Thunderstorms Do Not Get Butterflies

Dale R. Durran Department of Atmospheric Sciences, University of Washington, Seattle, Washington

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Jonathan A. Weyn Department of Atmospheric Sciences, University of Washington, Seattle, Washington

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Abstract

One important limitation on the accuracy of weather forecasts is imposed by unavoidable errors in the specification of the atmosphere’s initial state. Much theoretical concern has been focused on the limits to predictability imposed by small-scale errors, potentially even those on the scale of a butterfly. Very modest errors at much larger scales may nevertheless pose a more important practical limitation. We demonstrate the importance of large-scale uncertainty by analyzing ensembles of idealized squall-line simulations. Our results imply that minimizing initial errors on scales around 100 km is more likely to extend the accuracy of forecasts at lead times longer than 3–4 h than efforts to minimize initial errors on much smaller scales. These simulations also demonstrate that squall lines, triggered in a horizontally homogeneous environment with no initial background circulations, can generate a background mesoscale kinetic energy spectrum roughly similar to that observed in the atmosphere.

CORRESPONDING AUTHOR: Dale Durran, Department of Atmospheric Sciences, University of Washington, Box 351640, Seattle, WA 98195-1640, E-mail: drdee@uw.edu

A supplement to this article is available online (10.1175/BAMS-D-15-00070.2)

Abstract

One important limitation on the accuracy of weather forecasts is imposed by unavoidable errors in the specification of the atmosphere’s initial state. Much theoretical concern has been focused on the limits to predictability imposed by small-scale errors, potentially even those on the scale of a butterfly. Very modest errors at much larger scales may nevertheless pose a more important practical limitation. We demonstrate the importance of large-scale uncertainty by analyzing ensembles of idealized squall-line simulations. Our results imply that minimizing initial errors on scales around 100 km is more likely to extend the accuracy of forecasts at lead times longer than 3–4 h than efforts to minimize initial errors on much smaller scales. These simulations also demonstrate that squall lines, triggered in a horizontally homogeneous environment with no initial background circulations, can generate a background mesoscale kinetic energy spectrum roughly similar to that observed in the atmosphere.

CORRESPONDING AUTHOR: Dale Durran, Department of Atmospheric Sciences, University of Washington, Box 351640, Seattle, WA 98195-1640, E-mail: drdee@uw.edu

A supplement to this article is available online (10.1175/BAMS-D-15-00070.2)

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  • Anthes, R. A., Y.-H. Kuo, D. P. Baumhefner, R. M. Errico, and T. W. Bettge, 1985: Predictability of mesoscale atmospheric motions. Advances in Geophysics, Vol. 28, Academic Press, 159202, doi:10.1016/S0065-2687(08)60188-0.

    • 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, doi:10.1002/qj.20.

    • Search Google Scholar
    • Export Citation
  • Durran, D. R., and J. B. Klemp, 1983: A compressible model for the simulation of moist mountain waves. Mon. Wea. Rev., 111, 23412361, doi:10.1175/1520-0493(1983)111<2341:ACMFTS>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Durran, D. R., and M. Gingrich, 2014: Atmospheric predictability: Why butterflies are not important. J. Atmos. Sci., 71, 24762488, 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
  • Gage, K. S., 1979: Evidence for a k–5/3 law inertial range in mesoscale two-dimensional turbulence. J. Atmos. Sci., 36, 19501954, doi:10.1175/1520-0469(1979)036<1950:EFALIR>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Hohenegger, C., and C. Schär, 2007: 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
  • Kain, J. S., and Coauthors, 2010: Assessing advances in the assimilation of radar data and other mesoscale observations within a collaborative forecasting–research environment. Wea. Forecasting, 25, 15101521, doi:10.1175/2010WAF2222405.1.

    • Search Google Scholar
    • Export Citation
  • Leith, C., and R. Kraichnan, 1972: Predictability of turbulent flows. J. Atmos. Sci., 29, 10411058, doi:10.1175/1520-0469(1972)029<1041:POTF>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Lilly, D., 1983: Stratified turbulence and the mesoscale variability of the atmosphere. J. Atmos. Sci., 40, 749761, doi:10.1175/1520-0469(1983)040<0749:STATMV>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Lindborg, E., 2015: A Helmholtz decomposition of structure functions and spectra calculated from aircraft data. J. Fluid Mech., 762, R4, doi:10.1017/jfm.2014.685.

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

    • Search Google Scholar
    • Export Citation
  • Métais, O., and M. Lesieur, 1986: Statistical predictability of decaying turbulence. J. Atmos. Sci., 43, 857870, doi:10.1175/1520-0469(1986)043<0857:SPODT>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Nastrom, G., and K. Gage, 1985: A climatology of atmospheric wavenumber spectra of wind and temperature observed by commercial aircraft. J. Atmos. Sci., 42, 950960, doi:10.1175/1520-0469(1985)042<0950:ACOAWS>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Nuss, W., and D. Miller, 2001: Mesoscale predictability under various synoptic regimes. Nonlinear Processes Geophys., 8, 429438, doi:10.5194/npg-8-429-2001.

    • 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
  • Rotunno, R., and C. Snyder, 2008: A generalization of Lorenz’s model for the predictability of flows with many scales of motion. J. Atmos. Sci., 65, 10631076, doi:10.1175/2007JAS2449.1.

    • Search Google Scholar
    • Export Citation
  • Stratman, D. R., M. C. Coniglio, and S. E. Koch, 2013: Use of multiple verification methods to evaluate forecasts of convection from hot- and cold-start convection-allowing models. Wea. Forecasting, 28, 119138, doi:10.1175/WAF-D-12-00022.1.

    • Search Google Scholar
    • Export Citation
  • Surcel, M., I. Zawadzki, and M. K. Yau, 2015: A study on the scale dependence of the predictability of precipitation patterns. J. Atmos. Sci., 72, 216235, doi:10.1175/JAS-D-14-0071.1.

    • Search Google Scholar
    • Export Citation
  • Weisman, M., and J. Klemp, 1982: The dependence of numerically simulated convective storms on vertical wind shear and buoyancy. Mon. Wea. Rev., 110, 504520, doi:10.1175/1520-0493(1982)110<0504:TDONSC>2.0.CO;2.

    • 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, doi:10.1175/1520-0493(2002)130<1617:MPOTSS>2.0.CO;2.

    • 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. 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
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