Claire Doyle

Thesis Defense

Subgrid Scale Modeling of Turbulence and Cloud Microphysics Interactions 

Claire Doyle

Friday, June 21st, 2024

NWC 5600 / 10:00 am

Abstract: Clouds have a significant but uncertain impact on Earth’s climatological and hydrological cycles. In particular, the warm rain process has considerable inconsistencies between current theories and observations. One hypothesis to explain this is the role of turbulence in broadening the droplet size distribution. The primary methods of studying these processes have been through the Pi Chamber at Michigan Technological University, direct numerical simulations, and large eddy simulations. However, these processes are difficult to study as they occur on a broad range of scales. This makes large eddy simulations appealing for studying these processes as they remain computationally efficient by modeling the smallest, dissipative scales of motion. While past studies have made efforts to improve the subgrid-scale stress tensor and scalar flux vectors in large-eddy simulations, subgrid-scale terms related to cloud microphysics have received little attention. Specifically, subgrid-scale super
 saturation variance is important when considering a Lagrangian approach as it arises from the Langevin equation, and both supersaturation and concentration covariance and concentration variance arise from the filtered evolution equation for droplet size distribution. It is these terms that were the focus of this study.
This study computed the true subgrid-scale variance and covariance terms from data of direct numerical simulations of Rayleigh-Benard convection in the Pi Chamber. Five cases of varying injection rates were considered, each with a Rayleigh number of 7.9×106. Each of the true subgrid-scale terms was compared to two candidate models: the gradient model and the scale-similarity model. Statistical analysis consisting of probability density functions, joint probability density functions, and correlation coefficients was used to assess model performance. Results concluded that the gradient model had relatively poor agreement with the true subgrid-scale terms with joint probability density functions that did not follow the one-to-one line indicating good skill, and correlation coefficients between ρ = 0 − 0.4. In contrast, results from the similarity model indicated joint probability density functions that closely followed the one-to-one line, and correlation coefficients between ρ =
 0.3 − 0.9 suggesting good agreement between the true and modeled subgrid-scale term. Altogether, the similarity model showed promise for modeling the subgrid-scale supersaturation variance, supersaturation and concentration covariance, and concentration variance. However, future investigation with higher Rayleigh numbers is warranted to prove or disprove these results.