LOGIN TO YOUR ACCOUNT

Username
Password
Remember Me
Or use your Academic/Social account:

CREATE AN ACCOUNT

Or use your Academic/Social account:

Congratulations!

You have just completed your registration at OpenAire.

Before you can login to the site, you will need to activate your account. An e-mail will be sent to you with the proper instructions.

Important!

Please note that this site is currently undergoing Beta testing.
Any new content you create is not guaranteed to be present to the final version of the site upon release.

Thank you for your patience,
OpenAire Dev Team.

Close This Message

CREATE AN ACCOUNT

Name:
Username:
Password:
Verify Password:
E-mail:
Verify E-mail:
*All Fields Are Required.
Please Verify You Are Human:
fbtwitterlinkedinvimeoflicker grey 14rssslideshare1
Stephenson, David B.; Dolas-Reyes, Francisco J. (2011)
Publisher: Co-Action Publishing
Journal: Tellus A
Languages: English
Types: Article
Subjects:
For complex dynamical systems such as the atmosphere, improved estimates of future behaviourcan be obtained by making ensembles of forecasts starting from a set of Monte Carlo perturbedinitial conditions. Ensemble forecasting, however, generates an overwhelming amount of datathat is difficult to analyse in detail. Fortunately, the main features of interest are often summarisedby certain statistics estimated from the sample of forecasts. By considering an ensemble offorecasts as a realisation of a linear mapping from phase space to sample space, it is possibleto construct two types of sample covariance matrix. The ensemble covariance can be visualisedby constructing multidimensional scaling maps, which show at a glance the relative distancesbetween the different ensemble members. Multivariate skewness and kurtosis can also be estimatedfrom ensembles of forecasts and provide useful information on the reliability of the samplemean and covariance estimated from the ensemble. They can also give useful information onthe non-linearity of the evolution in phase space. Entropy can also be defined for an ensembleof forecasts and shows a regular increase due to the smooth and rapid loss of initial informationin the first 3 days of a meteorological forecast. These new tools for summarising ensembleforecasts are illustrated using a single ensemble of 51 weather forecasts made at the EuropeanCentre for Medium-Range Weather Forecasts for the period 20–30 December 1997.DOI: 10.1034/j.1600-0870.2000.d01-5.x
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

    • 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.
    • Atger, F. 1999. Tubing: an alternative to clustering for the classification of ensemble forecasts. Weather and Forecasting 14, 741-757.
    • Barkmeijer, J. 1996. Constructing fast-growing perturbations for the nonlinear regime. J. Atmos. Sci. 53, 2838-2851.
    • Barkmeijer, J., Buizza, R. and Palmer, T. N. 1999. 3D-Var Hessian singular vectors and their potential use in the ECMWF ensemble prediction system. Quart. J. Roy. Met. Soc. 125, 2333-2351.
    • Barkmeijer, J., Gijzen, Van M. and Bouttier, F. 1998. Singular vectors and estimates of the analysis error covariance metric Quart. J. Roy. Met. Soc. 124, 1695-1713.
    • Brankovic, C. and Palmer, T. N. 1997. Atmospheric seasonal predictability and estimates of ensemble size Mon. Wea. Rev. 125, 859-874.
    • Brankovic, C., Palmer, T. N., Molteni, F., Tibaldi, S. and Cubasch, U. 1990. Extended-range predictions with ECMWF models: time-lagged ensemble forecasting. Quart. J. Roy. Met. Soc. 116, 867-912.
    • Buizza, R. 1994. Sensitivity of optimal unstable structures. Quart. J. Roy. Met. Soc. 120, 429-451.
    • Buizza, R. 1997. Potential forecast skill of ensemble prediction and spread and skill distributions of the ECMWF ensemble prediction system. Mon. Wea. Rev. 125, 99-119.
    • Buizza, R., Gelaro, R., Molteni, F. and Palmer, T. N. 1997. The impact of increased resolution on predictability studies with singular vectors. Quart. J. Roy. Met. Soc. 123, 1007-1033.
    • Buizza, R. and Palmer, T. N. 1995. The singular-vector structure of the atmospheric global circulation. J. Atmos. Sci. 52, 1434-1456.
    • Buizza, R. , Petroliagis, T., Palmer, T. N., Barkmeijer, J., Hamrud, M., Hollingsworth, A., Simmons, A. and Wedi, N. 1998. Impact of model resolution and ensemble size on the performance of an ensemble prediction system. Quart. J. Roy. Met. Soc. 124, 1935-1960.
    • Burgers, G. and Stephenson, D. B. 1999. The ''Normality'' of El Ni n˜o. Geophys. Res. L etters 26, 1027-1030.
    • Carnevale, G. F. 1982. Statistical features of the evolution of two-dimensional turbulence. J. Fluid. Mech. 122, 143-153.
    • Carnevale, G. F., Frisch, U. and Salmon, R. 1981. H theorems in statistical fluid dynamics. J. Phys. A 14, 1701-1718.
    • Carnevale, G. F. and Holloway, G. 1982. Information decay and the predictability of turbulent flows. J. Fluid. Mech. 116, 115-121.
    • Charney, J. G., Fj o¨rtoft, R. and von Neumann, J. 1950. Numerical integration of the barotropic vorticity equation. T ellus 2, 237-254.
    • Cox, T. F. and Cox, M. A. A. 1994. Multidimensional scaling. Chapman and Hall, London.
    • Ehrendorfer, M. 1994. The Liouville equation and its potential usefulness for the prediction of forecast skill. Part I: Theory. Mon. Wea. Rev. 122, 703-713.
    • Ehrendorfer, M. and Tribbia, J. J. 1997. Optimal prediction of forecast error covariances through singular vectors. J. Atmos. Sci. 54, 286-313.
    • Epstein, E. S. 1969a The role of initial uncertainties in prediction. J. Appl. Meteor. 8, 190-198.
    • Epstein, E. S. 1969b. Stochastic dynamic prediction.T ellus 21, 739-759.
    • Hadamard, J. 1898. Les surfaces a courbures oppose´es et leurs lignes ge´odesiques. J. Math. Pure et Appl. 4, 27-73.
    • Hamill, T. M. and Colucci, S. J. 1998 Evaluation of EtaRSM ensemble probabilistic precipitation forecasts. Mon. Wea. Rev. 126, 711-724.
    • Houtekamer, P. L. and Derome, J. 1995. Methods for ensemble prediction. Mon. Wea. Rev. 123, 2181-2196.
    • Houtekamer, P. L., Lefaivre, L., Derome, J., Ritchie, H. and Mitchell, H. L. 1996. A system simulation approach to ensemble prediction. Mon. Wea. Rev. 124, 1225-1242.
    • Landau, L. D. and Lifshitz, E. M. 1980. Statistical physics. Butterworth Heinemann, 3rd edition, vol. 5.
    • Leith, C. E. 1971. Atmospheric predictability and twodimensional turbulence. J. Atmos. Sci. 28, 145-161.
    • Leith, C. E. 1974. Theoretical skill of Monte Carlo forecasts. Mon. Wea. Rev. 102, 409-418.
    • Leith, C. E. and Kraichnan, R. H. 1972. Predictability of turbulent flows. J. Atmos. Sci. 29, 1041-1058.
    • Lorenz, E. N. 1963. Deterministic nonperiodic flow. J. Atmos. Sci. 20, 130-141.
    • Lorenz, E. N. 1969a. The predictability of a flow which possesses many scales of motion. T ellus 21, 289-307.
    • Lorenz, E. N. 1969b. Three approaches to atmospheric predictability. Bulletin American Met. Soc. 50, 345-349.
    • Mardia, K. V. 1970. Measures of multivariate skewness and kurtosis with applications. Biometrika, 57, 519-530.
    • Mardia, K. V. 1974. Applications of some measures of multivariate skewness and kurtosis in testing normality and robustness studies. Sankhya¨. T he Indian Journal of Statistics 36, 115-128.
    • Mardia, K. V., Kent, J. T. and Bibby, J. M. 1979. Multivariate analysis. Academic Press Limited, London.
    • Molteni, F., Buizza, R., Palmer, T. N. and Petroliagis, T. 1996. The ECMWF ensemble prediction system: methodology and validation. Quart. J. Roy. Met. Soc. 122, 73-119.
    • Murphy, J. M. 1988. The impact of ensemble forecasts on predictability. Quart. J. Roy. Met. Soc. 114, 463-493.
    • Poincare´, H. 1890. Sur les equations de la dynamique de le proble`me de trois corps. Acta Math. 13, 1-270.
    • Ruelle, D. 1989. Chaotic evolution and strange attractors. Cambridge University Press, Cambridge.
    • Selten, F. M. 1997. Baroclinic empirical orthogonal functions as basis functions in an atmospheric model. Mon. Wea. Rev. 54, 2100-2114.
    • Sivillo, J. K., Ahlquist, J. E. and Toth, Z. 1997. An ensemble forecasting primer. Weather and Forecasting 12, 809-818.
    • Skelly, W. C. and Henderson-Sellers, A. 1996. Grid box or grid point: what type of data do GCMs deliver to climate impacts researchers? Int. J. Climatol. 16, 1079-1086.
    • Smagorinsky, J. 1969. Problems and promises of deterministic extended range forecasting. Bulletin American Met. Soc. 50, 286-311.
    • Smith, L. A. 1996. Accountability and error in ensemble forecasting. In: Palmer, T. (ed.): Predictabilty, ECMWF November 1997 Workshop on Predictability, vol. 1, pp. 351-368. Reading, UK. ECMWF.
    • Smith, L. A. and Gilmour, I. 1997. Accountability and internal consistence in ensemble formation. In: Palmer, T. (ed.): Predictabilty, ECMWF November 1997 Workshop on Predictability, vol. 2, p.15. Reading, UK. ECMWF.
    • Stephenson, D. B., Rupa Kumar, K., Doblas-Reyes, F.-J., Royer, J.-F., Chauvin, F. and Pezzulli, S. 1999. Extreme daily rainfall events and their impact on ensemble forecasts of the Indian monsoon. Mon. Wea. Rev. 127, 1954-1966.
    • Stephenson, D. B. 1997. Correlation of spatial climate/ weather maps and the advantages of using the mahalanobis metric in predictions. T ellus 49A, 513-527.
    • Strang, G. 1988. L inear algebra and its applications. Harcourt Brace Jovanovich, Inc., 3rd edition.
    • Stroe, R. and Royer, J.-F. 1993. Comparison of diVerent error growth formulas and predictability estimation in numerical extended-range forecasts. Ann. Geophysicae 11, 296-316.
    • Sutton, O. G. 1951. Mathematics and the future of meteorology. Weather 6, 291-296.
    • T he Economist, 27 March 1999. Economics focus: A` la mode. 350, 90.
    • Thompson, P. D. 1957. Uncertainty of initial state as a factor in the predictability of large scale atmospheric flow patterns. T ellus 9, 275-295.
    • Toth, Z. 1991. Circulation patterns in phase space: a multinormal distribution? Mon. Wea. Rev. 119, 1501-1511.
    • Toth, Z. 1995. Degrees of freedom in Northern Hemisphere circulation data. T ellus 47A, 457-472.
    • Toth, Z. and Kalnay, E. 1993. Ensemble forecasting at NMC: the generation of perturbations. Bull. Amer. Meteor. Soc. 74, 2317-2330.
    • Toth, Z. and Kalnay, E. 1997. Ensemble forecasting at NCEP and the breeding method. Mon. Wea. Rev. 125, 3297-3319.
    • Toth, Z., Kalnay, E., Tracton, S. M., Wobus, R. and Irwin, J. 1997. A synoptic evaluation of the NCEP ensemble. Weather and Forecasting 12, 140-153.
    • Wallace, J. M., Cheng, X. and Sun, D. 1991. Does lowfrequency atmospheric variability exhibit regime-like behavior? T ellus 43AB, 16-26.
    • Weinberg, S. 1972. Gravitation and cosmology. John Wiley & Sons, Inc.
    • Whitaker, J. S. and Loughe, A. F. 1998. The relationship between ensemble spread and ensemble mean skill. Mon. Wea. Rev. 126, 3292-3302.
    • Zwiers, F. W. 1996. Interannual variability and predictability in an ensemble of AMIP climate simulations conducted with the CCC GCM2. Climate Dynamics 12, 825-847.
  • No related research data.
  • No similar publications.

Share - Bookmark

Cite this article

Collected from