Publishers version - DTU Orbit

500 overlapping scenes for wind resource assessment. In comparison, the accuracy of wind
resources from mesoscale modeling is 10−15% (Badger et al., 2010a).
The advantages of wind class sampling are that fewer images are needed and the long-term
wind climatology may be obtained, even using SAR scenes covering a limited period.
15.12 SAR wind resource maps
In the Indian Ocean near India the mean wind speed from 164 Envisat ASAR wind fields is
presented in Figure 194. Hasager et al. 2011b provide further detail.
Figure 194: Mean wind speed map based on 164 Envisat ASAR wind fields at the Indian
Ocean, India. From Hasager et al. 2011b.
Takeyama et al. 2013 compared in-situ winds for offshore and onshore winds in Japan and
found large negative bias (above 1 ms −1 ) for offshore flow but small bias for onshore winds.
The wind maps are used to assess the mean wind speed in the region near Shirahama, see
Figure 195.
In Figure 196 the maps of the 10 m mean wind speed and energy density over Hangzhou
Bay in China are presented. Very high values (bright red) near the coastline are caused by a
high radar return from exposed sand or mud and are not associated with the wind (Badger,
2009).
The most recent wind resource wind map produced by DTU Wind Energy is based on
EnvisatASAR wideswathmodewindfieldsprocessedbyA.MoucheatCLSusingSOPRANO.
The result is published online at soprano.cls.fr (select Wind/Statistics L3/Norsewind).
The map covers the Northern European Seas and is based on 9,000 unique wind fields. It is
part of the FP7 Northern European Seas Wind Index database (NORSEWInD) project final
results (Hasager et al., 2012). The second moment fitting was chosen to be used for the
Weibull scale and shape parameters (Pryor et al., 2004; Barthelmie and Pryor, 2003) for the
final products in NORSEWInD. The uncertainty on mean wind speed and Weibull A is of the
order 0.08 ms −1 in most of the study area and around 0.18 ms −1 in parts of the Irish Sea
DTU Wind Energy-E-Report-0029(EN) 289