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

Abstract 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 free-fall 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. Significance Statement This work proposes and presents a novel multiview snowflake classification methodology leveraging recent developments in machine learning, multiview classification, and multiangle multicamera instruments for acquiring high-resolution photographs of frozen hydrometeors in free-fall from multiple views. The results for multiview classification show record accuracies for snowflake geometric classification, riming degree estimation, and melt/dry state estimation, respectively, significantly outperforming other classification models in each of the same categories. Automatic multiview machine learning–based winter hydrometeor classification enhances understanding, characterization, and quantification of geometrical, microphysical, and scattering properties of ice and snow hydrometeors. These improvements are essential for quantitative precipitation estimation algorithms and for microphysical parameterizations employed in numerical winter-weather forecast models and regional climate projections, with impacts on economy, safety, and everyday life.