EUROBRISA WORKSHOP, Paraty 17-19 March 2008, ECMWF System 3 1 The ECMWF Seasonal Forecast System-3 Magdalena A. Balmaseda Franco Molteni,Tim Stockdale

EUROBRISA WORKSHOP, Paraty 17-19 March 2008 , ECMWF System 3 1

The ECMWF Seasonal Forecast

System-3

Magdalena A. BalmasedaFranco Molteni,Tim Stockdale Laura Ferranti,

Paco Doblas-Reyes, Frederic Vitart

European Centre for Medium-Range Weather Forecasts, Reading, U.K.


Overview

• Introduction to Seasonal Forecasts End to End Seasonal Forecasting System Importance of Ocean Initial Conditions

• ECMWF Seasonal forecasting system 3 Overview Performance Web products

• Calibration of model output

• Multimodel (EUROSIP)

• Calibration + Multimodel

• Summary


Pro

bab

ilist

ic

fore

cast

calib

ratio

n Rel

iab

le p

rob

abili

ty f

ore

cast

sT

ailo

red

pro

du

cts

End to End

Forecasting System

atmosDA

atmos obs

SST analysis

oceanDA

ocean obs

ocean reanalysis

atmos reanalysis

land,snow…?

sea-ice?

initialconditions

initialconditions

AGCM

OGCM

ensemble

generation


1997-1998 El-Niño forecast

50OE 100OE 150OE 160OW 110OW 60OW 10OW

Longitude

50OS

0O

50ON

Latit

ude

Contour interval = 1 deg CSea Surface TemperatureECMWF S3 ocean analysis: Anomaly

Interpolated in y1981-2005 climatology

respect to19970716 (31 days mean)

1

1

1

1

2

2

3

-6-4-2.5-2-1.5-1-0.50.511.522.5346

MAGICS 6.11 bee17 - emos Wed Mar 21 13:27:57 2007

Initial Conditions Forecast


2007 La Niña

50OE 100OE 150OE 160OW 110OW 60OW 10OW

Longitude

50OS

0O

50ON

Latit

ude




1

1

1

1

1

-6-4-2.5-2-1.5-1-0.50.511.522.5346


50OE 100OE 150OE 160OW 110OW 60OW 10OW

Longitude

300

250

200

150

100

50

0

Dep

th (

met

res)

Contour interval = 1 deg CPotential Temperature along the EquatorECMWF S3 ocean analysis: Anomaly



-9.5-8.5-7.5-6.5-5.5-4.5-3.5-2.5-1.5-0.50.51.52.53.54.55.56.57.58.59.5


JUL2006

AUG SEP OCT NOV DEC JAN2007

FEB MAR APR MAY JUN JUL AUG SEP

-1

0

1

2

Ano

mal

y (d

eg C

)

-1

0

1

2

Monthly mean anomalies relative to NCEP adjusted OIv2 1971-2000 climatologyECMWF forecast from 1 Jan 2007

NINO3.4 SST anomaly plume

Produced from real-time forecast data

System 3

50OE 100OE 150OE 160OW 110OW 60OW 10OW

Longitude

50OS

0O

50ON

Latit

ude




-2

-1

-1

-1

1

1

124

-6-4-2.5-2-1.5-1-0.50.511.522.5346

MAGICS 6.11 bee19 - emos Tue Sep 11 15:33:41 2007

Initial Conditions


Magdalena A. Balmaseda

Slide 6

Observations used in the S3 ocean analysis


Impact on ECMWF-S3 Forecast Skill

In ECMWF S3, ocean Data Assimilation improves forecast skill in the Equatorial Pacific, especially in the

Western Part

0 1 2 3 4 5 6 7Forecast time (months)

0.4

0.5

0.6

0.7

0.8

0.9

1

An

om

aly

co

rre

latio

n

wrt NCEP adjusted OIv2 1971-2000 climatology

NINO4 SST anomaly correlation


0

0.2

0.4

0.6

0.8

Rm

s e

rro

r (d

eg

C)

Ensemble sizes are 3 (esj6) and 3 (esj6) 76 start dates from 19870101 to 20051001

NINO4 SST rms errors

Fc esj6/m1 Fc esj6/m0 Persistence Ensemble sd

MAGICS 6.10 hyrokkin - neh Thu Sep 7 19:11:46 2006

S3 Nodata S3 Assim

The impact of ocean initialization in the prediction of SST is comparable to the

impact of atmospheric model cycle

S2 S2ic_S3model S3


•COUPLED MODEL (IFS + OASIS2 + HOPE)•Recent cycle of atmospheric model (Cy31R1)•Atmospheric resolution TL159 and 62 levels•Time varying greenhouse gasses.•Includes ocean currents in wave model

•INITIALIZATION•Includes bias correction in ocean assimilation.•Includes assimilation of salinity and altimeter data. •ERA-40 data used to initialize ocean and atmosphere in hindcasts•Ocean reanalysis back to 1959, using ENACT/ENSEMBLES ocean data

•ENSEMBLE GENERATION•Extended range of back integrations: 11 members, 1981-2005.•Revised wind and SST perturbations. •Use EPS Singular Vector perturbations in atmospheric initial conditions.

•Forecasts extended to 7 months (to 13 months 4x per year).

The seasonal forecast System-3 (implem. March 07)


Rms error of forecasts has been systematically reduced (solid lines) ….

Rms error / spread in different ECMWF systems

0 1 2 3 4 5 6Forecast time (months)

0.4

0.5

0.6

0.7

0.8

0.9

1

An

om

aly

co

rre

latio

n


NINO3.4 SST anomaly correlation


0

0.2

0.4

0.6

0.8

1

Rm

s e

rro

r (d

eg

C)

Ensemble sizes are 5 (0001), 5 (0001) and 5 (0001)192 start dates from 19870101 to 20021201

NINO3.4 SST rms errors

Fcast S3 Fcast S2 Fcast S1 Persistence

MAGICS 6.11 cressida - net Tue Apr 17 16:45:18 2007

.. but ensemble spread (dashed lines) is still substantially less than actual forecast error.


0.4

0.5

0.6

0.7

0.8

0.9

1

An

om

aly

co

rre

latio

n


NINO3.4 SST anomaly correlation


0

0.2

0.4

0.6

0.8

1

Rm

s e

rro

r (d

eg

C)

Ensemble sizes are 5 (0001), 5 (0001) and 5 (0001)192 start dates from 19870101 to 20021201

NINO3.4 SST rms errors

Fcast S3 Fcast S2 Fcast S1 Persistence Ensemble sd

MAGICS 6.11 cressida - net Tue Apr 17 16:41:30 2007


0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18Calendar month

24

25

26

27

28

29

30

Ab

solu

te S

ST

NINO3.4 mean absolute SST

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18Calendar month

-3

-2

-1

0

1

2

3

Dri

ft (

de

g C

)

NINO3.4 mean SST drift

Fcast S3

MAGICS 6.11 cressida - net Mon Feb 5 17:34:33 2007

JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC

Verification month

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

An

om

aly

co

rre

latio

n


NINO3.4 SST anomaly correlation at 5 months


Verification month

0

0.2

0.4

0.6

0.8

1

1.2

Rm

s e

rro

r (d

eg

C)

Ensemble size is 11300 start dates from 19810101 to 20051201

NINO3.4 SST rms errors at 5 months

Fcast S3 Persistence Ensemble sd

MAGICS 6.11 cressida - net Tue Feb 6 09:26:44 2007



0.4

0.5

0.6

0.7

0.8

0.9

1

An

om

aly

co

rre

latio

n


ATL3 SST anomaly correlation


0

0.2

0.4

0.6

0.8

Rm

s e

rro

r (d

eg

C)


ATL3 SST rms errors




Verification month

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

An

om

aly

co

rre

latio

n


ATL3 SST anomaly correlation at 5 months


Verification month

0

0.2

0.4

0.6

0.8

1

Rm

s e

rro

r (d

eg

C)


ATL3 SST rms errors at 5 months



0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18Calendar month

24

25

26

27

28

29

30

Ab

solu

te S

ST

ATL3 mean absolute SST

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18Calendar month

-3

-2

-1

0

1

2

3

Dri

ft (

de

g C

)

ATL3 mean SST drift

Fcast S3

MAGICS 6.11 cressida - net Mon Feb 5 17:34:33 2007


ACC for seasonal-mean (1981-2005)

2m-T: DJF from 1 Nov 2m-T: JJA from 1 May

Precip: DJF from 1 Nov Precip: JJA from 1 May

Doblas-Reyes


New products in the web:ocean reanalysis

http://www.ecmwf.int/products/forecasts/d/charts/ocean/reanalysis/


New products from Sys-3: annual-range Nino indices

S2007

O N D J2008

F M A M J J A S O N D J2009

F M A-2

-1

0

1

2

3A

nom

aly

(deg

C)

-2

-1

0

1

2

3Monthly mean anomalies relative to NCEP adjusted OIv2 1971-2000 climatology

ECMWF forecast from 1 Feb 2008NINO3 SST anomaly plume

Forecast issue date: 15 Feb 2008

System 3


New products from Sys-3: ’tercile summary’


New products from Sys-3: climagrams

a) Teleconnection and monsoon

indices with verification

http://www.ecmwf.int/products/forecasts/d/charts/seasonal/forecast/

Feb

ruar

yM

arch

Apr

ilM

ayJu

neJu

lyA

ugus

t

-6

.3

-5.0

-3.8

-2.5

-1.3

-0.0

1.3

2.5

3.8

5.0

En

se

mb

le s

ize

: F

ore

ca

st=

41

Mo

de

l clim

ate

=2

75

An

aly

sis

clim

ate

=2

5 F

ore

ca

st

init

ial d

ate

: 2

00

8 2

01

Eq

ua

tori

al S

ou

the

rn O

sc

illa

tio

n

Feb

ruar

yM

arch

Apr

ilM

ayJu

neJu

lyA

ugus

t

-6

.3

-5.0

-3.8

-2.5

-1.3

-0.0

1.3

2.5

3.8

5.0

En

se

mb

le s

ize

: F

ore

ca

st=

41

Mo

de

l clim

ate

=2

75

An

aly

sis

clim

ate

=2

5 F

ore

ca

st

init

ial d

ate

: 2

00

8 2

01

Eq

ua

tori

al S

ou

the

rn O

sc

illa

tio

n

Feb

ruar

yM

arch

Apr

ilM

ayJu

neJu

lyA

ugus

t

-6

.3

-5.0

-3.8

-2.5

-1.3

-0.0

1.3

2.5

3.8

5.0

En

se

mb

le s

ize

: F

ore

ca

st=

41

Mo

de

l clim

ate

=2

75

An

aly

sis

clim

ate

=2

5 F

ore

ca

st

init

ial d

ate

: 2

00

8 2

01

Eq

ua

tori

al S

ou

the

rn O

sc

illa

tio

n

Feb

ruar

yM

arch

Apr

ilM

ayJu

neJu

lyA

ugus

t

-6

.3

-5.0

-3.8

-2.5

-1.3

-0.0

1.3

2.5

3.8

5.0

En

se

mb

le s

ize

: F

ore

ca

st=

41

Mo

de

l clim

ate

=2

75

An

aly

sis

clim

ate

=2

5 F

ore

ca

st

init

ial d

ate

: 2

00

8 2

01

Eq

ua

tori

al S

ou

the

rn O

sc

illa

tio

n

Feb

ruar

yM

arch

Apr

ilM

ayJu

neJu

lyA

ugus

t

-6

.3

-5.0

-3.8

-2.5

-1.3

-0.0

1.3

2.5

3.8

5.0

En

se

mb

le s

ize

: F

ore

ca

st=

41

Mo

de

l clim

ate

=2

75

An

aly

sis

clim

ate

=2

5 F

ore

ca

st

init

ial d

ate

: 2

00

8 2

01

Eq

ua

tori

al S

ou

the

rn O

sc

illa

tio

n

Feb

ruar

yM

arch

Apr

ilM

ayJu

neJu

lyA

ugus

t

-6

.3

-5.0

-3.8

-2.5

-1.3

-0.0

1.3

2.5

3.8

5.0

En

se

mb

le s

ize

: F

ore

ca

st=

41

Mo

de

l clim

ate

=2

75

An

aly

sis

clim

ate

=2

5 F

ore

ca

st

init

ial d

ate

: 2

00

8 2

01

Eq

ua

tori

al S

ou

the

rn O

sc

illa

tio

n

Feb

ruar

yM

arch

Apr

ilM

ayJu

neJu

lyA

ugus

t

-6

.3

-5.0

-3.8

-2.5

-1.3

-0.0

1.3

2.5

3.8

5.0

En

se

mb

le s

ize

: F

ore

ca

st=

41

Mo

de

l clim

ate

=2

75

An

aly

sis

clim

ate

=2

5 F

ore

ca

st

init

ial d

ate

: 2

00

8 2

01

Eq

ua

tori

al S

ou

the

rn O

sc

illa

tio

n

Feb

ruar

yM

arch

Apr

ilM

ayJu

neJu

lyA

ugus

t

-6

.3

-5.0

-3.8

-2.5

-1.3

-0.0

1.3

2.5

3.8

5.0

En

se

mb

le s

ize

: F

ore

ca

st=

41

Mo

de

l clim

ate

=2

75

An

aly

sis

clim

ate

=2

5 F

ore

ca

st

init

ial d

ate

: 2

00

8 2

01

Eq

ua

tori

al S

ou

the

rn O

sc

illa

tio

n

February March April May June July August -6.3

-5.0

-3.8

-2.5

-1.3

-0.0

1.3

2.5

3.8

5.0

Ensemble size: Forecast=41 Model climate=275 Analysis climate=25 Forecast initial date: 2008 201Equatorial Southern Oscillation


-5.0

-3.8

-2.5

-1.3

-0.0

1.3

2.5

3.8

5.0



-5.0

-3.8

-2.5

-1.3

-0.0

1.3

2.5

3.8

5.0



-5.0

-3.8

-2.5

-1.3

-0.0

1.3

2.5

3.8

5.0


Predictability barrier


Climagrams : area-averages of 2mT and rainfall


-1.0

-0.7

-0.3

0.0

0.3

0.7

1.0

1.4

1.7

Ensemble size: Forecast=41 Model climate=275 Analysis climate=25 Forecast initial date: 2008 201 2m temp. anomalies (K) latitude= 10.0 to -12.5 longitude= 285.0 to 310.0


-1.0

-0.7

-0.3

0.0

0.3

0.7

1.0

1.4

1.7



-1.0

-0.7

-0.3

0.0

0.3

0.7

1.0

1.4

1.7



-1.0

-0.7

-0.3

0.0

0.3

0.7

1.0

1.4

1.7


2m Temperature Amazones

!"##$% %$#&'( #& #&$#

& )#*#*"

!""#$ $# "%&' "% "%#"

% ()"*("*!)

Anomaly Correlation Temperature

Anomaly Correlation Precipitation





!"##$% %$#&'( #& #&$#

& )#*#*"

!""#$ $# "%&' "% "%#"

% ()"*("*!

North-East

Brasil

Target month is more predictable

Feb/March as a Window of

predictability





South America

Atlantic Coast

! "#$$%&!&%$'()!$'!$'%$

' $*$*#*

!!"##$%!!%$!#&'(!#&!#&$#

& )#*)#*"


Forecast System is not reliable:

RMS > Spread

To calibrate the model output

To sample model error (multi-model): EUROSIP

Both

A. Can we reduce the error? How much? (Predictability limit)

Is the ensemble spread sufficient? Are the forecast reliable?

B. Can we increase the spread by improving the ensemble generation?


Anomaly correlation of seasonal-mean rainfall

Franco Molteni


Can we predict tropical rainfall anomalies?


Prediction of All India Rainfall: EOF filtered fc. in JAS

CC = .50

Franco Molteni


Prediction of All India Rainfall

JJASCC = .25

JASCC = .46


Prediction of East Africa short rains: OND from Aug.

Unfiltered fc. : CC = 0.04 EOF-filt. : CC = 0.42

Franco Molteni


Persistence

ECMWF

ensemble spread

RMS error of Nino3 SST anomalies

EUROSIP

EUROSIP

ECMWF-UKMO-MeteoFrance

Sampling model error: The Real Time Multimodel


TROPICAL CYCLONES

80°S80°S

70°S 70°S

60°S60°S

50°S 50°S

40°S40°S

30°S 30°S

20°S20°S

10°S 10°S

0°0°

10°N 10°N

20°N20°N

30°N 30°N

40°N40°N

50°N 50°N

60°N60°N

70°N 70°N

80°N80°N

20°E

20°E 40°E

40°E 60°E

60°E 80°E

80°E 100°E

100°E 120°E

120°E 140°E

140°E 160°E

160°E 180°

180° 160°W

160°W 140°W

140°W 120°W

120°W 100°W

100°W 80°W

80°W 60°W

60°W 40°W

40°W 20°W

20°W

14.3 10.39.8 13.325.1 26.22 2.9

No Significance 90% Significance 95% Significance 99% Significance

Ensemble size = 40,climate size = 70Forecast start reference is 01/06/2005Tropical Storm FrequencyECMWF Seasonal Forecast

Significance level is 90%JASON

FORECAST CLIMATE

80°S80°S

70°S 70°S

60°S60°S

50°S 50°S

40°S40°S

30°S 30°S

20°S20°S

10°S 10°S

0°0°

10°N 10°N

20°N20°N

30°N 30°N

40°N40°N

50°N 50°N

60°N60°N

70°N 70°N

80°N80°N

20°E

20°E 40°E

40°E 60°E

60°E 80°E

80°E 100°E

100°E 120°E

120°E 140°E

140°E 160°E

160°E 180°

180° 160°W

160°W 140°W

140°W 120°W

120°W 100°W

100°W 80°W

80°W 60°W

60°W 40°W

40°W 20°W

20°W

15 10.38.8 13.327.4 26.23 2.9


Ensemble size = 41,climate size =225Forecast start reference is 01/06/2005Tropical Storm FrequencyMet Office Seasonal Forecast


FORECAST CLIMATE

Forecasts starting on 1st June 2005: JASON

80°S80°S

70°S 70°S

60°S60°S

50°S 50°S

40°S40°S

30°S 30°S

20°S20°S

10°S 10°S

0°0°

10°N 10°N

20°N20°N

30°N 30°N

40°N40°N

50°N 50°N

60°N60°N

70°N 70°N

80°N80°N

20°E

20°E 40°E

40°E 60°E

60°E 80°E

80°E 100°E

100°E 120°E

120°E 140°E

140°E 160°E

160°E 180°

180° 160°W

160°W 140°W

140°W 120°W

120°W 100°W

100°W 80°W

80°W 60°W

60°W 40°W

40°W 20°W

20°W

20.4 11.67.8 12.516.6 21.22.5 2.5


Ensemble size = 41,climate size = 55Forecast start reference is 01/06/2005Tropical Storm FrequencyMétéo-France Seasonal Forecast


FORECAST CLIMATE

ECMWF Met Office

Meteo-France

Obs July-November

AtlW-Pac E-Pac

80°S80°S

70°S 70°S

60°S60°S

50°S 50°S

40°S40°S

30°S 30°S

20°S20°S

10°S 10°S

0°0°

10°N 10°N

20°N20°N

30°N 30°N

40°N40°N

50°N 50°N

60°N60°N

70°N 70°N

80°N80°N

20°E

20°E 40°E

40°E 60°E

60°E 80°E

80°E 100°E

100°E 120°E

120°E 140°E

140°E 160°E

160°E 180°

180° 160°W

160°W 140°W

140°W 120°W

120°W 100°W

100°W 80°W

80°W 60°W

60°W 40°W

40°W 20°W

20°W

17.4 11.68.7 12.520.6 21.22.4 2.5

No Significance Sig at 10% level Sig at 5% level Sig at 1% level

Ensemble size =120,climate size =165Forecast start reference is 01/06/2005Tropical Storm FrequencyEUROSIP multi-model seasonal forecast


ECMWF/Met Office/Météo-France

FORECAST CLIMATE

0

5

10

15

20

25

30

Multi-model

1987-2004

2005

Frederic Vitart


MULIMODEL: EUROSIP

JUN2006

JUL AUG SEP OCT NOV DEC JAN2007

FEB MAR APR MAY JUN JUL AUG-2

-1

0

1

2

3

Ano

mal

y (d

eg C

)

-2

-1

0

1

2

3Monthly mean anomalies relative to NCEP adjusted OIv2 1971-2000 climatology

ECMWF forecast from 1 Dec 2006NINO3 SST anomaly plume

Produced from real-time forecast data

System 3

JUN2006

JUL AUG SEP OCT NOV DEC JAN2007

FEB MAR APR MAY JUN JUL AUG

-1

0

1

2

3

Ano

mal

y (d

eg C

)

-1

0

1

2

3

Monthly means plotted using NCEP adjusted OIv2 1971-2000 climatologyECMWF, Met Office, Météo-France

EUROSIP multi-model forecast from 1 Dec 2006NINO3 SST anomaly plume

Forecast issue date: 15 Dec 2006

Multi-model anomalies

MAY2007

JUN JUL AUG SEP OCT NOV DEC JAN2008

FEB MAR APR MAY JUN JUL

-4

-3

-2

-1

0

Ano

mal

y (d

eg C

)

-4

-3

-2

-1

0


EUROSIP multi-model forecast from 1 Nov 2007NINO3 SST anomaly plume

Forecast issue date: 15 Nov 2007


JUL2007



-4

-3

-2

-1

0

Ano

mal

y (d

eg C

)

-4

-3

-2

-1

0


EUROSIP multi-model forecast from 1 Jan 2008NINO3 SST anomaly plume

Forecast issue date: 15 Jan 2008


But sometimes the spread with EUROSIP is too large!!ECMWF

MULTI-MODEL


Bayesian Calibration of the Nino Indices:

• Based on the Forecast Assimilation Framework

It will produce a revised mean and variance

• Specific Ingredients:

1. Take into account that error in the models can be correlated (remove correlation from errors, not from the signal, by doing SVD of error covariance matrix)

2. Model for the errors:

3. Given the mean and variance, produce the individual plumes

2 2 2 2

2

2

2

of past performance

spread

i o Ei Si

o

Ei

Si

a b c

const

estimation

ensemble


EUROSIP: Bayesian Combination

MAY2007



-4

-3

-2

-1

0

Ano

mal

y (d

eg C

)

-4

-3

-2

-1

0





MAY2007



-2

-1

0

1

2

Ano

mal

y (d

eg C

)

-2

-1

0

1

2




Bayesian calibration

JUL2007



-4

-3

-2

-1

0

Ano

mal

y (d

eg C

)

-4

-3

-2

-1

0


EUROSIP multi-model forecast from 1 Jan 2008NINO3 SST anomaly plume



JUL2007


FEB MAR APR MAY JUN JUL AUG SEP-3

-2

-1

0

1

2

Ano

mal

y (d

eg C

)-3

-2

-1

0

1

2Monthly means plotted using NCEP adjusted OIv2 1971-2000 climatology

ECMWF, Met Office, Météo-FranceEUROSIP multi-model forecast from 1 Jan 2008

NINO3 SST anomaly plume


Bayesian calibration


Sampling model error: The Real Time Multimodel

Persistence

ECMWF

ensemble spread

RMS error of Nino3 SST anomalies

Bayesian Calibration

EUROSIP

EUROSIP

ECMWF-UKMO-MeteoFrance


Conclusions

• The new ECMWF seasonal forecast system-3 gives improved

predictions of tropical/summer variability respect the previous system.

• SST predictions are good in the tropical Pacific and eastern Indian Oc.,

but western Indian Oc. and tropical Atlantic are not better than

persistence in NH summer.

• Difficulty in getting the correct rainfall variability over land. Predictive

skill over land can be improved by exploiting teleconnections

(calibration)

• The Multi-Model (EUROSIP) provides skilful predictions of tropical

storms. In general it improves reliability, but sometimes the spread is

too large

• Bayesian Calibration can improve the products, but attention should be

paid to the estimation of the model error (sensitive to sampling size)


Interannual variability of tropical storms in EURO-SIP

Forecasts issued in June for the period July-NovemberCorrelation: 0.72 RMS error: 2.93

Frederic Vitart

Documents

EUROBRISA WORKSHOP, Paraty 17-19 March 2008, ECMWF System 3 1 The ECMWF Seasonal Forecast System-3 Magdalena A. Balmaseda Franco Molteni,Tim Stockdale