• Bengtsson, L., , H. Körnich, , E. Källén, , and G. Svensson, 2011: Large-scale dynamical response to subgrid-scale organization provided by cellular automata. J. Atmos. Sci., 68, 31323144.

    • Search Google Scholar
    • Export Citation
  • Berner, J., 2009: A spectral stochastic kinetic energy backscatter scheme and its impact on flow-dependent predictability in the ECMWF ensemble prediction system. J. Atmos. Sci., 66, 603626.

    • Search Google Scholar
    • Export Citation
  • Berner, J., , S.-Y. Ha, , J. Hacker, , A. Fournier, , and C. Snyder, 2011: Model uncertainty in a mesoscale ensemble prediction system: Stochastic versus multiphysics representations. Mon. Wea. Rev., 139, 19721995.

    • Search Google Scholar
    • Export Citation
  • Berre, L., 2000: Estimation of synoptic and mesoscale forecast error covariances in a limited area model. Mon. Wea. Rev., 128, 644667.

    • Search Google Scholar
    • Export Citation
  • Bougeault, P., and Coauthors, 2010: The THORPEX Interactive Grand Global Ensemble. Bull. Amer. Meteor. Soc., 91, 10591072.

  • Bowler, N., , and K. Mylne, 2009: Ensemble transform Kalman filter perturbations for a regional ensemble prediction system. Quart. J. Roy. Meteor. Soc., 135, 757766.

    • Search Google Scholar
    • Export Citation
  • Bowler, N., , A. Arribas, , K. Mylne, , K. Robertson, , and S. Beare, 2008: The MOGREPS short-range ensemble prediction system. Quart. J. Roy. Meteor. Soc., 134, 703722.

    • Search Google Scholar
    • Export Citation
  • Bowler, N., , A. Arribas, , S. Beare, , K. Mylne, , and G. Schutts, 2009: The local ETKF and SKEB: Upgrades to the MOGREPS short-range ensemble prediction system. Quart. J. Roy. Meteor. Soc., 135, 767776.

    • Search Google Scholar
    • Export Citation
  • Bright, D., , and S. Mullen, 2002: Short-range ensemble forecasts of precipitation during the Southwest Monsoon. Wea. Forecasting, 17, 10801100.

    • Search Google Scholar
    • Export Citation
  • Bröcker, J., , and L. Smith, 2007: Increasing the reliability of reliability diagrams. Wea. Forecasting, 22, 651661.

  • Brousseau, P., , L. Berre, , F. Bouttier, , and G. Desroziers, 2011: Background-error covariances for a convective scale data-assimilation system: Arome-France 3D-Var. Quart. J. Roy. Meteor. Soc., 137, 409422.

    • Search Google Scholar
    • Export Citation
  • Buizza, R., , and T. Palmer, 1999: Stochastic representation of model uncertainties in the ECMWF ensemble prediction system. Quart. J. Roy. Meteor. Soc., 125, 28872908.

    • Search Google Scholar
    • Export Citation
  • Candille, G., 2009: The multiensemble approach: The NAEFS example. Mon. Wea. Rev., 137, 16551665.

  • Candille, G., , and O. Talagrand, 2005: Evaluation of probabilistic prediction systems for a scalar variable. Quart. J. Roy. Meteor. Soc., 131, 21312150.

    • Search Google Scholar
    • Export Citation
  • Charron, M., , G. Pellerin, , L. Spacek, , P. Houtekamer, , N. Gagnon, , H. Mitchell, , and L. Michelin, 2010: Toward random sampling of model error in the Canadian ensemble prediction system. Mon. Wea. Rev., 138, 18771901.

    • Search Google Scholar
    • Export Citation
  • Clark, A., and Coauthors, 2011: Probabilistic precipitation forecast skill as a function of ensemble size and spatial scale in a convection-allowing ensemble. Mon. Wea. Rev., 139, 14101418.

    • Search Google Scholar
    • Export Citation
  • Courtier, P., , C. Freydier, , J.-F. Geleyn, , F. Rabier, , and M. Rochas, 1991: The ARPEGE project at Météo-France. Proc. ECMWF Seminar on Numerical Methods in Atmospheric Models, Vol. 2, Reading, United Kingdom, ECMWF, 193–232. [Available online at http://www.ecmwf.int/publications/.]

  • Desroziers, G., , L. Berre, , V. Chabot, , and B. Chapnik, 2009: A posteriori diagnostics in an ensemble of perturbed analyses. Mon. Wea. Rev., 137, 34203436.

    • Search Google Scholar
    • Export Citation
  • Frogner, I.-L., , and T. Iversen, 2002: High-resolution limited-area ensemble predictions based on low-resolution targeted singular vectors. Quart. J. Roy. Meteor. Soc., 128, 13211341.

    • Search Google Scholar
    • Export Citation
  • Gebhardt, C., , S. Theis, , P. Krahe, , and V. Renner, 2008: Experimental ensemble forecasts of precipitation based on a convection-resolving model. Atmos. Sci. Lett., 9, 6772.

    • Search Google Scholar
    • Export Citation
  • Hagedorn, R., , F. Doblas-Reyes, , and T. Palmer, 2005: The rationale behind the success of multi-model ensembles in seasonal forecasting. I: Basic concept. Tellus, 57A, 219233.

    • Search Google Scholar
    • Export Citation
  • Houtekamer, P., , M. Herschel, , and X. Deng, 2009: Model error representation in an operational ensemble Kalman filter. Mon. Wea. Rev., 137, 21262143.

    • Search Google Scholar
    • Export Citation
  • Jolliffe, I., 2007: Uncertainty and inference for verification measures. Wea. Forecasting, 22, 637650.

  • Leith, C., 1974: Theoretical skill of Monte Carlo forecasts. Mon. Wea. Rev., 102, 409418.

  • Li, X., , M. Charron, , L. Spacek, , and G. Candille, 2008: A regional ensemble prediction system based on moist targeted singular vectors and stochastic parameter perturbations. Mon. Wea. Rev., 136, 443462.

    • Search Google Scholar
    • Export Citation
  • Lin, J., , and J. Neelin, 2000: Influence of a stochastic moist convective parameterization on tropical climate variability. Geophys. Res. Lett., 27 (22), 36913694.

    • Search Google Scholar
    • Export Citation
  • Marsigli, C., , A. Montani, , F. Nerozzi, , T. Paccagnella, , S. Tibaldi, , F. Molteni, , and R. Buizza, 2001: A strategy for high-resolution ensemble prediction. II: Limited-area experiments in four Alpine flood events. Quart. J. Roy. Meteor. Soc., 127, 20952115.

    • Search Google Scholar
    • Export Citation
  • Migliorini, S., , M. Dixon, , R. Bannister, , and S. Ballard, 2011: Ensemble prediction for nowcasting with a convection-permitting model. I: Description of the system and the impact of radar-derived surface precipitation rates. Tellus, 63A, 468496.

    • Search Google Scholar
    • Export Citation
  • Molteni, F., , R. Buizza, , T. Palmer, , and T. Petroliagis, 1996: The ECMWF ensemble prediction system: Methodology and validation. Quart. J. Roy. Meteor. Soc., 122, 73119.

    • Search Google Scholar
    • Export Citation
  • Nicolau, J., 2002: Short-range ensemble forecasting. Proc. Tech. Conf. on Data Processing and Forecasting Systems, Cairns, Queensland, Australia, World Meteorological Organization/Commission on Basic Systems, 6 pp. [Available online at http://www.wmo.ch/pages/prog/www/DPS/TC-DPFS-2002/Papers-Posters/Topic1-Nicolau.pdf.]

  • Palmer, T., 2001: A nonlinear dynamical perspective on model error: A proposal for non-local stochastic-dynamic parametrization in weather and climate prediction models. Quart. J. Roy. Meteor. Soc., 127, 279304.

    • Search Google Scholar
    • Export Citation
  • Palmer, T., , R. Buizza, , F. Doblas-Reyes, , T. Jung, , M. Leutbecher, , G. Shutts, , M. Steinheimer, , and A. Weisheimer, 2009: Stochastic parametrization and model uncertainty. Tech. Rep. ECMWF RD Tech. Memo. 598, 42 pp. [Available online at http://www.ecmwf.int/publications/.]

  • Park, Y., , R. Buizza, , and M. Leutbecher, 2008: TIGGE: Preliminary results on comparing and combining ensembles. Quart. J. Roy. Meteor. Soc., 134, 20292050.

    • Search Google Scholar
    • Export Citation
  • Plant, R., , and G. Craig, 2008: A stochastic parameterization for deep convection based on equilibrium statistics. J. Atmos. Sci., 65, 87105.

    • Search Google Scholar
    • Export Citation
  • Raynaud, L., , L. Berre, , and G. Desroziers, 2011: An extended specification of flow-dependent background error variances in the Météo-France global 4D-Var system. Quart. J. Roy. Meteor. Soc., 137, 607619.

    • Search Google Scholar
    • Export Citation
  • Richardson, D., 2000: Skill and relative economic value of the ECMWF ensemble prediction system. Quart. J. Roy. Meteor. Soc., 126, 649667.

    • Search Google Scholar
    • Export Citation
  • Seity, Y., , P. Brousseau, , S. Malardel, , G. Hello, , P. Bénard, , F. Bouttier, , C. Lac, , and V. Masson, 2011: The AROME-France convective-scale operational model. Mon. Wea. Rev., 139, 976991.

    • Search Google Scholar
    • Export Citation
  • Shutts, G., 2005: A kinetic energy backscatter algorithm for use in ensemble prediction systems. Quart. J. Roy. Meteor. Soc., 131, 30793102.

    • Search Google Scholar
    • Export Citation
  • Shutts, G., , and T. Palmer, 2007: Convective forcing fluctuations in a cloud-resolving model: Relevance to the stochastic parameterization problem. J. Climate, 20, 187202.

    • Search Google Scholar
    • Export Citation
  • Stensrud, D., , H. Brooks, , J. Du, , S. Tracton, , and E. Rogers, 1999: Using ensembles for short-range forecasting. Mon. Wea. Rev., 127, 433446.

    • Search Google Scholar
    • Export Citation
  • Teixeira, J., , and C. Reynolds, 2008: Stochastic nature of physical parameterizations in ensemble prediction: A stochastic convection approach. Mon. Wea. Rev., 136, 483496.

    • Search Google Scholar
    • Export Citation
  • Tompkins, A., , and J. Berner, 2008: A stochastic convective approach to account for model uncertainty due to unresolved humidity variability. J. Geophys. Res., 113, D18101, doi:10.1029/2007JD009284.

    • Search Google Scholar
    • Export Citation
  • Toth, Z., , and E. Kalnay, 1993: Ensemble forecasting at NMC: The generation of perturbations. Bull. Amer. Meteor. Soc., 74, 23172330.

  • Trémolet, Y., 2007: Model-error estimation in 4D-Var. Quart. J. Roy. Meteor. Soc., 133, 12671280.

  • van Leeuwen, P., 2009: Particle filtering in geophysical systems. Mon. Wea. Rev., 137, 40894114.

  • Vié, B., , O. Nuissier, , and V. Ducrocq, 2011: Cloud-resolving ensemble simulations of Mediterranean heavy precipitating events: Uncertainty on initial conditions and lateral boundary conditions. Mon. Wea. Rev., 139, 403423.

    • Search Google Scholar
    • Export Citation
  • Whitaker, J., , and T. Hamill, 2002: Ensemble data assimilation without perturbed observations. Mon. Wea. Rev., 130, 19131924.

All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 197 197 32
PDF Downloads 87 87 22

Impact of Stochastic Physics in a Convection-Permitting Ensemble

View More View Less
  • 1 CNRM-GAME, CNRS, and Météo-France, Toulouse, France
© Get Permissions
Restricted access

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
Save