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Shape Classification of Cloud Particles Recorded by the 2D-S Imaging Probe Using a Convolutional Neural Network

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The airborne two-dimensional stereo (2D-S) optical array probe has been operating for more than 10 yr, accumulating a large amount of cloud particle image data. However, due to the lack of reliable and unbiased classification tools, our ability to extract meaningful morphological information related to cloud microphysical processes is limited. To solve this issue, we propose a novel classification algorithm for 2D-S cloud particle images based on a convolutional neural network (CNN), named CNN-2DS. A 2D-S cloud particle shape dataset was established by using the 2D-S cloud particle images observed from 13 aircraft detection flights in 6 regions of China (Northeast, Northwest, North, East, Central, and South China). This dataset contains 33,300 cloud particle images with 8 types of cloud particle shape (linear, sphere, dendrite, aggregate, graupel, plate, donut, and irregular). The CNN-2DS model was trained and tested based on the established 2D-S dataset. Experimental results show that the CNN-2DS model can accurately identify cloud particles with an average classification accuracy of 97%. Compared with other common classification models [e.g., Vision Transformer (ViT) and Residual Neural Network (ResNet)], the CNN-2DS model is lightweight (few parameters) and fast in calculations, and has the highest classification accuracy. In a word, the proposed CNN-2DS model is effective and reliable for the classification of cloud particles detected by the 2D-S probe.

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Correspondence to Haixia Xiao.

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Supported by the National Key Research and Development Program of China (2019YFC1510301), Key Innovation Team Fund of the China Meteorological Administration (CMA2022ZD10), and Basic Research Fund of the Chinese Academy of Meteorological Sciences (2021Y010).

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Zhang, R., Xiao, H., Gao, Y. et al. Shape Classification of Cloud Particles Recorded by the 2D-S Imaging Probe Using a Convolutional Neural Network. J Meteorol Res 37, 521–535 (2023).

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