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Abstract Cold fog refers to a type of fog that forms when the temperature is below 0°C. It can be composed of liquid, ice, and mixed‐phase fog particles. Cold fog happens frequently over mountainous terrain in the cold season, but it is difficult to predict. Using observations from the Cold Fog Amongst Complex Terrain (CFACT) field campaign conducted in Heber Valley, Utah, in the western United States during January and February of 2022, this study investigates the meteorological conditions in the surface and boundary layers that support the formation of wintertime ephemeral cold fog in a local area of small‐scale mountain valleys. It is found that fog formation is susceptible to subtleties in forcing conditions and is supported by several factors: (1) established high pressure over the Great Basin with associated local clear skies, calm winds, and a stable boundary layer; (2) near‐surface inversion with saturation near the surface and strong moisture gradient in the boundary layer; (3) warm (above‐freezing) daytime air temperature with a large diurnal range, accompanied with warm soil temperatures during the daytime; (4) a period of increased turbulence kinetic energy (above 0.5 m²·s⁻²), followed by calm conditions throughout the fog's duration; and (5) supersaturation with respect to ice. Then, the field observations and identified supporting factors for fog formation were utilized to evaluate high‐resolution (˜400 m horizontal grid spacing) Weather Research and Forecasting (WRF) model simulations. Results show that the WRF model accurately simulates the mesoscale conditions facilitating cold‐fog formation but misses some critical surface and atmospheric boundary conditions. The overall results from this paper indicate that these identified factors that support fog formation are vital to accurately forecasting cold‐fog events. At the same time, they are also critical fields for the NWP model validation. 

Abstract. It is a challenge to obtain accurate measurements of the microphysical properties of delicate, structurally complex, frozen, and semi-frozen hydrometeors. We present a new technique for the real-time measurement of the density of freshly fallen individual snowflakes. A new thermal-imaging instrument, the Differential Emissivity Imaging Disdrometer (DEID), has been shown through laboratory and field experiments to be capable of providing accurate estimates of individual snowflake and bulk snow hydrometeor density (which can be interpreted as the snow-to-liquid ratio or SLR). The method exploits the rate of heat transfer during the melting of a hydrometeor on a heated metal plate, which is a function of the temperature difference between the hotplate surface and the top of the hydrometeor. The product of the melting speed and melting time yields an effective particle thickness normal to the hotplate surface, which can then be used in combination with the particle mass and area on the plate to determine a particle density. Uncertainties in estimates of particle density are approximately 4 % based on calibrations with laboratory-produced particles made from water and frozen solutions of salt and water and field comparisons with both high-resolution imagery of falling snow and traditional snowpack density measurements obtained at 12 h intervals. For 17 storms, individual particle densities vary from 19 to 495 kg m−3, and storm mean snow densities vary from 40 to 100 kg m−3. We observe probability distribution functions for hydrometeor density that are nearly Gaussian with kurtosis of ≈ 3 and skewness of ≈ 0.01. 

We use a novel experimental setup to obtain the vertical velocity and acceleration statistics of snowflakes settling in atmospheric surface-layer turbulence, for Taylor microscale Reynolds numbers (Reλ) between 400 and 67 000, Stokes numbers (St) between 0.12 and 3.50, and a broad range of snowflake habits. Despite the complexity of snowflake structures and the non-uniform nature of the turbulence, we find that mean snowflake acceleration distributions can be uniquely determined from the value of St. Ensemble-averaged snowflake root mean square (rms) accelerations scale nearly linearly with St. Normalized by the rms value, the acceleration distribution is nearly exponential, with a scaling factor for the (exponent) of −3/2 that is independent of Reλ and St; kurtosis scales with Reλ, albeit weakly compared to fluid tracers in turbulence; gravitational drift with sweeping is observed for St < 1. Surprisingly, the same exponential distribution describes a pseudo-acceleration calculated from fluctuations of snowflake terminal fall speed in still air. This equivalence suggests an underlying connection between how turbulence determines the trajectories of particles and the microphysics determining the evolution of their shapes and sizes. 

Abstract New particle formation (NPF) is a complex atmospheric phenomenon defined by the gas‐to‐particle conversion that leads to the sudden burst and growth in aerosol particles. Although chemical mechanisms for aerosol nucleation and growth are well established, the role of physical processes, such as turbulent mixing, within the atmospheric boundary layer (ABL) is beginning to emerge with recent studies. This study, based on the observations from the 2022 CFACT (Cold Fog Amongst Complex Terrain) field study in the Heber Valley of northern Utah, demonstrates an interconnection between turbulence and the occurrence of NPF. Using a spatially distributed boundary layer instrumentation, a novel feature of CFACT, three case studies depict unique boundary layer conditions that modulate the development of NPF characterized by sustained turbulence and weak intermittent turbulence. Quantitative analysis using in situ measurements and derived variables demonstrate that periods of weak intermittent turbulence hinder particle growth, whereas sustained turbulence helps modulate NPF. These findings provide new insights into the physical drivers of NPF, underscoring the role of turbulence in impacting particle formation with the ABL. 

Recent works on wall-bounded flows have corroborated the coexistence of wall-attached eddies, whose statistical features are predicted through Townsend's attached-eddy hypothesis (AEH), and very-large-scale motions (VLSMs). Furthermore, it has been shown that the presence of wall-attached eddies within the logarithmic layer is linked to the appearance of an inverse-power-law region in the streamwise velocity energy spectra, upon significant separation between outer and viscous scales. In this work, a near-neutral atmospheric surface layer is probed with wind light detection and ranging to investigate the contributions to the streamwise velocity energy associated with wall-attached eddies and VLSMs for a very-high-Reynolds-number boundary layer. Energy and linear coherence spectra (LCS) of the streamwise velocity are interrogated to identify the spectral boundaries associated with eddies of different typologies. Inspired by the AEH, an analytical model for the LCS associated with wall-attached eddies is formulated. The experimental results show that the identification of the wall-attached-eddy energy contribution through the analysis of the energy spectra leads to an underestimate of the associated spectral range, maximum height attained and turbulence intensity. This feature is due to the overlap of the energy associated with VLSMs obscuring the inverse-power-law region. The LCS analysis estimates wall-attached eddies with a streamwise/wall-normal ratio of about 14.3 attaining a height of about 30 % of the outer scale of turbulence. 

To examine spatial and temporal scales of katabatic flow, a distributed temperature sensing (DTS) optical fiber was deployed 2 km down a mild slope irregularly interrupted by small-scale drainage features as part of the Mountain Terrain Atmospheric Modeling and Observation (MATERHORN) experiment conducted at the U.S. Army Dugway Proving Ground, Utah. The fiber was suspended at two heights near the surface, enabling measurement of variations in lapse rate near the surface at meter-scale spatial resolution with 1-min temporal resolution. Experimental results derived from the DTS and tower-mounted instrumentation indicate that airflow through small-scale drainage features regulated the local cooling rate whereas topographic slope and distance along the drainage strongly influenced the larger-scale cooling rate. Empirical results indicate that local cooling rate decays exponentially after local sunset and basin-wide cooling rate decreases linearly with time. The difference in the functional form for cooling rate between local and basin-wide scales suggests that small-scale features have faster timescales that manifests most strongly shortly after local sunset. More generally, partitioning drainage flow by scale provides insight and a methodology for improved understanding of drainage flow in complex terrain. 

Data from the Idealized Planar-Array experiment for Quantifying Spatial heterogeneity are used to perform a control volume analysis (400 × 400 × × 2 m^3) on the total derivative of the temperature tendency equation. Analysis of the heat-flux imbalance, which is defined as the ratio of the sum of advective, dispersive, and turbulence-flux terms to the turbulence-flux term, are presented. Results are divided amongst free-convective and forced-convective days, as well as high-wind-speed and quiescent nocturnal periods. Findings show that the median flux imbalance is greater on forced-convective days (a 168% turbulence-flux overestimation, or relative importance of the advection to dispersive flux to the turbulence flux) when compared to free-convective periods (79% turbulence-flux overestimation). During nocturnal periods, a median turbulence-flux underestimation of 146% exists for quiescent nights and a 43% underestimation of the flux for high-wind-speed nights. These results support the existing literature, suggesting that mean air-temperature heterogeneities lead to strong bulk advection and dispersive fluxes. A discussion of the impact of the flux imbalance on the surface energy balance and numerical-weather-prediction modelling is presented.