Phase state recognition of cloud particles is an important content in cloud physics research and also significant for inverting other cloud microphysical parameters. With the development of remote sensing detection technology, researchers have developed various recognition methods of cloud phase particles, such as decision tree recognition, classic statistical decision recognition, neural networks, clustering algorithms, and fuzzy logic algorithms. However, due to the complex characteristics of cloud particles, the radar information corresponding to different particles does not have absolute features, and there may be some overlap degree. Thus, recognition algorithms based on rigid threshold conditions are not well suitable for phase recognition and classification of cloud particles. Fortunately, the fuzzy logic recognition algorithm can improve this rigid threshold defect, but the accuracy of the T-function coefficients in fuzzy logic will directly determine the accuracy of the recognition results. To accurately and finely identify cloud phase states, we propose an optimization algorithm based on fuzzy logic to recognize the phase states of cloud particles. The optimized fuzzy logic algorithm can also recognize supercooled water and warm cloud droplets compared to the original fuzzy logic algorithm which can only recognize ice crystals, snow, mixed phases, liquid cloud droplets, drizzle, and raindrops.

Based on the induction and summary of a large number of aircraft and remote sensing instruments simultaneously observed data and comprehensive characteristic consideration of different cloud types, we adjust and optimize the T-function coefficients of fuzzy logic. A table of T-function coefficient parameters for different cloud phase particles is constructed as shown in Table 2. The corrected reflectivity factor, radial velocity, and spectral width detected by millimeter wave cloud radars with high spatiotemporal resolution, as well as the temperature detected by microwave radiometer, are adopted as input parameters for the optimized fuzzy logic algorithm. According to the phase recognition process of cloud particles shown in Fig. 1, snow, ice, mixed phase, supercooled water, warm cloud droplets, drizzle, and rain in cloud particles can be identified.

The cloud particle phase of a snowfall observed on 6 February 2022 in Xi'an is inverted to verify the effectiveness and accuracy of the optimized algorithm. Additionally, we input the parameters (corrected reflectivity factor, radial velocity, spectral width, and temperature) that can characterize the features of cloud particles in Fig. 3 into the optimized fuzzy logic algorithm, and obtain the phase recognition results of cloud particles shown in Fig. 5. The cloud phase distribution in Fig. 5 (near the ground area, at a height of about 200 m) is highly consistent with the particle phase changes recorded by the ground precipitation phenomenon meter. Meanwhile, we also compare the recognition results of the optimized fuzzy logic algorithm (Fig. 5) with the original fuzzy logic algorithm (Fig. 4) and find that the optimized algorithm can identify supercooled water that cannot be recognized by the original algorithm, which is beneficial for explaining the particle phase transformation process and precipitation mechanism research in clouds.

We propose an optimized fuzzy logic algorithm by optimizing the asymmetric T-function coefficients and considering the effects of reflectivity factor attenuation and temperature on the accuracy of recognition results. The corrected reflectivity factor, radial velocity, spectral width, and spatiotemporal continuous temperature detected by the microwave radiometer are leveraged as input parameters for the optimized fuzzy logic algorithm. The optimized algorithm can accurately identify snow, ice, mixed phase, supercooled water, warm cloud droplets, drizzle, and rain particles in clouds, which would help study and invert cloud microscopic parameters.