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).

In this study, we investigated the macro- and microstructures of layered precipitation clouds in spring in Jilin Province, China. The premise of the campaign was to observe cloud particles in the melting layer (ML). The weather was developed under the influence of the Mongolia cyclone, which brought a large range of precipitation to the northeast. Combining the Droplet Measurement Technology (DMT) and Particle Measuring Systems (PMS) data, small particles accounted for the majority of all particles at each level above and below the ML. In our observations, both ice crystals (50–300 μm) and snowflakes (>300 μm) had two peaks between −5 and −2 °C. The high concentration of ice crystals at a temperature of −2.65 °C (4865 m) attained a maximum value of 287 L⁻¹ and snowflakes with 47 L⁻¹, which was similar to the previous studies. The Hallett–Mossop ice multiplication process operated most effectively at the temperature of −5 °C in this study. Even at the cloud dissipation stage, new droplets were still generated between −5 and −6 °C, providing abundant liquid water content (LWC) for the upper cloud. Although irregulars were observed, needles and spheres dominated in the observed cloud region of low LWC (<0.1 g m⁻³) at temperatures of −6 to −3 °C. These cloud conditions fit into the Hallett–Mossop criteria.

Stratiform clouds with embedded convective cells is an important precipitation system. Precise knowledge of the cloud’s microphysical structure can be useful for the development of a numerical weather prediction model and precipitation enhancement. Airborne measurement is one of the important ways for determining the microphysical structure of clouds. However, cloud particle shattering during measurement poses a serious problem to the measured microphysical characterization of clouds. In order to study the different influences of the shattered ice particles on the standard cloud imaging probe (CIP) measurement in the stratiform cloud region and the convective cloud region, a time-variant threshold method to identify the shattered fragments is presented. After application of this algorithm, the shattered fragments were recognized and their impacts on the particle size distribution (PSD), particle number concentration and ice water content measurement were analyzed. It was found that the shattering effect on the PSD decreases with the increasing size of less than 400 μm, fluctuates between 400 μm and 1000 μm and slightly increases with the increasing size of larger than 1000 μm on average in a stratiform region and a convective region. However, the average ratio of PSD uncorrected to that corrected for shattering events using the presented algorithm in convective clouds is larger than that in the stratiform regions in the whole size, and nearly twice that in the size of less than 1000 μm. The measured number concentration can be overestimated by up to a factor of 3.9 on average in a stratiform region, while in a convective region, it is 7.7, nearly twice that of a stratiform region. The ice water content in a stratiform region can be overestimated by 29.5% on average, but by 60.7% in a convective region. These findings can be helpful for the cloud physics community to use the airborne CIP measurement data for numerical weather and climate models.

作为云微物理过程测量的重要利器，机载云降水粒子成像仪在云降水物理与人工影响天气研究中具有重要的作用.从采样结果来看，机载云降水粒子成像仪所测粒子图像中含有大量的粒子图像仅是粒子的一部分而已，即部分状粒子.因其数量较多，对该类粒子所选处理方法不同，会引起测量结果的很大差异.本文介绍并分析了现有部分状粒子处理方法的优劣，通过对部分状粒子的再定义与粒子形状分类，提出了一个融合粒子形状识别技术、“粒径重构”和“中心在内”方法的新的部分状粒子处理方法；利用实测数据，对所提方法与现有方法进行了云微物理参量处理结果的对比，发现本文所提方法与“粒径重构”方法处理结果比较一致，能较好地克服“整体在内”与“中心在内”两种方法存在的缺陷；同时，在针柱状粒子占比较多情形下，本文所提方法要比“粒径重构”方法处理后的结果相对合理.因此本文所提方法对仪器所测粒子数据处理具有更好的适应性.

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.

Accurate identification of supercooled water cloud (SWC) is critical for precipitation enhancement, reducing the risk of aircraft icing, and advancing our understanding of Earth’s radiative energy budget. However, SWC detection is frequently missed in the current official satellite products. To fill this observational gap and to investigate the potential use of the Himawari-8 satellite observation in the Northeast China Cold Vortex (NCCV) research, this study introduces an efficient algorithm to detect SWC from the Advanced Himawari Imager (AHI), which first uses a random forest (RF) model to classify the cloud into nine cloud types—high ice cloud, high water cloud, high mixed cloud, middle ice cloud, middle water cloud, middle mixed cloud, low water cloud, low ice cloud and low mixed cloud. Then, with the level 2 products, i.e., cloud optical thickness (COT), cloud effective radius (CER) and cloud-top temperature (CTT), the SWCs will further be identified from the water clouds and the mixed clouds. Validated by the dataset from AHI and labels derived from the Cloud-Aerosol LiDAR and Infrared Pathfinder Satellite Observation (CALIPSO) vertical feature mask (VFM), our algorithm can correctly detect 89.3% of SWC pixels in the dataset, which satisfies the requirements for operational cold-cloud seeding, enhances precipitation efficiency, and facilitates the integration of satellite remote sensing into weather modification practices. Based on AHI data in late spring 2021 during the NCCV weather event, SWCs exhibit significant spatial heterogeneity and distinct diurnal vertical evolution. These clouds occur most frequently in the southwest quadrant and evolve progressively from mid- and low-level layers to the high-level layer.