Snow event classification with a 2D video disdrometer — A decision tree approach

Abstract

Snowfall classification according to crystal type or degree of riming of the snowflakes is import for many atmospheric processes, e.g. wet deposition of aerosol particles. 2D video disdrometers (2DVD) have recently proved their capability to measure microphysical parameters of snowfall. The present work has the aim of classifying snowfall according to microphysical properties of single hydrometeors (e.g. shape and fall velocity) measured by means of a 2DVD. The constraints for the shape and velocity parameters which are used in a decision tree for classification of the 2DVD measurements, are derived from detailed on-site observations, combining automatic 2DVD classification with visual inspection. The developed decision tree algorithm subdivides the detected events into three classes of dominating crystal type (single crystals, complex crystals and pellets) and three classes of dominating degree of riming (weak, moderate and strong). The classification results for the crystal type were validated with an independent data set proving the unambiguousness of the classification. In addition, for three long-term events, good agreement of the classification results with independently measured maximum dimension of snowflakes, snowflake bulk density and surrounding temperature was found. The developed classification algorithm is applicable for wind speeds below 5.0ms-1 and has the advantage of being easily implemented by other users.