Assessing Orographic Cloud Seeding Impacts Through Integration of Remote Sensing from Multispectral Satellite, Radar Data, and In Situ Observations in the Western United States

Abstract

What are the main findings?

Combining GOES-R multispectral satellite data with NEXRAD radar revealed clear seeding-induced cloud microphysical changes, including droplet-to-ice phase transitions, cloud top cooling, and optical thickening.

Results varied by location: Tahoe showed stronger effects in comparison to Ruby and Santa Rosa mountains.

What is the implication of the main finding?

Results show that the effectiveness of orographic cloud seeding depends strongly on local atmospheric conditions, emphasizing the need for site-specific strategies.

Satellite and radar together provide a practical way to track seeding impacts.

Cloud seeding is a targeted weather modification strategy aimed at enhancing precipitation, particularly in regions facing water scarcity. This study evaluates the impacts of wintertime cloud seeding events in the western United States, focusing on three regions: the Lake Tahoe area, the Santa Rosa Range, and the Ruby Mountains, using an integrated remote sensing approach. Ground-based AgI generators were deployed to initiate seeding, and the atmospheric responses were assessed using multispectral observations from the Advanced Baseline Imager (ABI) aboard the GOES-R series satellites and regional radar reflectivity mosaics derived from NEXRAD data. Satellite-derived cloud microphysical properties, including cloud top brightness temperatures, optical thickness, and phase indicators, were analyzed in conjunction with radar reflectivity to evaluate microphysical changes associated with seeding. The analysis revealed significant regional variability: Tahoe events consistently exhibited strong seeding signatures, such as droplet-to-ice phase transitions, cloud top cooling, and thickened cloud structures, often followed by increased radar reflectivity. These outcomes were linked to favorable atmospheric conditions, including colder temperatures, elevated mid-to-upper tropospheric moisture, and sufficient supercooled liquid water. In contrast, events in the Santa Rosa Range generally showed weaker responses due to warmer, drier conditions and limited cloud development, while the Ruby Mountains presented mixed outcomes. This study improves the detection of seeding impacts by characterizing microphysical changes and precipitation development, capturing the progression from initial cloud phase transitions to hydrometeor development. The results highlight the importance of aligning seeding strategies with local atmospheric conditions and demonstrate the practical value of satellite-based tools for evaluating seeding effectiveness, particularly in data-sparse regions. Overall, this work contributes to advancing both the scientific insight and operational practices of weather modification through remote sensing.