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 The UK has an abundant offshore wind resource with offshore wind set to grow rapidly over the coming years. 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. While operational assessment methods which rely upon in-situ observations or flow models do little to explore the spatial variability of the wind resource, 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 km 2 accurate to within ±2 m/s. This paper 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 Two algorithms are explored that combine SAR imagery with NWP output; DWSA which estimates wind speed based upon NWP directions alone and SWRA where wind vector inversion is achieved using a statistical approach to combine NWP wind directions and speeds with backscatter information. Results show SWRA to produce superior wind speed estimates, generally within ~2 m/s of in situ observations. Additionally expected improved wind speed estimation of CMOD-5 over CMOD-4 at high wind speeds is demonstrated. 1 INTRODUCTION Renewable energy development is central to the meeting the UK government’s emission reduction targets under the terms of the Kyoto Protocol. Offshore wind energy is central to meeting these targets with 18 offshore wind farms currently consented for development, 3 of which are generating. By 2010 offshore energy is expected to provide around 4.3% of the UK energy capacity [1]. The sensitivity of turbine productivity to variations in wind speed combined with high installation and maintenance costs makes determination of the offshore wind climate, particularly the mean wind speed and wind speed distribution, critical for optimising the productivity of an offshore wind farm. The inaccessibility of the offshore environment obtaining accurate observations of the offshore wind regime presents a significant problem. While a long term in-situ record is often seen as the optimal observation technique there is a paucity of permanent offshore recording stations due to the expense of installation and maintenance. Consequently extrapolation methods such as measure-correlate-predict and the wind atlas approach are widely implemented to provide cost-effective determination of the offshore wind regime. Synthetic Aperture Radar (SAR) presents an attractive alternative for determining long term offshore wind regimes with a continuous archive of satellite observations dating to the launch of ERS-1 in 1991. The principle methods for retrieving the surface wind field from SAR in coastal regions utilise geophysical model functions (GMF’s) such as CMOD-4, CMOD-4 IFR-2 and CMOD-5 [2-4]. These model the measured ! 0 according to SAR geometry, surface wind speed and wind direction for C-band VV polarised SAR systems such as ERS-1/2 or ENVISAT. The use of polarisation ratios [5] allows HH polarised data, such as RADARSAT, to be analysed using these GMF’s. As a SAR scene is observed from a single look direction some background knowledge of the surface wind field is required to allow GMF inversion. Robust wind retrievals can be provided using wind directions estimated by wind aligned effects such as boundary layer rolls in the SAR imagery as background information. Methods for estimating the orientation of wind aligned effects include Fourier analysis [6-9] and image cross spectra analysis using single-look-complex data [10] which can provide estimates of wind direction at spatial scales of ~10km 2 grid cells. A particularly promising new technique is the local-gradients method [11, 12] which can provide wind directions at smaller spatial scales of up to 1 km 2 , providing wind direction retrieval in near shore situations. The relationship between the surface wind direction and wind aligned effects is not, however, always direct; it has been suggested the mean surface wind direction can deviate from the roll orientation by up to ± 30 o [9, 13-15]. An alternate inversion scheme, termed the statistical wind retrieval approach (SWRA), was proposed by [16]. Here the inversion is solved statistically by combining remotely sensed observations with background knowledge such as