Publishers version - DTU Orbit

Satellites in sun-synchronous polar orbit observe a given local area at a certain local time in
ascending (northbound) track and 12-hour shifted in descending (southbound) track. There
are differences in the local acquisition time of the various satellites carrying radar instruments.
If a local site has pronounced daily wind speed variations, the two fixed observational times
may not be fully representative of the diurnal cycle. Far offshore diurnal variation is typically
less pronounced than in some coastal areas, e.g. sites with pronounced land-sea breezes.
Using observations from several satellites with different fixed observational times would be a
possibility, but this has not yet been achieved.
Another challenge of using SAR for wind resource assessment is the relatively few observations
available. For some sites less than 200 scenes may be available. In European coastal
waters Envisat provides a somewhat higher number of observation. In the later years of operation
many wide swath mode scenes were recorded. This makes it a unique archive for wind
resource assessment. The available scenes can be seen at http://earth.esa.int/EOLi.
Barthelmie & Pryor (2003) and Pryor et al. (2004) investigated based on statistical analysis
and using offshore meteorological observations from the Baltic Sea and North Sea, the
number of randomly sampled wind observations necessary to carry out wind resource analysis
within certain statistical bounds. The conclusion was that around 70 samples are sufficient
to estimate mean wind speed and the Weibull scale parameter but around 2,000 samples are
needed to estimate energy density and the Weibull shape parameter at the 10% confidence
level (significance 95%). The result makes it clear that the accuracy of SAR-based wind resource
statistics is adequate for pre-feasibility studies. In other words, the SAR-based wind
resource map may be used as guide to site an offshore meteorological mast in a wind farm
project. Alternatively, if high-quality offshore observations exist, the local wind gradients at
10 m above sea level may be evaluated from SAR wind resource maps. Also, combining or
blending SAR wind resource statistics and mesoscale wind resource modelling is an option
(Badger et al., 2010b).
When planning clusters of wind farms that cover 100 km 2 or more, it is advisable to have
two meteorological masts if the wind resource should be assessed as accurately as possible. It
is most likely that significant wind gradients exist within such areas, in particular in coastal
zones (Barthelmie et al., 2007). At Horns Rev the first wind farm is located 14 to 21 km
offshore from the closest coastal point and the second wind farm is located 26 to 31 km
offshore. Using SAR, winds at 1 − 2 km grid scale allow quantification of the local wind
gradients. A third wind farm at Horns Rev in the North Sea is in planning.
15.10 S-WAsP
In-house software was developed for SAR wind resource wind mapping at DTU Wind Energy.
The Satellite-WAsP (S-WAsP) software reads SAR wind fields from ANSWRS and SARTool
(SOPRANO). In earlier versions of S-WAsP other formats were used as input (Nielsen et al.,
2004; Hasager et al., 2008). The first step is to ensure geographical collocation of the series
of wind fields (through a database) and select the desired data. The second step is to run
the wind resource application routines to calculate the wind resource statistics: mean wind
speed, Weibull scale, Weibull shape, and energy density and the statistical uncertainty for
each parameter. The final results are output maps of the parameters (Hasager et al., 2012).
The number of satellite observations in a single directional bin may be small. Therefore to
make it possible to fit a distribution, all data is used to derive the shape parameter. The shape
parameter is assumed valid for every bin. The Weibull scale parameter is then estimated by
the average wind speed in each sector. The frequency ofoccurrence in each sectoris uncertain
when observations are sparse and there is a risk of observing sectors without any observation
and no estimate of the mean wind. An alternative to simple bin counting is to sort all
observations after directions, estimate the probability density between the observations by
the angle separating between them, and finally resample the densities in the standard sectors
(Nielsen et al., 2004).
The available wind powerdensity, E W/m 2 that is proportional to the wind speed cubed,
DTU Wind Energy-E-Report-0029(EN) 287