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Elizabeth Tirone

Elizabeth Tirone

Elizabeth Tirone

Research Scientist

elizabeth.tirone@ou.edu
elizabeth.tirone@noaa.gov
National Weather Center 4340 E1

Education

  • PhD, Meteorology, Iowa State University
  • BS, Meteorology, State University of New York College at Oswego

Affiliated CIWRO Team

  • Developmental Research and Engineering for Applied Meteorology Team (DREAM)

About

Elizabeth is a research scientist within the Developmental Research and Engineering for Applied Meteorology Team at CIWRO/NSSL. Currently, she is leading research into the utility of aerial imagery collected by uncrewed aerial systems and satellite for severe storm damage assessments. Her research also focuses on automating damage detection with machine learning.

Research Focus

  • Uncrewed aerial systems
  • Severe storm damage assessments
  • Machine learning
  • Aerial imagery analysis

CIWRO Research Themes

  • Weather Radar and Observations
  • Mesoscale and Stormscale Modeling R&D
Publications
  • Tirone, E. A., S. Pal, W. A. Gallus, S. Dutta, R. Maitra, J. L. Newman and E. Weber, 2024: A machine learning approach to improve the usability of severe thunderstorm wind reports. Bulletin of the American Meteorological Society, https://doi.org/10.1175/BAMS‑D‑22‑0268.1
  • Wagner, M., M. Coniglio, E. Rasmussen, M. Satrio, D. Bodine, D. Candela, D. Kennedy, E. Tirone, 2024: Harnessing UAS and high-resolution satellite imagery to better characterize wind damage and understand tornado behavior. Bull. Amer. Meteor. Soc.
  • Tirone, E., W. A. Gallus, 2024: Exploring the explainability of a machine learning tool to improve severe thunderstorm wind reports. Atmosphere, 16(7), https://doi.org/10.3390/atmos16070881
Conference presentations
 
  • Tirone, E., M. Coniglio, R. Moore, L. Sims, D. Candela, 2025: Scales of tornado damage detectability using machine learning Artificial Intelligence for Satellite Observations, New Orleans, LA
  • Martinaitis, S. M., A. J. Schroeder, E. Tirone, D. Candela, C. Spannagle, L. Elizalde-Garcia, R. Pajela, J. Culin, A. M. Ward, M. Gittinger, B. Philips, C. Walker, J.J. Gourley, 2025: Analysis of the Historic Oklahoma Panhandle Flash Flood Event of 18-19 June 2024 through the Use of Aerial Imagery and the Flash Flood Severity Index 39th Conference on Hydrology, New Orleans, LA
  • Tirone, E., Z. Chen, D. Candela, M. Coniglio, 2024: Implementing Machine Learning into UAS Data Analysis International Society for Atmospheric Research using Remotely Piloted Aircraft, Tulsa, OK
  • Tirone, E. A., M. Wagner, Z. Chen, D. Candela, E. Rasmussen, M. Coniglio, 2024: Automated Treefall Detection using Zero‑Shot Deep Learning. 23rd Conference on Artificial Intelligence for Environmental Science 
  • Tirone, E. A., S. Pal, S. Dutta, R. Maitra, W. A. Gallus, J. L. Newman and E. Weber, 2022: Evaluating meteorological environments of severe wind report classifications by a machine learning tool. 31st Conference on Weather Analysis and Forecasting (WAF)/27th Conference on Numerical Weather Prediction (NWP) 
  • Tirone, E. A., W. A. Gallus, S. Pal, S. Dutta, J. L. Newman, E. Weber, and R. Maitra, 2022: Using Machine Learning to Quality Control Thunderstorm Wind Reports in the NCEI Storm Events Database. Severe Storms and Doppler Radar Conference 
  • Tirone, E. A., S. Pal, W. A. Gallus, S. Dutta, R. Maitra, J. L. Newman and E. Weber, 2022: Verifying Storm Prediction Center 1630UTC day 1 wind outlooks using a machine learning‑based weighting method. 21st Conference on Artificial Intelligence for Environmental Science 
  • Pal, S., E. A. Tirone, W. A. Gallus, S. Dutta, R. Maitra, J. L. Newman and E. Weber, 2022: ”Blowin’ in the wind” ‑ diagnosing the probability that a severe thunderstorm wind report is truly due to severe intensity wind event. 21st Conference on Artificial Intelligence for Environmental Science 
  • Thielen, J., R. S. Schumacher, A. Haberlie, W. A. Gallus, E. A.Tirone, K. K. Hugeback , 2022: VSVRIMG for detailed morphology: a crowdsourced dataset of convective morphology attribute labels on radar mosaic images. 21st Conference on Artificial Intelligence for Environmental Science 
  • Tirone, E. A., S. Pal, W. A. Gallus, S. Dutta, J. L. Newman, R. Maitra and E. Weber, 2021: Evaluating the performance of a machine learning‑based tool to predict the probability that a severe wind report is due to severe intensity winds. Artificial Intelligence for High‑Impact Weather 
  • Thielen, J. E., E. A. Tirone, W. A. Gallus, 2020: OpenMosaic: open‑source and extensible NEXRAD mosaic creation and storm object feature extraction in python. 11th Symposium on Advances in Modeling and Analysis Using Python 
  • Tirone, E. A., W. A. Gallus, S. Pal, S. Dutta, R. Maitra, J. L. Newman, and E. Weber, 2020: A machine learning tool to provide probabilities that thunderstorm wind damage reports are due to severe intensity winds. Severe Local Storms Symposium 
  • Tirone, E. A., 2019: Improved diagnosis of severe wind occurrence through machine learning. Great Lakes Atmospheric Science Symposium, Oswego, NY


 

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Updated 11/12/2025 by Cooperative Institute for Severe and High-Impact Weather Research and Operations (CIWRO): ciwro@ou.edu