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

Article English OPEN
Magnusson, Linus ; Nycander, Jonas ; Källén, Erland (2009)

Ensemble prediction relies on a faithful representation of initial uncertainties in a forecasting system. Early research on initial perturbation methods tested random perturbations by adding ‘white noise’ to the analysis. Here, an alternative kind of random perturbations is introduced by using the difference between two randomly chosen atmospheric states (i.e. analyses). It yields perturbations (random field, RF, perturbations) in approximate flow balance. The RF method is compared with the operational singular vector based ensemble at European Centre for Medium Range Weather Forecasts (ECMWF) and the ensemble transform (ET) method. All three methods have been implemented on the ECMWF IFS-model with resolution TL255L40. The properties of the different perturbation methods have been investigated both by comparing the dynamical properties and the quality of the ensembles in terms of different skill scores. The results show that the RF perturbations initially have the same dynamical properties as the natural variability of the atmosphere. After a day of integration, the perturbations from all three methods converge. The skill scores indicate a statistically significant advantage for the RF method for the first 2–3 d for the most of the evaluated parameters. For the medium range (3–8 d), the differences are very small.
  • References (58)
    58 references, page 1 of 6

    Anderson, J. L. 1997. The impact of dynamical constraints on the selection of initial conditions for ensemble predictions: low-order perfect model results. Mon. Wea. Rev. 125, 2969-2983.

    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. Rev. 133, 1825-1839.

    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.

    Barkmeijer, J., Buizza, R. and Palmer, T. N. 1999. 3D-Var Hessian singular vectors and their potential use in ECMWF Ensemble Prediction System. Quart. J. R. Meteorol. Soc. 125, 2333-2351.

    Barkmeijer, J., Buizza, R., Palmer, T. N., Puri, K. and Mahfouf, J.-F. 2001. Tropical singular vectors computed with linearized diabatic physics. Quart. J. R. Meteorol. Soc. 127, 685-708.

    Bengtsson, L. K., Magnusson, L. and Ka¨lle´n, E. 2008. Independent estimations of the asymptotic variability in an ensemble forecast system. Mon. Wea. Rev. 136, 4105-4112.

    Bishop, C. H. and Toth, Z. 1999. Ensemble transformation and adaptive mbservations. J. Atmos. Sci 56, 1748-1765.

    Boer, G. J. 2003. Predictability as a function of scale. Atmos.-Ocean 41, 203-215.

    Bowler, N. 2006. Comparison of error breeding, singular vectors, random perturbations and ensemble Kalman filter perturbation strategies on a simple model. Tellus 58A, 538-548.

    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.

  • Metrics
    No metrics available
Share - Bookmark