• Barker, D. M., , W. Huang, , Y-R. Guoa, , and A. J. Bourgeois, 2003: A three-dimensional variational (3DVAR) data assimilation system for use with MM5. NCAR Tech. Note NCAR/TN-453+STR, 68 pp.

  • Barker, D. M., , W. Huang, , Y-R. Guoa, , A. J. Bourgeois, , and Q. N. Xiao, 2004: A three-dimensional variational data assimilation system for MM5: Implementation and initial results. Mon. Wea. Rev., 132 , 897914.

    • Search Google Scholar
    • Export Citation
  • Barkmeijer, J., , M. van Gijzen, , and F. Bouttier, 1998: Singular vectors and estimates of the analysis-error covariance metric. Quart. J. Roy. Meteor. Soc., 124 , 16951713.

    • Search Google Scholar
    • Export Citation
  • Benjamin, S. O., , and N. L. Seaman, 1985: A simple scheme for objective analysis in curved flow. Mon. Wea. Rev., 113 , 11841198.

  • Bishop, C. H., , B. J. Etherton, , and S. J. Majumdar, 2001: Adaptive sampling with the ensemble transform Kalman filter. Part I: Theoretical aspects. Mon. Wea. Rev., 129 , 420436.

    • Search Google Scholar
    • Export Citation
  • Branstator, G., 1995: Organization of storm track anomalies by recurring low-frequency circulation anomalies. J. Atmos. Sci., 52 , 207226.

    • Search Google Scholar
    • Export Citation
  • Buizza, R., 1994: Sensitivity of optimal unstable structures. Quart. J. Roy. Meteor. Soc., 120 , 429451.

  • Buizza, R., , and P. Chessa, 2002: Prediction of the U.S. storm of 24–26 January 2000 with the ECMWF ensemble prediction system. Mon. Wea. Rev., 130 , 15311551.

    • Search Google Scholar
    • Export Citation
  • Cohn, S. E., 1997: An introduction to estimation theory. J. Meteor. Soc. Japan, 75 , 257288.

  • Cohn, S. E., , and D. F. Parrish, 1991: The behavior of forecast error covariances for a Kalman filter in two dimensions. Mon. Wea. Rev., 119 , 17571785.

    • Search Google Scholar
    • Export Citation
  • Daley, R., 1992: Forecast error statistics for homogeneous and inhomogeneous observation networks. Mon. Wea. Rev., 120 , 627643.

  • Dowell, D. C., , F. Zhang, , L. J. Wicker, , C. Snyder, , and N. A. Crook, 2004: Wind and temperature retrievals in the 17 May 1981 Arcadia, Oklahoma, supercell: Ensemble Kalman filter experiments. Mon. Wea. Rev., 132 , 19822005.

    • Search Google Scholar
    • Export Citation
  • Du, J., , S. L. Mullen, , and F. Sanders, 1997: Short-range ensemble forecasting of quantitative precipitation. Mon. Wea. Rev., 125 , 24272459.

    • Search Google Scholar
    • Export Citation
  • Dudhia, J., 1993: A nonhydrostatic version of the Penn State/NCAR Mesoscale Model: Validation tests and simulation of an Atlantic cyclone and cold front. Mon. Wea. Rev., 121 , 14931513.

    • Search Google Scholar
    • Export Citation
  • Errico, R., , and D. Baumhefner, 1987: Predictability experiments using a high-resolution limited area model. Mon. Wea. Rev., 115 , 488504.

    • Search Google Scholar
    • Export Citation
  • Evensen, G., 1994: Sequential data assimilation with a nonlinear quasi-geostrophic model using Monte Carlo methods to forecast error statistics. J. Geophys. Res., 99 , C5,. 1014310162.

    • Search Google Scholar
    • Export Citation
  • Farrell, B. F., 1990: Small error dynamics and the predictability of atmospheric flows. J. Atmos. Sci., 47 , 24092416.

  • Hamill, T. M., 2005: Ensemble-based atmospheric data assimilation. Predictability of Weather and Climate, R. Hagedorn and T.N. Palmer, Eds., Cambridge University Press, in press.

    • Search Google Scholar
    • Export Citation
  • Hamill, T. M., , and C. Snyder, 2000: A hybrid ensemble Kalman filter/3D-variational analysis scheme. Mon. Wea. Rev., 128 , 29052919.

  • Hamill, T. M., , and C. Snyder, 2002: Using improved background-error convariances from an ensemble Kalman filter for adaptive observations. Mon. Wea. Rev., 130 , 15521572.

    • Search Google Scholar
    • Export Citation
  • Holton, J. R., 1992: An Introduction to Dynamic Meteorology. Academic Press, 511 pp.

  • Houtekamer, P. L., , and H. L. Mitchell, 1998: Data assimilation using an ensemble Kalman filter technique. Mon. Wea. Rev., 126 , 796811.

    • Search Google Scholar
    • Export Citation
  • Houtekamer, P. L., , H. L. Mitchell, , G. Pellerin, , M. Buehner, , M. Charron, , L. Spacek, , and B. Hansen, 2005: Atmospheric data assimilation with an ensemble Kalman filter: Results with real observations. Mon. Wea. Rev., 133 , 604620.

    • Search Google Scholar
    • Export Citation
  • Jang, K-I., , X. Zou, , M. S. F. V. De Pondeca, , M. Shapiro, , C. Davis, , and A. Krueger, 2003: Incorporating TOMS ozone measurements into the prediction of the Washington, D.C., winter storm during 24–25 January 2000. J. Appl. Meteor., 42 , 797812.

    • Search Google Scholar
    • Export Citation
  • Kalnay, E., 2003: Atmospheric Modeling, Data Assimilation and Predictability. Cambridge University Press, 341 pp.

  • Keppenne, C. L., 2000: Data assimilation into a primitive-equation model with a parallel ensemble Kalman filter. Mon. Wea. Rev., 128 , 19711981.

    • Search Google Scholar
    • Export Citation
  • Keppenne, C. L., , and M. M. Rienecker, 2002: Initial testing of a massively parallel ensemble Kalman filter with the Poseidon isopycnal ocean general circulation model. Mon. Wea. Rev., 130 , 29512965.

    • Search Google Scholar
    • Export Citation
  • Langland, R. H., , M. A. Shapiro, , and R. Gelaro, 2002: Initial condition sensitivity and error growth in the forecast of the 25 January 2000 East Coast snowstorm. Mon. Wea. Rev., 130 , 957974.

    • Search Google Scholar
    • Export Citation
  • Lorenz, E. N., 1969: The predictability of a flow which possesses many scales of motion. Tellus, 21 , 289307.

  • Mesinger, F., 1998: Comparison of Quantitative Precipitation Forecasts by the 48- and by the 29-km Eta Model: An update and possible implications. Preprints, 16th Conf. on Weather Analysis and Forecasting/Symp. on Research Foci of the U.S. Weather Research Program, Phoenix, AZ, Amer. Meteor. Soc., J106–J107.

  • Mitchell, H. L., , P. L. Houtekamer, , and G. Pellerin, 2002: Ensemble size, balance and model-error representation in an ensemble Kalman filter. Mon. Wea. Rev., 130 , 27912808.

    • Search Google Scholar
    • Export Citation
  • Molteni, F., , R. Buizza, , T. N. Palmer, , and T. Petroliagis, 1996: The ECMWF ensemble prediction system: Methodology and validation. Quart. J. Roy. Meteor. Soc., 122 , 73120.

    • Search Google Scholar
    • Export Citation
  • Mullen, S. L., , J. Du, , and F. Sanders, 1999: The dependence of ensemble dispersion on analysis–forecast systems: Implications to short-range ensemble forecasting of precipitation. Mon. Wea. Rev., 127 , 16741686.

    • Search Google Scholar
    • Export Citation
  • Snyder, C., , and T. M. Hamill, 2003: Leading Lyapunov vectors of a turbulent baroclinic jet in a quasigeostrophic model. J. Atmos. Sci., 60 , 683688.

    • Search Google Scholar
    • Export Citation
  • Snyder, C., , and F. Zhang, 2003: Assimilation of simulated radar observations with an ensemble Kalman filter. Mon. Wea. Rev., 131 , 16631677.

    • Search Google Scholar
    • Export Citation
  • Snyder, C., , T. M. Hamill, , and S. B. Trier, 2003: Linear evolution of error covariances in a quasigeostrophic model. Mon. Wea. Rev., 131 , 189205.

    • Search Google Scholar
    • Export Citation
  • Stensrud, D. J., , H. E. Brooks, , J. Du, , M. S. Tracton, , and E. Rogers, 1999: Using ensembles for short-range forecasting. Mon. Wea. Rev., 127 , 433446.

    • Search Google Scholar
    • Export Citation
  • Talagrand, O., 1997: Assimilation of observations, an introduction. J. Meteor. Soc. Japan, 75 , 191209.

  • Toth, Z., , and E. Kalnay, 1993: Ensemble forecasting at NMC: The generation of perturbations. Bull. Amer. Meteor. Soc., 74 , 23172330.

  • Toth, Z., , and E. Kalnay, 1997: Ensemble forecasting at NCEP and the breeding method. Mon. Wea. Rev., 125 , 32973319.

  • Tracton, M. S., , and E. Kalnay, 1993: Operational ensemble prediction at the National Meteorological Center: Practical aspects. Wea. Forecasting, 8 , 379400.

    • Search Google Scholar
    • Export Citation
  • Tracton, M. S., , and J. Du, 2001: Application of the NCEP/EMC short-range ensemble forecast system (SREF) to predicting extreme precipitation events. Preprints, Symp. on Precipitation Extremes: Prediction, Impacts, and Responses, Albuqerque, NM, Amer. Meteor. Soc., 64–65.

  • Tribbia, J. J., , and D. P. Baumhefner, 2004: Scale interactions and atmospheric predictability: An updated perspective. Mon. Wea. Rev., 132 , 703713.

    • Search Google Scholar
    • Export Citation
  • Vukicevic, T., , and R. M. Errico, 1990: The influence of artificial and physical factors upon predictability estimates using a complex limit-area model. Mon. Wea. Rev., 118 , 14601482.

    • Search Google Scholar
    • Export Citation
  • Wandishin, M. S., , S. L. Mullen, , D. J. Stensrud, , and H. E. Brooks, 2001: Evaluation of a short-range multimodel ensemble system. Mon. Wea. Rev., 129 , 729747.

    • Search Google Scholar
    • Export Citation
  • Whitaker, J. S., , G. P. Compo, , X. Wei, , and T. M. Hamill, 2004: Reanalysis without radiosondes using ensemble data assimilation. Mon. Wea. Rev., 132 , 11901200.

    • 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.

    • 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.

  • Zhang, F., , C. Snyder, , and J. Sun, 2004: Impacts of initial estimate and observation availability on convective-scale data assimilation with an ensemble Kalman filter. Mon. Wea. Rev., 132 , 12381253.

    • Search Google Scholar
    • Export Citation
  • Zhang, S., , and J. L. Anderson, 2003: Impact of spatially and temporally varying estimates of error covariance on assimilation in a simple atmospheric model. Tellus, 55A , 126147.

    • Search Google Scholar
    • Export Citation
  • Zupanski, M., , D. Zupanski, , D. F. Parrish, , E. Rogers, , and G. DiMego, 2002: Four-dimensional variational data assimilation for the blizzard of 2000. Mon. Wea. Rev., 130 , 19671988.

    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 27 27 5
PDF Downloads 26 26 5

Dynamics and Structure of Mesoscale Error Covariance of a Winter Cyclone Estimated through Short-Range Ensemble Forecasts

View More View Less
  • 1 Department of Atmospheric Sciences, Texas A&M University, College Station, Texas
© Get Permissions
Restricted access

Abstract

Several sets of short-range mesoscale ensemble forecasts generated with different types of initial perturbations are used in this study to investigate the dynamics and structure of mesoscale error covariance in an intensive extratropical cyclogenesis event that occurred on 24–25 January 2000. Consistent with past predictability studies of this event, it is demonstrated that the characteristics and structure of the error growth are determined by the underlying balanced dynamics and the attendant moist convection. The initially uncorrelated errors can grow from small-scale, largely unbalanced perturbations to large-scale, quasi-balanced structured disturbances within 12–24 h. Maximum error growth occurred in the vicinity of upper-level and surface zones with the strongest potential vorticity (PV) gradient over the area of active moist convection. The structure of mesoscale error covariance estimated from these short-term ensemble forecasts is subsequently flow dependent and highly anisotropic, which is also ultimately determined by the underlying governing dynamics and associated error growth. Significant spatial and cross covariance (correlation) exists between different state variables with a horizontal distance as large as 1000 km and across all vertical layers. Qualitatively similar error covariance structure is estimated from different ensemble forecasts initialized with different perturbations.

Corresponding author address: Dr. Fuqing Zhang, Department of Atmospheric Sciences, Texas A&M University, 3150 TAMU, College Station, TX 77843-3150. Email: fzhang@tamu.edu

Abstract

Several sets of short-range mesoscale ensemble forecasts generated with different types of initial perturbations are used in this study to investigate the dynamics and structure of mesoscale error covariance in an intensive extratropical cyclogenesis event that occurred on 24–25 January 2000. Consistent with past predictability studies of this event, it is demonstrated that the characteristics and structure of the error growth are determined by the underlying balanced dynamics and the attendant moist convection. The initially uncorrelated errors can grow from small-scale, largely unbalanced perturbations to large-scale, quasi-balanced structured disturbances within 12–24 h. Maximum error growth occurred in the vicinity of upper-level and surface zones with the strongest potential vorticity (PV) gradient over the area of active moist convection. The structure of mesoscale error covariance estimated from these short-term ensemble forecasts is subsequently flow dependent and highly anisotropic, which is also ultimately determined by the underlying governing dynamics and associated error growth. Significant spatial and cross covariance (correlation) exists between different state variables with a horizontal distance as large as 1000 km and across all vertical layers. Qualitatively similar error covariance structure is estimated from different ensemble forecasts initialized with different perturbations.

Corresponding author address: Dr. Fuqing Zhang, Department of Atmospheric Sciences, Texas A&M University, 3150 TAMU, College Station, TX 77843-3150. Email: fzhang@tamu.edu

Save