Identifying Ice Crystal Chain Aggregates in Cold-Season Storms: Leveraging Machine Learning to Map Occurrence and Distribution

In situ observations of electrically induced aggregation of cloud ice and frozen droplets have primarily been observed in mid- to upper-level clouds of summertime storms. These aggregates, distinguished by their elongated, quasi-linear structure, are specifically termed as chain aggregates. Cloud chamber experiments reveal that chain aggregation is temperature-dependent, and their formation is enhanced in an electric field exceeding approximately 60 kV m^-1. However, various difficulties arise when connecting the laboratory experiments to in situ observations. While there is evidence that significant electric fields are required for chain aggregate formation, the precise locations and the mechanisms for chain aggregation within storms remain poorly understood. This knowledge gap hinders the accurate parameterization of chain aggregate formation processes in cloud models, impacting precipitation formation, radiative transfer, remote sensing retrievals, and precipitation forecasting. 

During NASA’s Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) field campaign, chain aggregates were observed in 30 of 34 research flights, across temperatures from –38.2 to 2.5 °C and altitudes from 1.5 to 9.7 km, including in weakly electrified winter storms. These frequent observations challenge prevailing assumptions and underscore the need for comprehensive analysis. Given that the Cloud Particle Imager (CPI) captured millions of particle images during IMPACTS, manual classification is infeasible. To address this, we developed a supervised convolutional neural network (CNN) classifier using transfer learning to distinguish chain aggregates from non-chains directly from CPI images. We benchmarked several common CNN backbones (ResNet18/34/50/101 and VGG16/19) and selected the final model using a precision-first criterion supported by PR-AUC/ROC-AUC and calibration metrics (log-loss/Brier). The resulting ResNet34 model provides reliable separation of chain aggregates vs. non-chains and achieves strong performance on unseen data (≈95% precision and ≈80% recall for the chain class), enabling confident campaign-scale mapping of chain aggregate occurrence and more robust comparisons with collocated ER-2 radar and lidar observations.