• Baldauf, M., A. Seifert, J. Förstner, D. Majewski, M. Raschendorfer, and T. Reinhardt, 2011: Operational convective-scale numerical weather prediction with the COSMO model: Description and sensitivities. Mon. Wea. Rev., 139, 38873905, https://doi.org/10.1175/MWR-D-10-05013.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bei, N., and F. Zhang, 2007: Impacts of initial condition errors on mesoscale predictability of heavy precipitation along the Mei-Yu front of China. Quart. J. Roy. Meteor. Soc., 133, 8399, https://doi.org/10.1002/qj.20.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bierdel, L., T. Selz, and G. C. Craig, 2017: Theoretical aspects of upscale error growth through the mesoscales: An analytical model. Quart. J. Roy. Meteor. Soc., 143, 30483059, https://doi.org/10.1002/qj.3160.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Buizza, R., M. Miller, and T. N. Palmer, 1999: Stochastic representation of model uncertainties in the ECMWF Ensemble Prediction System. Quart. J. Roy. Meteor. Soc., 125, 28872908, https://doi.org/10.1002/qj.49712556006.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Done, J. M., G. C. Craig, S. L. Gray, P. A. Clark, and M. E. B. Gray, 2006: Mesoscale simulations of organized convection: Importance of convective equilibrium. Quart. J. Roy. Meteor. Soc., 132, 737756, https://doi.org/10.1256/qj.04.84.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Durran, D. R., and M. Gingrich, 2014: Atmospheric predictability: Why butterflies are not of practical importance. J. Atmos. Sci., 71, 24762488, https://doi.org/10.1175/JAS-D-14-0007.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Durran, D. R., and J. A. Weyn, 2016: Thunderstorms do not get butterflies. Bull. Amer. Meteor. Soc., 97, 237243, https://doi.org/10.1175/BAMS-D-15-00070.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Fresnay, S., A. Hally, C. Garnaud, E. Richard, and D. Lambert, 2012: Heavy precipitation events in the Mediterranean: Sensitivity to cloud physics parameterisation uncertainties. Nat. Hazards Earth Syst. Sci., 12, 26712688, https://doi.org/10.5194/nhess-12-2671-2012.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Fuhrer, O., C. Osuna, X. Lapillonne, T. Gysi, B. Cumming, M. Bianco, A. Arteaga, and T. Schulthess, 2014: Towards a performance portable, architecture agnostic implementation strategy for weather and climate models. Supercomput. Front. Innovations, 1, 4562, https://doi.org/10.14529/jsfi140103.

    • Search Google Scholar
    • Export Citation
  • Gebhardt, C., S. E. Theis, M. Paulat, and Z. Ben Bouallègue, 2011: Uncertainties in COSMO-DE precipitation forecasts introduced by model perturbations and variation of lateral boundaries. Atmos. Res., 100, 168177, https://doi.org/10.1016/j.atmosres.2010.12.008.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hally, A., E. Richard, S. Fresnay, and D. Lambert, 2014: Ensemble simulations with perturbed physical parameterizations: Pre-HyMeX case studies. Quart. J. Roy. Meteor. Soc., 140, 19001916, https://doi.org/10.1002/qj.2257.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hohenegger, C., and C. Schär, 2007: Predictability and error growth dynamics in cloud-resolving models. J. Atmos. Sci., 64, 44674478, https://doi.org/10.1175/2007JAS2143.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hohenegger, C., D. Lüthi, and C. Schär, 2006: Predictability mysteries in cloud-resolving models. Mon. Wea. Rev., 134, 20952107, https://doi.org/10.1175/MWR3176.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Holton, J. R., and G. J. Hakim, 2013: An Introduction to Dynamic Meteorology. 5th ed. Academic Press, 552 pp.

    • Crossref
    • Export Citation
  • Keil, C., F. Heinlein, and G. C. Craig, 2014: The convective adjustment time-scale as indicator of predictability of convective precipitation. Quart. J. Roy. Meteor. Soc., 140, 480490, https://doi.org/10.1002/qj.2143.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Klasa, C., M. Arpagaus, A. Walser, and H. Wernli, 2018: An evaluation of the convection-permitting ensemble COSMO-E for three contrasting precipitation events in Switzerland. Quart. J. Roy. Meteor. Soc., 144, 744764, https://doi.org/10.1002/qj.3245.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kühnlein, C., C. Keil, G. C. Craig, and C. Gebhardt, 2014: The impact of downscaled initial condition perturbations on convective-scale ensemble forecasts of precipitation. Quart. J. Roy. Meteor. Soc., 140, 15521562, https://doi.org/10.1002/qj.2238.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lorenz, E. N., 1969: The predictability of a flow which possesses many scales of motion. Tellus, 21, 289307, https://doi.org/10.3402/tellusa.v21i3.10086.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lorenz, E. N., 1982: Atmospheric predictability experiments with a large numerical model. Tellus, 34, 505513, https://doi.org/10.3402/tellusa.v34i6.10836.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Luo, Y., and Y. Chen, 2015: Investigation of the predictability and physical mechanisms of an extreme-rainfall-producing mesoscale convective system along the Meiyu front in East China: An ensemble approach. J. Geophys. Res. Atmos., 120, 10 59310 618, https://doi.org/10.1002/2015JD023584.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lynch, P., 1988: Deducing the wind from vorticity and divergence. Mon. Wea. Rev., 116, 8693, https://doi.org/10.1175/1520-0493(1988)116<0086:DTWFVA>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lynch, P., 1989: Partitioning the wind in a limited domain. Mon. Wea. Rev., 117, 14921500, https://doi.org/10.1175/1520-0493(1989)117<1492:PTWIAL>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Melhauser, C., and F. Zhang, 2012: Practical and intrinsic predictability of severe and convective weather at the mesoscales. J. Atmos. Sci., 69, 33503371, https://doi.org/10.1175/JAS-D-11-0315.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Murphy, J. M., 1988: The impact of ensemble forecasts on predictability. Quart. J. Roy. Meteor. Soc., 114, 463493, https://doi.org/10.1002/qj.49711448010.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Nielsen, E. R., and R. S. Schumacher, 2016: Using convection-allowing ensembles to understand the predictability of an extreme rainfall event. Mon. Wea. Rev., 144, 36513676, https://doi.org/10.1175/MWR-D-16-0083.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Press, W. H., B. P. Flannery, S. A. Teukolsky, and W. T. Vetterling, 1992: Numerical Recipes in Fortran 77: The Art of Scientific Computing. 2nd ed. Cambridge University Press, 580 pp.

  • Schubert, W. H., J. J. Hack, P. L. Silva Dias, and S. R. Fulton, 1980: Geostrophic adjustment in an axisymmetric vortex. J. Atmos. Sci., 37, 14641484, https://doi.org/10.1175/1520-0469(1980)037<1464:GAIAAV>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Selz, T., and G. Craig, 2015: Upscale error growth in a high-resolution simulation of a summertime weather event over Europe. Mon. Wea. Rev., 143, 813827, https://doi.org/10.1175/MWR-D-14-00140.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sun, Y. Q., and F. Zhang, 2016: Intrinsic versus practical limits of atmospheric predictability and the significance of the butterfly effect. J. Atmos. Sci., 73, 14191438, https://doi.org/10.1175/JAS-D-15-0142.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sun, Y. Q., R. Rotunno, and F. Zhang, 2017: Contributions of moist convection and internal gravity waves to building the atmospheric −5/3 kinetic energy spectra. J. Atmos. Sci., 74, 185201, https://doi.org/10.1175/JAS-D-16-0097.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tan, Z.-M., F. Zhang, R. Rotunno, and C. Snyder, 2004: Mesoscale predictability of moist baroclinic waves: Experiments with parameterized convection. J. Atmos. Sci., 61, 17941804, https://doi.org/10.1175/1520-0469(2004)061<1794:MPOMBW>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Walser, A., D. Lüthi, and C. Schär, 2004: Predictability of precipitation in a cloud-resolving model. Mon. Wea. Rev., 132, 560577, https://doi.org/10.1175/1520-0493(2004)132<0560:POPIAC>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Weyn, J. A., and D. R. Durran, 2017: The dependence of the predictability of mesoscale convective systems on the horizontal scale and amplitude of initial errors in idealized simulations. J. Atmos. Sci., 74, 21912210, https://doi.org/10.1175/JAS-D-17-0006.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Xu, Q., J. Cao, and S. Gao, 2011: Computing streamfunction and velocity potential in a limited domain of arbitrary shape. Part I: Theory and integral formulae. Adv. Atmos. Sci., 28, 14331444, https://doi.org/10.1007/s00376-011-0185-6.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhang, F., C. Snyder, and R. Rotunno, 2002: Mesoscale predictability of the “surprise” snowstorm of 24–25 January 2000. Mon. Wea. Rev., 130, 16171632, https://doi.org/10.1175/1520-0493(2002)130<1617:MPOTSS>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhang, F., C. Snyder, and R. Rotunno, 2003: Effects of moist convection on mesoscale predictability. J. Atmos. Sci., 60, 11731185, https://doi.org/10.1175/1520-0469(2003)060<1173:EOMCOM>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhang, F., A. M. Odins, and J. W. Nielsen-Gammon, 2006: Mesoscale predictability of an extreme warm-season precipitation event. Wea. Forecasting, 21, 149166, https://doi.org/10.1175/WAF909.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhang, F., N. Bei, R. Rotunno, C. Snyder, and C. C. Epifanio, 2007: Mesoscale predictability of moist baroclinic waves: Convection-permitting experiments and multistage error growth dynamics. J. Atmos. Sci., 64, 35793594, https://doi.org/10.1175/JAS4028.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zimmer, M., G. C. Craig, C. Keil, and H. Wernli, 2011: Classification of precipitation events with a convective response timescale and their forecasting characteristics. Geophys. Res. Lett., 38, L05802, https://doi.org/10.1029/2010GL046199.

    • Crossref
    • Search Google Scholar
    • Export Citation
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On the Time Evolution of Limited-Area Ensemble Variance: Case Studies with the Convection-Permitting Ensemble COSMO-E

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  • 1 Institute for Atmospheric and Climate Science, ETH Zürich, Zurich, Switzerland
  • | 2 Federal Office of Meteorology and Climatology MeteoSwiss, Zurich, Switzerland
  • | 3 Institute for Atmospheric and Climate Science, ETH Zürich, Zurich, Switzerland
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Abstract

Dynamical processes determining the time evolution of difference kinetic energy (DKE) in a limited-area domain are investigated with the convection-permitting ensemble model COSMO-E for a forecasting period of 4 days. DKE is quantified by means of ensemble variance of the irrotational and nondivergent horizontal wind. For three case studies characterized by contrasting predictability levels of precipitation, it is shown that DKE of the irrotational wind strongly increases during periods of solar-forced moist convective activity and decreases when the latter ceases. The response of DKE of the nondivergent wind is also clearly related to the convective activity, but delayed by a few hours, pointing to interactions between both wind components. Apart from the impact of moist convection, DKE of the nondivergent wind is primarily governed by large-scale advection, imposed at the lateral domain boundaries of the limited-area ensemble. This forcing may also sustain or increase DKE of the irrotational wind when moist convection is absent. Consequently, the large-scale flow and diurnal solar forcing, associated with higher spatiotemporal predictability, determines the overall evolution of the limited-area ensemble variance of the horizontal wind, which increases in the presence of moist convective activity or strong synoptic-scale forcing, and stagnates or decreases otherwise, rendering forecasts of convection-permitting ensembles valuable beyond the very short forecast range.

© 2018 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Christina Klasa, christina.klasa@env.ethz.ch

Abstract

Dynamical processes determining the time evolution of difference kinetic energy (DKE) in a limited-area domain are investigated with the convection-permitting ensemble model COSMO-E for a forecasting period of 4 days. DKE is quantified by means of ensemble variance of the irrotational and nondivergent horizontal wind. For three case studies characterized by contrasting predictability levels of precipitation, it is shown that DKE of the irrotational wind strongly increases during periods of solar-forced moist convective activity and decreases when the latter ceases. The response of DKE of the nondivergent wind is also clearly related to the convective activity, but delayed by a few hours, pointing to interactions between both wind components. Apart from the impact of moist convection, DKE of the nondivergent wind is primarily governed by large-scale advection, imposed at the lateral domain boundaries of the limited-area ensemble. This forcing may also sustain or increase DKE of the irrotational wind when moist convection is absent. Consequently, the large-scale flow and diurnal solar forcing, associated with higher spatiotemporal predictability, determines the overall evolution of the limited-area ensemble variance of the horizontal wind, which increases in the presence of moist convective activity or strong synoptic-scale forcing, and stagnates or decreases otherwise, rendering forecasts of convection-permitting ensembles valuable beyond the very short forecast range.

© 2018 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Christina Klasa, christina.klasa@env.ethz.ch
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