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Synthetic Aperture Radar for Offshore Wind Resource Assessment and Wind Farm Development in the UK

Iain Cameron(1) , Parivash Lumsdon(2) , Nick Walker(3) , and Iain Woodhouse(1)

(1) University of Edinburgh, Drummond st, Edinburgh EH8 9XP, United Kingdom
(2) Macaulay Land Use Research Institute, Craigiebuckler, AB15 8QH, United Kingdom
(3) Vexcel UK, West Woodhay, RG20 0BP, United Kingdom


Offshore wind is set to grow rapidly over the coming years with 5% of the UK’s generation capacity expected to come from offshore wind by 2010 [1]. Optimisation of energy production is of the utmost importance given the high installation and maintenance costs of offshore turbines; accurate estimates of wind speed characteristics are critical during the planning process. Operational assessment methods which rely upon in-situ observations [2,3] or flow models [4] do little to explore the spatial variability of the wind resource; by contrast synthetic aperture radar (SAR) data from platforms such as ERS 1/ 2 and ENVISAT can provide wind field speed estimates at a spatial resolution of a few km2 accurate to within ±2 m/s [5,6,7].

Most methods for inversion of wind vectors from SAR backscatter use a geophysical model function (GMF) such as CMOD-4 [8] in combination with wind directions derived from wind aligned effects in the SAR image [6,9] to estimate wind speed. However, these approaches ultimately rely upon the presence of wind aligned roll or smear effects which may only be present intermittently [10]. An alternate inversion scheme, termed the statistical wind retrieval approach (SWRA), was proposed by [11]. Here the inversion is solved using Bayesian statistics to combine trial wind vectors from numerical weather prediction (NWP) output with the GMF. In addition to being independent of wind-aligned features, this scheme models backscatter variation as the result of variation in the whole wind vector rather than just the speed component and has been demonstrated to provide wind speed estimates accurate to within ±1m/s [11]. Challenges with SWRA wind retrieval include quantifying NWP model uncertainty and the need for temporal collocation between NWP output and SAR scenes.

This poster will present the results of the first phase of a project examining the use of SAR for wind resource assessment around the coast of the UK An approach similar to SWRA is adopted where wind vector inversion is achieved through linking UK Met Office Mesoscale Model output to the CMOD-4 and CMOD-5 [12] GMF’s using Bayesian statistics. Results are compared against in-situ observations for a number of offshore and coastal stations and, where appropriate, wind speed estimates derived using CMOD-4 and -5 coupled with wind directions estimated from wind aligned effects.


1. National Grid (2004) Interim Great Britain Seven Year Statement, Internal Report

2. Woods, J. C. and S. J. Watson (1997). "A new matrix method of predicting long-term wind roses with MCP." Journal Of Wind Engineering And Industrial Aerodynamics 66(2): 85-94.

3. Walmsley, J. L., R. J. Barthelmie, et al. (2001). "The statistical prediction of offshore winds from land-based data for wind-energy applications." Boundary-Layer Meteorology 101(3): 409-433.

4. Lange, B. and J. Hojstrup (2001). "Evaluation of the wind-resource estimation program WAsP for offshore applications." Journal Of Wind Engineering And Industrial Aerodynamics 89(3-4): 271-291.

5. Korsbakken, E., Johannessen, J.A., & Johannessen, O.M. (1998) Coastal wind field retrievals from ERS synthetic aperture radar images. Journal Of Geophysical Research-Oceans, 103, 7857-7874.

6. Fetterer, F., Gineris, D., & Wackerman, C.C. (1998) Validating a scatterometer wind algorithm for ERS-1 SAR. Ieee Transactions On Geoscience And Remote Sensing, 36, 479-492.

7. Hasager, C.B., Barthelmie, R.J., Christiansen, M., Nielsen, M., & Pryor, S.C. (2004) Quantifying offshore wind resources from satellite wind maps, study area the North Sea. In European Wind Energy Conference, pp. 29-33, London.

8. Stoffelen, A. & Anderson, D. (1997b) Sactterometer data interpretation: estimation and validation of the transfer function CMOD4. Journal of Geophysical Research, 102, 5767-5780.

9. Horstmann, J. & Koch, W. (2004) Evaluation of an Operational SAR Wind Field Retrieval Algorithm for ENVISAT ASAR. In IEEE International Geoscience and Remote Sensing Symposium, Alaska.

10. Etling, D. & Brown, R.A. (1993) Roll Vortices In The Planetary Boundary-Layer - A Review. Boundary-Layer Meteorology, 65, 215-248.

11. Portabella, M., Stoffelen, A., & Johannessen, J.A. (2002) Toward an optimal inversion method for synthetic aperture radar wind retrieval. Journal Of Geophysical Research-Oceans, 107, art. no.-3086.

12. Hersbach, H. (2003). CMOD5, An improved geophysical model for ERS C-band scatterometry. European Centre for Medium Range Weather Forecasts, Reading.


Full paper


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