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

Group announcements coming soon! Full list of announcements here

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)
 
 
 
 
 
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
 
 
 
 
 
Texas Tech University
Graduate Research Assistant
Texas Tech University
September 2012 – August 2019 Lubbock, TX
 
 
 
 
 
Texas Tech University
Instructor
Texas Tech University
July 2019 – June 2021 Lubbock, TX

Classes taught:

  • ATMO 1300: Introduction to Atmospheric Science
 
 
 
 
 
National Center for Atmospheric Research
Graduate Student Visitor
National Center for Atmospheric Research
June 2018 – August 2018 Boulder, CO
 
 
 
 
 
Texas Tech University
Graduate Writing Tutor
Texas Tech University
July 2016 – June 2018 Lubbock, TX

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
Data Assimilation
The application of convective-scale data assimilation to improve hazard forecasts
Data Assimilation
Machine Learning for Forecasting
Machine learning tools are being used to generate valuable products that aid operational forecasting
Machine Learning for Forecasting
Observation Targeting
Targeted observations are used to sample the atmosphere in strategic ways to improve forecasts
Observation Targeting
Severe Storm Dynamics
Improving the real-time detection of tornados through understanding of environmental precursors
Severe Storm Dynamics

Recent Publications

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(2024). Observation Definitions and their Implications in Machine Learning-based Predictions of Excessive Rainfall. Weather and Forecasting, in review..

(2024). Can Ingredients-Based Forecasting be Learned? Disentangling a Random Forest's Severe Weather Predictions. Weather and Forecasting, in review..

(2023). A New Paradigm for Medium-Range Severe Weather Forecasts: Probabilistic Random Forest–Based Predictions. Weather and Forecasting, 38, 251-272.

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(2021). Forecasting excessive rainfall with random forests and a deterministic convection-allowing model. Weather and Forecasting, 36, 1693-1711.

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Recent Presentations

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(2024). Ingredients-Based Explainability: Using Tree Interpreter to Disaggregate a Random Forest's Severe Weather Predictions. 23rd Conference on Artificial Intelligence for Environmental Science, Baltimore, MD.

(2024). Understanding Training Data Components for Excessive Rainfall Machine-Learning Models: A look inside the Unified Flooding Verification System. 23rd Annual AMS Student Conference, Baltimore, MD.

(2024). When Do Machine Learning Forecasts Succeed and Fail? Evaluating Synoptic Regimes Associated With a Random Forest's Good and Bad Severe Weather Predictions. 14th Conference on Transition of Research to Operations, Baltimore, MD.

(2023). Understanding and Improving Predictability of High-Impact Weather Hazards Through the Lens of Machine Learning.. American Geophysical Union Fall Meeting, virtual, December 2023.

(2023). AI and Machine Learning in NWP: A look at Excessive Rainfall with the CSU-MLP. Winter Weather Workshop, NWS OKX WFO, virtual, November 15th 2023.

Contact

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