As the core components of clouds, the measurement of cloud particle characteristic parameters, such as size, shape, phase, number concentration, and spectral distribution, plays a key role in atmospheric detection, aircraft icing warning, and artificial weather modification. At present, the most widely used method for in-situ measurement of cloud particles is the optical array probe (OAP) with a high-speed linear array detector. However, this method faces the challenge of low detection accuracy for the spectral distribution of small-sized cloud particles, which is mainly due to limitations in detector resolution and the diffraction effects of particles. Furthermore, OAP images are easily influenced by sampling space and lack texture information, making particle identification and classification more difficult. To improve the reliability of OAPs and the efficiency of in-situ cloud observations, we propose a method to identify the shape and rotation orientation of small-sized cloud particles using image diffraction features. In addition, a size feature extraction algorithm combining edge detection and contour smoothing is studied to reduce measurement errors. In this paper, we enable accurate measurement of characteristic parameters, such as size and shape, for heterogeneous multi-particles, and thus allow for high-precision spectral distribution information.

First, the single-particle diffraction propagation model is constructed using the LightPipes simulation software. Three plane geometric masks, including circular, regular hexagonal, and rectangular, are designed to represent spherical water droplets, plate-shaped ice crystals, and columnar ice crystals. The diffraction characteristics of these three particle shapes under different size conditions are then studied. Second, the characteristic parameters, such as array direction size (D_x), velocity direction size (D_y), maximum geometric size (D_max), and contour enclosing area (S), are calculated. An image processing algorithm combining edge detection and contour smoothing is proposed. A cloud particle diffraction imaging measurement system based on a linear array detector is developed, and an optical mask method is used to simulate real water droplets and ice crystal particles. The influence of sampling distance on diffraction imaging is analyzed to determine the detection limit of the system. Finally, a series of turntable calibration experiments are carried out to measure the size, shape, and rotation orientation of heterogeneous multi-particles, while obtaining the spectral distribution statistics of continuous random particle groups to verify the feasibility of the proposed method.

Simulation results show that spheroid droplets, plate ice crystals, and column ice crystals exhibit different diffraction stripes and bright spot characteristics, which can aid in contour identification and shape recognition (Fig. 2). By varying the sampling distance (l) and recording the size of the Poisson bright spot (D_p) in the diffraction image, the optimal sampling distance is found to be l=50 mm (Fig. 4). The numerical aperture of the system is inferred from the D_p data of small-sized particles, and the diffraction limit (D_dif≈14.42 μm) is estimated, which is greater than the resolution limit, D_res,pix=4.31 μm. The theoretical detection limit is considered to be 14.42 μm. The experimental detection limit of the system is found to be D_lim=15 μm, which aligns with the theoretical estimate (Fig. 5). The error is defined as the difference between the maximum geometric size (D_max) and the theoretical particle size (D). The turntable calibration experiments show that the size measurement error is the smallest for spheroid droplets and largest for 90° column ice crystals. However, the maximum error is only 4.51 μm (Fig. 6). In addition, rotation orientation experiments demonstrate that diffraction features can be used to identify the rotation angle of small-sized cloud particles (Fig. 7). The system can also perform spectral distribution statistics of continuous random particle groups. The error distribution for each particle size interval is between 2 and 4 μm (Fig. 8). In this paper, we address the problem of low measurement accuracy for small-scale cloud particle spectral distributions.

To address the challenge of low measurement accuracy and difficulty in accurately identifying the shape of small-sized cloud particles during in-situ measurement with OAPs, we propose a cloud particle diffraction imaging and characteristic parameter measurement method based on linear array detectors and Fresnel diffraction principle. Based on LightPipes simulation results, a cloud particle diffraction imaging measurement system is designed using linear array detectors. Optical masks are customized on the calibration turntable to simulate real cloud particles, including spherical water droplets and plate-shaped and columnar ice crystal particles in the atmosphere. The detection limit of the system is discussed in terms of both resolution and diffraction limits, and the effect of sampling distance on particle imaging is analyzed. The diffraction imaging behavior of heterogeneous multi-particles is explored, and the system’s ability to recognize ice crystal particles of different shapes and rotational orientations on a large scale is verified. The spectral distribution statistics of continuous random particle groups are also obtained. The experimental results show that the particle images exhibit diffraction characteristics consistent with simulations. The system’s size detection limit, at an optimal sampling distance of l=50 mm, is 15 μm. The size measurement range is between 15 and 1000 μm, with a full-scale error of less than 5 μm. For particles larger than 100 μm, the relative error is less than 5%. Compared to other in-situ cloud particle detection methods, this method improves the accuracy of contour recognition for particle shape, distinguishes particle rotation orientation, and acquires images in real time using diffraction features. However, this method still has the limitation of not being able to measure the three-dimensional information of particles. In summary, we provide a new technical approach to solve the problem of low measurement accuracy for small-sized cloud particles and offer a means for precise measurement of important meteorological parameters, such as particle concentration, liquid water content (LWC), and mean droplet diameter (MVD) in cloud microphysics.