Using super-large-scale particle image velocimetry (SLPIV), we investigate the spatial structure of the near-wall region in the fully rough atmospheric surface layer with Reynolds number$Re_{\unicode[STIX]{x1D70F}}\sim O(10^{6})$. The field site consists of relatively flat, snow-covered farmland, allowing for the development of a fully rough turbulent boundary layer under near-neutral thermal stability conditions. The imaging field of view extends from 3 m to 19 m above the ground and captures the top of the roughness sublayer and the bottom of an extensive logarithmic region. The SLPIV technique uses natural snowfall as seeding particles for the flow imaging. We demonstrate that SLPIV provides reliable measurements of first- and second-order velocity statistics in the streamwise and wall-normal directions. Our results in the logarithmic region show that the structural features identified in laboratory studies are similarly present in the atmosphere. Using instantaneous vector fields and two-point correlation analysis, we identify vortex structures sharing the signature of hairpin vortex packets. We also evaluate the zonal structure of the boundary layer by tracking uniform momentum zones (UMZs) and the shear interfaces between UMZs in space and time. Statistics of the UMZs and shear interfaces reveal the role of the zonal structure in determining the mean and variance profiles. The velocity difference across the shear interfaces scales with the friction velocity, in agreement with previous studies, and the size of the UMZs scales with wall-normal distance, in agreement with the attached eddy framework.

We report on optical field measurements of snow settling in atmospheric turbulence at $Re_{\unicode[STIX]{x1D706}}=940$. It is found that the snowflakes exhibit hallmark features of inertial particles in turbulence. The snow motion is analysed in both Eulerian and Lagrangian frameworks by large-scale particle imaging, while sonic anemometry is used to characterize the flow field. Additionally, the snowflake size and morphology are assessed by digital in-line holography. The low volume fraction and mass loading imply a one-way interaction with the turbulent air. Acceleration probability density functions show wide exponential tails consistent with laboratory and numerical studies of homogeneous isotropic turbulence. Invoking the assumption that the particle acceleration has a stronger dependence on the Stokes number than on the specific features of the turbulence (e.g. precise Reynolds number and large-scale anisotropy), we make inferences on the snowflakes’ aerodynamic response time. In particular, we observe that their acceleration distribution is consistent with that of particles of Stokes number in the range $St=0.1{-}0.4$ based on the Kolmogorov time scale. The still-air terminal velocities estimated for the resulting range of aerodynamic response times are significantly smaller than the measured snow particle fall speed. This is interpreted as a manifestation of settling enhancement by turbulence, which is observed here for the first time in a natural setting.

Super-large-scale particle image velocimetry (SLPIV) and the associated flow visualization technique using natural snowfall have been shown to be effective tools to probe the turbulent velocity field and coherent structures around utility-scale wind turbines (Hong et al.Nat. Commun., vol. 5, 2014, article 4216). Here, we present a follow-up study using the data collected during multiple deployments from 2014 to 2016 around the 2.5 MW turbine at the EOLOS field station. These data include SLPIV measurements in the near wake of the turbine in a field of view of 115 m (vertical) $\times$ 66 m (streamwise), and the visualization of tip vortex behaviour near the elevation corresponding to the bottom blade tip over a broad range of turbine operational conditions. The SLPIV measurements provide velocity deficit and turbulent kinetic energy assessments over the entire rotor span. The instantaneous velocity fields from SLPIV indicate the presence of intermittent wake contraction states which are in clear contrast with the expansion states typically associated with wind turbine wakes. These contraction states feature a pronounced upsurge of velocity in the central portion of the wake. The wake velocity ratio $R_{w}$, defined as the ratio of the spatially averaged velocity of the inner wake to that of the outer wake, is introduced to categorize the instantaneous near wake into expansion ($R_{w}<1$) and contraction states ($R_{w}>1$). Based on the $R_{w}$ criterion, the wake contraction occurs 25 % of the time during a 30 min time duration of SLPIV measurements. The contraction states are found to be correlated with the rate of change of blade pitch by examining the distribution and samples of time sequences of wake states with different turbine operation parameters. Moreover, blade pitch change is shown to be strongly correlated to the tower and blade strains measured on the turbine, and the result suggests that the flexing of the turbine tower and the blades could indeed lead to the interaction of the rotor with the turbine wake, causing wake contraction. The visualization of tip vortex behaviour demonstrates the presence of a state of consistent vortex formation as well as various types of disturbed vortex states. The histograms corresponding to the consistent and disturbed states are examined over a number of turbine operation/response parameters, including turbine power and tower strain as well as the fluctuation of these quantities, with different conditional sampling restrictions. This analysis establishes a clear statistical correspondence between these turbine parameters and tip vortex behaviours under different turbine operation conditions, which is further substantiated by examining samples of time series of these turbine parameters and tip vortex patterns. This study not only offers benchmark datasets for comparison with the-state-of-the-art numerical simulation, laboratory and field measurements, but also sheds light on understanding wake characteristics and the downstream development of the wake, turbine performance and regulation, as well as developing novel turbine or wind farm control strategies.