A snowfall rate estimate based on the snow types

Weather radar observations of snow depend on size, shape, orientation and density of the ice particles. Variability in these physical properties is one of the major error sources in quantitative snowfall estimation with radar (Mitchell et al. 1990). The classification of winter precipitation according to hydrometeor classes such as aggregates, crystals, and rimed particles can give guidance for refinement of snowfall estimate techniques. A snowfall algorithm based on the snow types has been developed. The method uses snow type identification to guide the choice of the particular parameters of power law relations of equivalent radar reflectivity factor–liquid equivalent snow rate. In this algorithm, snow types are categorized as snow, aggregate, rimed snow and high density ice (graupel). Data collected from the C-band operational Helsinki Vantaa radar (VAN) and ground instruments (Vaisala PWD-11 and Pluvio that located at the University of Helsinki Kumpula) are used to evaluate the performance of the proposed algorithm. The preliminary results show that the selective choice of power law parameters corresponding to snow types can provide more accurate snowfall estimation.