Caio A. S. CoelhoCentro de Previsão de Tempo e Estudos Climáticos (CPTEC)

Instituto Nacional de Pesquisas Espaciais (INPE)caio@cptec.inpe.br

CPTEC-IRI Workshop , Cachoeira Paulista (Brazil), 8 November 2006

PLAN OF TALK• History• Aims• Planned activities• Motivating results• Summary

EUROBRISA: A EURO-BRazilian Initiative for improving South American seasonal forecasts

History of EUROBRISA2001-2005: PhD work on forecast calibration and combination (Coelho 2005)

Developed conceptual framework for forecasting(Bayesian approach named forecast assimilation)

• Nino index (Coelho et al. 2003, 2004)

• Equatorial Pacific SST (Stephenson et al. 2005)

• South American rainfall (Coelho et al. 2006a)

• Regional rainfall and river flow downscaling (Coelho et al. 2006b)

2005: Preparation, submission and approval of EUROBRISA proposal by ECMWF council

2005/2006: Preparation, submission and approval of young investigator fellowship by FAPESP and start of EUROBRISA

)y(p

)x(p)x|y(p)y|x(p

i

iiiii

)x(p

)y(p)y|x(p)x|y(p

f

fffff

Data AssimilationForecast Assimilation

•

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, and agriculture)

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 Reading UK Stephenson, Challinor

Uni. of Sao Paulo Brazil Ambrizzi, Silva Dias

http://www.cptec.inpe.br/~caio/EUROBRISA/index.html

CIIFEN Ecuador Camacho

IRI USA Baethgen

UFRGS Brazil Bergamaschi

Affiliated institutions

Planned activities

• Produce probabilistic forecasts of precip. and temp. with empirical and dynamical coupled models

• Deliver objectively combined (dynamical + empirical) well-calibrated forecasts

• Compare skill 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 International 1987-2001

Meteo-France France 1993-2001

UKMO U.K. 1987-2005

Empirical modelPredictor: Atlantic and Pacific SSTPredictands: Precipitation and temperature

EmpiricalDEMETER

Multi-model (*)

Integrated

Correlation maps: DJF rainfall anomalies

Comparable level of determinist skillBetter skill in tropical and southeastern South America

* ECMWF, Meteo-France, UKMO (1959-2001), I.C. November

Mean Anomaly Correlation Coefficient

Most skill in ENSO years and forecast assimilation can improve skill

Multi-modelIntegrated

Empirical

limcBS

BS1BSS )0YPr(p tt

ENS

Forecast assimilation improved Brier Skill Score (BSS) in the tropics

Brier Skill Score for DJF rainfall Empirical Multi-model Integrated

Brier Score decomposition

1BS0)op(n

1BS

n

1k

2kk

)o1(o)oo(Nn

1)op(N

n

1BS

l

1i

2ii

l

1i

2iii

iNkk

ii1i o

N

1)p|o(po

n

1kkon

1o

reliability resolution uncertainty

limc

reliabreliab BS

BSBSS

Forecast assimilation improved reliability in many regions

Reliability component of the BSS Empirical Multi-model Integrated

limc

resolresol BS

BSBSS

Forecast assimilation improved resolution in the tropics

Resolution component of the BSSEmpirical Multi-model Integrated

Regional rainfall downscaling

Multi-model ensemble

3 DEMETER coupled models

ECMWF, Meteo-France, UKMO

3-month lead

Start: Aug NDJ

Period: 1959-2001

Forecast Correlation Brier Score

Multi-model 0.57 0.22

FA 0.74 0.17

South box: NDJ rainfall anomaly Multi-model

Forecast assimilation

Forecast assimilation improves skill substantially

- - - Observation Forecast

(Coelho et al. 2006b)

Forecast

Forecast Correlation Brier Score

Multi-model 0.62 0.21

FA 0.63 0.18

- - - Observation

Forecast assimilation improved skill marginally

North box: NDJ rainfall anomaly Multi-model

Forecast assimilation

(Coelho et al. 2006b)

Forecast Correlation Brier Score

Parana 0.16 0.25

Tocantins 0.29 0.22

Annual cycle

Harder to downscale river flow than rainfall

River flow predictions (NDJ)

(Coelho et al. 2006b)

Agricultural application

(Challinor et al. 2004)

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

• Collaborative activities with IRI are of great interest

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

•Challinor et al.,2004: “Design and optimisation of a large-area process-based model for annual crops”.Agricultural and Forest Meteorology, 124, 99-112.