1. Anna M. Sempreviva 1,3 Rebecca Barthelmie 2, Gregor Giebel 3, Bernard Lange 4 and Abha Sood 5 (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

2. EGU06-A-04593 POW’WOW – A Coordination Action on ERE1-1FR2P-0270 Prediction Of Waves, Wakes and Offshore Wind Gregor 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. Objectives This 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-labs Data 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 Workshops Wakes, 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 groups We 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 powwow.risoe.dk

3. 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 measurement For N years Cost money and TIME!! To develop new methodologies to generate data

4. 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

5. 1. Statistical Methodologies

6. CASE STUDY: OCEANOGRAPHIC Platform Offshore Venice in the Adriatic Sea The long-term site is the predictor The short-term site is the predictand.

7. Sector-wise correlations Venezia Tessera Rimini Platform

8. WAsP Obstacles Roughness Orography © Risø Local Wind Climate WIND ATLAS DATA WAsP Extrapolate above and Clean up local effect Predictor Station WAsP Extrapolate at ground and re-introduce local effect

9. WAsP: Effect of long term Climatology

10. 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.

11. Effect of atmospheric stability on the vertical wind profile

12. 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

13. Global reanalysis data NCEP/NCAR Mean wind speed [ms -1 ] Resolution: 2.5 degree (~275 km)

14. Mapping by meso-scale modelling Mesoscale model Output: annual averages of wind speed and power Regular horizontal grid Area: 10,000’s of km 2 Resolution: 3-5 km Met. measurements are not required but….. Super-computer and skilled staff needed! Uncertainty usually larger than observational wind atlas

15. 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

16. Numerical wind atlas Mesoscale modelling NCEP/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

17. The KAMM-WAsP methodology Met.station data

18. Offshore wind resource assessment with WAsP and MM5: German Bight. © Risø ForWind, University of Oldenburg, Germany. Deutsches Windenergie-Institut, Germany. ISET, UniKassel, Germany

19. 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 WIND CLIMATOLOGY IN THE MEDITERRANEAN AREA, COMPARISON OF QuikSCAT DATA WITH MODELS

20. Comparisons data methodologies Seasonal variations Comparison of seasonal U variation from three methodologies

21. Comparisons

22. Unsolved issues Use of Satellite data is an added value Computer power Need for better model resolution to resolve coastal zones and enclosed seas