Aim:
description
Transcript of Aim:
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Caio A. S. Coelho, D. B. Stephenson, F. J. Doblas-Reyes (*) and M.
Balmaseda (*) Department of Meteorology, University of Reading and ECMWF (*)
E-mail: [email protected]
The skill of empirical and combined/calibrated coupled multi-model South American seasonal
predictions during ENSO
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Aim:
•
To produce improved probability rainfall forecasts for S. America
Strategy:• Stage 1: Nino-3.4 index, 1 model (Coelho et al. 2003,2004)• Stage 2: Equatorial Pac. SST, 7 models (Stephenson et al. 2005)• Stage 3: S. American rainfall, 3 models (Coelho et al. 2005a,b)
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Plan of talk
1. Issues2. Conceptual framework (“Forecast Assimilation”)3. Examples of application: 0-d (Nino-3.4)
1-d (Eq. Pac. SST) 2-d (S. Amer.
rainfall) Downscaling
4. Conclusions
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1. Issues
• Why do forecasts need it?• How to do it?• How to get good probability
estimates?
Calibration
Combination • Why to combine?• How to combine?
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2. Conceptual framework
)y(p)x(p)x|y(p)y|x(p
i
iiiii
Data Assimilation “Forecast Assimilation”
)x(p)y(p)y|x(p)x|y(p
f
fffff
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y
Modelling the likelihood p(x|y)
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Forecast MAE(C)
MAESS (%)
BS BSS(%)
Uncert(C)
Climatol. 1.16 0 0.25 0 1.19
Empirical 0.53 55 0.05 79 0.61
ECMWF 0.57 51 0.18 29 0.33
Integrated 0.31 74 0.04 81 0.32
MAESS = [1- MAE/MAE(clim.)]*100%
Empirical ECMWF
Integrated
BSS = [1- BS/BS(clim.)]*100%
Example 1: Dec Niño3.4 forecasts (5-month lead)
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Example 2: Equatorial Pacific SST
Forecast Brier Score (BS)
BSS(%)
Climatol p=0.5 0.25 0Ensemble (ENS) 0.19 24Integrated (INT) 0.17 31
)0YPr(p tt
SST anomalies: Y (°C)Forecast probabilities: p
DEMETER: 7 coupled models; 6-month lead
BSS = [1- BS/BS(clim.)]*100%
Y 0YOBS OBS INT ENS
1BS0)op(n1BS
n
1k
2kk
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Brier Score as a function of longitude
Forecast assimilation reduces (i.e. improves) the Brier score in the eastern and western equatorial Pacific
ENS - - - INT
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Example 3: South American rainfall anomalies
(mm/day)
ENSO composites: 1959-200116 El Nino years13 La Nina years
• Empirical model (EMP):
ASO SST DJF
• Multi-model ensemble (ENS):
3 DEMETER coupled models
ECMWF, Meteo-France, Met Office
1-month lead
Start: Nov DJF
• Integrated (INT) forecast
Combines EMP and ENS
OBS(El Nino)
EMP(El Nino)
ENS(El Nino)
INT(El Nino)
OBS(La Nina)
EMP(La Nina)
ENS(La Nina)
INT(La Nina)
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Mean Anomaly Correlation Coefficient (ACC)
Generally low skill (c.f. ACC<0.31)Larger skill in ENSO years than in neutral yearsCalibration and combination improves skill
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EMP ENS INTCorrelation score for S.American rainfall
Comparable level of deterministic skillHigher skill in the tropics and southeastern S. America
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Brier Skill Score for S. American rainfall
Forecast assimilation improves the Brier Skill Score (BSS) in the tropics
limcBSBS1BSS )0YPr(p tt
EMP ENS INTENS
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Why has the skill been improved?
• How well calibrated the forecasts are (reliability)
• Ability to discriminate between different observed situations (resolution)
Forecast skill depends on:
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Brier Score decomposition
1BS0)op(n1BS
n
1k
2kk
)o1(o)oo(Nn1)op(N
n1BS
l
1i
2ii
l
1i
2iii
iNkk
ii1i o
N1)p|o(po
n
1kkon
1o
reliability resolution uncertainty
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Reliability component of the BSS
Forecast assimilation improves reliability over many regions
limc
reliabreliab BS
BSBSS
EMP ENS INT
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Resolution component of the BSS
Forecast assimilation improves resolution in the tropics
limc
resolresol BS
BSBSS
INTENSEMP
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Example 4: Downscaling of rainfall anomalies
• Multi-model ensemble (ENS):
3 DEMETER coupled models
ECMWF, Meteo-France, Met Office
1-month lead
Start: Nov DJF
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Forecast Correlation Brier Score ENS 0.57 0.22INT 0.74 0.17
South Box: DJF rainfall anomalies (1-month lead)ENS
INT
Forecast assimilation substantially improves forecast skill
- - - Observation Forecast
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Forecast Correlation Brier Score ENS 0.62 0.21INT 0.63 0.18
North Box : DJF rainfall anomalies (1-month lead)ENS
INT
Forecast assimilation slightly improves forecast skill
- - - Observation Forecast
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• Forecast assimilation improves the skill of probability forecasts
• South America rainfall example: - empirical and integrated predictions have
comparable level of deterministic skill - improved reliability and resolution in the tropics; - improved reliability in subtropical and central
regions - higher skill in ENSO years than neutral years - tropical and southeastern South America are the
two most predictable regions- first step towards an integrated system for South
America
4. Conclusions:
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•Coelho C.A.S., 2005: “Forecast Calibration and Combination: Bayesian Assimilation of Seasonal Climate Predictions”. PhD Thesis. University of Reading, 178 pp. • Coelho C.A.S., D. B. Stephenson, F. J. Doblas-Reyes and M. Balmaseda, 2005a: “From Multi-model Ensemble Predictions to Well-calibrated Probability Forecasts: Seasonal Rainfall Forecasts over South America 1959-2001”. CLIVAR Exchanges No 32, Vol. 10, No 1, 14-20.• Coelho C.A.S., D. B. Stephenson, M. Balmaseda, F. J. Doblas-Reyes and G. J. van Oldenborgh, 2005b: “Towards an integrated seasonal forecasting system for South America”. Submitted to J. Climate.•Stephenson, D. B., C.A.S. Coelho, F. J. Doblas-Reyes, and M. Balmaseda, 2005:“Forecast Assimilation: A Unified Framework for the Combination of Multi-Model Weather and Climate Predictions.” Tellus A, Vol. 57, 253-264.• 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
More information …
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Forecast assimilation improves reliability in the western Pacific
Reliability as a function of longitudeReliability as a function of longitude
ENS - - - INT
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Resolution as a function of longitude
Forecast assimilation improves resolution in the eastern Pacific
ENS - - - INT