2. SAR for offshore wind energy
Tobias Ahsbahs1, Charlotte Hasager1, Caroline Draxl2, Galen MacLaurin2, Alex Newcombe3, Nicolai G. Nygaard3, and Merete Badger1
1) Technical University of Denmark, Wind Energy Risø, Roskilde, Denmark
2) National Renewable Energy Laboratory, Golden, Colorado, USA
3) Ørsted Wind Power, Fredericia, Denmark

3. Outline Offshore wind – some key numbers
Application examples SAR for wind energy
Wind resource variation
Wind farm wake detection

4. Development of offshore wind farms in Europe
Offshore wind is growing fast Larger wind farms New markets (US, Taiwan, China…)

5. Where are offshore wind farms planned?
Europe:
North Sea
Baltic Sea
Close to shore
USA:
East Coast

6. What makes SAR wind fields attractive?
High spatial resolution
Large swath widths
Open data access via Copernicus

7. SAR wind archive at DTU Wind Energy
SAR winds available at:
NRT processing of Sentinel-1 and archive of Envisat

8. SAR for wind resource assessment
Wind climatology (wind atlas) from SAR wind fields
High accuracy needed: 𝐸𝑛𝑒𝑟𝑔𝑦 ~ 𝑈 3
Long time series necessary
Use multiple sensor (Envisat, Radarsat-1, Sentinel-1A, and B)
Need to inter-calibrate for consistency (Badger et. al .(2019), in review)

9. WRF SAR Mean wind speed maps US East Coast
25 designated areas
Meso-scale model WRF is available for planning of wind farm projects.
Satellite (SAR) and meso-scale model (WRF) based mean wind maps.
Gradients in the mean wind speed with the distance to shore.
More spatial variability in the mean wind speed from SAR.
WRF data from WIND Toolkit Draxl et.al. (2015)
SAR derived mean wind speed map of the US East Coast (a)
Mean wind speed from 7 year WRF model (b)
Increase of wind speed with distance to shore
How is this useful for wind energy?
To be published: Ahsbahs et.al. (2019)

10. Variation of wind resources within each wind farm
SAR
WRF
Extract mean wind speed within each potential wind farm area
Larger variation of wind resources from SAR than model
Hint towards underestimating spatial variability of wind resources?
What is shown?
Violin plot
X-axis: different potential wind farm locations
Y-axis: mean wind speed
Distributions of SAR and WRF mean wind speeds over the region
What does this mean?
Span of mean wind speeds higher from SAR
Explanation: Higher resolution SAR, edges and artefacts, lower sampling
=> SAR likely upper boundary
To be published: Ahsbahs et.al. (2019)

11. Wind turbine and farm wakes
What is the wind farm wake?
Energy extracted
Decreased wind speed
Increased turbulence
Wind farm wakes in SAR:
SAR observes the sea surface
Wakes at hub height
vertical extrapolation
U(z)

12. SAR wind fields in the presence of wind farm wakes
Wind direction
English East Coast
Wind farms
Wakes visible
Wind farms
SAR image of the English East Coast (27th of April 2018)
Easterly wind direction
Wakes clearly visible as reduced wind speed
But:
Can structures in the wake be resolved?
Is the wind retrieval accurate under wakes conditions?

13. Large scale reference data in the wake – the BEACon experiment
Two Doppler radars
Large scale measurements
Collocated SAR images
Doppler radar wind measurements provided by Ørsted’s BEACon experiment

14. Large scale reference data in the wake – the BEACon experiment
SAR
Doppler radar
SAR backscatter image (a)
Wind field at 100 m (b)
Features of the wake similarly present
Singular wakes detectable in SAR with correct position
Velocity deficit downstream (c)
Observation:

15. Large scale reference data in the wake – the BEACon experiment
Velocity deficit characterizes reduced winds in the wake. Structure of the wake similar in SAR and Doppler radar. First comparison at full scale wind farm.
Features of the wake similarly present
Singular wakes detectable in SAR with correct position

16. Summary High resolution wind resource maps
Identify variation in wind resources
Compare with meso-scale model outputs
Wind farm wakes offshore
Wakes are visible in SAR images
Structure of the wind farm wake can also be observed

17. Challenges
SAR observation at the surface vs. atmospheric processes higher in the atmosphere.
Wind turbines operate up to 250m height
Limited sampling rate and sampling biases from SAR.
The interaction of wind farm wakes with the ocean surface needs to be better understood.

18. Acknowledgements
Satellite SAR data from the ESA, Copernicus, and NOAA.
The SAR Ocean Products System (SAROPS) by the Johns Hopkins University, Applied Physics Laboratory and the US National Atmospheric and Oceanographic Administration (NOAA).
This work received funding from the EU H2020 program under grant agreement no (CEASELESS project)
Access to Doppler radar wind observation from the BEACon experiment provided by Ørsted

19. References
Draxl, C., Clifton, A., Hodge, B., McCaa, J.: The Wind Integration National Dataset ( WIND ) Toolkit, Appl. Energy, 151(August), 355–366, doi: /j.apenergy , 2015.
Ahsbahs, T., Maclaurin, G., Draxl, C, Jackson, C., Monaldo, F., Badger, M.: US East Coast synthetic aperture radar wind atlas for offshore wind Energy, submitted to Wind Energy Science.
Badger, M., Ahsbahs, T., Maule, P., Karagali, I. Inter-calibration of SAR data series for offshore wind resource assessment, Remote Sensing of Environment (in review), 2019