Gagne II, D. J., and Coauthors, 2024: Lessons Learned from Building Real-Time Machine Learning Testbeds for AI2ES. 14th Conference on Transition of Research to Operations, Baltimore, MD.
Green, M. R. L., A. J. Hill, and R. S. Schumacher, 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.
Mazurek, A. C., R. S. Schumacher, and A. J. Hill, 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.
Mazurek, A. C., A. J. Hill, R. S. Schumacher, and H. J. McDaniel, 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.
Hill, A. J. and R. S. Schumacher, 2023: How long of an observational record is needed for skillful ML-based forecasts of excessive rainfall? 32nd Conference on Weather Analysis and Forecasting, Madison, WI.
Hill, A. J., D. C. Dowell, and C. C. Weiss, 2023: An Initial Assessment of Environmental Influences on QLCS-tornadogenesis from PERiLS Field Campaign Datasets and High-Resolution Simulations. 28th Conference on Numerical Weather Prediction, Madison, WI.
James, E. P., R. S. Schumacher, and A. J. Hill, 2023: Testing random forests for prediction of excessive rainfall based on the High-Resolution Rapid Refresh (HRRR). 32nd Conference on Weather Analysis and Forecasting, Madison, WI.