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may be calculated from the two Weibull parameters, the scale parameter A and the shape
parameter k, using the gamma function Γ, and the air density ρ (≈ 1.245kgm−3 at 10◦C) as
E = 1
2 ρA3
Γ 1+ 3
(350)
k
The uncertainties for the four parameters; mean wind speed, Weibull A and k, and energy
density can be calculated in S-WAsP. The uncertainty calculation estimates the difference of
the Weibull A and k fitted function and the measured wind distribution based on the available
number of samples. We follow the equations from the appendix in (Pryor et al., 2004). We
assume that each SAR-based wind map is accurate and that the influence of time sampling
is insignificant to the estimates. In other words, we assume the diurnal wind pattern to be
described accurately using morning and evening observations only.
Studies from the North Sea and Baltic Sea have shown SAR wind maps to be a useful/valuable
source of information for the estimation of Weibull A and k (Badger et al.,
2010a; Christiansen et al., 2006; Hasager et al., 2011a).
15.11 The wind class method
Envisat ASAR and ERS-1/2 SAR scenes are nowadays freely available in large quantities
over Europe. In earlier times there were limitations. For commercial application a relatively
high cost was associated. This prompted a need for an alternative SAR-based wind resource
method in S-WAsP: the wind class method (Badger et al., 2010a). The method is based
on representative selected sampling of 135 SAR scenes each with wind conditions similar to
representative long-term wind conditions as evaluated from the global atmospheric model
results from NCAR NCEP re-analysis. Thus the first processing step is to evaluate the longterm
statistics from large-scale models and assess the weighting function for the selected
representative wind conditions. This method is also used in the KAMM/WAsP wind atlas
methodology (Frank et al., 2001). The second step is to distribute the SAR scenes amongst
the wind classes based on look-up tables with the specific dates and times when each given
wind situation occurs. The third step is to retrieve and process the SAR scenes to wind fields.
The fourth and final step is to use the relevant weighting functions from the first step to
produce representative SAR-based wind resource statistics from the series of SAR wind fields.
The final results are maps of Weibull A and k, mean wind speed and energy density. The
method was used in United Arab Emirates and compared well with mesoscale model results
(Badger et al., 2010b). Figure 193 shows the mean wind speed at 10 m over the United Arab
Emirates.
Figure 193: 10 m mean wind speed maps over the United Arab Emirates from (left) Envisat
ASAR wind fields and (right) KAMM mesoscale modeling. From Badger et al. (2010b).
The wind class method was evaluated in the North Sea using in-situ data for comparison.
Theresultswereverygood.Theoverallagreementwithmastobservationsofthewindresource
was within±5% for mean wind speed and Weibull scale parameterand within ±7% forenergy
density and Weibull shape parameter. Similar results were obtained from using more than
288 DTU Wind Energy-E-Report-0029(EN)