  Super-large-scale particle image velocimetry and flow visualization with
natural snowfall is used to collect and analyze multiple datasets in the near
wake of a 2.5 MW wind turbine. Each dataset captures the full vertical span of
the wake from a different perspective. Together, these datasets compose a
three-dimensional picture of the near-wake flow, including the effect of the
tower and hub and the variation of instantaneous wake expansion in response to
changes in turbine operation. A region of high-speed flow is observed directly
behind the hub, and a region of low-speed flow appears behind the tower.
Additionally, the hub produces a region of enhanced turbulence in its wake
while the tower reduces turbulence near the ground as it breaks up turbulent
structures in the boundary layer. Analysis of the instantaneous wake behaviour
reveals variations in wake expansion, and even periods of wake contraction,
occurring in response to changes in the angle of attack and blade pitch
gradient. This behaviour is found to depend on the region of operation of the
turbine. These findings can be incorporated into wake models and advanced
control algorithms for wind farm optimization and can be used to validate wind
turbine wake simulations.
