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
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
Classes taught:
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.