Unveiling In-Situ Ice Aggregation: Deep Learning, Causal Discovery, and Physics

Ice aggregation is a fundamental driver of cloud evolution and precipitation formation. However, quantifying its rate in natural environments remains challenging due to the difficulty of tracking particle history in a Lagrangian frame. To address this issue, we use a unique dataset from 21 targeted glaciogenic seeding experiments (CLOUDLAB, Henneberger et al., 2023) conducted in supercooled stratiform clouds ranging from -7.8 °C to -4.7 °C. This experimental design establishes a controlled initial state and advection time (5–10 minutes). Central to our methodology is IceDetectNet (Zhang et al., 2024), a deep learning architecture that applies in situ holographic imagery to detect and classify individual monomers within complex aggregates. Quantifying the number of collisions per aggregate at the monomer level allows us to reconstruct the initial ice crystal number concentration (ICNC_t0) directly from downwind observations.

To disentangle the microphysical and environmental drivers of aggregation, we implemented a comprehensive analytical workflow that integrated three distinct paradigms: data-driven causal inference, a theoretically derived physical equation, and machine learning regressors. These independent approaches converge on the conclusion that ICNC_t0 parameter is governing aggregation, significantly outweighing the influence of temperature, turbulence, or aspect ratio. Our analysis reveals a significant departure from classical collection theory: the aggregation rate exhibits sub-quadratic power-law dependence on initial concentration (mean exponent 0.92; 95% confidence interval CI: 0.88–0.97), contrasting with the traditional quadratic scaling assumed in kinetic collection kernels. We hypothesize that this scaling involves aggregation among smaller crystals, where subsequent diffusional growth masks the boundaries between monomers, making early collisions difficult to detect. Furthermore, benchmarking eleven machine learning architectures against the physically derived formulation revealed a clear trade-off. While CatBoost's gradient boosting ensembles achieved higher statistical accuracy (R² = 0.87), the theoretical model showed greater robustness and generalizability in sensitivity testing. This multi-perspective framework uses a combination of experimental atmospheric physics and AI-driven interpretation to demonstrate how data-driven plasticity and physically-based stability complement each other. It provides a practical approach to understanding complex microphysical processes.

 

Reference:

Henneberger J, Ramelli F, Spirig R, et al. Seeding of supercooled low stratus clouds with a UAV to study microphysical ice processes: an introduction to the CLOUDLAB project[J]. Bulletin of the American Meteorological Society, 2023, 104(11): E1962-E1979. https://doi.org/10.1175/BAMS-D-22-0178.1

Zhang H, Li X, Ramelli F, et al. IceDetectNet: a rotated object detection algorithm for classifying components of aggregated ice crystals with a multi-label classification scheme[J]. Atmospheric Measurement Techniques, 2024, 17(24): 7109-7128. https://doi.org/10.5194/amt-17-7109-2024