Hydrometeor classification from two-dimensional video disdrometer data
- 1Environmental Remote Sensing Laboratory (LTE), École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
- 2Laboratory of Geographic Information Systems (LASIG), École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
- 3Federal Office of Meteorology and Climatology, Meteo Swiss, Locarno-Monti, Switzerland
Abstract. The first hydrometeor classification technique based on two-dimensional video disdrometer (2DVD) data is presented. The method provides an estimate of the dominant hydrometeor type falling over time intervals of 60 s during precipitation, using the statistical behavior of a set of particle descriptors as input, calculated for each particle image. The employed supervised algorithm is a support vector machine (SVM), trained over 60 s precipitation time steps labeled by visual inspection. In this way, eight dominant hydrometeor classes can be discriminated. The algorithm achieved high classification performances, with median overall accuracies (Cohen's K) of 90% (0.88), and with accuracies higher than 84% for each hydrometeor class.