Anna M. Sempreviva1,3

Rebecca Barthelmie2, Gregor Giebel3, Bernard Lange4 and Abha Sood5

(1) Institute of Atmospheric Sciences and Climate, ISAC-CNR, Rome, Italy

(2) Institute for Energy Systems, The University of Edinburgh, Scotland

(3) Risoe National Laboratory, Department of Wind Energy, Roskilde, Denmark

(4) ISET, University of Kassel, Germany

(5) FORWIND, University of Oldenburg, Germany

Offshore wind resource assessment in European Seas

A survey within the FP6 POW’WOW Project

EGU06-A-04593EGU06-A-04593 POW’WOW – A Coordination Action on POW’WOW – A Coordination Action on ERE1-1FR2P-0270ERE1-1FR2P-0270 Prediction Of Waves, Wakes and Offshore WindPrediction Of Waves, Wakes and Offshore WindGregor Giebel, Risø National Laboratory, DK-4000 Roskilde. Rebecca Barthelmie University of Edinburgh; Anna Maria Sempreviva CNR-ISAC; Pierre Pinson, Henrik Madsen, Technical University of Denmark; Ignacio Martí Perez, CENER; Georges Kariniotakis, Armines; Ismael Sanchez, Julio Usaola,

Universidad Carlos III de Madrid; Lueder v. Bremen, Abha Sood, Carl v.Ossietzky Universität Oldenburg; Uli Focken, Matthias Lange, energy & meteo sys.; Bernhard Lange, ISET; George Kallos, IASA; Teresa Pontes, INETI; Katarzyna Michalowska,

ECBREC-IEO; Alexandre de Lemos Pereira, Pedro Rosas, Universidade Federal de Pernambuco.

ObjectivesThis poster describes a coordination action project harmonising approaches to wave and wind modelling offshore, helping

the short-term forecasting and wake research communities by establishing virtual laboratories, offering specialised workshops, and setting up expert groups with large outreach in the mentioned fields.

Vi-labsData needed: Numerical Weather Predictions,

evaluation of short-term forecasts (i.e. wind farm), offshore wake data

Offered: Compare your short-term forecasts or wake model performance

Contact: gregor.giebel@risoe.dk

WorkshopsWakes, Oldenberg 2008

Offshore meteorology at EWTEC Lisbon 2007 Short-term prediction: Dispatcher training (TBA,

2007)POW’WOW and Anemos workshop on short-

term prediction experiences, Delft 2006.

Expert groupsWe welcome your participation in the expert group on short-term forecasting or offshore

meteorology (wind and wave)Contact: gregor.giebel@risoe.dk

The project is funded by the European Commission (019898(SES6)).

Noticeboard

Short-term forecasting

Wakes

Offshore meteorology

Dissemination

pow

wow

.ris

oe.d

k

The issue

Are at least 5 year local wind data available?

To design a wind farm the local wind climatology is needed

YES NO

So far so good What shall we do?

Plan local measurementFor N years

Cost money and TIME!!

To develop new methodologies

to generate data

Alternatives

1. Statistical Methods

mainly correlations with coastal data (data or Weibull parameters)

2. Methods based on diagnostic models WAsP - Coastal Discontinuity Model

- Geo WAsP 3. Analysis o re-analysis programmes databases

- ECMWF (EUROPEAN) ERA-15 or ERA-40 - NCEP-NCAR (USA) 50 years

4. Downscaling from General Circulation Models

5. Climate Models 6. Satellite data

1. Statistical Methodologies

1.Venezia Plat 15 76-82

2.Venezia Tess. 10 61-96

3.Venezia S.N. 10 51-77

4.Rimini 10 51-96

5.Ronchi 10 67-96

Station PeriodHeight anemometer

CASE STUDY: OCEANOGRAPHIC Platform Offshore Venice in the Adriatic Sea

The long-term site is the predictor

The short-term site is the predictand.

Sector-wise correlationsV

en

ezi

a T

es

se

ra

Rim

ini

Platform Platform

WAsP

Obstacles

Roughness

Orography

© Risø

Local Wind Climate

WIND ATLAS DATA

WAsPExtrapolate

aboveand

Clean up local effect

PredictorStation WAsP

Extrapolate at ground

andre-introduce local effect

0 60 120 180 240 300Sectors

0

8

16

24

f (%

)

2

4

6

8

M (

m/s

)

W AsP VT 7 years

Platform data

0 60 120 180 240 300Sectors

0

8

16

24

f (%

) 2

4

6

8

M (

m/s

)

W AsP VT 35 years

Platform data

WAsP: Effect of long term Climatology

Coastal Discontinuity Model (CDM) developed as part of the EC “POWER” project.

The CDM works in a slightly different way to WAsP It uses air and sea temperature, geostrophic wind

speed time series (input data are six-hourly) over a 1x1º grid

It calculates the stability parameter (the Monin-Obukhov length) for each grid point at each time step

Equilibrium land and sea wind speed profiles are corrected for stability.

Uses the fetch distance to land to determine the internal boundary layer height accounting for the discontinuity caused in the profile by the IBL.

Effect of atmospheric stability on the vertical wind profile

6 6.4 6.8 7.2 7.6 8W ind speed (m /s)

10

100

He

igh

t (m

)

CD MLogarithm ic

Comments

• All these methodologies/models must only be applied if the coastal stations are in the same regional geostrophic area the offshore site.

• There is still needs to develop and verify methodologies:

Missing data, calms, stability effects are major issues

• Stability effects and Sea-Breeze recirculation are important

• Increasing the length of the climatology is still an issue to take into account

Global reanalysis data NCEP/NCAR

Mean wind speed [ms-1] Resolution: 2.5 degree (~275 km)

Mapping by meso-scale modelling

• Mesoscale model

• Output: annual averages of wind speed and power

• Regular horizontal grid

• Area: 10,000’s of km2

• Resolution: 3-5 km

• Met. measurements are not required but…..

• Super-computer and skilled staff needed!

• Uncertainty usually larger than observational wind atlas

ECMWF Re-analysis Limited Area Models – Q-BOLAM

A model can run for a number of overlapping years

i.e. Ratio QBOLAM/ECMWF, over 2 Years

Q-Bolam at 10x10 km grid

- 5 0 5 1 0 1 5 2 0 2 5 3 0 3 5

L o n g . ( d e g )

3 0

3 5

4 0

4 5

Lat.(

deg.

)

0 . 8 1 1 . 2 1 . 4 1 . 6 1 . 8

- 5 0 5 1 0 1 5 2 0 2 5 3 0 3 53 0

3 5

4 0

4 5

Numerical wind atlas

• Mesoscale modellingNCEP/NCAR reanalysis data

+ roughness + elevation map Predicted Wind Climate

• Analysis procedure (WAsP-like)Predicted Wind Climate

+ terrain descriptions Regional Wind Climate

• Application procedure (WAsP)Regional Wind Climate

+ terrain descriptions Predicted Wind Climate

The KAMM-WAsP methodology

Met.station data

Offshore wind resource assessment with WAsP and MM5:

German Bight.

© Risø

ForWind, University of Oldenburg, Germany.

Deutsches Windenergie-Institut, Germany.

ISET, UniKassel, Germany

ECMWF: Analyses 1999-2005, 50x50 km

LMDz: GCM, year 2000, downscaled to 80x80 km,

GeoWAsP: Surface Pressure from ECMWF, use of WAsP, 50x50 km, 1984 – 1997

QuikSCAT SeaWinds:

► 1999 - 2005 ► Scatterometer, 25 x 25km spatial resolution, ► Gridded to a 0.25 deg grid for the Mediterraneum

► U at 10 m retrieved from radar backscatter values assuming a neutral log profile

ESTIMATING OFFSHORE WINDCLIMATOLOGY IN THE MEDITERRANEAN AREA,

COMPARISON OF QuikSCAT DATA WITH MODELS

Comparisons data methodologiesSeasonal variations

L EGEN D in th e POSTERECMWF

Geo-WAsP

Q-Scat

DATA

1 2 3 4SEASO N

2

6

U (

ms-

1 )

3 4 N 2 3 E

1 2 3 4SEASO N

2

4

6

8U

(m

s-1 )

3 7 N 1 2 E

1 2 3 4SEASO N

2

4

6

8

U (

ms-

1 )

3 7 N 0 0 E

1 2 3 4SEASO N

2468

10

U (

ms-

1 )

4 0 N 0 5 E

1 2 3 4SEASO N

2

4

6

8U

(m

s-1 )

3 8 N 2 0 E

1 2 3 4SEASO N

2

4

6

8

U (

ms-

1 )

4 3 .5 N 1 5 E

1 2 3 4SEASO N

2

4

6

8

U (

ms-

1 )

4 2 N 1 7 E

1 2 3 4SEASO N

2

4

6

8

U (

ms-

1 )

3 8 N 1 7 E

1 2 3 4SEASO N

2

4

6

8U

(m

s-1 )

4 0 N 1 3 E

1 2 3 4SEASO N

4

6

8

U (

ms-

1 )

3 5 N 1 7 E

1 2 3 4SEASO N

4

6

8

U (

ms-

1 )

3 5 N 2 0 E

Comparison of seasonal U variation from three methodologies

1 2 3 4SEASO N

4

6

8

U (

ms-

1 )

4 0 N 0 7 E

1 2 3 4SEASO N

2

4

6

8

U (

ms-

1 )

3 8 N 0 9 E

Comparisons

0 5 10 15 20 25Long. (deg.)

32

37

42

Lat

. (d

eg.)

C o m p a riso n o f w ind fre q ue nc y d istrib utio ns fro m 3 m e tho d o lo g ie s

03 0

6 0

9 0

1 2 0

1 5 01 8 0

2 1 0

2 4 0

2 7 0

3 0 0

3 3 0

0 1 0 2 00

3 0

6 0

9 0

1 2 0

1 5 01 8 0

2 1 0

2 4 0

2 7 0

3 0 0

3 3 0

0 2 0

03 0

6 0

9 0

1 2 0

1 5 01 8 0

2 1 0

2 4 0

2 7 0

3 0 0

3 3 0

0 1 0 2 0

03 0

6 0

9 0

1 2 0

1 5 01 8 0

2 1 0

2 4 0

2 7 0

3 0 0

3 3 0

0 1 0 2 0

03 0

6 0

9 0

1 2 0

1 5 01 8 0

2 1 0

2 4 0

2 7 0

3 0 0

3 3 0

0 1 0 2 0

03 0

6 0

9 0

1 2 0

1 5 01 8 0

2 1 0

2 4 0

2 7 0

3 0 0

3 3 0

0 1 0 2 0

03 0

6 0

9 0

1 2 0

1 5 01 8 0

2 1 0

2 4 0

2 7 0

3 0 0

3 3 0

0 1 0 2 0 3 0

03 0

6 0

9 0

1 2 0

1 5 01 8 0

2 1 0

2 4 0

2 7 0

3 0 0

3 3 0

0 1 0

03 0

6 0

9 0

1 2 0

1 5 01 8 0

2 1 0

2 4 0

2 7 0

3 0 0

3 3 0

0 1 0 2 0

03 0

6 0

9 0

1 2 0

1 5 01 8 0

2 1 0

2 4 0

2 7 0

3 0 0

3 3 0

1 0 2 0

03 0

6 0

9 0

1 2 0

1 5 01 8 0

2 1 0

2 4 0

2 7 0

3 0 0

3 3 0

0 1 0 2 0

030

60

90

120

150180

210

240

270

300

330

0 10 20

43.5N 15.0E

030

60

90

120

150180

210

240

270

300

330

0 10 20

42.0N 17.0E

030

60

90

120

150180

210

240

270

300

330

0 10 20

44.5N 12.5E

L EGEN D in th e POSTERECMWF

Geo-WAsP

Q-Scat

DATA

Unsolved issues

• Use of Satellite data is an added value

• Computer power

• Need for better model resolution to resolve coastal zones and enclosed seas