Wind resource assessment from C-band SAR

Abstract

Wind fields retrieved from high-resolution synthetic aperture radar (SAR) images are valuable in wind resource assessment offshore. In contrast to in situ measurements, SAR wind maps provide spatial information which allows wind farm developers to compare the wind resource for different sites. Further advantages include the opportunity to obtain archived data and a low cost of satellite based assessments compared to the cost of installing a meteorological mast offshore. Using accurate inputs of wind speed is crucial in wind resource assessment, as predicted power is proportional to the wind speed cubed. Wind speeds retrieved from a series of 97 high-resolution ERS-2 SAR and Envisat ASAR images, at moderate wind speeds (2-15 m s-1), were validated against in situ measurements from an offshore mast in the North Sea. The wind direction input, necessary for SAR wind speed retrievals, was obtained from the meteorological mast and from a local gradient analysis of streaks in the SAR images. For the first method, a standard deviation of ~1.1 m s-1 was found. The second method, which worked independently of in situ measurements, yielded a standard deviation of ~1.3 m s-1. The performance of three geophysical model functions was compared. The best approximation to the in situ measurements of wind speed was found for CMOD-IFR2, despite a bias on the order of-0.3 m s-1. CMOD4 retrievals also underestimated the wind speed, whereas the bias on CMOD5 retrievals was negligible. The accuracy on SAR wind retrievals improved as cases with a long fetch and near-neutral atmospheric stability were analyzed separately. The mean wind speed, obtained from the 97 SAR scenes, was linked closely to the bias on SAR wind retrievals. Agreement to ±15% of the in situ measurements was found for all the wind retrieval methods tested.

Validation of Sentinel-1A SAR Coastal Wind Speeds Against Scanning LiDAR

“…Hasager et al [9] 149 to 197 Denmark/The Netherlands 1.27 to 1.65 Envisat Chang et al [10] 552 South Chinese Sea 2.09 Envisat Takeyama et al [11] 42 Japan 0.75 to 2.24 Envisat Chang et al [12] 522 East Chinese Sea 1.99 Envisat Takeyama et al [13] 33 to 73 Japan 0.64 to 2.34 Envisat Hasager et al [14] 875 Baltic Sea 1.17 Envisat Christiansen et al [15] 91 Denmark 1.1 to 1.8 ERS2 Hasager et al [16] 61 Denmark 0.9 to 1.14 ERS2…”

“…The influence of those effects can change when moving from the open ocean to the coastal zone. Table 1 shows an overview of selected past studies validating C-band SAR winds in coastal regions using meteorological masts [9][10][11][12][13][14][15][16]. Resulting Root Mean Square Errors (RMSE) ranged between 0.64 ms −1 and 2.34 ms −1 .…”

Methods and tools to evaluate the availability of renewable energy sources

“…However, their effectiveness and efficiency will have to be proved since their possible limitations are still under examination. Choisnard et al (2004) andBruun Christiansen et al (2006) present a methodology for wind resource assessment using a series of satellite synthetic aperture radar (SAR) images, a technique particularly useful for regions where year long time series are generally unavailable, such as offshore regions. Lackner et al (2008) investigate the use of an alternative monitoring strategy for wind resource assessment, the "round robin site assessment" method.…”

Correlation between Synthetic Aperture Radar Surface Winds and Deep Water Velocity in the Amundsen Sea, Antarctica

“…Hence, eastward winds drive a northward Ekman transport in the surface layer, i.e., the top 20 m. In order to compensate for that flow a return current in the deeper water is induced which the mooring measures ( Figure 1). Synthetic Aperture Radar (SAR) images of the ocean surface have been used to produce maps of wind fields with high resolution at offshore areas worldwide (e.g., [15,16]). These instantaneous measurements of the wind field complements the reanalysis products that are averaged in space at a coarser resolution.…”

“…The reason for the comparatively low correlation could be the low resolution of the ERA-interim data set or errors in the modeled data, possibly amplified during the ice-covered season. The wind data (both ERA interim and the SAR-derived) are less reliable when close to coastal or ice-covered areas [15][16][17]. The correlation between wind and currents is also likely smaller during the ice-covered season, since the sea-ice cover affects the momentum transfer between ocean and atmosphere.…”