Snowflakes in the atmospheric surface layer: observation of particle–turbulence dynamics

Abstract

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.

Multiscale preferential sweeping of particles settling in turbulence

“…This is in contrast to previous work (e.g. Wang & Maxey 1993;Yang & Lei 1998;Good et al 2014;Nemes et al 2017) where it has been argued that the relevant velocity scale determining u z (x p (t), t) is u ′ , which is associated with the large scales of the flow. We would argue that the results in those previous studies were strongly affected by the fact that the R λ values they considered were such that u ′ /u η was not very large.…”

Incoming flow measurements of a utility-scale wind turbine using super-large-scale particle image velocimetry

“…Figure 4 provides a sample image with raw, distortion corrected, and enhanced versions from Run 1. Note that the current spatial resolution (7.4 cm/pixel) is not capable of resolving individual snow particles like in cases with finer spatial resolution [43,44,46,47]. Thus, similar to Dasari et al [45], the current velocity field analysis relies on tracking coherent structures within the atmospheric boundary layer.…”

Automated droplet size distribution measurements using digital inline holography

“…DIH has been recently demonstrated for the analysis of the motion and morphology of individual aerosol particles (Berg & Holler, 2016;David et al, 2018;Giri et al, 2019). DIH has also been used successfully employed in a diverse array of fields, including the study of social behaviors of flies (Kumar et al, 2016), snowflake size distribution function and morphology (Nemes et al, 2017), atmospheric mixing and cloud formation (Beals et al, 2015), ocean sediment particle size and velocity transport (Graham & Nimmo Smith, 2010), oil droplets in oceans (Li et al, 2017), bubbly wake behind a ventilated supercavity (Shao et al, 2019), and preliminarily (without automated processing), droplets from sprays in compressible flow cross wind (Olinger et al, 2014). Building upon these studies, in this work our goal is to develop an automated and rigorous data analysis approach for DIH to specifically determine the size distribution functions of spray generated droplets.…”