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AbstractOffshore wind resources are quantified from satellite synthetic aperture radar (SAR) and satellite scatterometer observations at local and regional scale respectively at the Horns Rev site in Denmark. The method for wind resource estimation from satellite observations interfaces with the wind atlas analysis and application program (WAsP). An estimate of the wind resource at the new project site at Horns Rev is given based on satellite SAR observations. The comparison of offshore satellite scatterometer winds, global model data and in situ data shows good agreement. Furthermore, the wake effect of the Horns Rev wind farm is quantified from satellite SAR images and compared with state‐of‐the‐art wake model results with good agreement. It is a unique method using satellite observations to quantify the spatial extent of the wake behind large offshore wind farms. Copyright © 2006 John Wiley & Sons, Ltd.

The offshore wind climatology in the Northern European seas is analysed from ten years of Envisat synthetic aperture radar (SAR) images using a total of 9256 scenes, ten years of QuikSCAT and two years of ASCAT gridded ocean surface vector wind products and high-quality wind observations from four meteorological masts in the North Sea. The traditional method for assessment of the wind resource for wind energy application is through analysis of wind speed and wind direction observed during one or more years at a meteorological mast equipped with well-calibrated anemometers at several levels. The cost of such measurements is very high and therefore they are only sparsely available. An alternative method is the application of satellite remote sensing. Comparison of wind resource statistics from satellite products is presented and discussed including the uncertainty on the wind resource. The diurnal wind variability is found to be negligible at some location but up to 0.5 m s− 1 at two sites. Synergetic use of observations from multiple satellites in different orbits provides wind observations at six times in the diurnal cycle and increases the number of observations. At Horns Rev M2, FINO1 and Greater Gabbard satellite and in situ collocated samples show differences in mean wind speed of − 2%, − 1% and 3%, respectively. At Egmond aan Zee the difference is 10%. It is most likely due to scatterometer data sampled further offshore than at the meteorological mast. Comparing energy density with all samples at Horns Rev M2 shows overestimation 7–19% and at FINO1 underestimation 2–5% but no clear conclusion can be drawn as the comparison data are not collocated. At eight new offshore wind farm areas in Denmark, the variability in mean energy density observed by SAR ranges from 347 W m− 2 in Sejerøbugten to 514 W m− 2 at Horns Rev 3. The spatial variability in the near-shore areas is much higher than at areas located further offshore.

AbstractOffshore wind energy is progressing rapidly around Europe. One of the latest initiatives is the installation of multiple wind farms in clusters to share cables and maintenance costs and to fully exploit premium wind resource sites. For siting of multiple nearby wind farms, the wind turbine wake effect must be considered. Synthetic aperture radar (SAR) is an imaging remote sensing technique which offers a unique opportunity to describe spatial variations of wind speed offshore. For the first time an airborne SAR instrument was used for data acquisition over a large offshore wind farm. The aim was to identify the turbine wake effect from SAR‐derived wind speed maps as a downstream region of reduced wind speed. The aircraft SAR campaign was conducted on 12 October 2003 over the wind farm at Horns Rev in the North Sea. Nearly simultaneous measurements were acquired over the area by the SAR on board the ERS‐2 satellite. In addition, meteorological data were collected. Both aircraft and satellite SAR‐derived wind speed maps showed significant velocity deficits downstream of the wind farm. Wind speed maps retrieved from aircraft SAR suggested deficits of up to 20% downstream of the last turbine, whereas satellite SAR‐derived maps showed deficits of the order of 10%. The difference originated partly from the two different reference methods used for normalization of measured wind speeds. The detected region of reduced wind speed had the same width as the wind turbine array, indicating a low degree of horizontal wake dispersion. The downstream wake extent was approximately 10 km, which corresponds well with results from previous studies and with wake model predictions. Copyright © 2006 John Wiley & Sons, Ltd.

Around 2000 wind turbines in 58 offshore wind farms produce wind energy in the Northern European seas and many new wind farms are foreseen. The wind resource assessment is costly to observe using traditional meteorological masts and therefore atmospheric modeling is state of the art. However, to reduce the uncertainty on the model results on the offshore wind resource, it is necessary to compare model results with observations. Observations from ground‐based wind lidar and satellite remote sensing are the two main technologies that can provide new types of offshore wind data at relatively low cost. The advantages of microwave satellite remote sensing are (1) horizontal spatial coverage, (2) long data archives, and (3) high spatial detail both in the coastal zone and of far‐field wind farm wake. Passive microwave ocean wind speed data are available since 1987 with up to six observations per day with near‐global coverage. The data are particularly useful for investigation of long‐term wind conditions. Scatterometer ocean surface wind vectors provide a continuous series since 1999 with twice‐daily near‐global coverage. Both types of data have grid cells around 25 km. In contrast, synthetic aperture radar (SAR) wind maps can be retrieved at 1‐km grid resolution.SAR‐based wind maps have been used for wind resource assessment far offshore and in the coastal zones with good results when compared to e.g., meteorological data and mesoscale model results. High‐resolutionSARdata show very long far‐field wind farm wakes. Thus wind farm wake loss is foreseen in wind farm clusters.WIREs Energy Environ2014, 3:594–603. doi: 10.1002/wene.123This article is categorized under:Wind Power > Science and Materials

Leading edge erosion (LEE) of wind turbine blades causes decreased aerodynamic performance leading to lower power production and revenue and increased operations and maintenance costs. LEE is caused primarily by materials stresses when hydrometeors (rain and hail) impact on rotating blades. The kinetic energy transferred by these impacts is a function of the precipitation intensity, droplet size distributions (DSD), hydrometeor phase and the wind turbine rotational speed which in turn depends on the wind speed at hub-height. Hence, there is a need to better understand the hydrometeor properties and the joint probability distributions of precipitation and wind speeds at prospective and operating wind farms in order to quantify the potential for LEE and the financial efficacy of LEE mitigation measures. However, there are relatively few observational datasets of hydrometeor DSD available for such locations. Here, we analyze six observational datasets from spatially dispersed locations and compare them with existing literature and assumed DSD used in laboratory experiments of material fatigue. We show that the so-called Best DSD being recommended for use in whirling arm experiments does not represent the observational data. Neither does the Marshall Palmer approximation. We also use these data to derive and compare joint probability distributions of drivers of LEE; precipitation intensity (and phase) and wind speed. We further review and summarize observational metrologies for hydrometeor DSD, provide information regarding measurement uncertainty in the parameters of critical importance to kinetic energy transfer and closure of data sets from different instruments. A series of recommendations are made about research needed to evolve towards the required fidelity for a priori estimates of LEE potential.

Offshore wind farm cluster effects between neighboring wind farms increase rapidly with the large-scale deployment of offshore wind turbines. The wind farm wakes observed from Synthetic Aperture Radar (SAR) are sometimes visible and atmospheric and wake models are here shown to convincingly reproduce the observed very long wind farm wakes. The present study mainly focuses on wind farm wake climatology based on Envisat ASAR. The available SAR data archive covering the large offshore wind farms at Horns Rev has been used for geo-located wind farm wake studies. However, the results are difficult to interpret due to mainly three issues: the limited number of samples per wind directional sector, the coastal wind speed gradient, and oceanic bathymetry effects in the SAR retrievals. A new methodology is developed and presented. This method overcomes effectively the first issue and in most cases, but not always, the second. In the new method all wind field maps are rotated such that the wind is always coming from the same relative direction. By applying the new method to the SAR wind maps, mesoscale and microscale model wake aggregated wind-fields results are compared. The SAR-based findings strongly support the model results at Horns Rev 1.

Abstract. With the increasing level of offshore wind energy investment, it is correspondingly important to be able to accurately characterize the wind resource in terms of energy potential as well as operating conditions affecting wind plant performance, maintenance, and lifespan. Accurate resource assessment at a particular site supports investment decisions. Following construction, accurate wind forecasts are needed to support efficient power markets and integration of wind power with the electrical grid. To optimize the design of wind turbines, it is necessary to accurately describe the environmental characteristics, such as precipitation and waves, that erode turbine surfaces and generate structural loads as a complicated response to the combined impact of shear, atmospheric turbulence, and wave stresses. Despite recent considerable progress both in improvements to numerical weather prediction models and in coupling these models to turbulent flows within wind plants, major challenges remain, especially in the offshore environment. Accurately simulating the interactions among winds, waves, wakes, and their structural interactions with offshore wind turbines requires accounting for spatial (and associated temporal) scales from O(1 m) to O(100 km). Computing capabilities for the foreseeable future will not be able to resolve all of these scales simultaneously, necessitating continuing improvement in subgrid-scale parameterizations within highly nonlinear models. In addition, observations to constrain and validate these models, especially in the rotor-swept area of turbines over the ocean, remains largely absent. Thus, gaining sufficient understanding of the physics of atmospheric flow within and around wind plants remains one of the grand challenges of wind energy, particularly in the offshore environment. This paper provides a review of prominent scientific challenges to characterizing the offshore wind resource using as examples phenomena that occur in the rapidly developing wind energy areas off the United States. Such phenomena include horizontal temperature gradients that lead to strong vertical stratification; consequent features such as low-level jets and internal boundary layers; highly nonstationary conditions, which occur with both extratropical storms (e.g., nor'easters) and tropical storms; air–sea interaction, including deformation of conventional wind profiles by the wave boundary layer; and precipitation with its contributions to leading-edge erosion of wind turbine blades. The paper also describes the current state of modeling and observations in the marine atmospheric boundary layer and provides specific recommendations for filling key current knowledge gaps.