Articles | Volume 26, issue 2
https://doi.org/10.5194/acp-26-1459-2026
https://doi.org/10.5194/acp-26-1459-2026
Research article
 | 
28 Jan 2026
Research article |  | 28 Jan 2026

Inferring the controlling factors of ice aggregation from targeted cloud seeding experiments

Huiying Zhang, Fabiola Ramelli, Christopher Fuchs, Nadja Omanovic, Anna J. Miller, Robert Spirig, Zhaolong Wu, Yunpei Chu, Xia Li, Ulrike Lohmann, and Jan Henneberger

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Short summary
Ice crystals in clouds aggregate, shaping snow and rain, yet rates are hard to measure. Using cloud seeding, we sampled crystals downwind after known times. A deep-learning algorithm quantified aggregation by counting crystal components. Initial ice concentration was the main driver, confirmed by causal analysis, physics, and machine learning, though weaker than theory predicts. Temperature, size, and shape also mattered, while turbulence was negligible.
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