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Caio A. S. Coelho Is a group of forecasts better than one? Department of Meteorology, University of Reading Introduction Combination in meteorology Bayesian combination Conclusion and future directions Plan of talk
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Pierre-Simon Laplace (1749-1827) The Pioneer of combination a p Laplace (1818): Combination of two estimators slope residual
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+ numerical = methods Have combined forecasts better skill than individual forecasts? In modern times… Forecasts from
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Forecast combination literature Trenkler and Gotu (2000): ~600 publications (1970-2000) Widely applied in Economics and Meteorology Overlap of methods in these areas Combined forecasts are better than individual forecasts
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Some issues What is the best method for combining? Is it worth combining unbiased forecasts with biased forecasts?
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DEMETER coupled model forecasts Nino-3.4 index (Y) Period: 1987-99 9 members Jul -> Dec 5 months lead DEMETER web page: http://www.ecmwf.int/research/demeter ECMWF Meteo-France (MF) Max Planck Institut (MPI)
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December Nino-3.4 raw forecasts
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Forecast combination in meteorology Linear combination of M ensemble mean forecasts X constants models ensemble mean combined forecast
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Bias-removed multimodel ensemble mean forecast (Uem) Regression-improved multimodel ensemble mean forecast(Rem) Regression-improved multimodel forecast (Rall) Combination methods used in meteorology Kharin and Zwiers (2002) How to estimate w o and w i ?
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Kharin and Zwiers combined forecasts
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Y: Observed December Nino-3.4 index X: Ensemble mean forecast of Y for December Thomas Bayes ( 1701-1761) The process of belief revision on any event Y consists in updating the probability of Y when new information X becomes available The Bayesian approach Example: Ensemble mean (X=x=27 C) Likelihood:p(X=x|Y) Prior:p(Y) Posterior:p(Y|X=x)
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Prior: Likelihood: Posterior: calibration Bayesian multimodel forecast (B) bias
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July and December Reynolds OI V2 SST (1950-2001) Nino-3.4 index observational data Nino-3.4 index mean values: Jul: 27.1 C Dec: 26.5 C r: 0.87 R 2 =0.76
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All combined forecasts Uem Rall Rem B Why are Rall and B so similar?
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Combined forecasts in Bayesian notation ForecastLikelihoodPrior Uem Rem Rall B B prior Rem and Rall prior Bayesian combination Prior: Likelihood:
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Skill and uncertainty measures ForecastMSE ( C) 2 Skill Score (%) Uncert. ( C) 2 Climatology1.9801.20 ECMWF (raw)1.64180.49 ECMWF (bias-removed)0.18910.49 ECMWF (regression-improved)0.22890.47 MF (raw)0.27860.39 MF (bias-removed)0.22890.39 MF (regression-improved)0.20900.46 MPI (raw)15.24-6690.49 MPI (bias-removed)1.52230.49 MPI (regression-improved)1.06460.86 Uem0.25881.79 Rem0.46770.56 Rall0.12940.38 B0.11940.23 Skill Score = [1- MSE/MSE(climatology)]*100%
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Conclusions and future directions Forecast can be combined in several different ways Combined forecasts have better skill than individual forecasts Rall and B had best skill for Nino- 3.4 example Inclusion of very biased forecast has not deteriorated combination Extend method for South America rainfall forecasts
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