Preprints
https://doi.org/10.5194/egusphere-2023-290
https://doi.org/10.5194/egusphere-2023-290
10 May 2023
 | 10 May 2023
Status: this preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).

Analysis of insoluble particles in hailstones in China

Haifan Zhang, Xiangyu Lin, Qinghong Zhang, Kai Bi, Chan-Pang Ng, Yangze Ren, Huiwen Xue, Li Chen, and Zhuolin Chang

Abstract. Insoluble particles affect weather and climate indirectly by heterogeneous freezing process. Current weather and climate models have large uncertainty in freezing process simulation due to little regarding species and number concentration of heterogeneous ice-nucleating particles, mainly insoluble particles. Here, for the first time, size distribution and species of insoluble particles are analyzed in 30 shells of 12 hailstones in China, using scanning electron microscopy and energy dispersive X-ray spectrometry. Total 289,461 insoluble particles are detected and grouped into 3 species: organics, dust, and bioprotein by machine learning methods. The size distribution of insoluble particles of each species vary greatly in different hailstorms but little in shells. Further, classic size distribution modes of organics and dust were performed as logarithmic normal distributions, which may be adapted in future weather and climate models though uncertainty still exists. Our finding suggests that physical properties of aerosols should be considered in model simulation on ice freezing process.

Haifan Zhang et al.

Status: open (until 21 Jun 2023)

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Haifan Zhang et al.

Haifan Zhang et al.

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Short summary
This work is the first study to simultaneously analyze the number concentrations and species of insoluble particles in hailstones. The size distribution of insoluble particles for each species vary greatly in different hailstorms but little in shells. Two classic size distribution modes of organics and dust were fitted for the description of insoluble particles in deep convection. Combining this study with future experiments will lead to refinement of weather and climate models.