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Chapter 24 - Ensemble forecasts: can they provide useful early warnings?

Published online by Cambridge University Press:  03 December 2009

François Lalaurette
Affiliation:
Ecole Nationale de la Météorologie, Toulouse
Gerald van der Grijn
Affiliation:
European Centre for Medium-Range Weather Forecasts, Reading
Tim Palmer
Affiliation:
European Centre for Medium-Range Weather Forecasts
Renate Hagedorn
Affiliation:
European Centre for Medium-Range Weather Forecasts
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Summary

Medium-range forecasts

The design of ensemble methods for medium-range weather forecasting ten years ago was aiming mainly at addressing the problem of limited predictability of supra-synoptic weather regimes within the 6–10 days range. From this point of view, it has been shown since then that the ensembles have delivered improved forecasts compared with a purely deterministic approach, both improving single-value estimates by removing stochastic errors (ensemble mean) and providing reliable and still sharp estimates of the probability distributions of large scale flow patterns such as blocking (Chessa and Lalaurette, 2001; Pelly and Hoskins, 2003).

Severe weather forecasts seem, however, to be clearly beyond reach of such medium-range, global and therefore relatively coarse grid models. Indeed the experience in trying to use medium-range forecasting systems to be alerted of the risk of severe weather more than one day in advance is very limited. On most occasions, civil security services are alerted not earlier than the day before the event, while public warnings are only issued on the same day – tropical cyclones being a notable exception to this common rule. The reasons usually given by forecasters as to why they do not use numerical forecasts in the early medium range are mainly twofold:

  1. The global numerical models are generating nothing looking remotely like severe weather.

  2. If one takes signatures from the global models that are associated with severe weather, there is no consistency in the forecast from one day to the next: the rate of false alarms would be far too high to be considered.

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Publisher: Cambridge University Press
Print publication year: 2006

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References

Barkmeijer, J., Buizza, R., Palmer, T. N., Puri, K. and Mahfouf, J.-F. (2001). Tropical singular vectors computed with linearized diabatic physics. Quart. J. Roy. Meteor. Soc., 127, 685–708CrossRefGoogle Scholar
Cherubini, T., Ghelli, A. and Lalaurette, F. (2002). Verification of precipitation forecasts over the Alpine region using a high-density observing network. Weather Forecast., 238–492.0.CO;2>CrossRefGoogle Scholar
Chessa, P. and Lalaurette, F. (2001). Verification of the ECMWF Ensemble Prediction System forecasts: a study of large-scale patterns. Weather Forecast., 16, 611–192.0.CO;2>CrossRefGoogle Scholar
Lalaurette, F. (2003). Early detection of abnormal weather conditions using a probabilistic extreme forecast index. Quart. J. Roy. Meteor. Soc., 3037–57CrossRefGoogle Scholar
Pelly, J. L. and Hoskins, B. J. (2003). How well does the ECMWF ensemble prediction system predict blocking?Quart. J. Roy. Meteor. Soc., 129, 1683–702CrossRefGoogle Scholar
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Rubel, F. and Rudolf, B. (2001). Global daily precipitation estimates proved over the European Alps. Meteorologische Zeitschrift, vol. 10, pp. 407–18CrossRefGoogle Scholar
Sinclair, M. R. (1994). An objective cyclone climatology study for the southern hemisphere. Mon. Weather Rev., 122, 2239–562.0.CO;2>CrossRefGoogle Scholar
van der Grijn, G. (2002). Tropical Cyclones Forecasting at ECMWF: New Products and Validation. ECMWF Technical Memorandum, 386. Recent ECMWF Technical Memoranda can be retrieved in electronic format from http://www.ecmwf.int/publications/library/ecpublications/techmemos/tm00.html
Wilks, D. S. (1995). Statistical Methods in the Atmospheric Sciences. Academic Press

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