, CNRS, and Météo-France, 42 Av Coriolis, F31057, Toulouse, France. E-mail: francois.bouttier@meteo.fr
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