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Caio A. S. Coelho, S. Pezzulli, M. Balmaseda (*), F. J. Doblas-Reyes (*) and D. B. Stephenson Bayesian combination of ENSO forecasts Department of Meteorology,

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Presentation on theme: "Caio A. S. Coelho, S. Pezzulli, M. Balmaseda (*), F. J. Doblas-Reyes (*) and D. B. Stephenson Bayesian combination of ENSO forecasts Department of Meteorology,"— Presentation transcript:

1 Caio A. S. Coelho, S. Pezzulli, M. Balmaseda (*), F. J. Doblas-Reyes (*) and D. B. Stephenson Bayesian combination of ENSO forecasts Department of Meteorology, University of Reading and ECMWF (*) Aim Coupled forecasts Empirical forecasts Bayesian combination Conclusion and future directions Plan of talk

2 Aim Improve ENSO probability forecasts by using Bayesian approach to combine historical information with coupled model ensemble forecasts

3 ECMWF coupled model forecasts  Note: observations not within the 95% prediction interval! Nino-3 index DEMETER: 1987-98 9 members Jul -> Dec 5 months lead DEMETER web page: http://www.ecmwf.int/research/demeter R 2 =0.90

4 July and December Reynolds OI V2 SST (1950-2001) Nino-3 index observational data Nino-3 index mean values: Jul: 25.5  C Dec: 25.0  C r: 0.83 R 2 =0.69

5 Empirical persistence forecasts R 2 =0.79  Larger 95% prediction interval  More observations within the 95% prediction interval

6  : Observed December Nino-3 index X: Ensemble mean forecast of  for December Thomas Bayes ( 1701-1761) The process of belief revision on any event  consists in updating the probability of  when new information X becomes available The Bayesian approach Example: Ensemble mean (X=x=27  C) Likelihood:p(X=x|  ) Prior:p(  ) Posterior:p(  |X=x)

7 Modelling the likelihood p(X=x|  )  =8.55  C  =0.67  =9.88 R 2 =0.95

8 Combined forecasts  Note: more observations within the 95% prediction interval! R 2 =0.90

9 All forecasts ForecastMAE (  C) Skill Score (%) Uncert. (  C) Climatolog y 1.0801.23 Empirical0.50530.72 Raw0.53510.33 Combined0.35680.39 Skill Score = [1- MAE/MAE(climatology)]*100% R 2 =0.79 R 2 =0.90

10 Conclusions and future directions Bayesian combination improves the skill and uncertainty estimates of ENSO probability forecasts Methodology is now being extended to deal with multi-model DEMETER forecasts Extend method for South America rainfall forecasts e-mail: c.a.d.s.coelho@reading.ac.uk http://www.met.rdg.ac.uk/~swr01cac

11 Example: December 2002 forecasts Nino-3.4 Forecast Forecast + 1.96 std.dev (95% P.I) 1) Climatology 26.47 + 2.33  C 2) Empirical 27.82 + 1.14  C 3) Raw 27.13 + 0.54  C 4) Bias-corrected 27.56 + 0.54  C 5) Combined 28.13 + 0.71  C  Note: Best accuracy and reliability obtained through the combined forecast Observed Nino-3.4 value = 28.10  C


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