Aaron Hill

School of Meteorology, University of Oklahoma, Norman, OK

I am an Assistant Professor in the School of Meteorology at the University of Oklahoma. I am interested in machine learning applications for high-impact weather hazards and I have a general interest in all things predictability and forecasting, and in particular ensemble sensitivity analysis, targeted observing, convective-scale data assimilation, machine learning, and ensemble forecasting of high-impact weather.


Research Interests

Machine Learning for High-Impact Weather

Postprocessing numerical weather prediction (NWP) model output to improve point-based and localized forecasts has been a topic of research for over 50 years (e.g., MOS). Statistical models are used to deduce relationships between pre-defined predictors and the phenomena of interest. Machine learning (ML) has emerged within atmospheric science as a new method to postprocess large NWP datasets to explicitly predict events that are unresolvable by NWP model output (e.g., extreme precipitation, tornadoes). One benefit of ML models is they are capable at learning the underlying spatio-temporal biases in NWP model output, thereby "correcting" biases in ML-based forecasts. I am interested in applying ML and deep learning (DL) techniques with global convection-parameterizing and convection-allowing models to create probablistic hazard outlooks (e.g., Hill et al. 2020a, Hill and Schumacher 2021, Schumacher et al. 2021, Hill et al. 2023) that are beneficial to operational forecast centers (e.g., Storm Prediction Center, Weather Prediction Center). This work is being supported by two grants from the National Oceanic and Atmospheric Administration Joint Technology Transfer Initiative program.

Predictability of Convection

Predictability of convective processes is easily discerned through the application of Ensemble Sensitivity Analysis (ESA), which relates changes in model initial conditions to changes in a forecast metric through univariate regressions. ESA is capable of diagnosing dynamic regions and features that are critical to successful forecasts of convection initiation and intensity (e.g., Hill et al. 2016). I continue to employ ESA to examine dynamic sensitivies of convection forecasts with convection-allowing models (e.g., Hill et al. 2016, Hill et al. 2021) and am interested in exploring how sensitivities vary across convection regimes and climates.

Targeted Observing

Observations of small-scale atmospheric prcesses become critical for convection-allowing model forecasts when convective processes are explicitly simulated. However, the selection of additional observations to improve forecasts is often subjective, even though objective methods exist to select observations in dynamically relevant regions. Ensemble sensitivity analysis (ESA) provides a framework to determine additional observing locations that dynamically relate earlier state variables (e.g., 2-m temperature) and a particular forecast metric (e.g., reflectivity) to reduce forecast uncertainty after the new observation is assimilated. I have examined ESA-based targeting at convection-allowing resolutions in idealized simulations (e.g., Hill et al. 2020b) and continue to explore its utility for real-data cases and storm-scale applications, for instance, targeting in-storm observations with Unmanned Aerial Systems and assimilating them into CM1 model simulations.

Data Assimilation

Data assimilation procedures, which combine first-guess four-dimensional snap shots of the atmosphere with observations, have evolved significantly over the last two decades to improve forecasts at all scales of motion. I am particularly interested in the ensemble Kalman filter (EnKF), which has a broad application at convective scales, and methodological parameters that control inflation, localization, and ensemble spread, which help to create reliable ensemble forecasts. Specifically, as a component of the VORTEX-SE project, I am investigating the influence of near-surface thermodynamic observations on deep convection in the southeast U.S. through retrospective data assimilation experiments with the High Resolution Rapid Refresh Ensemble (e.g., Hill et al. 2021).

Severe Storm Dynamics

I have been involved in a number of field programs that aimed to better understand the dynamics of severe storms, either through targeted deployments of observational assets in and near supercell thunderstorms (e.g., Targeted Observations by Radars and UAS of Supercells, TORUS) or deployments of mobile and portable near-surface platforms to observe heterogenieties preceeding convection in the southeast U.S. (e.g., VORTEX-SE). These experiences have propelled an interest in engineering platforms designed to observe severe storms (e.g., small, lightweight platforms) as well as the observations obtained that may elucidate new dynamic information to help advance warnings of severe-storm hazards. These interests are being supported by a National Oceanic and Atmospheric Administration grant through the VORTEX-SE Program.



Teaching Interests

Severe Storm Dynamics

Cold-pool dynamics, supercell dynamics, tornadogenesis, tornado sturcture, radar remote sensing, microsphyics, electrification, single-cell thunderstorms, multi-cell thunderstorms

Mesoscale Meteorology

Parcel theory, fluid dynamics, atmospheric waves, dryline dynamics, frontogenesis, stability, shallow and deep convective processes, mesoscale convective systems, sounding analyses, low-level jets

Atmospheric Modeling and Data Assimilation

Discretization and finite differencing, governing dynamic equations, parameterization schemes, deterministic and ensemble forecasting, verification, data assimilation, chaos theory, predictability, WRF model, initial condition problems

Data Analytics and Numerical Methods

Least squares problem, maximum likelihood estimation, statistical filtering, objective analysis, coordinate transformations, random forests, neural networks, convolutional neural networks, advanced machine learning/deep learning methods

Introduction to Atmospheric Sciences

Energy cycle, atmospheric forces, vertical structure of the atmosphere, atmospheric composition, stability, thermodynamics and clouds, air masses, cyclones, climate cycles, thunderstorms, tornadoes, forecasting

Scientific Computing in the Atmospheric Sciences

Linux, bash scripting, Python, version control (git, GitHub), object-based programming, meteorological data packages


Education

Texas Tech University

Doctorate of Philosophy
Geosciences - Atmospheric Science Focus

Dissertation: Demonstration of ensemble sensitivity-based targeted observing for convective-scale applications: Perfect-model experiments

January 2015 - August 2019

Texas Tech University

Masters of Science
Atmospheric Science

Thesis: Mesoscale data assimilation and ensemble sensitivity analysis towards improved predictability of dryline convection

August 2012 - December 2014

University of Washington

Bachelors of Science
Atmospheric Science - Meteorology

Research Advisor: Dr. Robert Houze Jr.

September 2008 - June 2012

Experience

Assistant Professor

University of Oklahoma
August 2023 - Present

Research Scientist II

Colorado State University
July 2022 - August 2023

Research Scientist I

Colorado State University
July 2021 - June 2022

Postdoctoral Fellow

Colorado State University

Advisor: Dr. Russ Schumacher

July 2019 - June 2021

Graduate Research Assistant

Texas Tech University

Advisors: Drs. Christopher Weiss and Brian Ancell

August 2012 - August 2019

Instructor

Texas Tech University

ATMO 1300: Introduction to Atmospheric Science

Summer Session 2016
Summer Session 2017

Graduate Student Visitor

National Center for Atmospheric Research

July 2018 - September 2018

Graduate Writing Tutor

Texas Tech University

August 2017 - April 2018
August 2015 - May 2016

Publications

Submitted
Accepted
Published

Hill, A. J., R. S. Schumacher, and I. Jirak, 2023: A new paradigm for medium-range severe weather forecasts: probabilistic random forest-based predictions.. Weather and Forecasting, 38, 251–272, doi.org/10.1175/WAF-D-22-0143.1.

Hill, A. J. and R. S. Schumacher, 2021: Forecasting excessive rainfall with random forests and a deterministic convection-allowing model. Weather and Forecasting, 36, 1693-1711, doi:10.1175/WAF-D-21-0026.1.

Schumacher, R. S., A. J. Hill, M. Klein, J. Nelson, M. Erickson, S. M. Trojniak, and G. R. Herman, 2021: From random forests to flood forecasts: A research to operations success story. Bulletin of the American Meteorological Society, 102, E1742-E1755, doi:10.1175/BAMS-D-20-0186.1.

Hill, A. J., C. C. Weiss, and D. C. Dowell, 2021: Influence of a portable near-surface observing network on experimental ensemble forecasts of deep convection hazards during VORTEX-SE, Weather and Forecasting, 36, 1141-1167, doi:10.1175/WAF-D-20-0237.1.

Hill, A. J., C. C. Weiss, and B. C. Ancell, 2020b: Factors influencing ensemble sensitivity-based targeted observing prediction at convection-allowing resolutions. Monthly Weather Review, 148, 4497-4517, doi:10.1175/MWR-D-20-0015.1.

Hill, A. J., G. R. Herman, and R. S. Schumacher, 2020a: Forecasting severe weather with random forests. Monthly Weather Review, 148, 2135--2161, doi:10.1175/MWR-D-19-0344.1.

Hill, A. J., C. C. Weiss, and B. C. Ancell, 2016: Ensemble sensitivity analysis for mesoscale forecasts of dryline convection initiation. Monthly Weather Review, 144, 4161-4182. doi:10.1175/MWR-D-15-0338.1

Rasmussen, K. L., A. J. Hill, V. E. Toma, M. D. Zuluaga, P. J. Webster, and R. A. Houze, Jr., 2014: Multiscale analysis of three consecutive years of anomalous flooding in Pakistan Quart. J. Roy. Meteor. Soc., 141, 1259-1276. doi:10.1002/qj.2433.

Select Lead-Author Presentations

Hill, A. J. and R. S. Schumacher, 2023: Predictions of Severe Weather with Random Forests and the Global Ensemble Forecast System. European Conference on Severe Storms, Bucharest, Romania.

(invited) Hill, A. J., 2023: Probabilistic Predictions of Severe Weather with Machine Learning, NWS WFO Indianapolis Regional Training, virtual, April 2023.

(invited) Hill, A. J., 2023: Using Machine Learning to Identify Severe Weather and Excessive Rainfall Risk Areas, NWS Central Region Spring Seasonal Symposium, virtual, March/April 2023.

(invited) Hill, A. J., 2023: Moving beyond dynamics-based weather forecasting toward machine learning, University of Nebraska-Lincoln Stout Lecture, February 2023.

Hill, A. J. and A. Mazurek, 2023: The CSU-MLP Hazardous Weather Prediction System. National Weather Service Norman Weather Forecast Office, Norman, OK.

Hill, A. J. and A. Mazurek, 2023: The CSU-MLP Severe Weather Prediction System. Storm Prediction Center Spring Forecaster Training, Norman, OK.

Hill, A. J., R. S. Schumacher, 2023: Exploring Definitions of Excessive Rainfall when Generating Machine Learning-based Probabilistic Excessive Rainfall Forecasts from a Global Reforecast Dataset. 37th Conference on Hydrology, Denver, CO, 13B.5.

Hill, A. J., R. S. Schumacher, and I. Jirak, 2023: Understanding and Interpreting Medium-Range Predictions of Severe Weather with Random Forests. 22nd Conference on Artificial Intelligence for Environmental Science, Denver, CO, 5A.3.

Hill, A. J., V. A. Gensini, and R. S. Schumacher, 2023: Medium-Range Machine Learning Forecasts for Severe Convective Storms. 22nd Conference on Artificial Intelligence for Environmental Science, Denver, CO, 11A.3.