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

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

1st EUROBRISA workshop, Paraty, 17-19 March 2008

PLAN OF TALK1. Introduction2. EUROBRISA integrated forecasting system3. Forecasts for 2007-20084. Skill of the hindcasts 5. Summary

The EUROBRISA operational system

1. Seasonal climate forecasts

•

Current forecast approaches• Empirical/statistical models• Dynamical atmospheric models• Dynamical coupled (ocean-atmosphere) models

Forecasts of climate conditions for the next 3-6 months

• • •• • •Nov Dec Jan Feb MarApr May

DJF

0 1 2 3 4 5 61-month lead for DJF

Empirical modelPredictors: Atlantic e Pacific SSTPredictand: Precipitation

Hindcast period: 1987-2001

2. EUROBRISA integrated forecasting system for South America

Integrated

U.K.UKMO

InternationalECMWF System 3

CountryCoupled model

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

The Empirical model

Y|Z ~ N (M (Z - Zo),T)

TYZ

1ZZYZYY

o

1ZZYZ

SSSST

MZYZM

SSM

Y: DJF precipitation

Z: October sea surface temp. (SST)

Model uses first three leading Maximum CovarianceAnalysis (MCA) modes of the matrix YT Z.

vnZ

qnY

:

:

qqT :

Y Z

Coelho et al. (2006)

Data sources:• SST: Reynolds OI v2

Reynolds et al. (2002)

• Precipitation: GPCP v2Adler et al. (2003)

5

First mode (71%)

Second mode (7.7%)

Observed Oct 2007 SST

DJF 2007 forecast Corr. DJF

Empirical forecast: DJF 2007/08

Issued: November 2007

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

Stephenson et al. (2005)

7

),(~ 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: forecasts (coupled + empir.)Y: DJF precipitation

)(

)()|()|(

Xp

YpYXpXYp

Stephenson et al. (2005)

8Web site launched in Oct 2007: http://www6.cptec.inpe.br/eurobrisa/

Real time and verification products

1-month lead precip. forecastsEUROSIP: ECMWF UKMO Meteo-FranceEmpirical (SST based)Integrated (Combined)

3. EUROBRISA forecasts for 2007-2008

10

Empirical Integrated

Examples of forecast productsProbability of most likely precip. tercile:

DJF 2007/08

Issued: Nov 2007

ECMWF UKMO

11

Empirical Integrated

Categorical forecast: DJF 2007/08 precip.

ECMWF UKMO

Issued: Nov 2007

12

Empirical Integrated

Prob. above average precip: DJF 2007/08

ECMWF UKMO

Issued: Nov 2007

13

Empirical IntegratedECMWF UKMO

Prob. precip. in lower tercile: DJF 2007/08

Issued: Nov 2007

EUROBRISA integrated forecast for AMJ 2007

Issued: March 2007

Gerrity score(tercile

categories)

Prob. of most likelyprecip. tercile (%)

Hindcasts: 1987-2001

Observed precip.tercile

Obs. SST anomaly Feb 2007

EUROBRISA integrated forecast for MJJ 2007

Hindcasts: 1987-2001

Prob. of most likelyprecip. tercile (%)

Observed precip.tercile

Issued: April 2007

Obs. SST anomaly Mar 2007

Gerrity score(tercile

categories)

EUROBRISA integrated forecast for JJA 2007

Issued: May 2007

Obs. SST anomaly Apr 2007

Prob. of most likelyprecip. tercile (%)

Observed precip.tercile

Gerrity score(tercile

categories)

Hindcasts: 1987-2001

EUROBRISA integrated forecast for JAS 2007

Issued: Jun 2007

Hindcasts: 1987-2001

Gerrity score(tercile

categories)

Prob. of most likelyprecip. tercile (%)

Obs. SST anomaly May 2007

Observed precip.tercile

Hindcasts: 1987-2001

EUROBRISA integrated forecast for ASO 2007

Obs. SST anomaly Jun 2007

Issued: Jul 2007

Gerrity score(tercile

categories)

Prob. of most likelyprecip. tercile (%)

Observed precip.tercile

EUROBRISA integrated forecast for SON 2007

Obs. SST anomaly Jul 2007

Issued: Aug 2007

Hindcasts: 1987-2001

Prob. of most likelyprecip. tercile (%)

Gerrity score(tercile

categories)

Observed precip.tercile

EUROBRISA integrated forecast for OND 2007

Issued: Sep 2007

Obs. SST anomaly Aug 2007

Hindcasts: 1987-2001

Prob. of most likelyprecip. tercile (%)

Observed precip.tercile

Gerrity score(tercile

categories)

EUROBRISA forecastsfor NDJ 2007/08

Issued: Oct 2007

Obs. SST anomaly Sep 2007

Integrated UKMOEmpirical

Prob. of most likely precip. tercile (%)

ECMWF

EUROBRISA forecastsfor DJF 2007/08

Issued: Nov 2007

Obs. SST anomaly Oct 2007

Integrated UKMOEmpirical

Prob. of most likely precip. tercile (%)

ECMWF

EUROBRISA forecastsfor JFM 2008

Issued: Dec 2007

Obs. SST anomaly Nov 2007

Integrated UKMOEmpirical

Prob. of most likely precip. tercile (%)

ECMWF

EUROBRISA forecastsfor FMA 2008

Issued: Jan 2008

Obs. SST anomaly Dec 2007

Integrated UKMOEmpirical

Prob. of most likely precip. tercile (%)

ECMWF

EUROBRISA forecastsfor MAM 2008

Issued: Feb 2008

Obs. SST anomaly Jan 2008

Integrated UKMOEmpirical

Prob. of most likely precip. tercile (%)

ECMWF

4. Skill of the hindcasts

27

Empirical IntegratedCorrelation btw. obs. and fcst. DJF precip. anom.

UKMOECMWF

Examples of verification products

• Hindcast period: 1987-2001• Coupled models with I.C. 1st Nov (1-month lead for DJF)• Empirical model uses Oct SST as predictor for DJF precip.• Integrated forecasts (coupled + empirical) with forecast assimilation

Best skill in tropical and southeast South America

28

Empirical Integrated

Brier Skill Score (pos. or neg. anomaly): DJF precipitation

UKMOECMWF

• Hindcast period: 1987-2001• Coupled models with I.C. 1st Nov (1-month lead for DJF)• Empirical model uses Oct SST as predictor for DJF precip.• Integrated forecasts (coupled + empirical) with forecast assimilation

limcBS

BS1BSS

n2

k kk 1

1BS (p o )

n

29

Empirical Integrated

Reliability diagram (pos. or neg. anomaly): DJF precipitation

UKMOECMWF

• Hindcast period: 1987-2001• Coupled models with I.C. 1st Nov (1-month lead for DJF)• Empirical model uses Oct SST as predictor for DJF precip.• Integrated forecasts (coupled + empirical) with forecast assimilation

30

Empirical Integrated

ROC curve (pos. or neg. anomaly): DJF precipitation

UKMOECMWF

• Hindcast period: 1987-2001• Coupled models with I.C. 1st Nov (1-month lead for DJF)• Empirical model uses Oct SST as predictor for DJF precip.• Integrated forecasts (coupled + empirical) with forecast assimilation

31

Empirical Integrated

ROC skill score (pos. or neg. anomaly): DJF precipitation

UKMOECMWF

• Hindcast period: 1987-2001• Coupled models with I.C. 1st Nov (1-month lead for DJF)• Empirical model uses Oct SST as predictor for DJF precip.• Integrated forecasts (coupled + empirical) with forecast assimilation

1A2ROCSS A is the area under the ROC curve

32

Empirical IntegratedUKMOECMWF

• Hindcast period: 1987-2001• Coupled models with I.C. 1st Nov (1-month lead for DJF)• Empirical model uses Oct SST as predictor for DJF precip.• Integrated forecasts (coupled + empirical) with forecast assimilation

limcRPS

RPS1RPSS 3K;BSRPS

K

1mm

Ranked probability skill score (tercile categories): DJF precipitation

33

Empirical Integrated

Gerrity score (tercile categories): DJF precipitation

UKMOECMWF

• Hindcast period: 1987-2001• Coupled models with I.C. 1st Nov (1-month lead for DJF)• Empirical model uses Oct SST as predictor for DJF precip.• Integrated forecasts (coupled + empirical) with forecast assimilation

5. Summary•EUROBRISA integrated forecasting system: First operational hybrid (empirical-dynamical) probabilistic seasonal forecasting system for South America

•Current operational system: SST-based empirical model + two dynamical coupled models (ECMWF and UKMO)

•Good performance in 2007 over regions where forecasts have historically moderate to good skill

•Web products include a range of forecast and verification products for the EUROBRISA integrated forecasting system in addition to Meteo-France coupled model forecasts

•Additional information at http://www6.cptec.inpe.br/eurobrisa and in Coelho et al.(2007)-CLIVAR Exchanges No 43 (Volume 12 No 4)

35

•Adler, R.F., G.J. Huffman, A. Chang, R. Ferraro, P. Xie, J. Janowiak, B. Rudolf, U. Schneider, S. Curtis, D. Bolvin, A. Gruber, J. Susskind, P. Arkin (2003), The Version 2 Global Precipitation Climatology Project (GPCP) Monthly Precipitation Analysis (1979-Present). J. Hydrometeor., 4,1147-1167.•Coelho C.A.S., D. B. Stephenson, F. J. Doblas-Reyes, M. Balmaseda and R. Graham, 2007: Integrated seasonal climate forecasts for South America. CLIVAR Exchanges. No.43. Vol. 12, No. 4, 13-19.• Coelho C.A.S., D. B. Stephenson, F. J. Doblas-Reyes and M. Balmaseda, 2005: 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, 2006: “Towards an integrated seasonal forecasting system for South America”.J. Climate., Vol. 19, 3704-3721.•Reynolds, R. W., N. A. Rayner, T. M. Smith, D. C. Stokes and W. Wang (2002), An improved in situ and satellite SST analysis for climate. J. Climate, 15, 1609-1625.•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.

References