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

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

2nd EUROBRISA workshop, Dartmoor, Devon, 21-24 July 2009

PLAN OF TALK1. Current operational system2. Investigation on identified issues3. Advances on use of upper level circulation4. Applications: river flow and dengue risk transmission prediction

5. Future plans6. Summary

Towards a new EUROBRISA operational system

Empirical modelPredictors: Atlantic and Pacific SSTPredictand: PrecipitationCoelho et al. (2006) J. Climate, 19, 3704-3721

Hindcast period: 1987-2001

EUROBRISA integrated forecasting system for South America

Integratedforecast

U.K.UKMO (GloSea 3)

InternationalECMWF System 3

CountryCoupled model

Combined and calibrated coupled + empirical precip. forecastsHybrid multi-model probabilistic system

Most recentEUROBRISA integrated forecast for ASO 2009

Issued: Jul 2009

Obs. SST anomaly Jun 2009

IntegratedUKMOEmpirical

Prob. of most likely precip. tercile (%)

ECMWF

4

),(~ CYNY b

1

111

)(

)()(

))((

SGCGCGL

CLGICGSGD

YYGXLYY

TT

T

obba

)),((~| SYYGNYX o

Prior:

Likelihood:

Posterior:

1 YYXY SSGGYXGYo

TYYXX GGSSS

),(~| DYNXY a

Calibration and combination procedure: Forecast Assimilation

qq:D

qn:Y

pn:X

qq:C q1:Yb

pp:S qn:Ya

Matrices

Forecast assimilation uses the first three MCA modes of the matrix YT X.

X: precip. fcsts (coupled + empir.)Y: DJF precipitation

)(

)()|()|(

Xp

YpYXpXYp Stephenson et al. (2005)

Tellus, 57A, 253-264

5

YYb

)XX(LYY ba

)D),XX(L(N~X|Y o

If prior param.:

FA becomes:1

XXYXSSL LXYLXo

TYX

1XXYXYY SSSSD

Calibration and combination procedure: Forecast Assimilation

qq:D

qn:Y

pn:X

qq:C q1:Yb

qn:Ya

MatricesYYSC

Posterior:

)D,Y(N~X|Y a

Stephenson et al. (2005)Tellus, 57A, 253-264

X: precip. fcsts (coupled + empir.)Y: DJF precipitation

Multivariate regression

(MCA on YT X: 3 modes)Principal component regression

at each grid point (EOF on X: 1 mode)

Why is skill negative for some grid points?

Correlation skill: Integrated forecast (precipitation)Issued: Nov Valid: DJF (1987-2001)

Predictor:First PC of X

First PC of X

Y

1988 1990 1992 1994 1996 1998 2000

Param. estimates sensitive to removal of indiv. data points

Grid point with neg. corr. skill

How stable are cross-validated predictorsand regression parameter estimates?

Grid point with pos. corr. skill

• Stable• ENSO

First PC of X

Y

Robust

How influential is each data point?

Leverage is a function of the predictor alone, and measures the potential for a data point to affect the model parameter estimates

YX~

)X~

X~

(X~ˆX

~Y

YX~

)X~

X~

(ˆ

X~

Y

H

T1T

T1T

H is the hat matrixLeverage: diag(H)

n =15 data pointsp =1 PC

2p/n

:X~

1 col. matrix (1st PC of X)

Can precipitation forecasts over the Pacifichelp improve forecasts over land?

Source: Franco Molteni (ECMWF)

South America domain (270o, 300o, 60oS, 15 oN)

South America+Pacific domain(100o, 300o, 60oS, 15 oN)

Correlation skill: Integrated forecast Issued: Nov Valid: DJF (1987-2001)

Use of precip. fcsts over Pac. does help improve fcst. skill in S. America

South America domain:ECMWF, UKMO and empirical

(limited to common hindcast period)

South America + Pacific domain:ECMWF, UKMO, MF, CPTEC

and empirical (diff. hind. periods)

Can skill be improved by adding more models to the system?

1987-2001 1981-2005

Correlation skill: Integrated forecast (precipitation) Issued: Nov Valid: DJF

Adding more models does help improve skill in S. America

Can model predicted circulation variables help improve precip. forecast skill?

Use calibration procedure to explore atmospheric teleconnections

Rationale for the use of circulation patterns as predictor for seasonal

precip.Precip. is influenced by atmospheric circulation patterns

On seasonal timescales the frequency of occurrence of such patterns is influenced by anomalous patterns ofsea surface temperatures (particularly in the tropics)

The link between tropical SSTs and global circulation patternsinvolves the generation of quasi-stationary upper level wave trains from tropical diabatic heat sources toremote regions (e.g. ENSO teleconnections to South America)

If upper level circulation is well simulated by seasonal climatemodels, it may then be possible to use upper level circulationpredictions to produce precip. predictions for South America(i.e. precip. downscaling from upper level circulation )

How well do coupled seasonal forecast models simulate upper

level circulation?ECMWF UK Met Office (GloSea 3)

Obs NCEP/NCAR Reanalysis Kalnay et al. (1996)BAMS, 77(3), 437-471

Generally good skill in the tropics

Pert. stream func. (’)

Veloc. Poten. ()

Correlation skill: 1-month lead forecasts for DJF

Hindcasts: 1987-2005

Downscaling procedure: Forecast Assimilation

pn:X

qn:Y

Matrices

Forecast assimilation uses first three leading MCA modes of the matrix YT X.

Y: DJF precipitation X: 1-month lead 200 hPa ( ’, ) pred. for DJF (ECMWF + UKMO)

)(

)()|()|(

Xp

YpYXpXYp

Stephenson et al. (2005), Tellus A . Vol. 57, 253-264.

ECMWF UKMO

(’,)

Forecast Assimilation

Correlation skill: 1-month lead precipitation forecasts for DJF

Downscaled forecasts obtained with forecast assimilation have improved skill in North and Southeast South America compared to individual model predictions

’

How does this compare with circulation-based and SST-based empirical predictions?

Correlation skill: 1-month lead precipitation forecasts for DJF

Fcst Assim. Emp: Circ-based Emp: SST-based

Predictor: 1-month lead ’ pred.for DJF (ECMWF + UKMO)

Predictor: Obs ’ in previous Oct

Predictor: Obs SST in previous Oct.

Seasonal forecast applications:

Flow prediction: Paraná riverIssued: Nov Valid: Dec• Flow (ONS) 1982-2003: F

• Oct SST (Reynolds et al. 2002): PC1, PC2• Precip. GPCP (Adler et al. 2003): P• Integrated precip. forecasts (EUROBRISA) ECMWF, UKMO, MF, CPTEC: Pr

D5O4O3O2O10D Pr2PC1PCPFF

Issued: Nov Valid: Feb

Issued: Nov Valid: Jan

J5O4O3O2O10J Pr2PC1PCPFF

F5O4O3O2O10F Pr2PC1PCPFF

Corr: 0.38

Corr: 0.43

Corr: 0.02

obs fcts

Dengue risk trans. model:• Degalier et al. (2005)Environ, Risques & Santé 4 (2), 1-5

• Favier et al. (2006)Trop. Med. and Int. Health11 (3), 332–340

Morse et al. (2005) Tellus, 57A(3), 464-475

Dengue risk transmission index predictionsNCEP/NCAR Reanalysis:

Kalnay et al. (1996)BAMS, 77(3), 437-471

ECMWF System 3:Anderson et al. (2007)ECMWF Tech. Memo,

503, pp 56

T, RH

Sim. risk Fcst. risk

Hindcast period: 1981-20050 to 5 month lead predictions; 11 ensemble members

Bias corr. TRH (climat.)

Work by:Caio CoelhoRachel LoweNicolas Degallier

10010ln

)m/nln(R

m: Environmental (climatic) capacity to sustain thedevelopment of the vector (optimum disease reproduction rate)

n: climatic capacity to ensure transmission of the pathogen(larva/hab. ~ vector density capable of sustaining stable transmission)

Both m and n are modelled as function of T and RH

if n=m (R=0) if n>m (R>0) favorable conditions for transmissionif n<m (R<0) unfavorable conditions for transmission

50<R<100: endemic riskR>100: epidemic risk

Dengue risk transmission index (R)

Source: Nicolas Degallier (IRD)

Skill assessment: Dengue risk transmission index prediction issued in Nov. (Gerrity score: terc. cat.)

Valid: Nov Valid: Dec Valid: Jan

Valid: Feb Valid: Mar Valid: Apr

0-month lead 1-month lead

3-month lead

2-month lead

4-month lead 5-month lead

Example: Dengue risk transmission index prediction issued in Nov 1997, valid for Apr 1998

Brasília

Salvador

5-month lead fcst Obs Corr. skill

Nov Dec Jan Feb Mar Apr Nov Dec Jan Feb Mar Apr

Future plans• Investigate alternative methods of dimensionality reduction for the multivariate regression in the FA procedure

• Implement new version of EUROBRISA forecasting system - able to accommodate models with different hindcast periods - incorporate Meteo-France System 3 and CPTEC forecasts- how to proceed with UK Met Office GloSea 4

• Research on seasonal forecast applications (agriculture, hydropower and health)

• Implement new approaches to visualise forecasts

• Produce joint EUROBRISA publication

• Early stage of El Niño: EUROBRISA forecast for ASO 2009 is for below normal precip. in N South America and above normal precip. in SE South America

• Use of precip. forecasts over Pacific improves robustness of predictors and forecast skill over South America

• Adding more models to the integrated system helps improve forecast skill

• Coupled model upper level circulation predictions can be successfully used for producing skilful precip. forecasts for South America

• Preliminary results on application are encouraging for further developing research using seasonal forecasts

• New web link http://eurobrisa.cptec.inpe.br

Summary