Outlook - Surface Based Remote Sensing of the Atmospheric Boundary Layer - page 149

A special challenge in boundary layer remote sensing connected to the gener-
ation of electricity from the wind is the exploration of the wakes of single wind
turbines as well as of entire wind parks. The analysis of these wakes is essential
for the assessment of the influence of a windward wind turbine on further turbines
downwind in a wind park. The result of such assessments is an optimized distance of
turbines in a wind park. Likewise, wind parks affect each other if they are too close
together. Therefore, not only the turbine distance within a park must be tuned but
also the distance between entire wind parks must be optimized for most effective
use of the available space for wind energy conversion (Emeis 2010 ). Main fea-
tures of such wakes are reduced wind speeds and enhanced turbulence intensities
in limited areas behind the disturbing objects. Their analysis requires spatially and
temporally highly resolved wind profile measurements. Thus, optical techniques are
probably the most ideal methods for this task. First experiments have been made in
recent years with small wind LIDARs, but much further work on this issue is neces-
sary. One example is the operation of a horizontally looking scanning wind LIDAR
mounted on the nacelle of a wind turbine that scans the wake of the turbine (Bingöl
et al. 2007 ).
First attempts have been performed to obtain the three-dimensional structure of
the wakes by simultaneous observation with two or even three ground-based wind
LIDARs. For that purpose, three pulsed LIDARs were used in staring mode, placed
so that their beams cross close to a 3D sonic anemometer mounted at a mast 78
m above the ground. The results show generally very good correlation between the
LIDAR and the sonic times series, except that the variance of the velocity mea-
sured by the LIDAR is attenuated due to spatial filtering. The amount of attenuation
can, however, be predicted theoretically by use of a spectral tensor model of the
atmospheric surface layer turbulence (Mann et al. 2009 ).
A further, tightly related example is the optimal operation of a wind turbine in a
turbulent wind field. Optimizing turbine orientation and blade angles requires short-
term prediction on upcoming turbulence elements in the incident flow. Suggestions
have been made to operate small wind LIDARs on turbine nacelles, which look
horizontally into the approaching wind flow (e.g. Harris et al. 2007 ).
The second application demand that fosters further instrument development is
monitoring of greenhouse gas emissions. With progressing climate change, the
supervision of these emissions increasingly becomes a major task. Vertical profiling
of the diurnal variation of such gas concentrations could provide input for determin-
ing the source strengths of these gases via budget methods (Denmead et al. 1996 ,
2000 ;Emeis 2008 ). Direct emission measurements will be increasingly made from
turbulent flux determination.
The third application demand requires among other tasks concentration mea-
surements and the determination of ABL features, which limit the dispersion of
trace gases. Two of these ABL features are the mixing-layer height and the vertical
wind profile above the surface layer. The variables “mixing layer height” and “atmo-
spheric boundary layer height” are important for the description of vertical profiles
within the boundary layer (Peña et al. 2008 , 2010 ) and for air quality assessments
(e.g. Emeis et al. 2008 ). These variables can neither be measured nor modelled