caio a. s. coelho centro de previs ã o de tempo e estudos clim á ticos (cptec)
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EUROBRISA : A EURO - BR azilian I nitiative for improving S outh A merican seasonal forecasts. Caio A. S. Coelho Centro de Previs ã o de Tempo e Estudos Clim á ticos (CPTEC) Instituto Nacional de Pesquisas Espaciais (INPE) [email protected]. PLAN OF TALK Aims Activities - PowerPoint PPT PresentationTRANSCRIPT
Caio A. S. CoelhoCentro de Previsão de Tempo e Estudos Climáticos (CPTEC)
Instituto Nacional de Pesquisas Espaciais (INPE)[email protected]
ENSEMBLES meeting on seasonal to decadal prediction Barcelona, 7-8 June 2007
PLAN OF TALK• Aims• Activities• Results• Summary
EUROBRISA: A EURO-BRazilian Initiative for improving South American seasonal forecasts
The EUROBRISA Projectkey Idea: To improve seasonal forecasts in S. America:a region where there is seasonal forecast skill and useful value.
Aims• Strengthen collaboration and promote exchange of expertise and information between European and S. American seasonal forecasters
• Produce improved well-calibrated real-time probabilistic seasonal forecasts for South America
• Develop real-time forecast products for non-profitable governmental use (e.g. reservoir management, hydropower production, agriculture and health)
Involved institutions Country Partners
CPTEC Brazil Coelho, Cavalcanti, CostaSilva Dias, Pezzi
ECMWF EU Anderson, Balmaseda, Doblas-Reyes, Stockdale
INMET Brazil Moura, Silveira, Lucio
Met Office UK Graham, Davey, Colman
Météo France France Déqué
UFPR Brazil Guetter
Uni. of Exeter UK Stephenson
Uni. of São Paulo Brazil Ambrizzi, Silva Dias
http://www.cptec.inpe.br/~caio/EUROBRISA/
CIIFEN Ecuador Camacho
IRI USA Baethgen, Goddard
UFRGS Brazil Bergamaschi
Affiliated institutions
EUROBRISA activities
• Probabilistic forecasts of precip. and temp. with empirical and dynamical coupled models
• Production of objectively combined (dynamical + empirical) well-calibrated integrated forecasts
• Skill assessment of empirical, dynamical and combined forecasts using deterministic and probabilistic measures
• Dynamical and statistical downscaling• Seasonal predictability studies
Climate prediction research and development
Impacts (collaborative work with users)• Hydrology: Downscaling of seasonal forecasts for
river flow predictions and use in hydrological models
• Agriculture: Research on the use of seasonal forecasts in agricultural activities; Downscaling of seasonal forecasts for use in crop models
EUROBRISA multi-model ensemble system
4 coupled global circulation models + 1 empirical model
Coupled Model Country Hindcast period
CPTEC Brazil 1982-2001
ECMWF S3 International 1981-2005
Meteo-France France 1993-2004
UKMO U.K. 1987-2005
Empirical modelPredictor: Atlantic and Pacific SSTPredictands: Precipitation and temperature
Longest common period: 1987-2001 (CPTEC,ECMWF,UKMO)
Empirical Integrated
Correlation maps: JJA precip. anomalies
Better skill in tropical South AmericaIntegrated forecasts have improved skill in tropical South America and Southeast Argentina
• Hindcast period: 1987-2001• Coupled models with I.C. 1st May (1-month lead)• Empirical model uses April SST as predictor• Integrated forecasts (coupled + empirical) with forecast assimilation
ECMWF UKMO
Empirical Integrated
Gerrity score for JJA tercile precip. categories
Better skill in tropical South AmericaIntegrated forecasts have improved skill in tropical South America and Southeast Argentina
ECMWF UKMO
• Hindcast period: 1987-2001• Coupled models with I.C. 1st May (1-month lead)• Empirical model uses April SST as predictor• Integrated forecasts (coupled + empirical) with forecast assimilation
Empirical Integrated
ROC skill score for JJA positive anomalies
Integrated forecasts have improved skill in tropical South America and Southeast Argentina
ECMWF UKMO
• Hindcast period: 1987-2001• Coupled models with I.C. 1st May (1-month lead)• Empirical model uses April SST as predictor• Integrated forecasts (coupled + empirical) with forecast assimilation
Empirical Integrated
Brier skill score for JJA precip. in the upper tercile
Integrated forecasts have improved skill in tropical S. America
ECMWF UKMO
• Hindcast period: 1987-2001• Coupled models with I.C. 1st May (1-month lead)• Empirical model uses April SST as predictor• Integrated forecasts (coupled + empirical) with forecast assimilation
Empirical Integrated
Reliability component of the Brier skill score for JJA precip. in the upper tercile
Integrated forecasts have improved reliability in tropical S. America
ECMWF UKMO
• Hindcast period: 1987-2001• Coupled models with I.C. 1st May (1-month lead)• Empirical model uses April SST as predictor• Integrated forecasts (coupled + empirical) with forecast assimilation
Empirical Integrated
Resolution component of the Brier skill score for JJA precip. in the upper tercile
Integrated forecasts have improved resolution in north Brazil
ECMWF UKMO
• Hindcast period: 1987-2001• Coupled models with I.C. 1st May (1-month lead)• Empirical model uses April SST as predictor• Integrated forecasts (coupled + empirical) with forecast assimilation
Empirical Integrated
Example: JJA 2007 precipitation forecast
Most likely tercile category forecast: upper tercile (wet conditions) in North South America
and lower tercile (dry conditions) in central South America
Issued: May 2007
ECMWF UKMO
EUROBRISA summary• Challenging initiative for improving the quality of
South American seasonal forecasts
• Facilitate exchange and transfer of technology, knowledge and expertise between participating institutions
• Valuable opportunity to:- develop an objectively integrated (i.e. dynamical + empirical) forecasting system for
South America- work closely with end-users to evaluate our forecasting system in terms of user variables rather than solely on traditional climate variables
• Preliminary results on seasonal precipitation are encouraging
• More results will be available at http://www.cptec.inpe.br/~caio/EUROBRISA
References: • 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.• 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”. J. Climate, 17, 1504-1516. • Coelho C.A.S. 2005: “Forecast Calibration and Combination: Bayesian Assimilation of Seasonal ClimatePredictions”. PhD Thesis. University of Reading. 178 pp. • Coelho C.A.S., D. B. Stephenson, M. Balmaseda, F. J. Doblas-Reyes and G. J. van Oldenborgh, 2006a: Towards an integrated seasonal forecasting system for South America. J. Climate , 19, 3704-3721. • Coelho C.A.S., D. B. Stephenson, F. J. Doblas-Reyes, M. Balmaseda, A. Guetter and G. J. vanOldenborgh, 2006b: A Bayesian approach for multi-model downscaling: Seasonal forecasting of regionalrainfall and river flows in South America. Meteorological Applications, 13, 73-82. • Stephenson, D. B., Coelho, C. A. S., Doblas-Reyes, F.J. and Balmaseda, M., 2005: “Forecast Assimilation: A Unified Framework for the Combination of Multi-Model Weather and Climate Predictions.” Tellus A, Vol. 57, 253-264.
Available from http://www.cptec.inpe.br/~caio