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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.

Abstract Satellite data are used to characterize the near-surface winds over the Northern European Shelf Seas. We compare mean winds from QuikSCAT with reanalysis fields from the Weather Research and Forecasting (WRF) model and in situ data from the FINO-1 offshore research mast. The aim is to evaluate the spatial and temporal variability of the near-surface wind field, including the inter- and intra-annual variability for resource assessment purposes. This study demonstrates the applicability of satellite observations as the means to provide information useful for selecting areas to perform higher resolution model runs or for mast installations. Comparisons between QuikSCAT and WRF reanalyses show biases ranging mostly between 0.6 and −0.6 m s−1 with a standard deviation of 1.8–2.8 m s−1. The combined analyses of inter- and intra-annual indices and the wind speed and direction distributions allow the identification of 3 sub-domains with similar intra-annual variability. Local characteristics observed from the long-term QuikSCAT wind rose distributions are depicted in high-resolution satellite Synthetic Aperture Radar (SAR) wind fields. The winds derived from the WRF reanalysis dataset miss seasonal features observed by QuikSCAT and at FINO-1.

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.

ABSTRACTThe QuikSCAT mission provided valuable daily information on global ocean wind speed and direction from July 1999 until November 2009 for various applications including numerical weather prediction, ocean and atmospheric modelling. One new and important application for wind vector satellite data is offshore wind energy, where accurate and frequent measurements are required for siting and operating modern wind farms. The greatest advantage of satellite observations rests in their extended spatial coverage. This paper presents analyses of the 10 year data set from QuikSCAT, for the overview of the wind characteristics observed in the North and Baltic Seas, where most of Europe's offshore wind farms operate and more will be constructed. Significant issues in data availability are identified, directly related to the flagging schemes. In situ observations from three locations in the North Sea are used for comparisons. Mean biases (in situ minus satellite) are close to zero for wind speed and ‐2.7° for wind direction with a standard deviation of 1.2 m s  − 1 and 15°, respectively. The impact of using QuikSCAT and in situ measurements extrapolated to 10 m for wind power density estimations is assessed, accounting for possible influences of rain‐contaminated retrievals, the sample size, the atmospheric stability effects and either fitting the Weibull distribution or obtaining the estimates from the time series of wind speed observations.Copyright © 2012 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.

Abstract. The increasing demand for wind energy offshore requires more hub-height relevant wind information while larger wind turbine sizes require measurements at greater heights. In situ measurements are harder to acquire at higher atmospheric levels; meanwhile the emergence of machine-learning applications has led to several studies demonstrating the improvement in accuracy for vertical wind extrapolation over conventional power-law and logarithmic profile methods. Satellite wind retrievals supply multiple daily wind observations offshore, however only at 10 m height. The goal of this study is to develop and validate novel machine-learning methods using satellite wind observations and near-surface atmospheric measurements to extrapolate wind speeds to higher heights. A machine-learning model is trained on 12 years of collocated offshore wind measurements from a meteorological mast (FINO3) and space-bourne wind observations from the Advanced Scatterometer (ASCAT). The model is extended vertically to predict the FINO3 vertical wind profile. Horizontally, it is validated against the NORA3 meso-scale model reanalysis data. In both cases the model slightly over-predicts the wind speed with differences of 0.25 and 0.40 m s-1 respectively. An important feature in the model training process is the air-sea temperature difference, thus satellite sea surface temperature observations were included in the horizontal extension of the model, resulting in 0.20 m s-1 differences with NORA3. A limiting factor when training machine-learning models with satellite observations is the small finite number of daily samples at discrete times; this can skew the training process to higher/lower wind speed predictions depending on the average wind speed at the satellite observational times. Nonetheless, results shown in this study demonstrate the applicability of using machine learning techniques to extrapolate long-term satellite wind observations when enough samples are available.