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Snowfall is an atmospheric phenomenon that can cause significant impacts to many aspects of daily life in Missouri. Further, no two snowfall events are exactly the same, as even small differences in environmental characteristics can result in differing snow crystal types dominating the event, which in turn can result in differing impacts from event to event. Therefore, it is necessary to understand snowfall behavior so that better forecasts and in situ analyses may be made. In this study, snowflake maximum dimension and fall velocity measurements were recorded using the OTT Parsivel Laser Disdrometer. In conjunction with distribution of measured maximum dimensions, RAP Analysis soundings were used to determine snow crystal type. From there, the relationships between fall velocity and maximum dimension and the particle size distributions of snowflakes from many snowfall events were analyzed. Observed relationships between fall velocity and maximum dimension were compared with previously derived relationships, and it was found that, in most cases, no single curve represented the relationship in the observed data well, with discrepancies caused by instrumentation error and lack of a single dominant crystal type. To analyze particle size distributions, several distribution functions were fit to the observed distribution using a least-squares regression method in MATLAB. It was found that, overall, the triple Gaussian distribution function performed the best in modeling particle size distributions in snow, but there were some instances where the gamma function modeled the distribution best. Further study, especially with the inclusion of field observations in addition to instrument observations, is necessary to develop a better understanding of these snowfall events.

Assessing fall velocity-maximum dimension relationships and particle size distributions for snowfall