Estimating the concentration of silver iodide needed to detect unambiguous signatures of glaciogenic cloud seeding

Abstract

Detecting an unambiguous radar reflectivity signature is vital for investigating cloud-seeding impacts. The radar reflectivity change attributed to seeding depends on both the cloud conditions and the concentration of silver iodide (AgI) particles. In this study, the reflectivity change induced by glaciogenic seeding using different AgI particle concentrations is investigated under various cloud conditions using a 1D ice growth model coupled with an AgI nucleation parameterization. In addition, an algorithm is developed to estimate the minimum AgI particle concentration needed for a measurable glaciogenic cloud-seeding signature, assuming there is sufficient supercooled liquid water. The results show that the 1D model captures the ice growth habit compared to available observations and yields an unambiguous reflectivity change that is consistent with 3D model simulations and previous observational studies. Simulations indicate that seeding at a temperature of about −15 °C has the highest probability of detecting the radar seeding signature. This finding is consistent with the fact that the seeding temperature was about −15 °C or slightly warmer in most documented cases of unambiguous seeding signatures. Using the 1D model, 2500 numerical experiments are conducted, and the outputs are used to develop a parameterization to estimate the AgI particle concentration needed to detect an unambiguous seeding signature. Application of this parameterization to a real case suggests that seeding between −21 and −11 °C may possibly produce unambiguous seeding signatures and that seeding at about −15 °C requires the lowest AgI particle concentration. Seeding at warmer temperatures in precipitating clouds requires an extremely high amount of AgI and a very high content of supercooled liquid water. The results shown in this study deepen our understanding of the relationship between AgI particle concentration and radar seeding signatures under different cloud conditions. The parameterization can be used in operational seeding-based decision-making regarding the optimal amount of AgI dispersed.