Investigating Seeded Orographic Cloud Microphysics during SNOWIE Using WRF-WxMod

Previous studies demonstrate that the Weather Research and Forecasting Weather Modification (WRF-WxMod) model can reasonably simulate the physical processes associated with glaciogenic cloud seeding in orographic clouds over the mountains of SW Idaho, USA (Xue et al 2022; JAMC). Xue et al simulated a case from the Seeded and Natural Orographic Wintertime clouds – the Idaho Experiment (SNOWIE) that contained little natural precipitation and showed distinct seeding signals in the observational data. These simulations were validated by observations from a suite of instruments deployed during the SNOWIE field campaign that included precipitation gauges, in situ cloud probes, ground-based and airborne radars. Model results showed that seeding effects range from 65 to 331 acre-feet of precipitation at the surface, in good agreement with the 64 to 157 acre-feet based on observations reported by Friedrich et al (2020; PNAS).

We simulate another case from SNOWIE using WRF-WxMod. The event occurred on January 8th, 2017following a deep overrunning snow band associated with a fast-moving jet streak. This case produced more widespread natural precipitation than the one simulated by Xue et al (2022; JAMC). Shallow clouds produced ample freezing drizzle with widespread light snow and isolated pockets of moderate natural snow at the surface. Ground radar-observations showed widespread regions of weak echo related to the natural precipitation. Isolated cells of more intense reflectivity embedded within the weaker echoes were associated with precipitation production resulting from glaciogenic seeding.

Here, we present analyses from observationally constrained numerical simulations of this case to understand the natural and seeding-induced microphysical evolution of the cloud. We aim to investigate the microphysical factors controlling the efficacy of cloud seeding in orographic winter clouds by examining contrasts between control and seeded runs. Lastly, we quantify seeding’s influence on precipitation reaching the surface using mass and volume metrics.