What the study found
Ice crystal number concentration at the initial time point was the main factor controlling ice aggregation rates in persistent supercooled stratiform clouds. The study also found that the aggregation rate increased less than quadratically with ice crystal number concentration, which differs from theoretical expectations.
Why the authors say this matters
The authors conclude that these findings offer new insight into the microphysical and environmental controls of ice aggregation. They also say the work provides a robust methodological foundation for studying aggregation processes in natural clouds.
What the researchers tested
The researchers used in situ measurements from targeted glaciogenic seeding experiments in which ice crystals were nucleated upwind and measured downwind after a known advection time. They used a deep-learning-based detection algorithm, IceDetectNet, to count the monomers in ice aggregates and estimate the initial ice crystal number concentration (the number of ice crystals present at the start of the observation period). They examined several possible controls on aggregation, including initial ice crystal number concentration, temperature, ice crystal size, aspect ratio, and turbulence.
What worked and what didn't
Three independent approaches — causal inference, a physical equation, and machine learning models — all identified initial ice crystal number concentration as the dominant control on aggregation rates. The reported dependence was subquadratic, with a mean exponent of about 0.92 and a 95% confidence interval of 0.88 to 0.97, rather than the quadratic dependence expected from theory. For prediction, CatBoost had the best statistical performance among 11 machine learning models, while the physically based model was more robust in sensitivity tests.
What to keep in mind
The abstract says the subquadratic result has one possible explanation: aggregation may also involve smaller ice crystals, but this is described as hypothetical. The summary provided does not describe other limitations beyond the scope of the experiments in persistent supercooled stratiform clouds.
Key points
- Initial ice crystal number concentration was the dominant control on aggregation rates.
- The measured relationship between aggregation rate and ice crystal number concentration was subquadratic, with a mean exponent of about 0.92.
- Causal inference, a physical equation, and machine learning all pointed to the same main control factor.
- CatBoost performed best statistically among 11 machine learning models.
- The physically based model was more robust in sensitivity tests.
- A hypothetical explanation is that smaller ice crystals may also be involved in aggregation.
Disclosure
- Research title:
- Ice crystal concentration is the main driver of aggregation rates
- Authors:
- Huiying Zhang, Fabiola Ramelli, Christopher Fuchs, Nadja Omanovic, Anna J. Miller, Robert Spirig, Zhaolong Wu, Yunpei Chu, Xia Li, Ulrike Lohmann, Jan Henneberger
- Institutions:
- ETH Zurich, Peking University, Leibniz Institute for Tropospheric Research
- Publication date:
- 2026-01-28
- OpenAlex record:
- View
- Image credit:
- Photo by Nils Söderman on Unsplash · Unsplash License
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