Targeting seedable clouds with silver iodide in complex terrain adds considerable uncertainty in weather modification studies. This study explores the geographic and temporal distribution of silver iodide associated with an active cloud seeding program in central Idaho snowpack using trace chemistry. Over 4,000 snow samples were analyzed for the presence of a cloud seeding silver iodide (AgI) signature over two winter seasons. The results indicate the following. (1) At sites within 70 km of AgI sources, silver enrichments were detected at 88% of cases involving seeding efforts from ground generators, but none from aircraft seeded cases.(2) Real-time snow collection methods were replicable within 0.41 ppt and confirmed seeding signatures for the entire duration of a seeded storm ( = 3). (3) Sites sampled beyond 70 km of AgI sources ( = 13) lacked detectable seeding signatures in snow. The results of this study demonstrate some of the strengths and limitations of chemical tracers to evaluate cloud seeding operations and provide observational data that can inform numerical simulations of these processes. The results also indicate that this chemical approach can be used to help constrain the spatiotemporal distribution of silver from cloud seeding efforts.

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Glaciogenic cloud seeding with silver iodide (AgI) has been used to enhance precipitation for over 60 years. Assessments of AgI impact and dispersion are often quantified using atmospheric processes models with impact assessed by comparing models with and without inclusion of cloud seeding modules.  However, there is inherent uncertainty in these aerosol models and physical validation of AgI distribution is of value to both validate and improve model performance. The purpose of this study is to demonstrate the capacity to physically validate the dispersion of AgI by measuring silver enrichments in snow.Field and laboratory methods were developed to detect trace seeding signatures in snowpack. Unique laboratory layout and protocols were developed to reduce contamination potential within a traditional ICP-MS laboratory setting (not housed in a Class 100 Clean Room). Using these methods, we sampled a series of snow profiles within the target area of active cloud seeding in the central mountains of Idaho. Our results demonstrate the ability the ability to reproduce distinct evidence of elevated Ag at concentrations at a hillslope (0.25 km2) and at the basin (2,400 km2) scale. The construction of 8 snow pits at one site (hillslope scale) and 6 sites along a 65 km transect (basin scale) reliably identified both of the seeded storm layers sampled. The location of the peaks in Ag concentration within the snow profiles generally corresponds in timing to known cloud seeding events. Distinct seeded storm layers were reliably identified seeding signatures more than 60 km from the AgI sources, where silver concentrations were only enhanced 1-3 parts per trillion. Ag enriched snow in these chemical profiles generally correspond to downwind target locations and AgI seeding times.

Their extremely detailed (and accurate) forecasts ensured that we collected relevant data every time we headed out in the field. I also wish to thank all the individuals contributing to the sampling effort: Robert Florence, Andy Karlson, Kerrie Weppner, Clay Roehner, and the Idaho Power River Engineering team. A very special thanks to Larry Thomas Oltheim (aka "TomO") for sampling conducting real-time snow at abnormal times of the day (midnight).I also wish to thank Reggie Walters, Katelyn Watson, and Miguel Aguayo for providing useful Python scripts to analyze large SNOTEL and/or WRF data efficiently.Marion Lytle was essential to the success of this projecther expertise in finding optimal mass spectrometer settings, her assistance analyzing every sample (and, at times, analyzing samples by herself), and her ability to swiftly make adjustments in the laboratory necessary to lower the contamination potential at the part per trillion level.I would like to give a big thanks to my primary advisor, Shawn Benner, for guiding this project and for teaching me how to make this project successful and learning the "soft skills" needed in the workforce. Shawn permitted me the flexibility to study whatever aspect I saw fit but frequently checked in to make sure I was on track.Lastly, but most importantly, I thank my friends and family for the constant encouragement and support. They made my graduate experience a joyful one. v ABSTRACT Glaciogenic cloud seeding increases the fraction of super cooled liquid water precipitating from a given storm. Orographic clouds tend to be inefficient at higher cloud temperatures due to the lack of active natural ice nuclei. Adding artificial ice nuclei active at temperatures greater than -12 o C (where most natural ice nuclei are inactive) may result in an increase in snow precipitation, especially in orographic clouds. Silver iodide (AgI) is typically the artificial nucleating agent for winter orographic cloud seeding.Recent estimates suggest the addition of AgI to orographic storm clouds enhance precipitation by 3 -15%. However, the National Research Council stated "the areas affected by AgI remains an open question".In this study, we seek to understand how well AgI is delivered to regions intended for cloud seeding in the central mountains of Idaho. To accomplish this, we develop and validate methods to detect sub-part-per-trillion silver concentrations in snow. These methods were specific to an ICP-MS laboratory not housed in a Class 100 Clean room.Unique laboratory layout and protocols are employed to reduce laboratory contamination potential. Using clean field methods, we sample a series of snow profiles within the target area of active cloud seeding. The results demonstrate the ability of these new methods to reproduce distinct elevated Ag concentrations over a small scale (0.25 km 2 ) and at the basin scale (2,400 km 2 ). A localized enrichment factor highlighted silver enrichments likely from AgI rather than from other local sources. This enrichment factor can delineate a seeding si...