Upscale Error Growth in a High-Resolution Simulation of a Summertime Weather Event over Europe

Tobias Selz Meteorologisches Institut, Ludwig-Maximilians-Universität, München, Germany

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George C. Craig Meteorologisches Institut, Ludwig-Maximilians-Universität, München, Germany

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Abstract

The growth of small-amplitude, spatially uncorrelated perturbations has been studied in a weather forecast of a 4-day period in the summer of 2007, using a large domain covering Europe and the eastern Atlantic and with explicitly resolved deep convection. The error growth follows the three-stage conceptual model of Zhang et al., with rapid initial growth (e-folding time about 0.5 h) on all scales, relaxing over about 20 h to a slow growth of the large-scale perturbations (e-folding time 12 h). The initial growth was confined to precipitating regions, with a faster growth rate where conditional instability was large. Growth in these regions saturated within 3–10 h, continuing for the longest where the precipitation rate was large. While the initial growth was mainly in the divergent part of the flow, the eventual slow growth on large scales was more in the rotational component.

Spectral decomposition of the disturbance energy showed that the rapid growth in precipitating regions projected onto all Fourier components; however, the amplitude at saturation was too small to initiate the subsequent large-scale growth. Visualization of the disturbance energy showed it to expand outward from the precipitating regions at a speed corresponding to a deep tropospheric gravity wave. These results suggest a physical picture of error growth with a rapidly growing disturbance to the vertical mass transport in precipitating regions that spreads to the radius of deformation while undergoing geostrophic adjustment, eventually creating a balanced perturbation that continues to grow through baroclinic instability.

Supplemental information related to this paper is available at the Journals Online website: http://dx.doi.org/10.1175/MWR-D-14-00140.s1.

Corresponding author address: Tobias Selz, Meteorologisches Institut, Theresienstrasse 37, 80333 München, Germany. E-mail: tobias.selz@lmu.de

This article is included in the Predictability and Dynamics of Weather Systems in the Atlantic-European Sector (PANDOWAE) Special Collection.

Abstract

The growth of small-amplitude, spatially uncorrelated perturbations has been studied in a weather forecast of a 4-day period in the summer of 2007, using a large domain covering Europe and the eastern Atlantic and with explicitly resolved deep convection. The error growth follows the three-stage conceptual model of Zhang et al., with rapid initial growth (e-folding time about 0.5 h) on all scales, relaxing over about 20 h to a slow growth of the large-scale perturbations (e-folding time 12 h). The initial growth was confined to precipitating regions, with a faster growth rate where conditional instability was large. Growth in these regions saturated within 3–10 h, continuing for the longest where the precipitation rate was large. While the initial growth was mainly in the divergent part of the flow, the eventual slow growth on large scales was more in the rotational component.

Spectral decomposition of the disturbance energy showed that the rapid growth in precipitating regions projected onto all Fourier components; however, the amplitude at saturation was too small to initiate the subsequent large-scale growth. Visualization of the disturbance energy showed it to expand outward from the precipitating regions at a speed corresponding to a deep tropospheric gravity wave. These results suggest a physical picture of error growth with a rapidly growing disturbance to the vertical mass transport in precipitating regions that spreads to the radius of deformation while undergoing geostrophic adjustment, eventually creating a balanced perturbation that continues to grow through baroclinic instability.

Supplemental information related to this paper is available at the Journals Online website: http://dx.doi.org/10.1175/MWR-D-14-00140.s1.

Corresponding author address: Tobias Selz, Meteorologisches Institut, Theresienstrasse 37, 80333 München, Germany. E-mail: tobias.selz@lmu.de

This article is included in the Predictability and Dynamics of Weather Systems in the Atlantic-European Sector (PANDOWAE) Special Collection.

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  • 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, doi:10.1175/MWR-D-10-05013.1.

    • Search Google Scholar
    • Export Citation
  • Berner, J., G. Shutts, M. Leutbecher, and T. Palmer, 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, doi:10.1175/2008JAS2677.1.

    • Search Google Scholar
    • Export Citation
  • Bierdel, L., P. Friederichs, and S. Bentzien, 2012: Spatial kinetic energy spectra in the convection-permitting limited-area NWP model COSMO-DE. Meteor. Z., 21, 245258, doi:10.1127/0941-2948/2012/0319.

    • Search Google Scholar
    • Export Citation
  • Bretherton, C. S., and P. K. Smolarkiewicz, 1989: Gravity waves, compensating subsidence and detrainment around cumulus clouds. J. Atmos. Sci., 46, 740759, doi:10.1175/1520-0469(1989)046<0740:GWCSAD>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Buizza, R., M. Milleer, and T. Palmer, 1999: Stochastic representation of model uncertainties in the ECMWF ensemble prediction system. Quart. J. Roy. Meteor. Soc., 125, 28872908, doi:10.1002/qj.49712556006.

    • 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, doi:10.1256/qj.04.84.

    • 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, 2476–2488, doi:10.1175/JAS-D-14-0007.1.

    • Search Google Scholar
    • Export Citation
  • Durran, D. R., P. A. Reinecke, and J. D. Doyle, 2013: Large-scale errors and mesoscale predictability in Pacific Northwest snowstorms. J. Atmos. Sci., 70, 14701487, doi:10.1175/JAS-D-12-0202.1.

    • Search Google Scholar
    • Export Citation
  • Errico, R. M., 1985: Spectra computed from a limited area grid. Mon. Wea. Rev., 113, 15541562, doi:10.1175/1520-0493(1985)113<1554:SCFALA>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Gill, A. E., 1982: Atmosphere-Ocean Dynamics. Academic Press, 261 pp.

  • Hohenegger, C., and C. Schär, 2007a: Atmospheric predictability at synoptic versus cloud-resolving scales. Bull. Amer. Meteor. Soc., 88, 17831793, doi:10.1175/BAMS-88-11-1783.

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

    • Search Google Scholar
    • 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, 480–490, doi:10.1002/qj.2143.

  • Leoncini, G., R. S. Plant, S. L. Gray, and P. A. Clark, 2010: Perturbation growth at the convective scale for CSIP IOP18. Quart. J. Roy. Meteor. Soc., 136, 653670, doi:10.1002/qj.587.

    • Search Google Scholar
    • Export Citation
  • Leutbecher, M., and T. N. Palmer, 2008: Ensemble forecasting. J. Comput. Phys., 227, 35153539, doi:10.1016/j.jcp.2007.02.014.

  • Lorenz, E. N., 1969: The predictability of a flow which possesses many scales of motion. Tellus, 21, 289307, doi:10.1111/j.2153-3490.1969.tb00444.x.

    • Search Google Scholar
    • Export Citation
  • Lorenz, E. N., 1996: Predictability: A problem partly solved. Proc. Seminar on Predictability, Vol. 1, Reading, United Kingdom, ECMWF, 1–18.

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

    • Search Google Scholar
    • Export Citation
  • Plant, R. S., and G. C. Craig, 2008: A stochastic parameterization for deep convection based on equilibrium statistics. J. Atmos. Sci., 65, 87105, doi:10.1175/2007JAS2263.1.

    • Search Google Scholar
    • Export Citation
  • Rodwell, M. J., and Coauthors, 2013: Characteristics of occasional poor medium-range weather forecasts for Europe. Bull. Amer. Meteor. Soc., 94, 13931405, doi:10.1175/BAMS-D-12-00099.1.

    • 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, doi:10.1175/1520-0469(2004)061<1794:MPOMBW>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Wulfmeyer, V., and Coauthors, 2011: The Convective and Orographically-induced Precipitation Study (COPS): The scientific strategy, the field phase, and research highlights. Quart. J. Roy. Meteor. Soc., 137, 330, doi:10.1002/qj.752.

    • 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, doi:10.1175/1520-0469(2003)060<1173:EOMCOM>2.0.CO;2.

    • 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, doi:10.1175/WAF909.1.

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

    • Search Google Scholar
    • Export Citation
  • Zhu, H., and A. Thorpe, 2006: Predictability of extratropical cyclones: The influence of initial condition and model uncertainties. J. Atmos. Sci., 63, 14831497, doi:10.1175/JAS3688.1.

    • Search Google Scholar
    • Export Citation
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