Abstract
A model was developed for simulating the effects of airborne silver iodide (AgI) glaciogenic cloud seeding using the weather research and forecasting (WRF) model with a modified Morrison cloud microphysics scheme. This model was used to hindcast the weather conditions and effects of seeding for three airborne seeding experiments conducted in 2016. The spatial patterns of the simulated precipitation and liquid water path (LWP) qualitatively agreed with the observations. Considering the observed wind fields during the seeding, the simulated spatiotemporal distributions of the seeding materials, AgI, and snowfall enhancements were found to be reasonable. In the enhanced snowfall cases, the process by which cloud water and vapor were converted into ice particles after seeding was also reasonable. It was also noted that the AgI residence time (>1 hr) above the optimum AgI concentration (105 m−3) and high LWP (>100 g m−2) were important factors for snowfall enhancements. In the first experiment, timing of the simulated snowfall enhancement agreed with the observations, which supports the notion that the seeding of AgI resulted in enhanced snowfall in the experiment. The model developed in this study will be useful for verifying the effects of cloud seeding on precipitation.
Highlights
It has been reported that cloud seeding is economically beneficial for securing water resources [1, 2]
In the winter of 2016, six airborne cloud seeding experiments were performed in the Pyeongchang region of South Korea for precipitation enhancement [3]
Kim et al [11] modified the Morrison scheme [12] in the weather research and forecasting (WRF) model and performed numerical simulations for continental and maritime aerosols with ground-based cloud seeding experiments using AgI; this method is only applicable to ground-based seeding and cannot be applied in airborne cloud seeding experiments in which the seeding point moves with time at high altitude
Summary
It has been reported that cloud seeding is economically beneficial for securing water resources [1, 2]. Ground observation equipment was used to monitor the changes in the cloud microphysical properties (i.e., particle sizes, concentrations, etc.) and the snowfall rates before and after seeding [4]. Researchers found it challenging to distinguish natural snowfall from the effects of seeding. It is impossible to repeat and verify experiments under the same meteorological conditions For these reasons, it is desirable to develop a model that can simulate the effects of cloud seeding on the microphysical processes and precipitation. If the timing of the simulated snowfall enhancement is coincident with the observation, this increases the likelihood that the precipitation was due to the seeding. Numerical simulations were carried out at the National Center for Atmospheric Research (NCAR) in which an AgI (seeding material) module was inserted into the Thompson
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
