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Nonlinear Processes in Geophysics An interactive open-access journal of the European Geosciences Union
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Volume 10, issue 6
Nonlin. Processes Geophys., 10, 469–475, 2003
https://doi.org/10.5194/npg-10-469-2003
© Author(s) 2003. This work is licensed under
the Creative Commons Attribution-NonCommercial-ShareAlike 2.5 License.

Special issue: Quantifying Predictability

Nonlin. Processes Geophys., 10, 469–475, 2003
https://doi.org/10.5194/npg-10-469-2003
© Author(s) 2003. This work is licensed under
the Creative Commons Attribution-NonCommercial-ShareAlike 2.5 License.

  31 Dec 2003

31 Dec 2003

Can an ensemble give anything more than Gaussian probabilities?

J. C. W. Denholm-Price J. C. W. Denholm-Price
  • School of Mathematics, Kingston University, Kingston upon Thames, KT1 2EE, UK

Abstract. Can a relatively small numerical weather prediction ensemble produce any more forecast information than can be reproduced by a Gaussian probability density function (PDF)? This question is examined using site-specific probability forecasts from the UK Met Office. These forecasts are based on the 51-member Ensemble Prediction System of the European Centre for Medium-range Weather Forecasts. Verification using Brier skill scores suggests that there can be statistically-significant skill in the ensemble forecast PDF compared with a Gaussian fit to the ensemble. The most significant increases in skill were achieved from bias-corrected, calibrated forecasts and for probability forecasts of thresholds that are located well inside the climatological limits at the examined sites. Forecast probabilities for more climatologically-extreme thresholds, where the verification more often lies within the tails or outside of the PDF, showed little difference in skill between the forecast PDF and the Gaussian forecast.

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