I worked on a technically complicated project focusing on the intersection between mesoscale meteorology and atmospheric turbulence. I used large-eddy simulations (LES) to investigate the impact of different near-surface turbulence parameterizations on tornadic supercell simulations, to see how a more realistic treatment of the lower boundary condition might affect model output. I used the National Center for Atmospheric Research's (NCAR) supercomputers to execute my simulations, and I became proficient in Python through the analysis of my results via NCAR's HPC JupyterHub. I developed my own technical scripts for visualization, analysis, and interpretation of my gridded model output, and I became comfortable working in Linux/UNIX environments. Additionally, I became capable of independently troubleshooting the supercomputing process as well as CM1's Fortran code. I regularly and effectively communicated my results with both written and oral progress reports, as well as informal and formal presentations. My work has been accepted for oral presentation at the American Meteorological Society's (AMS) Severe Local Storms Conference in October 2024, and I am currently working on submitting this work to a peer-reviewed publication in addition to having successfully defended and published my Master's thesis.

As an undergraduate research assistant, I was funded by NOAA to assist in the creation of GridRad-Severe, an archive of composite radar data volumes from severe weather events across the CONUS from 2010-2019. I became familiar with working in Linux/UNIX environments, and scheduling and monitoring automated jobs. I also gained experience analyzing Doppler radar products, performing forensic case studies of severe weather events, and writing complicated technical scripts to match Storm Prediction Center (SPC) wind, hail, and tornado reports to corresponding radar-identified storm tracks. I co-authored a peer-reviewed publication as a result of this work (DOI: 10.1175/MWR-D-23-0017.1).Additionally, I conducted an independent research project within this position where I used IDL programming to investigate statistical relationships between a storm's characteristics and its likelihood of becoming significantly tornadic.