1. S CHOOL of G EO S CIENCES Offshore wind mapping using synthetic aperture radar and meteorological model data Iain Cameron David Miller Nick Walker Iain Woodhouse

2. S CHOOL of G EO S CIENCES Offshore Wind UK has largest offshore wind resource in EU 10 km off shore ~ 25% more energy than on land BUT more expensive than land based technology Accurate understanding of wind resource vital Blade Diameter 90m Tower Height 80m 2 MW Vestas

3. S CHOOL of G EO S CIENCES Overview Retrieved Wind Synthetic Aperture Radar Data UKMO Unified Mesoscale Model (UMM) SAR Wind Inversion Analysis cells Hilbre Island

4. S CHOOL of G EO S CIENCES Retrieved Wind SAR Scenes UMM Data Inversion Envisat ASAR ASAR (advanced SAR C-band (5.6 cm λ, 5.3 GHz) Multiple modes of operation Image mode –High res (12.5 m 2 ) –Low repeat time (~25 days) Wide swath –Medium resolution (75 m 2 ) –High repeat time (~3-5 days)

5. S CHOOL of G EO S CIENCES Apriori Data Retrieved Wind SAR Scenes UMM Data Inversion UKMO Unified Mesoscale Model (UMM) 6 hourly analysis levels –Interpolated to SAR time –Interpolated to ~2.5 km Grid

6. S CHOOL of G EO S CIENCES Forward Model Retrieved Wind SAR Scenes UMM Data Inversion NRCS (σ 0 ) Wind direction + Wind speed GMF = CMOD5

7. S CHOOL of G EO S CIENCES Model Inversion Retrieved Wind SAR Scenes UMM Data Inversion a) Image Directions Roll vortices/streaks Fourier, wavelet, Sobel filters, cross spectra analysis Not visible in all scenes (~60% of cases) b) NWP winds Always available Poor resolution –Spatial (0.125 deg) –Temporal (every 6 hrs)

8. S CHOOL of G EO S CIENCES Model Inversion Retrieved Wind SAR Scenes UMM Data Inversion Retrieves wind speed assuming NWP wind direction is true Problems Assumes SAR variation only due to wind speed changes Doesn’t account for known retrieval errors 1) “Directional Wind Speed Algorithm” (DWSA)

9. S CHOOL of G EO S CIENCES Model Inversion Retrieved Wind SAR Scenes UMM Data Inversion Estimates optimal wind vector given the σ 0 and apriori wind vector Observation Term Apriori Term 2) Maximum Aposteriori Probability (MAP) Apply Gauss-Newton minimisation Stabilises within 3-5 iterations

10. S CHOOL of G EO S CIENCES Sensitivity Analysis Generate σ 0 using wind speeds 5-25 ms -1 and directions 0-180 o Add 5% Gaussian noise to σ 0 Retrieve speed

11. S CHOOL of G EO S CIENCES Validation Results R 2 = 0.715 RMSE = 1.57 m/s R 2 = 0.576 RMSE = 1.7 m/s UKMO UMM R 2 = 0.609 RMSE = 2.28 m/s

12. S CHOOL of G EO S CIENCES Mean Wind Speeds UKMO UMMMAP CMOD5DWSA CMOD5 010 Speed m/s MAX 16

13. S CHOOL of G EO S CIENCES Conclusions & Future Directions The MAP methodology shows promise for SAR wind field retrieval BUT there are limitations in the resolution of the weather model data Future work will: –Introduce SAR wind direction analysis –Consider the applicability of these data products for wind farm planning

14. S CHOOL of G EO S CIENCES Offshore wind mapping using synthetic aperture radar and meteorological model data Iain Cameron David Miller Iain Woodhouse

15. S CHOOL of G EO S CIENCES Sensitivity Analysis Gaussian Noise on apriori Why the high speed, Downwind bias?

16. S CHOOL of G EO S CIENCES Methodology Hierarchical Inversion Method For Improving Retrieval Resolution

17. S CHOOL of G EO S CIENCES Sensitivity Analysis CMOD5 shows increased saturation effects at high speeds Wind direction relative to antenna σ0σ0