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Research on the settling dynamics of snow particles, considering their complex morphologies and real atmospheric conditions, remains scarce despite extensive simulations and laboratory studies. Our study bridges this gap through a comprehensive field investigation into the three-dimensional (3-D) snow settling dynamics under weak atmospheric turbulence, enabled by a 3-D particle tracking velocimetry (PTV) system to record over a million trajectories, coupled with a snow particle analyser for simultaneous aerodynamic property characterization of four distinct snow types (aggregates, graupels, dendrites, needles). Our findings indicate that while the terminal velocity predicted by the aerodynamic model aligns well with the PTV-measured settling velocity for graupels, significant discrepancies arise for non-spherical particles, particularly dendrites, which exhibit higher drag coefficients than predicted. Qualitative observations of the 3-D settling trajectories highlight pronounced meandering in aggregates and dendrites, in contrast to the subtler meandering observed in needles and graupels, attributable to their smaller frontal areas. This meandering in aggregates and dendrites occurs at lower frequencies compared with that of graupels. Further quantification of trajectory acceleration and curvature suggests that the meandering frequencies in aggregates and dendrites are smaller than that of morphology-induced vortex shedding of disks, likely due to their rotational inertia, and those of graupels align with the small-scale atmospheric turbulence. Moreover, our analysis of vertical acceleration along trajectories elucidates that the orientation changes in dendrites and aggregates enhance their settling velocity. Such insights into settling dynamics refine models of snow settling velocity under weak atmospheric turbulence, with broader implications for more accurately predicting ground snow accumulation. 

SUMMARY We present a new 3-D radially anisotropic seismic velocity model EARA2024 of the crust and mantle beneath East Asia and the northwestern Pacific using adjoint full-waveform inversion tomography. We construct the EARA2024 model by iteratively minimizing the waveform similarity misfit between the synthetic and observed waveforms from 142 earthquakes recorded by about 2000 broad-band stations in East Asia. Compared to previous studies, this new model renders significantly improved images of the subducted oceanic plate in the upper mantle, mantle transition zone, and uppermost lower mantle along the Kuril, Japan, Izu-Bonin and Ryukyu Trenches. Complex slab deformation and break-offs are observed at different depths. Moreover, our model provides new insights into the origins of intraplate volcanoes in East Asia, including the Changbaishan, Datong-Fengzhen, Tengchong and Hainan volcanic fields. 

The statistical properties of uniform momentum zones (UMZs) are extracted from laboratory and field measurements in rough wall turbulent boundary layers to formulate a set of stochastic models for the simulation of instantaneous velocity profiles. A spatiotemporally resolved velocity dataset, covering a field of view of$$8 \times 9\,{\rm m}^2$$, was obtained in the atmospheric surface layer using super-large-scale particle image velocimetry (SLPIV), as part of the Grand-scale Atmospheric Imaging Apparatus (GAIA). Wind tunnel data from a previous study are included for comparison (Heiselet al.,J. Fluid Mech., vol. 887, 2020, R1). The probability density function of UMZ attributes such as their thickness, modal velocity and averaged vertical velocity are built at varying elevations and modelled using log-normal and Gaussian distributions. Inverse transform sampling of the distributions is used to generate synthetic step-like velocity profiles that are spatially and temporally uncorrelated. Results show that in the wide range of wall-normal distances and$$Re_\tau$$up to$$\sim O(10^6)$$investigated here, shear velocity scaling is manifested in the velocity jump across shear interfaces between adjacent UMZs, and attached eddy behaviour is observed in the linear proportionality between UMZ thickness and their wall normal location. These very same characteristics are recovered in the generated instantaneous profiles, using both fully stochastic and data-driven hybrid stochastic (DHS) models, which address, in different ways, the coupling between modal velocities and UMZ thickness. Our method provides a stochastic approach for generating an ensemble of instantaneous velocity profiles, consistent with the structural organisation of UMZs, where the ensemble reproduces the logarithmic mean velocity profile and recovers significant portions of the Reynolds stresses and, thus, of the streamwise and vertical velocity variability. 

Abstract Understanding the organization and dynamics of turbulence structures in the atmospheric surface layer (ASL) is important for fundamental and applied research in different fields, including weather prediction, snow settling, particle and pollutant transport, and wind energy. The main challenges associated with probing and modeling turbulence in the ASL are: i) the broad range of turbulent scales associated with the different eddies present in high Reynolds-number boundary layers ranging from the viscous scale (𝒪(mm)) up to large energy-containing structures (𝒪(km)); ii) the non-stationarity of the wind conditions and the variability associated with the daily cycle of the atmospheric stability; iii) the interactions among eddies of different sizes populating different layers of the ASL, which contribute to momentum, energy, and scalar turbulent fluxes. Creative and innovative measurement techniques are required to probe near-surface turbulence by generating spatio-temporally-resolved data in the proximity of the ground and, at the same time, covering the entire ASL height with large enough streamwise extent to characterize the dynamics of larger eddies evolving aloft. To this aim, the U.S. National Science Foundation sponsored the development of the Grand-scale Atmospheric Imaging Apparatus (GAIA) enabling super-large snow particle image velocimetry (SLPIV) in the near-surface region of the ASL. This inaugural version of GAIA provides a comprehensive measuring system by coupling SLPIV and two scanning Doppler LiDARs to probe the ASL at an unprecedented resolution. A field campaign performed in 2021–2022 and its preliminary results are presented herein elucidating new research opportunities enabled by the GAIA measuring system.