Cloud Seeding Effects on Snowfall: Insights from Microphysical Model and Satellite Remote Sensing

Cloud seeding is a weather modification technique aimed at enhancing precipitation by introducing various substances (e.g., silver iodide) into clouds. The basic idea behind glaciogenic cloud seeding is to add ice nuclei to mixed-phase clouds that have numerous supercooled water droplets while not sufficient ice crystals. While striking a balance between efficiency and precision usually involves sacrificing accuracy, the inclusion of a multitude of microphysical processes in climate models are computationally expensive.

The goal of this study is to gain further insight into the mechanisms of cloud seeding and develop machine learning algorithms as a powerful predictive tool to better quantify the effects of cloud seeding. This research employs the Snow Growth Model for Rimed snowfall (SGMR) that predicts the vertical evolution of ice particle size spectra based on the relative humidity or supersaturation. The model is formulated in terms of analytically interrelated procedures involving ice crystal nucleation, vapor deposition, aggregation, and cloud updrafts and it thereby estimates snowfall rate. The model provides an accurate description of the microphysical processes with reduced computation time.

We aim to conduct various model experiments initialized with different meteorological conditions under seeding vs. non-seeding scenarios. We will use an ensemble of in-situ and satellite remotely sensed observations to develop an ML-based model in order to further investigate changes in snowfall due to cloud seeding.