Winds 2

Downstream of operating wind farms the mean wind speed is reduced as compared to the upwind conditions. In the offshore environment it is of particular interest to quantify the wind farm wake because turbine arrays are often located in the vicinity of other wind farms. The wakes reduce the annual energy production in clustered wind farms. Envisat ASAR, Radarsat-2 and Sentinel-1 are used in the study covering wind farms in the North Sea and Kattegat Strait. Three types of analysis are performed.

The first is a case based on a Radarsat-2 Scan-SAR wide VV scene (30^th April 2013 at 17:41 UTC) with winds around 8-9 m/s from the northeast and eight operating wind farms all showing long wind farm wakes. The longest wake is around 55 km. The case has been modelled using an industry-standard engineering microscale wake model (PARK) and using mesoscale model (WRF) including a parametrization for wind farm wake. Both models reproduce the observed very long wind farm wakes convincingly regarding their direction and extent.

The second analysis is based on 835 Envisat ASAR wide-swath-mode scenes from 2003 to 2012 (Hasager et al. 2015a) covering the Horns Rev-1 wind farm near the Danish North Sea coast. The wind farm covers an area of around 4 km by 5 km and three concentric circles centered at the wind farm are used for extraction of results. The selected radii are 6, 10 and 13 km. The mean wind speeds in each of the three circles (geo-collocated) quantify the coastal wind speed gradient. Next step is rotation of the data such that all scenes are aligned with inflow and downstream (wake region) based on the wind direction in the wind field maps. The rotation is done at 1 degree intervals. The data from rotated circles (not geo-collocated) are normalized with the winds at the side-lobes. Side-lobes are regions expected to be undisturbed by the wind farm wake. The key result of the analysis is the significant wind wake deficit at the inner circle, decreasing at outer circles, as expected. The SAR-based results strongly support the wake model results based on PARK and WRF (Hasager et al. 2015b).

The third analysis is based on Sentinel-1 covering the Anholt wind farm located 56.6 °N, 11.25 °E in the Kattegat Strait. The 111 wind turbines, each 3.6 MW, are positioned in irregular lay-out with most turbines at the outer rim. Figure 1 shows Sentinel-1 on 11^th September 2015 at 05:32 GMT with winds around 11-12 m/s from the southeast and wind farm wake west of the park with winds around 10 m/s. The wind turbines are visible as hard targets. Cases with winds from 6 to 14 m/s are under investigation. The potential of synergetic use of Sentinel-1a and Radarsat-2 with only few minutes time lag and the forthcoming Sentinel-1b with around 6 hour will increase sampling rate.

The project was supported by FP7 EERA DTOC, ESA Envisat, Copernicus Sentinel and MDA Radarsat.

Hasager, C.B.; Mouche, A.; Badger, M.; Bingöl, F.; Karagali, I.; Driessenaar, T.; Stoffelen, A.;

Peña, A.; Longépé, N. Offshore wind climatology based on synergetic use of Envisat ASAR,

ASCAT and QuikSCAT. Remote Sens. Environ. 2015a, 156, 247–263.

Hasager, C.B., Vincent, P., Badger, J., Badger, M. Di Bella, A., Peña, A. Husson, R., Volker, P. (2015b) Using satellite SAR to characterize the wind flow around offshore wind farms. Energies, 8(6), 5413-5439.

This paper reviews several wind quality-sensitive parameters derived from Ku-band scatterometer data. The objective is to assess their sensitivity to wind data quality in order to optimize the quality control (QC) for Ku-band scatterometers. The current scatterometer wind data processor uses the inversion residual or maximum likelihood estimator (MLE) value in QC. A large inconsistency between the measured backscatter set andthe geophysical model function (GMF) results in a largeMLE value, which indicates geophysical conditions other than those modeled by the wind GMF, such as rain,sea ice, highlocal wind variability,orconfused sea state.Therefore, the MLE value provides a good indication of the quality of the retrieved winds. Besides, an image processing technique, known as Singularity Analysis (SA), has been recently proposed as a complementaryQC tool to the current Advanced Scatterometer (ASCAT) MLE-based QC. In general, MLE is a proxy for sub- wind vector cell (WVC) wind variability, where large positive MLEs are usually found near (gust) fronts, squall lines, and convective systems. The SA-derived singularity exponent (SE) is based on spatial derivatives and therefore mostly represents the inter-WVC variability. By using a spatial filter approach similar to the SE derivation, one can also study the inter-WVC variability with the spatially-averaged MLE, which may further improve the wind quality control.

A comprehensive wind quality assessment is carried out forthe ongoing Ku-band rotating pencil-beam scatterometers, namely RapidSCAT installed on the International Space Station and HSCAT onboard the Chinese HY-2A satellite. Since ASCAT QC near rain (< 1%) has been investigated recently and the C-band ASCAT sensitivity to rain is limited as compared to that of Ku-band scatterometers, ASCAT winds can be used as reference for tuning the Ku-band QC. Moreover, thanks to the inclined orbit of RapidScat, a large amount of well collocated (in pace and time) ASCAT and RapidSCAT wind data can be used to fine tune the Ku-band scatterometer QC near rain.The RapidSCAT and HSCAT derived wind dataare further collocated with the European Centre for Medium-Range Weather Forecasts (ECMWF) winds, the Tropical Rainfall Measuring Mission’s Microwave Imager (TRMM TMI) rain data, in order to characterize and validate the proposed quality indicators. Moreover, buoy wind collocations are also used for validating the proposed QC algorithm. A set of MLE and SE thresholds are defined to optimize the QC effectiveness, i.e., to maximize poor-quality wind rejections (particularly contaminated by rain) while preserving fair-quality data.

The purpose of this study is to fit the dependence of the azimuth wavelength cut-off (λ_C) estimated from X and C band Synthetic Aperture Radar (SAR) data from, respectively, the COSMO-SkyMed (CSK) and the Sentinel 1 missions on the incidence angle, the 10-meters ocean wind speed (U₁₀), and the sea state. The parameters used to characterize the sea state are the significant wave height (SWH) and the Charnock parameter. The final aim is to fit a Geophysical Model Function (GMF)  to retrieve the wind speed from independent SAR measurements and validate it with independent buoy wind measurements. The training datasets include collocated scatterometer winds and the European Centre for Medium-range Weather Forecasts (ECMWF) ERA-Interim reanalysis SWH and Charnock data, while the validation dataset includes wind and SWH data from the National Data Buoy Center (NDBC) network.

All the SAR images are multi-look and vertically polarized (VV) and have been acquired in strip-map mode.

In particular, the X-band training dataset consists of 90 images, part of which has been acquired over the Gulf of Guinea and the rest over the Mediterranean Basin. With this choice we can appreciate the differences of the model performances for open ocean cases and for a semi-enclosed basin. For the GMF fit, the CSK images are co-located in space and time with QuikSCAT scatterometer winds. The X-band validation dataset consists of 26 CSK images co-located with the NDBC datalocated in East Bahamas, Hawaii and the Carribean Sea.

The C-band training dataset consists of 198 Sentinel 1 images. All of them have been acquired close to the Hawaii archipelago and have been co-located in space and time with the Advanced Scatterometer (ASCAT onboard Metop) winds. The C-band validation dataset consists of 67 Sentinel 1 images co-located with the NDBC data  located off-shore the Hawaii archipelago.

The λ_C has been evaluated by means of the auto-correlation function (ACF) through an inverse Fourier transform of the range-averaged SAR image power spectral density. The ACF has been finally fitted with a Gaussian function. The power spectrum has been computed from 10-km side squared image blocks centred on the co-located scatterometer grid point. The dependence of the λ_C on each of the geophysical parameters has been assumed linear. The dependence of the λ_C on each geophysical parameter separately as well as  on all the possible combinations has been analyzed.

All the fitted GMFs have been evaluated and compared in terms of bias and root mean square error with respect to both the auxiliary parameters used in the fit and the independent NDBC measurements. The preliminary results show that for similar wind and sea state conditions, the computed λ_C values from CSK images are smaller than those computed from Sentinel 1 images. This is expected because of the smaller range to platform velocity ratio of CSK with respect to Sentinel 1. Furthermore, this study confirms that the λ_C is mainly dependent on the wind speed, on the SWH and on the incidence angle. The dependence on the incidence angle is qualitatively in agreement with the SAR modulation transfer function. A better agreement may be probably achieved by considering also the direction of the sea spectrum. The validation exercises show that the bias of the retrieved wind speed is not significant from a statistical point of view and the rmse is around 2.5 m/s.

The increasing popularity of renewable energy resources has promoted the construction of offshore wind farms, particularly in the German Bight. While the wind farm layout consists of an array of large turbines, the interrelation of wind turbine wakes with the remaining array is of substantial interest. The downstream spatial evolution of turbulent wakes is very complex and depends on manifold parameters such as wind speed, wind direction and ambient atmospheric stability conditions. Besides the wake properties of single turbines, the interrelation of adjacent wind farms is a major subject of interest. To complement and validate existing numerical models, corresponding observations with high spatial resolution are needed.

Over the past four decades, spaceborne remote sensing of sea surface winds with microwave sensors has developed into a mature technology. It is now playing an important role in Earth Observation (EO) due to the capability to measure with high spatial coverage and almost unaffected by weather conditions. Hence, the observations are commonly used for the improvement of numerical weather prediction (NWP). While scatterometers yield only coarse-resolution data, synthetic aperture radar (SAR) has proven to measure local wind related sea surface roughness with both, high resolution and wide coverage.

On the other hand, earth-bound remote sensing techniques have evolved significantly. Especially Doppler LiDAR has become an important tool for offshore wind energy related research in the last years. Ground-based LiDAR windscanners enable to measure vertical wind profiles or horizontal planar scans of the radial wind speed within a range of several kilometres.

While both methods have been successfully validated against meteorological models and in-situ point measurements, we present a direct comparison of platform based LiDAR measurements and high resolution 2D sea surface wind fields derived from spaceborne SAR. The analysis is further supported by a comparison with data from the numerical meteorological model COSMO-DE run by the German Weather Service (DWD).

Our contribution will contain a comparison of a series of simultaneous wind field observations around the offshore wind farms “alpha ventus” and “Riffgat” in the North Sea taken with TerraSAR-X, Sentinel-1 and two scanning long range LiDAR systems. Both techniques are compared with a special focus on small scale variations in wind fields both, upstream and downstream from the wind facilities. Methods that have been developed to account for different measuring heights and scan duration of the two independent datasets will be presented and discussed regarding the applicability for turbulent atmospheric conditions predominant in the wake structures downstream of the wind turbines.

Our initial results indicate that high resolution SAR-based wind fields are well-suited to identify and quantify small scale wind variations and therefore represent a valuable complementary data source for numerical models of offshore wind farms and NWP in general.