Simulations, from mesoscale numerical models, and analyses of in-situ and remote sensing data from offshore wind farms in Denmark, are used to examine both horizontal and vertical gradients of wind speeds in the coastal zone. Results suggest that the distance from the coastline over which wind speed vertical profiles are not at equilibrium with the sea surface (which defines the coastal zone) extends to 20 km and possibly 70 km from the coast. Using this operational definition of the coastal zone, these results thus imply the typical width of the coastal zone in northern Europe is between 20 and 70 km. The width of the coastal zone, and the wind's vertical (shear) and horizontal gradients within the coastal zone, depend on atmospheric stability. Although vertical wind speed profiles above 50 m are likely responding to additional factors such as the height of the boundary-layer, using a stability correction improves predictions of wind speed compared with the logarithmic profile. Modelling indicates that within the coastal zone, wind speeds at typical turbine hub-heights can change by 2 m/s over the horizontal extent of a large wind farm, depending on stability. However, if the fetch is sufficiently long (or the windfarm is further from the coast) both horizontal and vertical wind speed gradients over the area of the wind farm appear to be small and negligible.

New developments for obtaining offshore wind maps by meso-scale modeling and satellite Synthetic Aperture Radar (SAR) images are presented. Results for Maddalena in Italy are compared to earlier results from Horns Rev in Denmark. The wind field in coastal regions is simulated with the Karlsruhe Atmospheric Mesoscale Model 2 (KAMM2). Data (4 times daily) from the global reanalysis of NCEP/NCAR is used to obtain the geostrophic wind and other large scale forcings which are suitable as input to the mesoscale model. This approach has mainly been used for regions on land. In coastal areas the wind fields can be complicated due to stability effects and the influence of land topography for offshore wind directions. The results of the simulations are compared with wind speeds and directions derived from satellite SAR images. In addition, results from WAsP of Risø National Laboratory are incorporated. The current empirical algorithms used for obtaining the wind speed from the radar backscatter arecalibrated for the open sea. Therefore, the mesoscale model and WAsP are also useful for comparison with the SAR-derived wind speeds close to the shore.

A new optimised clustering method is presented for generating wind classes for mesoscale modelling to produce numerical wind atlases. It is compared with the existing method of dividing the data in 12 to 16 sectors, 3 to 7 wind-speed bins and dividing again according to the stability of the atmosphere.Wind atlases are typically produced using many years of on-site wind observations at many locations. Numerical wind atlases are the result of mesoscale model integrations based on synoptic scale wind climates and can be produced in a number of hours of computation. 40 years of twice daily NCEP/NCAR Reanalysis geostrophic wind data (approximately 200 km resolution) are represented in typically around 150 classes, each with a frequency of occurrence. The mean wind-speed and direction in each class is used as input data to force the mesoscale model, which downscales the wind to a 5 km resolution while adapting to the local topography. The purpose of forming classes is to minimise the computational time for the mesoscale model while still representing the synoptic climate features.Only tried briefly in the past, clustering has traits that can be used to improve the existing class generation method by optimising the representation of the data and by automating the procedure more. The Karlsruhe Atmospheric Mesoscale Model (KAMM) is combined with the WAsP analysis to produce numerical wind atlases for two sites, Ireland and Egypt. The model results are compared with wind atlases made from measurements at specific sites. The sources are The New Irish Wind Resource Atlas and the Wind Atlas for the Gulf of Suez. The new clustering method has the ability to include wind-speed, direction and thermal stability from different heights for the classification. It is shown that the clustering method is able to produce results at least as accurate as the existing method for both sites. A refined, general clustering procedure is devised which could improve the results for both sites, where the existing method requires two different configurations.

A wind resource estimation study based on a series of 62 satellite wind field maps is presented. The maps were retrieved from imaging synthetic aperture radar (SAR) data. The wind field maps were used as input to the software RWT, which calculates the offshore wind resource based on spatial averaging (footprint modelling) of the wind statistic in each satellite image. The calculated statistics can then be input to the program WA(s)P and used in lieu of in-situ observations by meteorological instruments. A regional wind climate map based on satellite SAR images delineates significant spatial wind speed variations. The site of investigation was Horns Rev in the North Sea, where a meteorological time series is used for comparison. The advantages and limitations of these new techniques, which seem particularly useful for mapping of the regional wind climate, are discussed. Copyright (c) 2005 John Wiley & Sons, Ltd.

Offshore wind farms may soon contribute an important source of renewable energy. The energy production of a wind farm is closely connected to the wind climate and the local position, and the expected outcome is traditionally calculated based on least one year of accurate wind measurements. Satellite Synthetic Aperture Radar (SAR) wind mapping can be a useful tool in the process of selecting the optimal site for these measurements and may therefore increase the cost-effectiveness when planning wind farms, e.g. in feasibility studies. In the present study SAR, in situ and model output from three test sites have been analysed and a tool for effectively the retrieving wind from SAR images and utilising this in WAsP has been developed. Testing of the WEMSAR tool at Horns Rev in Denmark is ongoing.

Offshore wind farms have started to contribute important supplies of renewable energy. The energy production of a wind farm depends upon the local wind climate and so may be predicted in advance. Usually, the prediction is based on at least one year of accurate wind measurements. Satellite Synthetic Aperture Radar (SAR) wind mapping can be a useful tool in selecting optimal sites and may therefore increase the cost-effectiveness of planning wind farms, e.g., in feasibility studies. In the WEMSAR project1 wind fields from SAR, in situ measurements and model output from three test-sites have been analysed [1]. Subsequently, a WEMSAR tool for effectively retrieving wind data from SAR images and utilising them in the WAsP micrositing model, has been developed. Testing of the WEMSAR tool at Horns Rev offshore wind farm in Denmark is ongoing.

. By applying Proper Orthogonal Decomposition (POD) one is able to extract a limited amount of data which characterizes a flow of interest. The modes resulting from the decomposition form a basis in the phase space on which a Galerkin projection of the equations of motion can be performed. By carrying out such a procedure one obtains a low-dimensional model consisting of a reduced set of Ordinary Differential Equations (ODEs) which models the original equations. A technique called Sequential Proper Orthogonal Decomposition (SPOD) is developed to perform decompositions suitable for low-dimensional models. SPOD is capable of transforming data organized in different sets separately while still producing orthogonal modes. A low-dimensional model is constructed and used for analyzing bifurcations occurring in the flow in the lid-driven cavity with a rotating rod. The model allows one of the free parameters to appear in the inhomogeneous boundary conditions without the addition of any constraints. This is necessary because both the driving lid and the rotating rod are controlled simultaneously. Apparently, the results reported for this model are the first to be obtained for a low-dimensional model based on projections on POD modes for more than one free parameter.

The wind field in coastal regions is simulated with the Karlsruhe Atmospheric Mesoscale Model 2 (KAMM2). Data (4 times daily) from the global reanalysis of NCEP/NCAR is used to obtain the geostrophic wind and other large scale forcings which are suitable as input to the mesoscale model. The results of the simulations are compared with wind fields derived from satellite Synthetic Aperture Radar (SAR) images and results from the Linearized Computational Model (LINCOM) of Risø National Laboratory. SAR images provide ocean wind speed maps with a 400 m spatial resolution covering areas of 100 km * 100 km as snap-shots 3 times a month, thus offering a unique possibility of evaluating the performance of the mesoscale model at a relatively low cost. The SAR-derived wind speed is obtained from radar backscatter due to the water roughness generated by the interaction between the wind and capillary and short gravity waves. In fetch-limited seas additional parameters may influence the roughness of the sea as compared to that of the open sea. However, the current empirical algorithms used for obtaining the wind speed from the backscatter are calibrated for the open sea. Therefore, the mesoscale model and data from LINCOM are useful for comparison with the SAR-derived wind speeds close to the shore.

Offshore wind farms are a growing business worldwide. The outcome of a wind farm can be predicted by knowing the local wind climate. Usually, the wind climatology is based on at least one year of accurate wind measurements. Before such data are available at a site, satellite- based wind mapping can be a helpful tool in giving the first estimates of the wind conditions. In the WEMSAR project wind fields from SAR, in situ measurements and model output from three test-sites have been analysed. Subsequently, a tool for retrieving wind maps from SAR images and utilising them in the Wind Atlas analysis and application Programme (WAsP) has been developed.