Aaron Hill

Aaron Hill

Assistant Professor of Meteorology

University of Oklahoma

Biography

Dr. Aaron Hill is a professor of meteorology in the School of Meteorology at the University of Oklahoma. His research interests include weather forecasting, data assimilation, numerical weather prediction, predictability of severe convective weather, artificial intelligence and machine learning, Python programming, innovative observing systems, and operational weather forecasting tools. Dr. Hill leads the CHAOS research group which specializes in Convection and weather Hazards with Artificial intelligence, Observations, and Simulations. The group is currently involved in developing machine learning tools for high-impact weather forecasting, exploring predictability of storms in warming climates, and understanding the dynamics of tornadogenesis in Quasi-Linear Convective Systems. Interested in joining the CHAOS group? Contact Dr. Hill: ahill@ou.edu

Interests
  • Numerical Weather Prediction and Weather Forecasting
  • Artificial Intelligence, Machine Learning, and Data Science
  • Severe Storm Dynamics
Education
  • PhD in Geosciences, 2019

    Texas Tech University

  • MS in Atmospheric Science, 2014

    Texas Tech University

  • BS in Atmospheric Science; Minor in Applied Mathematics, 2012

    University of Washington

Group Announcements

Full list of announcements here

01/08/2025
Some overdue announcements…the CHAOS group welcomed Rebecca Oh and Evan White to the group this fall as undergraduate researchers! Rebecca is working on a project to understand how climate change impacts severe storm predictability while Evan is researching how to apply AI postprocessing techniques to global AI weather models to produce hazardous weather forecasts.

Dr. Hill presented some of Evan White’s early postprocessing work at the Severe Local Storms conference this past October in Virginia Beach, and will follow that up with a presentation at the AMS Annual Meeting in January.

Dr. Hill mentored a Senior Capstone group this fall and they successfully presented their work showcasing an AI application to predict updraft strenghthening in supercells - congrats Gabriel, Andrew, Victor, and Evan!

Dr. Hill also attended the AGU Fall Meeting, giving a presentation on how AI can improve impact-based decision support services, and he visited the Texas A&M Department of Atmospheric Sciences this fall for their Colloquium series.

Finally, a number of members of the CHAOS group will be presenting their work at the AMS annual meeting in New Orleans.

07/08/2024
Welcome Evan Sudler to the research group! Evan is pursuing his B.S. degree in Meteorology in the School of Meteorology at OU. Evan will be working on evaluating the skill of global AI-based weather forecast models for high-impact weather.

4/26/2024
Excited to announce three new members to the Hill Group at OU! Kelly Geiger (SUNY Albany), Hanna McDaniel (Florida State University), and Christian McGinty (University of Colorado - Boulder) will all be joining in the Fall as M.S. students!

Dr. Hill recently presented research at the European Geophyics Union General Assembly in Vienna, Austria

Experience

 
 
 
 
 
University of Oklahoma
Assistant Professor
University of Oklahoma
August 2023 – Present Norman, Oklahoma

Classes taught:

  • METR 1313: Introduction to Programming for Meteorology (Spring 2024, 2025)
  • METR 5970: AI for Environmental Science (Fall 2024)
 
 
 
 
 
Colorado State University
Research Scientist (I/II)
Colorado State University
July 2021 – August 2023 Fort Collins, CO
 
 
 
 
 
Colorado State University
Postdoctoral Research Fellow
Colorado State University
July 2019 – June 2021 Fort Collins, CO

Research Areas

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Convection Predictability
Improving forecasts of convection through understanding how forecasts are sensitive to small-scale changes in the environment
Convection Predictability
Machine Learning for Forecasting
Machine learning tools are being used to generate valuable products that aid operational forecasting
Machine Learning for Forecasting
Sensitivity Analysis and Targeted Observing
Targeted observations are used to sample the atmosphere in strategic ways to improve forecasts
Sensitivity Analysis and Targeted Observing
Severe Storm Dynamics
Improving the real-time detection of tornados through understanding of environmental precursors
Severe Storm Dynamics

Recent Presentations

Full list of presentations here

McClung, B. T., D. Schvartzman, A. J. Hill, M. Stock, and A. McGovern, 2025: BoltCast: Deep Learning for Long Term Lightning Prediction. 12th Conference on the Meteorological Application of Lightning Data, New Orleans, LA.

Clark, A., A. J. Hill, K. Hoogewind, A. Berrington, and E. Loken, 2025: Extended range machine-learning severe weather guidance based on the operational GEFS. 33rd Conference on Weather Analysis and Forecasting, New Orleans, LA.

Erickson, N., A. McGovern, and A. J. Hill, 2025: Deep Learning for Short-Term Characterization of Tornado Intensity. 24th Conference on Artificial Intelligence for Environmental Science, New Orleans, LA.

Hill, A. J., D. J. Bodine, S. M. Cavallo, B. G. Ilston, Z. J. Lebo, and D. Schvartzman, 2025: Preparing the Next Generation of Meteorological Data Scientists: Redesigning Curricula for Student Success. 34th Conference on Education, New Orleans, LA.

Hill, A. J., E. White, and J. Radford, 2025: An AI-Machine Learning Probabilities (AI-MLP) Forecast System for Hazardous Weather Prediction. 33rd Conference on Weather Analysis and Forecasting, New Orleans, LA.

Hill, A. J. and R. S Schumacher, 2025: Machine Learning Probability Ensembles for Medium-Range Excessive Rainfall Prediction. 24th Conference on Artificial Intelligence for Environmental Science, New Orleans, LA.

Madsen M., A. McGovern, D. Harrison, A. Clark, M. Baldwin, S. Ernst, J. T. Ripberger, and A. J. Hill, 2025: Perceptions and Performance of Global AI Models in the 2024 NOAA Hazardous Weather Testbed. 24th Conference on Artificial Intelligence for Environmental Science, New Orleans, LA.

Schumacher, R. S. and A. J. Hill, 2025: Quality-controlled databases of US extreme rainfall events based on gridded precipitation estimates and convection-permitting model output. 39th Conference on Hydrology, New Orleans, LA.

Contact

  • ahill@ou.edu
  • 120 David L. Boren Blvd Suite 5900, Norman, OK 73072