Ph.D. Atmospheric Science,
University of California, Davis 

Exploring Improvements to Snow Hydrology Modeling Under a Multilayer Advance Canopy Modeling Framework
Major Adviser: Kyaw Tha Paw U
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​Explored the use of a multilayer soil vegetation atmosphere numerical model with higher order closure in place of a single layer model to reproduce observed energy budgets and snow depths within a forest. The highly resolved multilayer model was shown to outperform a single layer model in ability to reproduce energy and water budgets. In addition, the multilayer model was used to reevaluate results of a previous Mountain Pine Beetle (MPB) study that used a single layer canopy model. On annual and monthly scales, the two models perform well. On daily and hourly scales, the multilayer model outperformed the single layer model. Furthermore, when exploring leaf density sensitivity, it was found the multilayer model had greater differences in hydrological effects within the range of leaf density likely due to its higher resolution.

Evaluated the impact of differing snow-covered vertical forest canopy structures on snow water and energy balance processes. Alterations to only the vertical canopy structure resulted in differences in average peak snowpack depths up to 0.15 m and up to a 10 days difference in time to snowpack melt out. The results demonstrate that the vertical canopy structure influences the seasonal snow depth, extent, and melt of snow in forested areas. Hence, resolving or parameterizing for vertical canopy structure and snow-processes in snow-forest models maybe a critical step in improving estimates of annual water budgets from forested areas.

Examined simulations of snow unloading from canopies verse no-unloading within a given snow-covered canopy and found the unloading simulations resulted in decreased snow canopy content, decreased sublimation, and increased below canopy snow depth compared to the no-unloading simulations. The magnitude of the differences in energy and water budgets between unloading and no unloading simulations for given tree structures were found to be relatively unchanged among tree types. Therefore, under these conditions, and with the given parameterization, the energy impact of differing tree structures is greater than unloading verse no unloading effects.

​M.S. Meteorology,
Pennsylvania State University 

Detectability of Anthropogenic Emissions
Major Adviser: Ken Davis

       Conducted research at Pennsylvania State using WRF-ARW, a mesoscale atmospheric transport model, to determine the detectability of anthropogenic emissions from a city given current measurement density and modeling techniques.

      The study was focused over Indianapolis, Indiana. Indianapolis is the location of INFLUX network, a network of towers that sample GHG concentrations (http://influx.psu.edu/). Within the simulations, tracers were used to track biogenic and anthropogenic surface fluxes, and lateral boundary inflow. The tracers were used to break city mole fraction measurements into their specific surface flux origins. The proportion of the mole fraction from anthropogenic fluxes to all other fluxes and background signals is used as a proxy for the detectability of anthropogenic city emissions.

​PDF PSU Exit Seminar

​"Education is a progressive discovery of our own ignorance."
-Will Durant 

​B.A. Geography, University of Delaware 
M.S. Geography, University of Delaware 

Snowpack Influence on Nor’easters
Major Adviser: Brian Hanson
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        Used WRF-ARW to examine the influence of a snowpack covering the Northeast United States on nor’easters. The research found the snowpack created a strong decrease in the surface energy leading to a decrease in lower atmospheric temperatures, an increase in pressure and stability; however these atmospheric changes did not significantly alter the modeled nor’easters. There was only a slight increase in central low pressure and total precipitation (while convective precipitation decreased) and the storm tracks were largely unchanged.

PDF UD Exit Seminar, PDF UD Exit Seminar Short Version