The EUROBRISA operational system
Caio A. S. Coelho
Centro de Previsão de Tempo e Estudos Climáticos (CPTEC)
Instituto Nacional de Pesquisas Espaciais (INPE)
[email protected]
PLAN OF TALK
1. Introduction
2. EUROBRISA integrated forecasting system
3. Forecasts for 2007-2008
4. Skill of the hindcasts
5. Summary
1st EUROBRISA workshop, Paraty, 17-19 March 2008
1. Seasonal climate forecasts
Forecasts of climate conditions for the next 3-6 months
DJF
•
•
•
•
• • •
Nov Dec Jan Feb Mar Apr May
0
1
2
3
4
5
6
1-month lead for DJF
Current forecast approaches
•
•
•
Empirical/statistical models
Dynamical atmospheric models
Dynamical coupled (ocean-atmosphere) models
2. EUROBRISA integrated
forecasting system for South America
Combined and calibrated coupled + empirical precip. forecasts
Hybrid multi-model probabilistic system
Coupled model
Country
ECMWF System 3 International
UKMO
U.K.
Integrated
Empirical model
Predictors: Atlantic e Pacific SST
Predictand: Precipitation
Hindcast period: 1987-2001
The Empirical model
Y
Data sources:
• SST: Reynolds OI v2
Reynolds et al. (2002)
Z
• Precipitation: GPCP v2
Adler et al. (2003)
Y|Z ~ N (M (Z - Zo),T)
Y: DJF precipitation
Z: October sea surface temp. (SST)
1
M  SYZ S ZZ
Y : nq
 M Zo  Y  ZM
Z : nv
1 T
T  SYY  SYZ S ZZ
SYZ
T :qq
Model uses first three leading Maximum Covariance
Analysis (MCA) modes of the matrix YT Z.
Coelho et al. (2006)
Empirical forecast: DJF 2007/08
First mode (71%)
Second mode (7.7%)
Issued: November 2007
Observed Oct 2007 SST
DJF 2007 forecast
Corr. DJF
5
Conceptual framework
Data Assimilation
“Forecast Assimilation”
p( y i | x i ) p( x i )
p( x i | y i ) 
p( y i )
p( x f | y f ) p( y f )
p( y f | x f ) 
p( x f )
Stephenson et al. (2005)
Calibration and combination procedure:
p
(
X
|Y
)
p
(
Y
)
Forecast Assimilation
p
(
Y
|X
)
p
(
X
)
Stephenson et al. (2005)
X: forecasts (coupled + empir.)
Prior:
Y~N(Yb,C) Y: DJF precipitation
|Y
~
N
(
G
(
Y

Y
),
S
)
o
Likelihood: X
Matrices
1
XY YY
GS S
GY
Y
G
o X
X : n p
Y : n q
T
SS

GS
G
Yb : 1 q
XX
YY
C : qq
(Y
,
D
)
Posterior: Y| X~N
a
S : p p
Y
Y

L
(
X

G
(
Y

Y
))
a
b
b
o
Ya : n  q
T
1

1
D

(
G
S
G

C
)1
(
I
LG
)
C D : qq
T
T

1
L

CG
(
GCG

S
)
7
Forecast assimilation uses the first three MCA modes of the matrix YT X.
1-month lead precip. forecasts
EUROSIP: ECMWF
UKMO
Meteo-France
Empirical (SST based)
Integrated (Combined)
Real time and verification products
8
Web site launched in Oct 2007: http://www6.cptec.inpe.br/eurobrisa/
3. EUROBRISA forecasts
for 2007-2008
Examples of forecast products
Probability of most likely precip. tercile:
DJF 2007/08
ECMWF
UKMO
Empirical
Integrated
Issued: Nov 2007
10
Categorical forecast: DJF 2007/08 precip.
ECMWF
UKMO
Empirical
Integrated
Issued: Nov 2007
11
Prob. above average precip: DJF 2007/08
ECMWF
UKMO
Empirical
Integrated
Issued: Nov 2007
12
Prob. precip. in lower tercile: DJF 2007/08
ECMWF
UKMO
Empirical
Integrated
Issued: Nov 2007
13
EUROBRISA integrated
forecast for AMJ 2007
Obs. SST anomaly Feb 2007
Issued: March 2007
Prob. of most likely
precip. tercile (%)
Observed precip.
tercile
Gerrity score
(tercile categories)
Hindcasts: 1987-2001
EUROBRISA integrated
forecast for MJJ 2007
Obs. SST anomaly Mar 2007
Issued: April 2007
Prob. of most likely
precip. tercile (%)
Observed precip.
tercile
Gerrity score
(tercile categories)
Hindcasts: 1987-2001
EUROBRISA integrated
forecast for JJA 2007
Obs. SST anomaly Apr 2007
Issued: May 2007
Prob. of most likely
precip. tercile (%)
Observed precip.
tercile
Gerrity score
(tercile categories)
Hindcasts: 1987-2001
EUROBRISA integrated
forecast for JAS 2007
Obs. SST anomaly May 2007
Issued: Jun 2007
Prob. of most likely
precip. tercile (%)
Observed precip.
tercile
Gerrity score
(tercile categories)
Hindcasts: 1987-2001
EUROBRISA integrated
forecast for ASO 2007
Obs. SST anomaly Jun 2007
Issued: Jul 2007
Prob. of most likely
precip. tercile (%)
Observed precip.
tercile
Gerrity score
(tercile categories)
Hindcasts: 1987-2001
EUROBRISA integrated
forecast for SON 2007
Obs. SST anomaly Jul 2007
Issued: Aug 2007
Prob. of most likely
precip. tercile (%)
Observed precip.
tercile
Gerrity score
(tercile categories)
Hindcasts: 1987-2001
EUROBRISA integrated
forecast for OND 2007
Obs. SST anomaly Aug 2007
Issued: Sep 2007
Prob. of most likely
precip. tercile (%)
Observed precip.
tercile
Gerrity score
(tercile categories)
Hindcasts: 1987-2001
EUROBRISA forecasts
for NDJ 2007/08
Obs. SST anomaly Sep 2007
Issued: Oct 2007
Prob. of most likely precip. tercile (%)
Integrated
Empirical
ECMWF
UKMO
EUROBRISA forecasts
for DJF 2007/08
Obs. SST anomaly Oct 2007
Issued: Nov 2007
Prob. of most likely precip. tercile (%)
Integrated
Empirical
ECMWF
UKMO
EUROBRISA forecasts
for JFM 2008
Obs. SST anomaly Nov 2007
Issued: Dec 2007
Prob. of most likely precip. tercile (%)
Integrated
Empirical
ECMWF
UKMO
EUROBRISA forecasts
for FMA 2008
Obs. SST anomaly Dec 2007
Issued: Jan 2008
Prob. of most likely precip. tercile (%)
Integrated
Empirical
ECMWF
UKMO
EUROBRISA forecasts
for MAM 2008
Obs. SST anomaly Jan 2008
Issued: Feb 2008
Prob. of most likely precip. tercile (%)
Integrated
Empirical
ECMWF
UKMO
4. Skill of the hindcasts
Examples of verification products
Correlation btw. obs. and fcst. DJF precip. anom.
ECMWF
•
•
•
•
UKMO
Empirical
Integrated
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
27
Brier Skill Score (pos. or neg. anomaly):
DJF precipitation
ECMWF
•
•
•
•
UKMO
Empirical
Integrated
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
n
BS
1
BSS  1 
BS 
(p k  o k ) 2
28
BSc lim
n k 1

Reliability diagram (pos. or neg. anomaly):
DJF precipitation
ECMWF
•
•
•
•
UKMO
Empirical
Integrated
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
29
ROC curve (pos. or neg. anomaly):
DJF precipitation
ECMWF
•
•
•
•
UKMO
Empirical
Integrated
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
ROC skill score (pos. or neg. anomaly):
DJF precipitation
ECMWF
•
•
•
•
UKMO
Empirical
Integrated
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
ROCSS  2A 1
A is the area under the ROC curve
31
Ranked probability skill score
(tercile categories): DJF precipitation
ECMWF
•
•
•
•
UKMO
Empirical
Integrated
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
RPS
RPSS  1 
RPS c lim
K
RPS   BS m ; K  3
m 1
32
Gerrity score
(tercile categories): DJF precipitation
ECMWF
•
•
•
•
UKMO
Empirical
Integrated
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
33
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)
References
•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 multimodel 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.
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