The habits of cloud particles are a significant factor impacting microphysical processes in clouds. The accurate identification of cloud particle shapes within clouds is a fundamental requirement for calculating various cloud microphysical parameters. In this study, we established a cloud particle image dataset encompassing nine distinct habit categories, totaling 8100 images. These images were captured using three probes with varying resolutions: the Cloud Particle Imager (CPI), the Two-Dimensional Stereo Probe (2D-S), and the High-Volume Precipitation Spectrometer (HVPS). Furthermore, this study performs a comparative analysis of ten different transfer learning (TL) models based on this dataset. It was found that the VGG-16 model exhibits the highest classification accuracy, reaching 97.90%. This model also demonstrates the highest recall, precision, and F1 measure. The results indicate that the VGG-16 model can reliably classify the shapes of ice crystal particles measured by both line scan imagers (2D-S, HVPS) and an area scan imager (CPI).

Ice crystal particle shape is an important factor affecting cloud microphysical processes. Accurately identifying the shapes of ice crystal particle within clouds is a fundamental requirement for calculating various cloud microphysical parameters. In this study, we set up an ice crystal image dataset, encompassing nine distinct habit categories with 8100 images. These images were captured using three probes with varying resolutions: the Cloud Particle Imager (CPI), the Two-dimensional Stereo Probe (2D-S), and the High Volume Precipitation Spectrometer (HVPS). In addition, we introduce a deep convolutional neural network (CNN) based on transfer learning for ice crystal particle shape classification model, TL-AlexNet, which demonstrates the capability to simultaneously classify ice crystal particle habits observed by both the Line Scan Imager and the Area Scan Imager. The results indicate that the TL-AlexNet model could achieve superior performance in ice crystal shapes classification for two types of imagers, and the classification with the accuracy of 97.16%. It is much higher than the traditional shape recognition methods, and has certain application value for Climate and cloud microphysics research.

This paper studies design of data processing software for raindrop spectrometer. It includes reading, decompression, data processing, storage and display. The difficulties are data decompression and the software architecture design. This software applies digital image processing technology to process the raindrop data measured by dual-line raindrop spectrometer data processing software (DRDPS) based on LabVIEW, realizes the display from data file to particle image, stores the obtained information and geometric parameters of precipitation particles, and the obtained meteorological information of precipitation intensity, precipitation particle shape, precipitation type and precipitation particle spectrum is displayed in the front panel of the software. The test results show that the software meets the system requirements completely and can be applied to the research of cloud precipitation and other atmospheric science relevant fields.