Probabilistic Weather Forecasting

  • George C. Craig
Part of the Research Topics in Aerospace book series (RTA)


Weather forecasts are approaching the physical limits of predictability. A prediction of a cyclone more than a week in advance, or a thunderstorm a few hours ahead, will have a large degree of uncertainty. New techniques using ensembles of forecasts to predict probabilities of weather events are being developed to increase the skill of weather forecasting and the value of forecasts to users when exact predictions are impossible.


Ensemble Member Ensemble Forecast Probabilistic Forecast Numerical Weather Prediction Model Precipitation Forecast 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. Done, J.M., Craig, G. C., Clark, P. A., Gray, S. L., Gray, M. E. B.: Mesoscale simulations of organized convection: Importance of convective equilibrium. Q. J. R. Meteorol. Soc. 132, 737–756 (2006). doi: 10.1256/qj.04.84
  2. Gebhardt, C., Theis, S.E., Paulat, M., Ben Bouallègue, Z.: Uncertainties in COSMO-DE precipitation forecasts introduced by model perturbations and variation of lateral boundaries. Atmos. Res. (2010). doi: 10.1016/j.atmosres.2010.12.008 Google Scholar
  3. Groenemeijer, P., Craig, G.C.: Ensemble forecasting with a stochastic convective parametrization based on equilibrium statistics. Atmos. Chem. Phys. Disc. 11, 30457–30485 (2011). doi: 10.5194/acpd-11-30457-2011 CrossRefGoogle Scholar
  4. Keil, C., Craig, G.C.: Regime-dependent forecast uncertainty of convective precipitation. Meteorol. Z. 20, 145–151 (2011). doi: 10.1127/0941-2948/2011/0219 CrossRefGoogle Scholar
  5. Kober, K., Craig, G.C., Keil, C., Dörnbrack, A.: Blending a probabilistic nowcasting method with a high resolution ensemble for convective precipitation forecasts. Q. J. R. Meteorol. Soc. (2011). doi: 10.1002/qj.939
  6. Leutbecher, M., Palmer, T.N.: Ensemble forecasting. J. Comp. Phys. 227, 3515–3539 (2008). doi: 10.1016/ MathSciNetADSzbMATHCrossRefGoogle Scholar
  7. Lorenz, E.N.: The essence of chaos. University of Washington Press, Seattle (1996)Google Scholar
  8. Plant, J.M., Craig, G.C.: A stochastic parameterization for deep convection based on equilibrium statistics. J. Atmos. Sci. 65, 87–105 (2008). doi: 10.1175/2007JAS2263.1 ADSCrossRefGoogle Scholar
  9. Richardson, D.S.: Skill and relative economic value of the ECMWF ensemble prediction system. Q. J. R. Meteorol. Soc. 126, 649–667 (2000). doi: 10.1002/qj.49712656313 ADSCrossRefGoogle Scholar
  10. Richardson, D.S.: Predictability and economic value. In: Proceedings of the ECMWF Seminar 2002: Predictability of weather and climate, pp. 321–332, 2003Google Scholar
  11. Richardson, D.S., Bidlot, J., Ferranti, L., Ghelli, A., Haiden, T., Hewson, T., Janousek, M., Prates, F., Vitart, F.: Verification statistics and evaluations of ECMWF forecasts in 2010–2011, ECMWF Technical Memorandum, vol. 564, pp. 53 (2011)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.Ludwig-Maximilians-Universität München (LMU), Meteorological Institute Munich (MIM)MünchenGermany
  2. 2.DLR, Institute of Atmospheric Physics (IPA)OberpfaffenhofenGermany

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