Novel Multiview Machine Learning Classification of Snowflakes: Harnessing Convolutional Neural Networks and Multiangle Multicamera Instruments

Classification of snowflakes based on their geometric shape, degree of riming, and melt/dry state can improve understanding, characterization, and quantification of other geometrical, microphysical, and scattering properties of ice particles. For example, classification provides essential ground truth data for interpreting polarimetric radar signatures of snow while validating and advancing radar-based quantitative precipitation estimation. High-resolution photographs of snowflakes obtained by emerging multicamera instruments are well suited for snowflake classification, which, coupled with recent machine learning techniques based on Convolutional Neural Networks (CNNs), enable methods for accurate and fast automatic classification of snowflakes using images. Given that the appearance of a snowflake generally changes significantly with viewing angle, this work proposes and presents a novel multiview snowflake classification methodology based on the high-resolution photographs of frozen hydrometeors in freefall from multiple views collected by the multicamera instruments. The approach employs machine/deep learning algorithms leveraging multiangle camera systems and enhanced supervised CNN-based techniques to achieve precise classification of snowflakes based on their geometrical categories and accurate and reliable estimates of specific snowflake properties, such as riming degree and melt/dry state. This represents the first multiview snowflake-classification framework that takes full advantage of multiview camera systems. Presented multiview classification results show record accuracies of 98.57%, 98.22%, and 95.83% for geometric classes, riming degree, and melt/dry state, respectively.