1. Comparison of wind climatology from dynamical downscaling with other methodologies. E. Avolio 1,2, S. Federico 1, Claudia Calidonna 1, C.Transerici 1, Birgitte Furevik 3 Anna M. Sempreviva 1,4, 1. Institute of Atmospheric Sciences and Climate, ISAC-CNR, Lamezia Terme, Italy 2. CRATI s.c.r.l., Lamezia Terme, Italy 3. Norwegian Meteorological office, met.no, Bergen, Norway 4. DTU, Wind Division, Risoe Campus, Roskilde, Denmark Lamezia Terme OFFSHORE WIND RESOURCE ASSESSMENT IN THE CENTRAL MEDITERRANEAN AREA.

2. Motivation Wind resources assessment methodologies Dynamical downscaling Results and comparisons Concluding remarks Outline of the presentation

3. Also  Coastal waters are deep with environmental constraints.  There is strong need of reliable data: Buoys are sparse and with missing data Vertical wind profiles are missing  Coastal methodologies tested in the North Sea can not be applied due to Stability effects and Sea-Breeze recirculation FP7 EU ORECCA PROJECT Offshore Renewable Energy Conversion – Coordination Action www.orecca.euwww.orecca.eu BUT THERE ARE OPPORTUNITIES:  Technological progress in buoyant deep offshore wind farms  Less harsh meteorological conditions Motivations : Challenges in the Mediterranean QUESTION: How the Mediterranean wind potential compares to the North Sea? ANSWER: The Mediterranean is less windy than the North Sea.

4. 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? Develop methodologies to generate data Motivations Plan local measurement For N years Cost money and TIME!!

5. 1. STATISTICAL : Correlations with coastal data 2. DIAGNOSTIC MODELS: WAsP 3. DATABASES FROM ANALYSIS O RE-ANALYSIS PROGRAMMES  ECMWF (EUROPEAN) i.e. ERA - 40  NCEP-NCAR (USA) 50 years 4. SPACE-BORN Observations 5. DYNAMICAL DOWNSCALING FROM GENERAL CIRCULATION MODELS Methodologies

6. Advantages: Spatial and temporal coverage Spatial resolution: 10 - 2 km Weak points: Super-computer and skilled staff needed! Uncertainties under evaluation, need data Modeling: Dynamical downscaling THE RAMS MODEL: 30 years (1975-2004)

7. Annual average wind speed: 80 mAnnual average wind speed: 150 m Meand Wind Climatology 80m 150m

8. MODEL: Intra - annual Cycle

9. IA=IA= year Total Period North GRID South GRID MODEL: Inter - annual Indices ±12 %

10. ► Availability: 10 Years, 01/08/1999 - 31/10/2009 ► Two passes per day ► Wind speed U retrieved from radar backscatter at surface and extrapolated to 10 m using a neutral log profile Space-born remote sensing: QuikSCAT SeaWinds microwave radar onboard QuikSCAT:

11. 11 QuikScat mean wind speed (m s −1 ) (2000–2007) and mean wind direction. The wind speeds are reduced using the correction of the ECMWF for winds above 19 m s −1 B.R. Furevik, A. M. Sempreviva, L. Cavaleri., J.M. Lefèvre, C. Transerici, “Eight years of wind measurements from scatterometer for wind resource mapping in the Mediterranean Sea”, Wind Energ. (2011), DOI: 10.1002/we QuikSCAT: Offshore Wind Map

12. 0.65 <R < 0.75 0.75< R<0.85 R>0.85 Correlation RAMS – QuikSCAT

13. Comparisons Indices RAMS – Observation Intra-annual indices: Venice, Ustica, and Lampedusa Intra-annual indices month Yearly Total Im=Im= Intra-annual index

14. Comparisons RAMS – data at Lampedusa Island

15. An example: Venice Offshore Research Platform, Adriatic Sea Lavagnini A., A.M. Sempreviva and R. Barthelmie, Wind Energy 2002 Statistical Methodologies: coastal to offshore Sea Breeze effect

16. Diagnostic models : 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 The long-term site is the predictor The short-term site is the predictand.

17. WAsP @ Venice: Effect of time series length

18.  Inter-annual variation is ± 10-12%  QuikSCAT might overestimate the wind speed  This dataset could be seen in a “climatological” perspective: Intra-annual and inter-annual indices for evaluating climate projections Single out areas with same wind climatology. For high resolution modeling  Work in progress:  Extending the RAMS with the ERA interim to present;  Extreme wind analysis  Collecting new data from buoys THANK YOU FOR YOUR ATTENTION Concluding remarks