Flow-dependent versus flow-independent initial perturbations for ensemble prediction

References

• Anderson, J. L. 1997. The impact of dynamical constraints on the selec-tion of initial conditions for ensemble predictions: low-order perfect model results. Mon. Wea. Re v. 125, 2969–2983.
   Google Scholar
• Arribas, A., Robertson, K. B. and Mylne, K. R. 2005. Test of a poor man’s ensemble prediction system for short-range probability forecasting. Mon. Wea. Re v. 133, 1825–1839.
   Google Scholar
• Barkmeijer, J., van Gijzen, M. and Bouttier, F. 1998. Singular vectors and estimates of the analysis-error covariance metric. Quart. J. R. Meteorol. Soc. 124, 1695–1713.
   Web of Science ®Google Scholar
• Barkmeijer, J., Buizza, R. and Palmer, T. N. 1999.3D-Var Hessian sin-gular vectors and their potential use in ECMWF Ensemble Prediction System. Quart. J. R. Meteorol. Soc. 125, 2333–2351.
   Web of Science ®Google Scholar
• Barkmeijer, J., Buizza, R., Palmer, T. N., Pun, K. and Mahfouf, J.-E 2001. Tropical singular vectors computed with linearized diabatic physics. Quart. J. R. Meteorol. Soc. 127, 685–708.
   Web of Science ®Google Scholar
• Bengtsson, L. K., Magnusson, L. and Knillen, E. 2008. Independent esti-mations of the asymptotic variability in an ensemble forecast system. Mon. Wea. Rev. 136, 4105–4112.
   Web of Science ®Google Scholar
• Bishop, C. H. and Toth, Z. 1999. Ensemble transformation and adaptive mbservations. J. Atmos. Sci 56, 1748–1765.
   Web of Science ®Google Scholar
• Boer, G. J. 2003. Predictability as a function of scale. Atmos.-Ocean 41, 203–215.
   Web of Science ®Google Scholar
• Bowler, N. 2006. Comparison of error breeding, singular vectors, ran-dom perturbations and ensemble Kalman filter perturbation strategies on a simple model. Tellus 58A, 538–548.
   Google Scholar
• Bowler, N., Arribas, A., Mylne, K. R., Robertson, K. B. and Beare, S. E. 2008. The MOGREPS short-range ensemble prediction system. Quart. J. R. Meteorol. Soc. 134, 703–722.
   Web of Science ®Google Scholar
• Buizza, R. 2008. Comparison of a 51-member low-resolution (TL399L62) ensemble with a 6-member high-resolution (TL799L91) lagged-forecast ensemble. Mon. Wea. Rev. 136, 3343–3362.
   Web of Science ®Google Scholar
• Buizza, R., Miller, M. and Palmer, T. 1999. Stochastic representation of model uncertainties in the ECMWF Ensemble Prediction System. Quart. J. R. Meteorol. Soc. 125, 2887–2908.
   Web of Science ®Google Scholar
• Buizza, R., Houtekamer, P. L., Toth, Z., Pellerin, G., Wei, M. and co-authors. 2005. A comparison of the ECMWF, MSC and NCEP global ensemble prediction systems. Mon. Wea. Rev. 133, 1067-1097.
   Google Scholar
• Candille, G. and Talagrand, J. 2005. Evaluation of probabilistic predic-tion systems for a scalar variable.Quart. J. R. Meteorol. Soc. 131, 2131-2150.
   Google Scholar
• Descamps, L. and Talagrand, J. 2007. On some aspects of the definition of initial conditions for ensemble prediction.Mon. Wea. Rev. 135, 3260-3272.
   Google Scholar
• Fisher, M. and Courtier, P. 1995. Estimating the covariance matrices of analysis and forecast error in variational data assimilation, Technical Memorandum 220, ECMWF.
   Google Scholar
• Gustafsson, N. 2007. Discussion on `4D-Var or EnKF?’. Tellus 59A, 774–777.
   Google Scholar
• Hamill, T. H. and Whitaker, J. S. 2005. Accounting for the error due to unresolved scales in ensemble data assimilation: a comparison of different approches. Mon. Wea. Rev. 133, 3132–3147.
   Web of Science ®Google Scholar
• Hollingsworth, A. 1979. An experiment in Monte Carlo forecasting, In: Proceedings of Workshop on Stochastic Dynamic Forecasting, Reading, UK, ECMWF.
   Google Scholar
• Holton, J. R. 2004. An Introduction to Dynamic Meteorology. Academic Press, London, p. 535.
   Google Scholar
• Hoskins, B. J. and Hodges, K. I. 2002. New perspectives on the Northern Hemisphere winter storm tracks. J. Atmos. Sci. 59, 1041–1061.
   Web of Science ®Google Scholar
• Hoskins, B. J., Buizza, R. and Badger, J. 2000. The nature of singular vector growth and structure. Quart. J. R. Meteorol. Soc. 126, 1565–1581.
   Web of Science ®Google Scholar
• Houtekamer, P. L., Lefaivre, L., Derome, J., Ritchie, H. and Mitchell, H. 1996. A system simulation approach to ensemble prediction. Mon. Wea. Rev. 124, 1225–1242.
   Web of Science ®Google Scholar
• Houtekamer, P. L., Mitchell, H. and Deng, X. 2008. Model error resp-resentation in an operational ensemble Kalman filter.Mon. Wea. Rev. in press.
   Google Scholar
• Judd, K., Reynolds, C. A., Rosmond, T. E. and Smith, L. A. 2008. The geometry of model error. J. Atmos. Sci. 65, 1749–1772.
   Web of Science ®Google Scholar
• Källen, E. and Huang, X.-Y. 1988. The influence of isolated observations on short-range numerical weather forecasts. Tellus 40A, 324–336.
   Google Scholar
• Leith, C. 1974. Theoretical skill of Monte Carlo forecasts. Mon. Wea. Rev. 1974, 409–418.
   Google Scholar
• Leutbecher, M. and Palmer, T. N. 2008. Ensemble forecasting. J. Com-put. Phys. 227, 3515–3539.
   Web of Science ®Google Scholar
• Lindzen, R. S. and Farrell, B. 1980. A simple approximate result for the maximum growth rate of baroclinic instabilities. J. Atmos. Sci. 37, 1648–1654.
   Web of Science ®Google Scholar
• Lorenz, E. N. 1963. Deterministic nonperiodic flow. J. Atmos. Sci. 20, 130–141.
   Web of Science ®Google Scholar
• Lorenz, E. N. 1965. A study of the predictability of a 28-variable atmo-spheric model. Tellus 67, 321–333.
   Google Scholar
• Lorenz, E. N. 1969. The predictability of a flow which posses many scales of motion. Tellus 21, 289–307.
   Google Scholar
• Lorenz, E. N. 1982. Atmospheric predictability experiments with a large numerical model. Tellus 34, 505–513.
   Google Scholar
• Magnusson, L., Källen, E. and Nycander, J. 2008. Initial state pertur-bations in ensemble forecasting. Nonlinear Processes Geophy. 15, 751–759.
   Web of Science ®Google Scholar
• Magnusson, L., Leutbecher, M. and Källen, E. 2008. Comparison be-tween singular vectors and breeding vectors as initial perturbations of ECMWF Ensemble Prediction System. Mon. Wea. Rev. 136, 4092–4104.
   Web of Science ®Google Scholar
• McLay, J. G., Bishop, C. H. and Reynolds, C. A. 2008. Evaluation of the ensemble transform analysis perturbation scheme at NRL. Mon. Wea. Rev. 136, 1093–1108.
   Web of Science ®Google Scholar
• Mureau, R., Molteni, F. and Palmer, T. 1993. Ensemble prediction using dynamically conditioned perturbations. Quart. J. R. Meteorol. Soc. 119, 299–323.
   Web of Science ®Google Scholar
• Murphy, A. H. 1973. A new vector partition of the pobability score. J. AppL Meteorol. 12, 595–600.
   Google Scholar
• Palmer, T. N. 1993. Extended-range atmospheric prediction and the Lorenz Model. Bull. Am. Meteorol. Soc. 74, 49–66.
   Web of Science ®Google Scholar
• Reinhold, B. 1986. Structural determinism of linear baroclinic waves and simple nonlinear equilibration. J. Atmos. Sci. 43, 1484–1504.
   Web of Science ®Google Scholar
• Reynolds, C. A., Teixeira, J. and McLay, J. G. 2008. Impact of stochastic convection on the ensemble transform. Mon. Wea. Rev. 136, 4517–4526.
   Web of Science ®Google Scholar
• Roulston, M. S. and Smith, L. A. 2002. Evaluating probabilistic forecasts using information theory. Mon. Wea. Rev. 130, 1653–1660.
   Web of Science ®Google Scholar
• Schubert, S. D. and Suarez, M. 1989. Dynamical predictability in a simple general circulation model: average error growth. J. Atmos. Sci. 46, 353–370.
   Web of Science ®Google Scholar
• Szunyogh, I., Kostelich, E. J., Gyarmati, G., Kalnay, E., Hunt, B. R., and co-authors. 2008. A local ensemble transform Kalman filter data assimilation system for the NCEP global model. Tellus 60A, 113-130.
   Google Scholar
• Talagrand, J., Vautard, R. and Strauss, B. 1999. Evaluation of probabilis-tic prediction system, In: Proceedings of Workshop on Predictability, October 1997, ECMWF.
   Google Scholar
• Talagrand, J., Candille, G. and Descamps, L. 2007. On some aspects of validation of probabilistic prediction, In: Proceedings of Workshop on Ensemble prediction, November 2007, ECMWF.
   Google Scholar
• Toth, Z. and Kalnay, E. 1993. Ensemble forecasting at NCEP: the generation of perturbation. Bull. Am. MeteoroL Soc. 74, 2317–2330.
   Web of Science ®Google Scholar
• Toth, Z. and Kalnay, E. 1997. Ensemble forecasting at NCEP and the breeding method. Mon. Wea. Rev. 125, 3297–3319.
   Web of Science ®Google Scholar
• Trevisan, A. 1993. Impact of transient error growth on global average predictability measures. J. Atmos. Sci. 50, 1016–1028.
   Web of Science ®Google Scholar
• Tribbia, J. J. and Baumhefner, D. P. 1988. Estimates of the predictability of low-frequency variability with a spectral general circulation model. J. Atmos. Sci. 45, 2306–2317.
   Web of Science ®Google Scholar
• Vallis, K. G. 2006, Atmospheric and Oceanic Fluid Dynamics. Cam-bridge University Press, Cambridge, p. 745.
   Google Scholar
• Wang, X. and Bishop, C. 2003. A comparison of breeding and ensemble transform Kalman filter ensemble forecast schemes. J. Atmos. Sci. 60, 1140–1158.
   Web of Science ®Google Scholar
• Wang, X., Bishop, C. and Julier, S. 2004. Which is better, an ensemble of positive-negative pairs or a centered spherical simplex ensemble? Mon. Wea. Rev. 132, 1590–1605.
   Web of Science ®Google Scholar
• Wei, M. and Toth, Z. 2003. A new measure of ensemble performance: perturbation versus error correlation analysis (PECA). Mon. Wea. Rev. 131, 1549–1565.
   Web of Science ®Google Scholar
• Wei, M., Toth, Z., Wobus, R. and Zhu, Y. 2008. Initial perturbations based on the ensemble transform (ET) technique in the NCEP global ensemble forecast systems. Tellus 60A, 62–79.
   Google Scholar
• Wilks, D. S. 1997. Resampling hypothesis tests for autocorrelated fields. J. Clim. 10, 65–82.
   Web of Science ®Google Scholar
• Wilks, D. S. 2006, Statistical Methods in the Atmospheric Sciences 2nd Edition. Academic Press, London, p. 627.
   Google Scholar
• Zhang, F. 2005. Dynamics and structure of mesoscale error covariance of a winter cyclone estimated through short-range ensemble forecasts. Mon. Wea. Rev. 133, 2876–2893.
   Web of Science ®Google Scholar