Editorial Type:
Article
Article Type:
Research Article

Joint Medium-Range Ensembles from The Met. Office and ECMWF Systems

R. E. Evans The Met. Office, Bracknell, Berkshire, United Kingdom

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M. S. J. Harrison The Met. Office, Bracknell, Berkshire, United Kingdom

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R. J. Graham The Met. Office, Bracknell, Berkshire, United Kingdom

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K. R. Mylne The Met. Office, Bracknell, Berkshire, United Kingdom

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Print Publication:
01 Sep 2000
DOI:
https://doi.org/10.1175/1520-0493(2000)128<3104:JMREFT>2.0.CO;2
Page(s):
3104–3127
Restricted access

Abstract

One possible method of incorporating model sensitivities into ensemble forecasting systems is to combine ensembles run from two or more models. Furthermore, the use of more than one analysis, to which perturbations are added, may provide further unstable directions for error growth not present with a single analysis.

Results are presented from recent investigations into the potential benefit of combining ensembles from the systems of the European Centre for Medium-Range Weather Forecasts and The Met. Office of the United Kingdom. The multimodel and multianalysis ensemble significantly outperforms either individual system in many performance aspects, including deterministic and probabilistic forecast skill, spread–skill correlations, and breadth of synoptic information. It is demonstrated that these improvements are achieved through the combination of independent, useful information contained in the individual systems, and not through simple cancellation of biases that could occur when ensembles from two different forecast systems are combined. In addition, results indicate that model dependencies are at least comparable with analysis dependencies on medium-range timescales, and so in general both models and both analyses are required in the joint ensemble for the largest benefits.

* Current affiliation: World Meteorological Organization, Geneva, Switzerland.

Corresponding author address: Ruth Evans, The Met. Office, London Road, Bracknell RG12 2SZ, United Kingdom.

Email: reevans@meto.gov.uk

Abstract

One possible method of incorporating model sensitivities into ensemble forecasting systems is to combine ensembles run from two or more models. Furthermore, the use of more than one analysis, to which perturbations are added, may provide further unstable directions for error growth not present with a single analysis.

Results are presented from recent investigations into the potential benefit of combining ensembles from the systems of the European Centre for Medium-Range Weather Forecasts and The Met. Office of the United Kingdom. The multimodel and multianalysis ensemble significantly outperforms either individual system in many performance aspects, including deterministic and probabilistic forecast skill, spread–skill correlations, and breadth of synoptic information. It is demonstrated that these improvements are achieved through the combination of independent, useful information contained in the individual systems, and not through simple cancellation of biases that could occur when ensembles from two different forecast systems are combined. In addition, results indicate that model dependencies are at least comparable with analysis dependencies on medium-range timescales, and so in general both models and both analyses are required in the joint ensemble for the largest benefits.

* Current affiliation: World Meteorological Organization, Geneva, Switzerland.

Corresponding author address: Ruth Evans, The Met. Office, London Road, Bracknell RG12 2SZ, United Kingdom.

Email: reevans@meto.gov.uk

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  • Anderson, J. L., 1996: Selection of initial conditions for ensemble forecasts in a simple perfect model framework. J. Atmos. Sci.,53, 22–36.

  • Atger, F., 1999: Tubing: An alternative to clustering for the classification of ensemble forecasts. Wea. Forecasting,14, 741–757.

  • Brier, G. W., 1950: Verification of forecasts expressed in terms of probability. Mon. Wea. Rev.,78, 1–3.

  • Brown, B. G., and A. H. Murphy, 1996: Improving forecasting performance by combining forecasts: The example of road surface temperature forecasts. Meteor. Appl.,3, 257–265.

  • 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.

  • ——, M. Miller, and T. N. Palmer, 1999: Stochastic representation of model uncertainties in the ECMWF Ensemble Prediction System. Quart. J. Roy. Meteor. Soc.,125, 2887–2908.

  • Cullen, M. J. P., 1993: The unified forecast/climate model. Meteor. Mag.,122, 81–93.

  • DeGroot, M. H., 1989: Probability and Statistics. Addison-Wesley, 723 pp.

  • Graham, R. J., A. D. L. Evans, K. R. Mylne, M. S. J. Harrison, and K. B. Robertson, 2000: An assessment of seasonal predictability using atmospheric general circulation models. Quart. J. Roy. Meteor. Soc., in press.

  • Hamill, T. M., and S. J. Colucci, 1997: Verification of Eta–RSM short-range ensemble forecasts. Mon. Wea. Rev.,125, 1312–1327.

  • ——, and ——, 1998: Evaluation of Eta–RSM ensemble probablistic precipitation forecasts. Mon. Wea. Rev.,126, 711–724.

  • Harrison, M. S. J., T. N. Palmer, D. S. Richardson, R. Buizza, and T. Petroliagis, 1995: Joint medium range ensembles from UKMO and ECMWF models and analyses. Proc. ECMWF, Seminar on Predictability, Vol. II, Reading, United Kingdom, ECMWF, 61–120.

  • ——, ——, ——, and ——, 1999: Analysis and model dependencies in medium-range ensembles: Two transplant case studies. Quart. J. Roy. Meteor. Soc.,125, 2487–2515.

  • Houtekamer, P. L., L. Lefaivre, J. Derome, H. Ritchie, and H. L. Mitchell, 1996: A system simulation approach to ensemble prediction. Mon. Wea. Rev.,124, 1225–1242.

  • Molteni, F., R. Buizza, T. N. Palmer, and T. Petroliagia, 1996: The ECMWF Ensemble Prediction System: Methodology and validation. Quart. J. Roy. Meteor. Soc.,122, 73–119.

  • Murphy, A. H., 1973: A new vector partition of the probability score. J. Appl. Meteor.,12, 595–600.

  • ——, and R. L. Winkler, 1992: Diagnostic verification of probability forecasts. Int. J. Forecasting,7, 435–455.

  • Rabier, F., E. Klinker, P. Courtier, and A. Hollingsworth, 1996: Sensitivity of forecast errors to initial conditions. Quart. J. Roy. Meteor. Soc.,122, 121–150.

  • Stanski, H. R., L. J. Wilson, and W. R. Burrows, 1989: Survey of common verification methods in meteorology. World Weather Watch Tech. Rep. 8, WMO/TD-358, 114 pp.

  • Swets, J. A., and R. M. Pickett, 1982: Evaluation of Diagnostic Systems—Methods from Signal Detection Theory. Academic Press, 253 pp.

  • Vislocky, R. L., and J. M. Fritsch, 1995: Improved model output statistics forecasts through model consensus. Bull. Amer. Meteor. Soc.,76, 1157–1164.

  • Ward, J. H., 1963: Hierarchical grouping to optimize an objective function. J. Amer. Stat. Assoc.,58, 236–244.

  • Whitaker, J. S., and A. F. Loughe, 1998: The relationship between ensemble spread and ensemble mean skill. Mon. Wea. Rev.,126, 3292–3302.

  • Wilks, D. S., 1995: Statistical Methods in the Atmospheric Sciences. Academic Press, 467 pp.

  • Wobus, R. L., and E. Kalnay, 1995: Three years of operational prediction of forecast skill at NMC. Mon. Wea. Rev.,123, 2149–2164.

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