Abstract
Wet snow may cause significant damage to humans and property, and thus, it is necessary to estimate the corresponding liquid fraction (FL). Consequently, the FL of wet snow was estimated using a novel technique; specifically, the particle shape irregularity (Ir) was estimated through the particle coordinate information obtained using 2-D video disdrometer (2DVD) measurements. Moreover, the possibility of quantitively estimating FL via Ir, based on the temperature (T), was examined. Eight snowfall cases from 2014 to 2016 were observed through a 2DVD installed in Jincheon, South Korea, to analyze the dominant properties of physical variables of snowflakes (i.e., the terminal velocity (VT), particle density (ρs), Ir, and FL) and the corresponding relationships according to the T ranges (−4.5 < T (°C) < 2.5) in which wet snow can occur. It was clarified that the volume-equivalent particle diameter (D)–FL and D–Ir relationships depended on T, and a relationship existed between Ir and FL. The analysis results were verified using the Yong-In Testbed (YIT) S-band weather radar and T-matrix scattering simulation. The D–FL relationship was implemented in the scattering simulation, and the results indicated that the simulated reflectivity (ZS) was highly correlated with the observed reflectivity (ZO) under all T classes. These features can provide a basis for radar analysis and quantitative snowfall estimation for wet snow with various FL values.
Highlights
Snow occurs in many types and phases, such as the liquid, solid, and combination phases
The characteristics of the physical variables and relationships according to each T class
To analyze the characteristics of the physical variables (VT, ρs, FL, and Ir) of wet snow according to T, statistical analysis was performed on eight snowfall cases under various conditions of surface T, as observed using the 2-D video disdrometer (2DVD) in Jincheon, S
Summary
Snow occurs in many types and phases, such as the liquid, solid, and combination phases. The accuracy of radar-based snowfall estimations is mainly affected by the radar properties, weather conditions, and Z–S relationships, which depend on the physical variables of the snowflakes [21,22,23,24]. The standard deviation of the fall speed of wet snow was noted to be 120–230% larger than that of dry snow [30] In this context, the reliable estimation of ground-based snowfall is difficult due to the wind-driven horizontal movement of snowflakes caused by the relatively lower snowflake density and large variability in the snowflake density as a function of the vertical structure of the atmospheric temperature (hereafter, “temperature,” T) and humidity [31,32].
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