Biography:
Prof. Will Cantrell is the Associate Provost for Graduate Education and Dean of the Graduate School at Michigan Technological University. He holds an academic appointment as Professor in the Department of Physics. Over the last 20 years, Prof. Cantrell’s research has primarily focused on laboratory investigations of ice nucleation and other aerosol-cloud interactions. He is one of the leaders of the Pi Chamber facility, a unique convection-cloud chamber, enabling studies of cloud droplet spectral broadening, mixed-phase clouds, the interaction of aerosol particles in a turbulent flow, and entrainment, among other topics.
Prof. Cantrell joined Michigan Technological University in 2001, following a two-year postdoctoral role at Indiana University's Chemistry department. He received his PhD in Atmospheric Science from the University of Alaska Fairbanks in 1999, gaining extensive experience in field measurements of aerosol properties. Prof. Cantrell's current research not only contributes to the fundamental understanding of cloud physics, but also holds promise for practical applications in climate research and environmental sustainability.
Project Brief:
“Laboratory and Modeling Studies of Cloud Susceptibility to Hygroscopic Seeding”
The project aims to quantify the ‘modifiability’ of clouds through hygroscopic seeding or electrical charging effects. Clouds with low droplet concentrations (clean clouds) or high concentrations of small droplets (polluted clouds) are less likely to respond to seeding. Even clouds that are susceptible to seeding may be inadvertently overseeded, potentially hindering precipitation rather than enhancing it. The project aims to identify the specific cloud characteristics and conditions under which hygroscopic seeding or modification by electrical effects will produce changes in the cloud which lead to precipitation enhancement.
The project’s experimental approach is enabled by a unique facility at Michigan Tech known as the Pi Chamber, named for its 3.14 cubic meter internal volume. Using the Pi Chamber, the team aims to create and sustain steady cloud conditions over extended periods, enabling the collection of high-fidelity, high-resolution data on cloud characteristics. The investigation into cloud seeding involves creating a cloud in the chamber and introducing hygroscopic seeding material into that cloud to observe the changes in cloud characteristics produced by the seeding.
The seeding materials will include hygroscopic salts (including commercially used hygroscopic flare materials), dust, and a unique NaCl-TiO2 core-shell material from a previous UAEREP cycle. Furthermore, the project will investigate the effect of introducing charge into the cloud, either through ions from a corona discharge, or from highly charged aerosol particles. Changes in cloud characteristics include the number concentration of cloud droplets, the relative distribution of droplet sizes, and the number aerosol particles in the cloud that have not yet become droplets. The appearance of larger droplets is of particular interest.
The project’s experimental approach is closely integrated with modeling efforts, which will extend the range of scenarios that can be examined. Its hierarchy of models ranges from the explicit parcel mixing model, which can run on a laptop to a large eddy simulation, requiring supercomputing resources. By combining results from experiments and simulations of the models, the project seeks to explore a wider range of conditions and more quickly determine the efficacy of seeding in different cloud conditions.
The team aims to establish a baseline through investigations of highly polluted clouds, which they expect be too costly to seed (meaning that the amount of seeding material required to enhance precipitation would be quite large). Once this baseline established, the amount of seeding material (or charge) introduced into the cloud will be adjusted, along with the cloud droplet number concentration, until a combination that leads to a broadening of the drop size distribution is identified. This will be the first step toward runaway collision-coalescence and rain formation. Furthermore, parallel simulations will provide additional context and pathways for scaling, facilitating adoption in operational contexts.
