Investigating Snowfall Enhancement through Cloud Seeding: A Microphysical Modeling and Remote Sensing Approach
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Authors
Mehdizadeh, Ghazal
Issue Date
2025
Type
Dissertation
Language
en_US
Keywords
Alternative Title
Abstract
Cloud seeding has been employed as a weather modification strategy to enhance precipitation, particularly in arid and semi-arid regions where water scarcity is a constant concern. Despite its widespread use, the scientific understanding of its effectiveness remains incomplete. Many operational programs rely on limited observational data or simplified assumptions, which can obscure the physical mechanisms behind seeding outcomes. This dissertation aims to bridge that gap by combining high-resolution microphysical modeling with satellite-based remote sensing to evaluate the effects of glaciogenic cloud seeding in the western United States. Through a series of case studies and broader regional analyses, this research significantly deepens our understanding of cloud seeding mechanisms and provides insights for future cloud seeding studies.A central tool in this dissertation is the Snow Growth Model for Rimed Snowfall (SGMR), a model developed to simulate key processes involved in ice crystal development, such as nucleation by silver iodide, vapor deposition, aggregation, and riming. The model was first applied to five cloud seeding events in the Lake Tahoe area. For each event, inputs such as cloud top and base heights, temperatures, liquid water content, and ice water content from MERRA-2 and CERES datasets were used to drive the model. The SGMR provided detailed estimates of snowfall rate, particle size, and ice crystal concentration, allowing for an in-depth comparison between seeded and unseeded scenarios.
Building on this initial analysis, the study was expanded to include 13 cloud seeding events across three distinct regions: Lake Tahoe, Santa Rosa Range, and Ruby Mountains. In these cases, ground-based silver iodide generators were used, and the response of the atmosphere was assessed using a combination of GOES-R satellite data and radar reflectivity mosaics. Key spectral channels from the Advanced Baseline Imager (ABI) were analyzed to extract information about cloud-top temperatures, optical thickness, cloud phase, and water vapor profiles. These data were used not only to track cloud evolution but also to assess whether observable changes in cloud structure and precipitation occurred following seeding.
Results from both modeling and satellite analyses point to a clear conclusion: cloud seeding effectiveness is highly dependent on pre-existing atmospheric conditions. Successful seeding events, those that showed increases in snowfall rates and ice crystal concentrations, typically occurred under conditions of abundant supercooled liquid water, colder cloud tops, and moist mid- to upper-tropospheric layers. These conditions favor ice nucleation and growth processes, allowing the seeded particles to initiate or enhance precipitation. On the other hand, events that lacked these favorable environmental characteristics showed minimal response to seeding, reinforcing the idea that not all clouds are good candidates for weather modification.
Another important outcome of this work is the demonstration of how satellite remote sensing, particularly from geostationary platforms like GOES-R, can be used to support and evaluate cloud seeding operations in near-real time. By monitoring cloud microphysical properties and vertical moisture structure, remote sensing provides a valuable supplement to conventional ground-based measurements and offers a pathway toward more data-driven, adaptive cloud seeding strategies.
In summary, this dissertation contributes to a more nuanced and physically grounded understanding of cloud seeding. By integrating advanced modeling with satellite observation, it provides a framework for identifying optimal seeding conditions and assessing developments with greater certainty. The findings have practical implications for the design and evaluation of weather modification programs and offer a foundation for integrating cloud seeding into broader regional water management and climate adaptation efforts.