Impact of a stochastic parametrization of cumulus convection, using cellular automata, in a mesoscale ensemble prediction system

Quarterly Journal of the Royal Meteorological Society

Volumn 142, Issue 695, 2016, Pages 1150-1159

  Bengtsson, Lisa ^a   Körnich, Heiner ^a  

^a SWEDISH METEOROLOGICAL AND HYDROLOGICAL INSTITUTE   (Sweden)

Author keywords

Deep convection parametrization; Ensemble prediction; Mesoscale modelling; Stochastic physics

Indexed keywords

CELLULAR AUTOMATA; FORECASTING; NATURAL CONVECTION; STOCHASTIC MODELS; UNCERTAINTY ANALYSIS;

DETERMINISTIC FORECASTS; ENSEMBLE PREDICTION; ENSEMBLE PREDICTION SYSTEMS; MESOSCALE MODELLING; PARAMETRIZATIONS; PROBABILISTIC PREDICTION; STOCHASTIC PARAMETRIZATION; UNCERTAINTY ESTIMATES;

STOCHASTIC SYSTEMS;

CELLULAR AUTOMATON; CONVECTIVE CLOUD; CUMULUS; ENSEMBLE FORECASTING; MESOSCALE METEOROLOGY; PARAMETERIZATION; PERFORMANCE ASSESSMENT; PREDICTION; WEATHER FORECASTING;

EID: 84961644627     PISSN: 00359009     EISSN: 1477870X     Source Type: Journal    
DOI: 10.1002/qj.2720     Document Type: Article

References (38)

1. Arakawa A. 2004. The cumulus parametrization problem: Past, present, and future. J. Clim. 17: 2493-2525.
2. Arakawa A, Schubert W. 1974. Interaction of a cumulus cloud ensemble with the large-scale environment, part I. J. Atmos. Sci. 31: 674-701.
3. Bengtsson L, Körnich H, Källén E, Svensson G. 2011. Large-scale dynamical response to subgrid scale organization provided by cellular automata. J. Atmos. Sci. 68: 3132-3144.
4. Bengtsson L, Tijm S, Váňa F, Svensson G. 2012. Impact of flow-dependent horizontal diffusion on resolved convection in AROME. J. Clim. Appl. Meteorol. 51: 54-67.
5. Bengtsson L, Steinheimer M, Bechtold P, Geleyn JF. 2013. A stochastic parametrization for deep convection using cellular automata. Q. J. R. Meteorol. Soc. 139: 1533-1543.
6. Berner J, Doblas-Reyes F, Palmer T, Shutts G, Weisheimer A. 2008. Impact of a quasi-stochastic cellular automaton backscatter scheme on the systematic error and seasonal prediction skill of a global climate model. Philos. Trans. A Math. Phys. Eng. Sci. 366: 2561-2579.
7. Bouttier F, Vié B, Nuissier O, Raynaud L. 2012. Impact of stochastic physics in a convection permitting ensemble. Mon. Weather Rev. 140: 3706-3721.
8. Bryan GH, Wyngaard JC, Fritsch JM. 2003. Resolution requirements for the simulation of deep moist convection. Mon. Weather Rev. 131: 2394-2416.
9. Buizza R, Miller M, Palmer T. 1999. Stochastic representation of model uncertainties in the ECMWF ensemble prediction system. Q. J. R. Meteorol. Soc. 125: 2887-2908.
10. Buizza R, Houtekamer PL, Toth Z, Pellerin G, Wei M, Zhu Y. 2005. A comparison of the ECMWF, MSC and NCEP global ensemble prediction systems. Mon. Weather Rev. 133: 1067-1097.
11. Byun YH, Hong SY. 2007. Improvements in the subgrid-scale representation of moist convection in a cumulus parametrization scheme: The single-column test and its impact on seasonal prediction. Mon. Weather Rev. 135: 2135-2154.
12. Charron M, Pellerin G, Spacek L, Houtekamer P, Gagnon N, Mitchell H, Michelin L. 2010. Toward random sampling of model error in the Canadian ensemble prediction system. Mon. Weather Rev. 138: 1877-1901.
13. Chopard B, Droz M. 1998. Cellular Automata Modeling of Physical Systems. Cambridge University Press: Cambridge, UK.
14. Deng A, Stauffer DR. 2006. On improving 4-km mesoscale model simulations. J. Appl. Meteorol. Climatol. 45: 361-381.
15. Dorrestijn J, Crommelin D, Siebesma P, Jonker H, Jakob C. 2014. Stochastic parametrization of convective area fractions with a multicloud model inferred from observational data. J. Atmos. Sci. 72: 854-869.
16. Frenkel Y, Majda A, Khouider B. 2012. Using the stochastic multicloud model to improve tropical convective parametrization: A paradigm example. J. Atmos. Sci. 69: 1080-1105.
17. Geleyn JF, Girard C, Louis JF. 1982. A simple parametrization of moist convection for large-scale atmospheric models. Beitr. Phys. Atmos. 55: 325-334.
18. Gerard L. 2015. Bulk mass-flux perturbation formulation for a unified approach of deep convection at high resolution. Mon. Weather Rev. 143: 4038-4063.
19. Gerard L, Geleyn JF. 2005. Evolution of a subgrid deep convection parametrization in a limited-area model with increasing resolution. Q. J. R. Meteorol. Soc. 131: 2293-2312.
20. Gerard L, Piriou JM, Brožková R, Geleyn JF, Banciu D. 2009. Cloud and precipitation parametrization in a meso-gamma scale operational weather prediction model. Mon. Weather Rev. 137: 3960-3977.
21. Groenemeijer P, Craig GC. 2012. Ensemble forecasting with a stochastic convective parametrization based on equilibrium statistics. Atmos. Chem. Phys. 12: 4555-4565.
22. Gustafsson N, Berre L, Hörnquist S, Huang XY, Lindskog M, Navascués B, Mogensen K, Thorsteinsson S. 2004. Three-dimensional variational data assimilation for a limited area model. Tellus A 53: 425-446.
23. Huang XY. 1988. 'Nonlinear effects in models of convection', PhD thesis. Stockholm University: Stockholm.
24. Jung T, Leutbecher M. 2008. Scale-dependent verification of ensemble forecasts. Q. J. R. Meteorol. Soc. 134: 973-984.
25. Keane RJ, Craig GC, Keil C, Zängl G. 2014. The Plant-Craig stochastic convection scheme in ICON and its scale-adaptivity. J. Atmos. Sci. 71: 3404-3415, doi: 10.1175/JAS-D-13-0331.1.
26. Khouider B, Biello J, Majda A. 2010. A stochastic multicloud model for tropical convection. Commun. Math. Sci. 8: 187-216.
27. Kober K, Foerster A, Craig G. 2015. Evaluation of a stochastic and deterministic convection parametrization in the COSMO model. Mon. Weather Rev. 143: 4088-4103.
28. Plant R, Craig G. 2008. A stochastic parametrization for deep convection based on equilibrium statistics. J. Atmos. Sci. 65: 87-105.
29. Plant RS, Bengtsson L, Whitall MA. 2015. Stochastic aspects of convective parametrization. In Parameterization of Atmospheric Convection, Plant R, Yano JI. (eds.) Current Issues and New Theories 2, Chapter 20: 135-172. Imperial College Press London.
30. Randall DA. 2013. Beyond deadlock. Geophys. Res. Lett. 40: 5970-5976, doi: 10.1002/2013GL057998.
31. Sanchez C, Williams KD, Shutts G, Collins M. 2014. Impact of a Stochastic Kinetic Energy Backscatter scheme across time-scales and resolutions. Q. J. R. Meteorol. Soc. 140: 2625-2637.
32. Shutts G. 2005. A kinetic energy backscatter algorithm for use in ensemble prediction systems. Q. J. R. Meteorol. Soc. 131: 3079-3102.
33. Shutts G. 2015. A stochastic convective backscatter scheme for use in ensemble prediction systems. Q. J. R. Meteorol. Soc. 141: 2602-2616, doi: 10.1002/qj.2547.
34. Sušelj K, Teixeira J, Chung D. 2013. A unified model for moist convective boundary layers based on a stochastic eddy-diffusivity/mass-flux parametrization. J. Atmos. Sci. 70: 1929-1953.
35. Teixeira J, Reynolds C. 2008. The stochastic nature of physical parametrizations in ensemble prediction: a stochastic convection approach system. Mon.Weather Rev. 136: 483-496.
36. Tennant W, Shutts G, Arribas A, Thompson S. 2011. Using a stochastic kinetic energy backscatter scheme to improve MOGREPS probabilistic forecast skill. Mon. Weather Rev. 139: 1190-1206.
37. Yanai M, Esbensen S, Chu JH. 1973. Determination of bulk properties of tropical cloud clusters from large-scale heat and moisture budgets. J. Atmos. Sci. 30: 611-627.
38. Yano JI, Bister M, Fuchs Z, Gerard L, Phillips V. 2013. Phenomenology of convection-parametrization closure. Atmos. Chem. Phys. 13: 4111-4131.