Large-scale dynamical response to subgrid-scale organization provided by cellular automata

Journal of the Atmospheric Sciences

Volumn 68, Issue 12, 2011, Pages 3132-3144

  Bengtsson, Lisa ^a   Körnich, Heiner ^b   Källén, Erland ^c   Svensson, Gunilla ^b  

^a SWEDISH METEOROLOGICAL AND HYDROLOGICAL INSTITUTE   (Sweden)

^b STOCKHOLM UNIVERSITY   (Sweden)

^c EUROPEAN CENTRE FOR MEDIUM RANGE WEATHER FORECASTS   (United Kingdom)

Author keywords

Convective parameterization; Model errors; Parameterization; Stochastic models; Subgrid scale processes

Indexed keywords

ACTIVE REGIONS; BACK-SCATTERED; DEEP CONVECTION; DYNAMICAL RESPONSE; ENERGY SPECTRA; EQUATORIAL WAVES; GRID-BOX; KELVIN MODES; KINETIC ENERGY SPECTRA; LARGE-SCALE FLOWS; LIMITED RESOLUTION; MESO-SCALE SYSTEMS; MODEL ERRORS; MODEL SCALE; NON-LINEAR WAVE INTERACTIONS; NUMERICAL WEATHER PREDICTION MODELS; PARAMETERIZED; PHYSICAL PROCESS; RANDOM PERTURBATIONS; SHALLOW WATERS; SPATIAL SCALE; STOCHASTIC REPRESENTATIONS; SUB-GRIDS; SUBGRID SCALE; TIME-SCALES;

AUTOMATA THEORY; CELLULAR AUTOMATA; ENERGY TRANSFER; MODELS; PARAMETERIZATION; SPECTROSCOPY; STOCHASTIC SYSTEMS;

STOCHASTIC MODELS;

CELLULAR AUTOMATON; CLUSTER ANALYSIS; CONVECTIVE SYSTEM; GRAVITY WAVE; MATHEMATICAL ANALYSIS; NUMERICAL MODEL; PARAMETERIZATION; PERTURBATION; STOCHASTICITY; UPDRAFT; WAVE PROPAGATION; WEATHER FORECASTING;

EID: 84855838601     PISSN: 00224928     EISSN: None     Source Type: Journal    
DOI: 10.1175/JAS-D-10-05028.1     Document Type: Article

References (23)

1. Bechtold, P., M. Köhler, T. Jung, F. Doblas-Reyes, M. Leutbecher, M. Rodwell, F. Vitart, and G. Balsamo, 2008: Advances in simulating atmospheric variability with the ECMWF model: From synoptic to decadal time-scales. Quart. J. Roy. Meteor. Soc., 134, 1337-1351.
2. Bengtsson, L., L. Magnusson, and E. Källén, 2008: Independent estimations of the asymptotic variability in an ensemble forecast system. Mon. Wea. Rev., 136, 4105-4112.
3. Berner, J., F. Doblas-Reyes, T. Palmer, G. Shutts, and A. Weisheimer, 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. Roy. Soc., 366, 2559-2577.
4. Buizza, R., M. Miller, and T. Palmer, 1999: Stochastic representation of model uncertainties in the ECMWF Ensemble Prediction System. Quart. J. Roy. Meteor. Soc., 125, 2887-2908.
5. Chopard, B., and M. Droz, 1998: Cellular Automata Modeling of Physical Systems. Cambridge University Press, 341 pp.
6. Gustafsson, N., 1998: The numerical scheme and lateral boundary conditions for the spectral HIRLAM and its adjoint. Proc. ECMWF Seminar on Recent Developments in Numerical Methods for Atmospheric Modelling, Reading, United Kingdom, ECMWF, 335-363.
7. Hodges, K. I., and C. D. Thorncroft, 1997: Distribution and statistics of African mesoscale convective weather systems based on the ISCCP Meteosat imagery. Mon. Wea. Rev., 125, 2821-2837.
8. Huang, X.-Y., 1988: The organization of moist convection by internal gravity waves. Tellus, 42A, 270-285.
9. Kiladis, G., M. Wheeler, P. Haertel, K. Straub, and P. E. Roundy, 2009: Convectively coupled equatorial waves. Rev. Geophys., 47, RG2003, doi:10.1029/2008RG000266.
10. Lin, J., and J. Neelin, 2002: Considerations for stochastic convective parameterization. J. Atmos. Sci., 59, 959-975.
11. Magnusson, L., 2009: Sampling uncertainties in ensemble weather forecasting. Ph.D. thesis, Stockholm University, 52 pp.
12. Palmer, T. N., 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, 279-304.
13. Palmer, T. N., and P. Williams, 2009: Stochastic Physics and Climate Modelling. Cambridge University Press, 480 pp.
14. Plant, R., and G. Craig, 2008: A stochastic parameterization for deep convection based on equilibrium statistics. J. Atmos. Sci., 65, 87-105.
15. Shutts, G., 2005: A kinetic energy backscatter algorithm for use in ensemble prediction systems. Quart. J. Roy. Meteor. Soc., 131, 3079-3102.
16. Straub, K., and G. Kiladis, 2002: The observed structure of convectively coupledKelvin waves: Comparison with simplemodels of coupled wave instability. J. Atmos. Sci., 60, 1655-1668.
17. Teixeira, J., and C. Reynolds, 2008: The stochastic nature of physical parameterizations in ensemble prediction: A stochastic convection approach system. Mon. Wea. Rev., 136, 483-496.
18. Tennant, W., G. Shutts, A. Arribas, and S. Thompson, 2011: Using a stochastic kinetic energy backscatter scheme to improve MOGREPS probabilistic forecast skill. Mon. Wea. Rev., 139, 1190-1206.
19. Tompkins, A. M., 2001: Organization of tropical convection in low vertical wind shears: The role of water vapor. J. Atmos. Sci., 58, 529-545.
20. Wheeler, M., and G. Kiladis, 1999: Convectively coupled equatorial waves: Analysis of clouds and temperature in the wavenumber-frequency domain. J. Atmos. Sci., 56, 374-399.
21. Wheeler, M., G. Kiladis, and P. Webster, 2000: Large-scale dynamical fields associated with convectively coupled equatorial waves. J. Atmos. Sci., 57, 613-640.
22. Wolfram, S., 1983: Statistical mechanics of cellular automata. Rev. Mod. Phys., 55, 601-644.
23. Zagar, N., N. Gustafsson, and E. Källén, 2004: Variational data assimilation in the tropics: The impact of a background-error constraint. Quart. J. Roy. Meteor. Soc., 130, 103-125.