Electrostatic and Surfactant-Enhanced Cellulose Nanocrystal Cloud Seeding With Machine Learning-Based Spatiotemporal Targeting

Cloud seeding success depends on both the seeding material and atmospheric conditions during deployment. This study developed a more environmentally friendly cloud-seeding formulation and a machine-learning method for identifying favorable deployment windows. Six conditions were tested in a cloud chamber: distilled water, sodium chloride, cellulose nanocrystals (CNCs), CNCs passed through polytetrafluoroethylene (PTFE) tubing, CNCs with saponin, and CNCs with both modifications. A 650 nm laser and light sensor measured cloud formation onset, minimum transmitted illuminance, and persistence across five trials per condition. Compared with unmodified CNCs, the combined formulation reduced mean onset time by 61% (27.0 to 10.6 s), reduced minimum transmitted illuminance by 93.5% (3818 to 248 lux), and increased persistence by 85% (90.8 to 168.0 s). Welch's tests produced p = 0.021, p = 0.008, and p < 0.001, respectively. Saponin produced most of the improvement, while PTFE routing provided a smaller additional benefit. In parallel, an Extreme Gradient Boosting (XGBoost) classifier was trained on approximately 41 million records from the European Centre for Medium-Range Weather Forecasts Reanalysis v5 (ERA5) and Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG). Using precipitation during the following six hours to identify favorable windows, the model achieved a receiver operating characteristic area under the curve of 0.802 on a 2023 holdout set. Location and season emerged as important predictors, demonstrating the spatiotemporal dependence of cloud-seeding opportunities. Together, these results support direct testing of the combined formulation and provide a practical method for identifying favorable deployment windows.