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Abstract Cloud seeding of wintertime orographic clouds in the western United States has been attempted to enhance snow production and snowpack. Due to the scarcity of long-term, high-resolution cloud and precipitation observations over complex terrain, few studies have explored variations in orographic snowfall amounts by comparing environmental conditions and cloud characteristics with surface snowfall distribution and quantity. This study analyzes the environmental conditions and cloud characteristics in relation to surface snowfall patterns for the 24 snowfall events observed during the 2017 Seeded and Natural Orographic Wintertime Clouds: The Idaho Experiment (SNOWIE). The investigation aims to understand: 1) What is the influence, if any, of wind, turbulence, and updraft strength on snowfall amounts, rates, and distribution? 2) What is the relationship, if any, of cloud properties and precipitation-forming effectiveness? and 3) Can cloud seeding modify controlling cloud characteristics sufficiently to increase precipitation in otherwise inefficient orographic clouds? The analysis over a 7200-km²observational domain revealed that the accumulated liquid-equivalent snowfall was <0.9 × 10⁷m³and snowfall rates were <0.45 mm h⁻¹for about half of the events. Low snowfall events were characterized by cloud-top temperatures >−20°C, fewer larger droplets, higher liquid water content, and lower ice water content compared to the other events. Cases with minimal background natural snowfall also permitted radar observation of seeding lines. In these cases, cloud seeding was mainly responsible for snowfall. The amount of silver iodide (AgI) released during cloud seeding did not correlate well with snowfall amount and rate. Significance StatementThis study illustrates the complexities of estimating snowfall in wintertime orographic clouds, underscoring the frequent inefficiency of these clouds in generating snowfall—a pivotal concern for regions dependent on snowpack for water resources. By analyzing environmental and cloud characteristics against snowfall patterns during the Seeded and Natural Orographic Wintertime Clouds: The Idaho Experiment (SNOWIE), the research provides critical insights into the complexities of precipitation formation. The findings, particularly on the impact of cloud seeding in enhancing snowfall under specific conditions, contribute significantly to our understanding of weather modification techniques. This research not only is vital for advancing scientific knowledge in understanding wintertime mountain cloud systems but also holds profound implications for water resource management, agriculture, and disaster preparedness in snow-dependent regions. 

Abstract Snowpack melting is a crucial water resource for local ecosystems, agriculture, and hydropower in the Intermountain West of the United States. Glaciogenic seeding, a method widely used in mountain regions to enhance precipitation, has been subject to numerous field studies aiming to understand and validate this mechanism. However, investigating precipitation distribution and amounts in mountainous areas is complicated due to the intricate interplay of synoptic circulation patterns and local complex topography. These interactions significantly influence microphysical processes, ultimately affecting the amount and distribution of surface precipitation. To address these challenges, this study leverages Weather Research and Forecasting (WRF) Model simulations, providing high-resolution (900 m), hourly data, spanning the Payette region of Idaho from January to March 2017. We applied the self-organizing map approach to categorize the most representative synoptic circulation patterns and conducted a multiscale analysis to explore their associated environmental conditions and microphysical processes, aiming to assess the cloud seeding potential. The analysis identified four primary synoptic patterns: cold zonal flow (CZF), cold southwesterly flow (CSWF), warm zonal flow (WZF), and warm southwesterly flow (WSWF), constituting 21.3%, 23.1%, 30.0%, and 25.5%, respectively. CSWF and WSWF demonstrated efficiency in generating natural precipitation. These patterns were characterized by abundant supercooled liquid water (SLW) and ice particles, facilitating cloud droplet growth through seeder–feeder processes. On the other hand, CZF exhibited the least SLW and limited potential for cloud seeding, while WZF displayed a lower ice water content but substantial SLW in the diffusion/dendritic growth layer, suggesting a favorable scenario for cloud seeding. Significance StatementUnderstanding snowfall amounts and distribution in the mountains and how it is linked to topography, synoptic flow, and microphysical processes will help in the development of effective strategies for cloud seeding operations, managing runoff, reservoir, and mitigating flood risks, garnering substantial interest from stakeholders and the government agencies.