A number of vector and volume averaging considerations arise in relation to remote sensing, and in particular, Lidar. 1)
Remote sensing devices obtain vector averages. These values are often compared to the scalar averages associated with
cup anemometry. The magnitude of a vector average is less than or equal to the scalar average obtained over the same
period. The use of Lidars in wind power applications has entailed the estimation of scalar averages by vector averages
and vice versa. The relationship between the two kinds of average must therefore be understood. It is found that the ratio
of the averages depends upon wind direction variability according to a Bessel function of the standard deviation of the
wind direction during the averaging interval. 2) The finite probe length of remote sensing devices also incurs a volume
averaging bias when wind shear is non-linear. The sensitivity of the devices to signals from a range of heights produces
volume averages which will be representative of wind speeds at heights within that range. One can distinguish between
the effective or apparent height the measured wind speeds represent as a result of volume averaging bias, and the
configuration height at which the device has been set to measure wind speeds. If the wind shear is described by a
logarithmic wind profile the apparent height is found to depend mainly on simple geometrical arguments concerning
configuration height and probe length and is largely independent of the degree of wind shear. 3) The restriction of the
locus of points at which radial velocity measurements are made to the circumference of a horizontally oriented disc at a
particular height is seen to introduce ambiguity into results when dealing with wind vector fields which are not
irrotational.
Lidar has in recent years matured into a reliable and versatile technology for remotely measuring wind speeds at all
heights across the rotor diameter. A laser beam is used to acquire the radial wind velocity in a number of directions at a
given height from the Doppler shift of the backscattered light. From this the wind velocity at that height can be derived.
Lidar allows wind flow model validation. Deployment of a Lidar to sites where different runs of modeling have
produced divergent results can help select which input parameter set is most useful for characterizing wind flow, by
taking measurements that allow differentiation between models. The cost of data acquisition for offshore wind resource
assessment can be reduced by adopting Lidar methods. Less stringent specifications are imposed for platform
installation, and approaches that dispense with the need for a platform are being developed. Operational turbine
performance monitoring can be helpfully augmented by using Lidar to obtain data describing the wind flow impinging
upon a turbine or in its wake. Lidar is also useful in obtaining details of wind shear, turbulence, vertical inflow and wind
veer at proposed and operational turbine locations. Some of the uses Lidar has been applied to, some of its limitations,
and the developing role Lidar will grow into in the future of wind resource assessment, are reviewed here.
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