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Abstract Numerical model predictions of precipitation rates rely heavily on representations of how fast hydrometeors fall, assuming settling is determined only by the opposing force balance of gravity and drag. Here, we use a novel suite of ground‐based winter measurements to show large departures of the mean snowflake settling speed from the terminal fall speed of a particle falling broadside. Where is lower than the air root‐mean‐square turbulent velocity fluctuation , settling is sub‐terminal by up to a factor of five, and if it is higher, then settling is super‐terminal by up to a factor of three. Mean winds and aerodynamic lift appear to play an unexpectedly important role, by tilting snowflake orientations edge‐on while slowing their mean rate of descent. New parameterizations are provided for relating winds and small‐scale turbulence to hydrometeor orientations, drift angles, and precipitation rate reductions and enhancements. 

Abstract Large‐scale integral constraints on cloud behaviors can guide their more precise representation in present and future climates. We show theoretically that the mean moist static energy at cloud edge is nearly equivalent to the mean saturated static energy of the entire atmospheric domain, whether for a given level or for the cloudy troposphere as a whole. A numerical model simulation of a deep‐convective cloud field shows this equivalence holds over a 10 K range in sea surface temperature. The constraint offers a simple method for estimating the mean level of convective neutral buoyancy, one that rises linearly with the energy of sea surface air. The simulations suggest an interesting second constraint, that the maximum deviation of the saturated static energy profile from its tropospheric mean value is equivalent to the tropospheric mean energy of the saturation deficit. 

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.