Since the discovery of the dry ice and silver iodide effects on clouds by Shaefer and Vonnegut in the late 1940s and early 1950s, artificial weather modification operations that deliberately introduce seeding materials into clouds to impact precipitation have been practiced all around the world. Along with the intentional activities, clouds and precipitation have been inadvertently changed by human activities such as industrial air pollution and urbanization. Despite the inherent difficulty in assessing the effect of intentional and inadvertent weather modification, various materials, methods, technologies, and applications of conducting and evaluating weather modification have been developed. During the last several decades, fundamental theories, observational instruments, and especially numerical models have advanced dramatically in the fields of cloud microphysics, dynamics, and cloud-aerosol interactions to a level that the effects of intentional and inadvertent weather modification can be more or less quantitatively assessed. In this special issue on intentional and inadvertent weather modification research, we have invited several papers that use experimental, observational, and numerical techniques to study the effect.

On the experimental and observational side, one paper demonstrates that the measurements of chemical tracers’ concentration within snow samples in Idaho, USA, are useful in evaluating the targeting efficacy (which is not the same as seeding efficacy) of an operational or research cloud seeding program and validating the numerical model simulation of seeding material dispersion, transportation, and deposition. The chemical tracer concentrations from the ground-based seeding operations were found to be larger than those from the airborne operations. Another paper analyzes comprehensive observations from soundings, aircraft in situ measurements, and ground-based remote sensing platforms, in situ instruments, and snow sample images for 12 cases collected in the Haituo Mountains in northern Beijing, China, to provide an assessment of precipitation conditions in this area during wintertime. Suitable seeding conditions were identified in 10 out of 12 cases, which justifies setting up a glaciogenic cloud seeding operation in the Haituo Mountains to enhance snowfall for the 2022 Winter Olympic Games.

On the numerical side, two papers investigate the potential seeding effects from glaciogenic seeding operations in the eastern mountains of South Korea. Both studies, one focusing on ground-based seeding and the other on airborne seeding, apply a silver iodide cloud seeding parameterization implemented in the Morrison microphysics scheme and conduct Weather Research and Forecasting (WRF) model simulations on four ground-based and three airborne seeding cases. By comparing observations with model results, it is found that the model can reasonably reproduce the observed features. Both studies find that when the seeding conditions are suitable and the seeding materials are delivered to the right conditions, snow is enhanced in both observations and model results. Terrain blocking and too high of a wind speed are identified as unfavorable conditions for both ground-based and airborne seeding operations. The numerical models that simulate cloud seeding physics are shown to be very useful for cloud seeding research and effect evaluation.

Within the special issue, there is one more paper investigating the inadvertent modification of orographic precipitation in the Rocky Mountains, USA, by anthropogenic pollution and dust particles. Sensitivity simulations of orographic precipitation using different cloud condensation nuclei (CCN), giant CCN, and ice nuclei (IN) concentrations were performed by the Regional Atmospheric Modeling System (RAMS) in one winter season. It is found that orographic precipitation responds to CCN/IN changes differently depending on the environmental conditions. The quantitative impact of inadvertent weather modification, by anthropogenic pollution and dust, on precipitation provide a background for the impact of intentional weather modification projects, which typically are more local.

Lulin Xue
Bart Geerts
Xueliang Guo
István Geresdi
Steven Siems