Date of Award




Document Type


Degree Name

Doctor of Philosophy (PhD)


Department of Atmospheric and Environmental Sciences

Content Description

1 online resource (xi, 234 pages) : color illustrations, color maps.

Dissertation/Thesis Chair

Kara J. Sulia

Committee Members

Ryan Torn, Justin Minder, Fangqun Yu


Ensemble, Microphysics, Numerical Weather Prediction, Sensitivity, Cloud physics, Ensemble learning (Machine learning), Atmospheric physics, Ice clouds, Atmospheric nucleation

Subject Categories

Atmospheric Sciences


Through ensemble sensitivity analysis, this dissertation aims to identify the amount of forecast uncertainty that stems from the representation of mixed-phase cloud microphysics within the Weather Research and Forecasting Model (WRF). The first research thrust focuses on how the evolution of ice crystal shape and choice of ice nucleation parameterization in the Adaptive Habit Microphysics Model (AHM) influences the lake-effect storm that occurred during Intensive Operating Period 4 (IOP4) of the Ontario Winter Lake Effect Systems (OWLeS) Field Campaign. This localized snowstorm produced total liquid-equivalent precipitation amounts up to 17.92 mm during a 16-hour time period, providing a natural laboratory to investigate the ice-liquid partitioning within the cloud and various microphysical process rates, as well as the accumulated precipitation magnitude and its associated spatial distribution. Two nucleation parameterizations were implemented, and aerosol data from a size-resolved Advanced Particle Microphysics (APM) model were ingested into the AHM for use in parameterizing ice and cloud condensation nuclei. Simulations allowing ice crystals to grow nonspherically produced 1.6–2.3% greater precipitation while altering the nucleation parameterization changed the type of accumulating hydrometeors. In addition, all simulations were highly sensitive to the domain resolution and the source of initial and boundary conditions.