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Improved Seasonal Predictions Through Forecast Assimilation: Examples and Conclusions

This talk discusses the issues involved in calibration and combination of seasonal predictions, and presents the conceptual framework of forecast assimilation. It explores the application of forecast assimilation in various scenarios and highlights the benefits it brings in terms of improved reliability and resolution. Examples include Niño-3.4 index forecasts, Equatorial Pacific SST predictions, and South American rainfall forecasts.

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Improved Seasonal Predictions Through Forecast Assimilation: Examples and Conclusions

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  1. Forecast Assimilation of DEMETER Coupled Model Seasonal Predictions Caio A. S. Coelho e-mail: c.a.d.s.coelho@reading.ac.uk Supervisors: D. B. Stephenson, F. J. Doblas-Reyes (*) Thanks to CAG, S. Pezzulli and M. Balmaseda (*) Department of Meteorology, University of Reading and ECMWF (*)

  2. Plan of talk • Issues • Conceptual framework (“Forecast Assimilation”) • DEMETER • Examples of application: 0-d, 1-d, 2-d. • Conclusions •

  3. 1. Issues Calibration • Why do forecasts need it? • Which are the best ways • to calibrate? • How to get good probability • estimates? Combination • Why to combine? • Should model predictions be • selected? • How best to combine?

  4. 2. Conceptual framework “Forecast Assimilation” Data Assimilation

  5. 3. Multi-model ensemble approach DEMETER Development of a European Multi-Model Ensemble System for Seasonal to Interannual Prediction Model formulation Errors: Initial conditions Multi-model Ensemble Solution: http://www.ecmwf.int/research/demeter

  6. DEMETER Multi-model ensemble system 7 coupled global circulation models 9 member ensembles ERA-40 initial conditions SST and wind perturbations 4 start dates per year (Feb, May, Aug and Nov) 6 month hindcasts Hindcast period: 1980-2001 (1959-2001)

  7. 4. Examples of application • • Niño-3.4 index (0-d) • Equatorial Pacific SST (1-d) • South American rainfall (2-d)

  8. Example 1: Niño-3.4 forecasts 95% P.I. Well-calibrated: Most observations in the 95% prediction interval (P.I.)

  9. ECMWF coupled model ensemble forecasts m=9 DEMETER: 5-month lead • Observations not within the 95% prediction interval! • Coupled model forecasts need calibration

  10. Univariate X and Y Prior: Likelihood: Posterior: Bayes’ theorem:

  11. Modelling the likelihood p(X|Y) y

  12. Combined forecasts  Note: most observations within the 95% prediction interval!

  13. All forecasts Empirical Coupled Combined MAESS = [1- MAE/MAE(clim.)]*100% BSS = [1- BS/BS(clim.)]*100%

  14. Multivariate X and Y bias Prior: Likelihood: Matrices Posterior:

  15. Example 2: Equatorial Pacific SST DEMETER: 7 coupled models; 6-month lead BSS = [1- BS/BS(clim.)]*100% Forecast probabilities: p SST anomalies: Y (°C)

  16. Brier Score as a function of longitude Forecast assimilation reduces (i.e. improves) the Brier score in the eastern and western equatorial Pacific

  17. Brier Score decomposition uncertainty reliability resolution

  18. Reliability as a function of longitude Reliability as a function of longitude Forecast assimilation improves reliability in the western Pacific

  19. Resolution as a function of longitude Forecast assimilation improves resolution in the eastern Pacific

  20. Why South America?  Seasonal climate potentially predictable El Niño (DJF) DEMETER Multi-model La Niña (DJF) Source: Climate Prediction Center (http://www.cpc.ncep.noaa.gov) Correlation: DJF rainfall

  21. Why South American rainfall? • Agriculture • Electricity: More than 90% produced by hydropower stations • e.g. Itaipu (Brazil/Paraguay): • • World largest hydropower plant • • Installed power: 12600 MW • • 18 generation units (700 MW each) • • ~25% electricity consumed in Brazil • • ~95% electricity consumed in Paraguay

  22. Itaipu

  23. Example 3: South American rainfall anomalies Forecast Assimilation Obs Multi-model DEMETER: 3 coupled models (ECMWF, CNRM, UKMO) 1-month lead Start: Nov DJF ENSO composites: 1959-2001 • 16 El Nino years • 13 La Nina years r=0.51 r=0.97 r=0.28 r=0.82 (mm/day)

  24. South American DJF rainfall anomalies Forecast Assimilation Obs Multi-model r=0.59 r=-0.09 r=0.32 r=0.56 (mm/day)

  25. South American DJF rainfall anomalies Forecast Assimilation Obs Multi-model r=0.32 r=0.04 r=0.08 r=0.38 (mm/day)

  26. Brier Skill Score for S. American rainfall Forecast assimilation improves the Brier Skill Score (BSS) in the tropics

  27. Reliability component of the BSS Forecast assimilation improves reliability over many regions

  28. Resolution component of the BSS Forecast assimilation improves resolution in the tropics

  29. 5. Conclusions: • unified framework for the calibration and combination of predictions – “forecast assimilation” • improves the skill of probability forecasts • Example 1: Niño-3.4 • improved mean forecast value and • prediction uncertainty estimate • Example 2: Equatorial Pacific SST •  improved reliability (west) and resolution (east) • Example 3: South American rainfall •  improved reliability and resolution in the tropics  improved reliability over subtropical and central regions

  30. More information … • Coelho C.A.S. “Forecast Calibration and Combination: Bayesian Assimilation of Seasonal Climate Predictions”. PhD Thesis. University of Reading (to be submitted) • Coelho C.A.S., D. B. Stephenson, F. J. Doblas-Reyes and M. Balmaseda: “From Multi-model Ensemble Predictions to Well-calibrated Probability Forecasts: Seasonal Rainfall Forecasts over South America 1959-2001”.CLIVAR Exchanges (submitted). • Stephenson, D. B., Coelho, C. A. S., Doblas-Reyes, F.J. and Balmaseda, M. • “Forecast Assimilation: A Unified Framework for the Combination of • Multi-Model Weather and Climate Predictions.” • Tellus A - DEMETER special issue (in press). • Coelho C.A.S., S. Pezzulli, M. Balmaseda, F. J. Doblas-Reyes and D. B. Stephenson, 2004: “Forecast Calibration and Combination: A Simple Bayesian Approach for ENSO”. Journal of Climate. Vol. 17, No. 7, 1504-1516. • Coelho C.A.S., S. Pezzulli, M. Balmaseda, F. J. Doblas-Reyes and D. B. Stephenson, 2003: “Skill of Coupled Model Seasonal Forecasts: A Bayesian Assessment of ECMWF ENSO Forecasts”. ECMWF Technical Memorandum No. 426, 16pp. Available at http://www.met.rdg.ac.uk/~swr01cac

  31. Reliability diagram (Multi-model) (oi) o (pi)

  32. Reliability diagram (FA 58-01) (oi) o (pi)

  33. Operational Seasonal forecasts for S. America • Coupled models Europe: http://www.ecmwf.int U.K: http://www.metoffice.com • Atmospheric models forced by persisted/forecast SSTs U.S.A: http://iri.columbia.edu Brazil: http://www.cptec.inpe.br

  34. Mean Anomaly Correlation Coefficient

  35. Momentum measure of skewness Measure of asymmetry of the distribution

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