Herein the authors introduce the Snowflake Video Imager (SVI), which is a new instrument for characterizing frozen precipitation. An SVI utilizes a video camera with sufficient frame rate, pixels, and shutter speed to record thousands of snowflake images. The camera housing and lighting produce little airflow distortion, so SVI data are quite representative of natural conditions, which is important for volumetric data products such as snowflake size distributions. Long-duration, unattended operation of an SVI is feasible because datalogging software provides data compression and the hardware can operate for months in harsh winter conditions. Details of SVI hardware and field operation are given. Snowflake size distributions (SSDs) from a storm near Boulder, Colorado, are computed. An SVI is an imaging system, so SVI data can be utilized to compute diverse data products for various applications. In this paper, the authors present visualizations of frozen particles (i.e., snowflake aggregates as well as individual crystals), which provide insight into the weather conditions such as temperature, humidity, and winds.

This study uses snow events from the Biogenic Aerosols–Effects on Clouds and Climate (BAECC) 2014 campaign to investigate the connection between properties of snow and radar observations. The general hydrodynamic theory is applied to video-disdrometer measurements to retrieve masses of falling ice particles. Errors associated with the observation geometry and the measured particle size distribution (PSD) are addressed by devising a simple correction procedure. The value of the correction factor is determined by comparison of the retrieved precipitation accumulation with weighing-gauge measurements. Derived mass–dimensional relations are represented in the power-law form m = . It is shown that the retrieved prefactor am and exponent bm react to changes in prevailing microphysical processes. From the derived microphysical properties, event-specific relations between the equivalent reflectivity factor Ze and snowfall precipitation rate S (Ze = ) are determined. For the studied events, the prefactor of the Ze–S relation varied between 53 and 782 and the exponent was in the range of 1.19–1.61. The dependence of the factors azs and bzs on the m(D) relation and PSD are investigated. The exponent of the Ze–S relation mainly depends on the exponent of the m(D) relation, whereas the prefactor azs depends on both the intercept parameter N0 of the PSD and the prefactors of the m(D) and υ(D) relations. Changes in azs for a given N0 are shown to be linked to changes in liquid water path, which can be considered to be a proxy for degree of riming.

Abstract. In this study measurements collected during winters 2013/2014 and 2014/2015 at the University of Helsinki measurement station in Hyytiälä are used to investigate connections between ensemble mean snow density, particle fall velocity and parameters of the particle size distribution (PSD). The density of snow is derived from measurements of particle fall velocity and PSD, provided by a particle video imager, and weighing gauge measurements of precipitation rate. Validity of the retrieved density values is checked against snow depth measurements. A relation retrieved for the ensemble mean snow density and median volume diameter is in general agreement with previous studies, but it is observed to vary significantly from one winter to the other. From these observations, characteristic massdimensional relations of snow are retrieved. For snow rates more than 0.2 mm h −1 , a correlation between the intercept parameter of normalized gamma PSD and median volume diameter was observed.

[1] Results from a rain and gas exchange experiment (Bio2 RainX III) at the Biosphere 2 Center demonstrate that turbulence controls the enhancement of the air-sea gas transfer rate (or velocity) k during rainfall, even though profiles of the turbulent dissipation rate e are strongly influenced by near-surface stratification. The gas transfer rate scales with e 1 = 4 for a range of rain rates with broad drop size distributions. The hydrodynamic measurements elucidate the mechanisms responsible for the rain-enhanced k results using SF 6 tracer evasion and active controlled flux technique. High-resolution k and turbulence results highlight the causal relationship between rainfall, turbulence, stratification, and air-sea gas exchange. Profiles of e beneath the air-sea interface during rainfall, measured for the first time during a gas exchange experiment, yielded discrete values as high as 10 À2 W kg À1 . Stratification modifies and traps the turbulence near the surface, affecting the enhancement of the transfer velocity and also diminishing the vertical mixing of mass transported to the air-water interface. Although the kinetic energy flux is an integral measure of the turbulent input to the system during rain events, e is the most robust response to all the modifications and transformations to the turbulent state that follows. The Craig-Banner turbulence model, modified for rain instead of breaking wave turbulence, successfully predicts the near-surface dissipation profile at the onset of the rain event before stratification plays a dominant role. This result is important for predictive modeling of k as it allows inferring the surface value of e fundamental to gas transfer.