Email: kyle.gillett@und.edu
Welcome!
I’m a Master’s Student of Atmospheric Science in the John D. Odegard School of Aerospace Science at the University of North Dakota. I also chase and document extreme weather and maintain python software.
My Master’s thesis research at the University Of North Dakota involves high-resolution numerical experiments of storm interactions that result in tornadogenesis. You’ll find those details below.
My interests in meteorology primarily include severe convective storm dynamics (particularly storm interactions, tornadogenesis, & supercell behavior). I also have interests in developing data visualization tools for meteorology and weather’s impact of Great Lakes maritime disasters.
Research, Tools, & Resources
Open the dropdowns below for details
Under Construction! More coming soon!
A Numerical Investigation into the Impacts of a Rear Flank Storm Interaction on Storm Behavior and Tornadogenesis: A Master’s Thesis presented to the University of North Dakota Department of Atmospheric Science; July 2026
Committee: Dr. Catherine Finley (advisor), Dr. Bruce D. Lee (member), Dr. Cameron J. Nixon (member) with special thanks to Dr. Matthew Brown, Dr. Jordan Christian, Dr. Jacob Carstens, & Chris Broyles
Thesis Abstract
Observationally, storm interactions have been associated with changes in severe convective storm behavior and evolution, including the development and maintenance of tornadoes. In particular, the interaction of neighboring convection in or near the rear-flank of a supercell thunderstorm has frequently been associated with tornadogenesis. Although statistical relationships for these changes in behavior have been established, the underlying physical mechanisms governing their evolution remain poorly understood, owing in part to limited spatial and temporal resolution of most observational datasets. Previous modeling efforts also lack the design and resolution required to sufficiently examine this problem. This study aims to compliment and build upon existing findings by investigating the impact and effect of auxiliary convection near the rear-flank of a weakly tornadic supercell thunderstorm. Presented are two sets of numerical experiments, each consisting of five idealized model simulations. One experiment set simulates a single discrete “parent” supercell thunderstorm, while the other simulates the same “parent” subjected to a rear-flank interaction with a nascent “neighbor” cell. The interaction simulations reveal drastic changes in overall storm behavior, including the accumulated duration of tornado-like-vortices, which is up to 75% higher (35 minutes longer) than control simulations. This study provides further evidence that rear-flank storm interactions can modulate overall storm behavior, which in turn modulates tornado development and maintenance.
UND GRAD Poster Presentation
A poster featuring preliminary findings presented to the University of North Dakota Graduate Student Research Achievement Day, March 2026, Grand Forks, North Dakota (click to enlarge)
- Gillett, K., J., March 2026: A Numerical Investigation into the Impacts of a Rear Flank Storm Interaction on Storm Behavior and Tornadogenesis, 2026 Graduate Research Achievement Day (GRAD), University of North Dakota, Grand Forks, North Dakota
Convective Environments Preceding Landfall in Tornadic Tropical Cyclones: An Analysis of Airborne Observations. Presented at the 106th AMS Annual Meeting in Houston, Texas
Advisor: Dr. Jacob D. Carstens
Synopsis: An analysis of ~30,000 dropsondes deployed from recon aircraft between 1995-2021 (Nguyen et al. 2019) are sorted by storm and then into two categories: weakly/non-tornadic Tropical Cyclones (TCs) and highly tornadic TCs using the Tropical Cyclone Tornado database (TCTOR, Edwards, 2010). Weakly/non-tornadic (highly-tornadic) TCs are defined as TCs producing <15 (>=15) tornadoes. Individual dropsonde points for both subsets were then spatially organized into 50 x 50 km bins relative to TC-center. After quality controlling the data (removing points beyond standard deviation criteria, interpolating over missing data, etc.), mean vertical profiles were constructed for each bin, revealing a 50 x 50 km area-averaged vertical profile in each bin for both subsets. A few relevant convective storm environment parameters and vertical profiles were compared to evaluate the differences between the two subsets. Preliminary takeaways from this work suggest that highly tornadic TCs exhibit “more favorable” thermodynamic and kinematic environmental configurations in their right-front storm-relative quadrant than weakly/non-tornadic TCs. (click to enlarge)
- Belzer, B. W*., Gillett, K. J., Carstens, J., D., January 2026: Convective Environments Preceding Landfall in Tornadic Tropical Cyclones: An Analysis of Airborne Observations, 106th AMS Annual Meeting, Houston, TX, https://doi.org/10.13140/RG.2.2.29219.11047
* denotes student coauthor
SounderPy is an open-source atmospheric science Python package for vertical profile analysis. This tool is designed to get data, ‘clean it up’ for simple use, and plot the data on advanced-sounding plots. SounderPy was developed with the goal in mind to keep the code simple and efficient for users of all experience levels and for reliability in all use cases.
SounderPy Journal of Open Source Science Article
An article published in The Journal of Open Source Software in August 2025:
- Gillett, K. J., 2025: SounderPy: An atmospheric sounding visualization and analysis tool for Python. J. Open-Source Software, 10 (112), 8087,
https://doi.org/10.21105/joss.08087
SounderPy Abstract
SounderPy is a simple, open-source Python package for retrieving and plotting vertical profile (sounding) data. Built for simplicity and reliability for all uses and users, this project’s goal is to provide a uniform method for sounding analysis across multiple data types. Severe weather analysis and forecasting requires a sound comprehension of thermodynamic and kinematic properties of the environment. SounderPy makes this possible with robust access to data and custom visualizations. The tool creates complex yet effective sounding and hodograph plots with high readability which are designed specifically for severe weather analysis and forecasting. SounderPy is capable of retrieving and plotting model forecast data, observed radiosonde data, Aircraft Communications Addressing and Reporting System (ACARS) observation data, and model reanalysis data. All of this functionality can be completed in three simple lines of code or less, making SounderPy an accessible tool for both Python experts and novices. A number of scientific Python libraries build the base of SounderPy’s efficient and durable functionality, such as NumPy, Matplotlib, xarray, Metpy, and SHARPpy. SounderPy is available through GitHub and PyPi and is distributed under an MIT license.
SounderPy Poster Presentation
A poster featuring SounderPy functionality and capabilities presented to the 27th Annual Central Iowa National Weather Association Severe Storms and Doppler Radar Conference, Ankeny Iowa, 2024 (click to enlarge)
- Gillett, K. J., March 2024: SounderPy: A Sounding Visualization Tool for Severe-Weather Analysis and Forecasting, 27th Annual Central Iowa National Weather Association Severe Storms and Doppler Radar Conference, Ankeny Iowa, https://doi.org/10.13140/RG.2.2.15823.29600
Co-Founder, Co-Developer, Contributor to the Chase Archive Project: The web’s largest storm data archive.
Introductory surveys of Skew-T Log-p diagram and hodograph applications in severe weather forecasting. Some prior basic understanding of Skew-Ts and hodographs is recommended.
An Analysis of the 20 May 2017 North-Central Indiana Localized Tornado Outbreak — A case study for undergraduate Mesoscale Dynamics
The Influences of Effective Inflow Layer Streamwise Vorticity and Storm Relative Flow on Supercell Updraft Properties — a publication review for undergraduate Dynamics II
Forecasting the Witch of November. A post event reanalysis of 9-10 November 1975
Curriculum Vitae
* denotes student co-author, ** denotes invited presentation
Publications
- Gillett, K. J., 2025: SounderPy: An atmospheric sounding visualization and analysis tool for Python. J. Open-Source Software, 10 (112), 8087, https://doi.org/10.21105/joss.08087
Oral Presentations
- **Gillett, K., J., May 2026: SounderPy: A Sounding Analysis and Visualization Platform for Severe Weather Forecasting., Environment Canada Storm Prediction Centre Winnipeg Spring Change of Seasons Workshop, Dartmouth, Nova Scotia, Canada
- **Gillett, K., J., May 2026: Storm Interactions: Their Relationship with Storm Behavior and Tornadogenesis., Environment Canada Storm Prediction Centre Winnipeg Spring Change of Seasons Workshop, Dartmouth, Nova Scotia, Canada
- **Gillett, K., J., May 2026: SounderPy: A Sounding Analysis and Visualization Platform for Severe Weather Forecasting., Environment Canada Storm Prediction Centre Winnipeg Spring Change of Seasons Workshop, Winnipeg, Manitoba, Canada
- **Gillett, K., J., May 2026: Storm Interactions: Their Relationship with Storm Behavior and Tornadogenesis., Environment Canada Storm Prediction Centre Winnipeg Spring Change of Seasons Workshop, Winnipeg, Manitoba, Canada
- Gillett, K., J., April 2026: A Theory on the Relationship Between Rear Flank Storm Interactions, Storm Behavior, and Tornadogenesis., Symposium on Local Atmospheric Research, University of North Dakota, Grand Forks, North Dakota
- **Gillett, K., J., April 2026: Storm Interactions: Their Relationship with Storm Behavior and Tornadogenesis., Grand Forks National Weather Service Change of Seasons Workshop, Grand Forks, North Dakota
- Gillett, K., J., March 2026: A Theory on the Relationship Between Rear Flank Storm Interactions, Storm Behavior, and Tornadogenesis., 29th Annual Central Iowa National Weather Association Severe Storms and Doppler Radar Conference, Ames, Iowa
- Nixon, C., Gillett, K., Tang, J., Fowkes, H., February 2025: Chase Archive: Introducing the biggest collection of storm chases on the web. For chasers, by Chasers., National Storm Chaser Summit.
- Gillett, K., May 2025: A Simple Guide to Forecasting the Northern Lights: Auroral Substorms, Seminar for Local Atmospheric Research, Grand Forks, ND.
Posters
- Gillett, K., J., March 2026: A Numerical Investigation into the Impacts of a Rear Flank Storm Interaction on Storm Behavior and Tornadogenesis, 2026 Graduate Research Achievement Day (GRAD), University of North Dakota, Grand Forks, North Dakota
- Belzer, B. W*., Gillett, K. J., Carstens, J., D., January 2026: Convective Environments Preceding Landfall in Tornadic Tropical Cyclones: An Analysis of Airborne Observations, 106th AMS Annual Meeting, Houston, TX, https://doi.org/10.13140/RG.2.2.29219.11047
- Gillett, K. J., March 2024: SounderPy: A Sounding Visualization Tool for Severe-Weather Analysis and Forecasting, 27th Annual Central Iowa National Weather Association Severe Storms and Doppler Radar Conference, Ames Iowa, https://doi.org/10.13140/RG.2.2.15823.29600
Featured In
- Synoptic Analysis and Forecasting: An Introductory Toolkit, 2nd Edition, Shawn Milrad, 2025.
Kyle J Gillett Photography 