Evaluation of Glaciogenic Cloud Seeding using Trace Chemistry

Abstract:

Glaciogenic cloud seeding is an important scientific technology for enhancing water resources across in the Western United States. Cloud seeding enriches orographic super cooled liquid water layers with plumes of ice nuclei, increasing the water yield of a storm event. Weather model assessments of cloud seeding estimate controlled releases of the ice nucleating agent, silver iodide (AgI), increases snow precipitation between 3-15% annually. However, efficacy of cloud seeding programs are difficult to assess using Weather Research and Forecasting (WRF) models alone. Therefore, this study validated the spatial and temporal distribution of AgI predicted by WRF models using ultra-trace snow chemistry data collected in the target area.

Field methods utilized in this study are unique to previous trace chemical assessments of cloud seeding in two ways. First, nearly all snow samples were collected within 24 hours of deposition. Focusing on freshly deposited snow was more effective at constraining AgI plume timing and spatial extent with greater precision (via WRF, SNOTEL, and chemistry data). Second, this study employed geostatistics to describe AgI variability on the pit scale, site scale, and regional scale to optimize on the amount of samples to collect. The analysis revealed 4 columns of vials per snow pit and 1 snow pit per sampling site are optimal. Identifying the seeding signature at the regional scale was also successfully accomplished over a 40 mile sampling transect. All 6 sites had an identical AgI seeding signature despite drastic differences in of canopy cover, aspect, and distance from AgI source. The furthest sites from the AgI source were difficult to identify with Ag concentrations alone. Therefore, enrichment factors were essential to locating AgI influence at the most distal sites.