Search for: All records

Abstract Cloud seeding has been widely used for enhancing wintertime snowfall, particularly to augment water resources. This study examines microphysical responses to airborne glaciogenic seeding with silver iodide (AgI) during a specific case from the Seeded and Natural Orographic Wintertime Clouds: Idaho Experiment (SNOWIE) on 11 January 2017. Ground-based and airborne remote sensing and in situ measurements were employed to assess the impact of cloud seeding on cloud properties and precipitation formation. On 11th January, AgI propagated downwind along prevailing winds, and any potential ice and snow particles created from it were identified by ground-based radar as zigzag lines of enhanced reflectivity compared to background reflectivity. As the aircraft flew several times through these seeded clouds, microphysical properties within seeded clouds can be compared to those observed in unseeded clouds. The results indicate that seeded clouds exhibited significantly enhanced ice water content (IWC; reaching up to 0.20 g m⁻³) and precipitating-size (>400μm) ice particle concentrations (>7 L⁻¹) relative to unseeded clouds. Additionally, seeded clouds exhibited a 30% decrease in the mean liquid water content (LWC) and cloud droplet concentrations, indicating efficient glaciation processes influenced by AgI. Precipitating snow development in seeded clouds occurred within 15–40 min following AgI release, marked by a transition from mixed-phase clouds with abundant supercooled liquid water (SLW) to ice clouds, with lidar-measured linear depolarization ratio (LDR) increasing to >0.3. These findings underscore the effectiveness of cloud seeding in enhancing snowfall by facilitating ice initiation and growth. Significance StatementThis study investigates the microphysical response of wintertime orographic clouds to airborne glaciogenic seeding, highlighting its role in enhancing precipitation. By introducing silver iodide (AgI) into clouds with supercooled liquid water, the seeding process facilitates ice particle formation, leading to increased snowfall. Through a detailed analysis of microphysical conditions using advanced in situ and remote sensing instruments, the study reveals enhanced ice water content and efficient conversion of liquid water to ice in seeded clouds. These findings provide critical insights into cloud-seeding efficacy, particularly in regions with abundant supercooled liquid water, offering a scientific foundation for enhancing snowpack in water-scarce mountainous areas. 

Abstract A dry-air intrusion induced by the tropopause folding split the deep cloud into two layers resulting in a shallow orographic cloud with a supercooled liquid cloud top at around −15°C and an ice cloud above it on 19 January 2017 during the Seeded and Natural Orographic Wintertime Clouds: The Idaho Experiment (SNOWIE). The airborne AgI seeding of this case was simulated by the WRF Weather Modification (WRF-WxMod) Model with different configurations. Simulations at different grid spacing, driven by different reanalysis data, using different model physics were conducted to explore the ability of WRF-WxMod to capture the properties of natural and seeded clouds. The detailed model–observation comparisons show that the simulation driven by ERA5 data, using Thompson–Eidhammer microphysics with 30% of the CCN climatology, best captured the observed cloud structure and supercooled liquid water properties. The ability of the model to correctly capture the wind field was critical for successful simulation of the seeding plume locations. The seeding plume features and ice number concentrations within them from the large-eddy simulations (LES) are in better agreement with observations than non-LES runs mostly due to weaker AgI dispersion associated with the finer grid spacing. Seeding effects on precipitation amount and impacted areas from LES seeding simulations agreed well with radar-derived values. This study shows that WRF-WxMod is able to simulate and quantify observed features of natural and seeded clouds given that critical observations are available to validate the model. Observation-constrained seeding ensemble simulations are proposed to quantify the AgI seeding impacts on wintertime orographic clouds. Significance Statement Recent observational work has demonstrated that the impact of airborne glaciogenic seeding of orographic supercooled liquid clouds is detectable and can be quantified in terms of the extra ground precipitation. This study aims, for the first time, to simulate this seeding impact for one well-observed case. The stakes are high: if the model performs well in this case, then seasonal simulations can be conducted with appropriate configurations after validations against observations, to determine the impact of a seeding program on the seasonal mountain snowpack and runoff, with more fidelity than ever. High–resolution weather simulations inherently carry uncertainty. Within the envelope of this uncertainty, the model compares very well to the field observations.