Aaron Hill

Department of Atmospheric Sciences, Colorado State University, Fort Collins, CO

I am a postdoctoral fellow at Colorado State University researching machine learning applications for extreme rainfall and severe weather. I have a general interest in all things predictability and forecasting, and in particular ensemble sensitivity analysis, targeted observing, convective-scale data assimilation, statistical applications (i.e. machine learning), and ensemble forecasting of high-impact weather.


Research Interests

Machine Learning for High-Impact Weather

Global dynamic forecast models have significant deficiencies in small-scale process applications (e.g., extreme precipitation, tornadoes) as they do not have the requisite grid spacing to properly simulate these events. Model variables are often combined in creative ways to create "surrogate" events and reports that represent a particular weather hazard (e.g., hail). Statistical models (e.g., machine-learning models) do not have these same deficiences, and are useful alternatives to create probabilistic forecasts of severe storm hazards, e.g., flash flooding, hail, tornadoes, and wind. I am interested in applying machine learning (ML) techniques with global convection-parameterizing and convection-allowing models to create probablistic hazard outlooks (e.g., Hill et al. 2020a, Hill and Schumacher 2020), some of which are being used by the Weather Prediction Center for Day 1 excessive rainfall outlooks.

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 the 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, both at convection-allowing model resolutions and smaller-scale simulations of single storms, which provides addition strain on the linear, univariate regressions of ESA. See our paper: Ensemble sensitivity analysis for mesoscale forecasts of dryline convection initiation.

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 in the NCAR CM1 model.

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) statistical filter, 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. 2020c).

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.


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

Geophysical Data Fields and Numerical Methods

Least squares, maximum likelihood estimation, statistical filtering, objective analyses, coordinate transformations, time-of-arrival problem, random forests, neural networks, convolutional neural networks, generalized machine learning methods

Introduction to Atmospheric Sciences

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

Scientific Computing in the Atmospheric Sciences

Linux, Fortran, Python, version control, object-based programming, Lorenz model,


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

Postdoctoral Fellow

Colorado State University

Advisor: Dr. Russ Schumacher

July 2019 - Present

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

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, in press.

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.

In preperation

Hill, A. J., C. C. Weiss, D. C. Dowell, and C. Alexander, 2020c: Influence of a mobile mesoscale near-surface observing network on ensemble forecasts during VORTEX-SE. In preparation for submission to Weather and Forecasting.

Schumacher, R. S., A. J. Hill, M. Klein, J. Nelson, M. Erickson, and G. R. Herman, 2020: From random forests to flood forecasts: A research to operations success story. In preparation for submission to Bulletin of the American Meteorology Society.

Hill, A. J. and R. S. Schumacher, 2020: Forecasting floods with Random Forests: Sensitivity to flooding proxy. In preparation for submission to Weather and Forecasting.

Select Lead-Author Presentations

Hill, A. J. and R. S. Schumacher, 2021: Medium-range severe weather forecasts with random forests, 20th Conference on Artificial Intelligence for Environmental Science.

Hill, A. J. and R. S. Schumacher, 2021: Short-term excessive rainfall forecasts using random forests and a deterministic convection-allowing model, 20th Conference on Artificial Intelligence for Environmental Science.

(invited) Hill, A. J. and R. S. Schumacher, 2020: Forecasting our future: machine learning and AI for high-impact weather. National Weather Association Annual Meeting, virtual.

(invited) Hill, A. J., 2020: Statistical tools for high impact weather. Naval Postgradaute School, Monterey, CA.

Hill, A. J. and R. S. Schumacher, 2020: Heavy precipitation and flash flood forecasts using random forests and convection-allowing models. 30th Conference on Weather Analysis and Forecasting/26th Conference on Numerical Weather Prediction, Boston, MA, J71.2.

Hill, A. J., C. C. Weiss, and B. C. Ancell, 2018: Towards improving forecasts of severe convection along the dryline through targeted observing with ensemble sensitivity analysis. 29th Conference on Severe Local Storms, Stowe, VT, paper 14.2.

Hill, A. J., C. C. Weiss, and D. C. Dowell, 2018: Exploring the utility of assimilating observations from a mesoscale network of StickNet platforms during VORTEX-SE with the High Resolution Rapid Refresh Ensemble. 29th Conference on Severe Local Storms, Stowe, VT, paper 74.

(invited) Hill, A. J., 2018: The utility of ensemble-sensitivity analysis for targeted observing, ensemble subsetting, and investigating environmental controls on storm characteristics. Cooperative Institute for Research in the Atmosphere, Fort Collins, CO.

Hill, A. J., C. C. Weiss, and B. C. Ancell, 2018: Ensemble-sensitivity analysis based observation targeting experiments for mesoscale convection forecasts and factors influencing observation-impact prediction. 22nd Conference on Integrated Observing and Assimilation Systems for the Atmosphere, Oceans, and Land Surface, Austin, TX, paper 613.

Hill, A. J., C. C. Weiss, and B. C. Ancell, 2017: Ensemble sensitivity-based observation targeting experiments for Southern Plains dryline convection. 21st Conference on Integrated Observing and Assimilation Systems for the Atmosphere, Oceans, and Land Surface, Seattle, WA, paper 15.5.

Hill, A. J., C. C. Weiss, and B. C. Ancell, 2016: Ensemble sensitivity-based observation targeting OSSEs for Southern Plains dryline convection. 20th Conference on Integrated Observing and Assimilation Systems for the Atmosphere, Oceans, and Land Surface, New Orleans, LA, paper J7.7.

Hill, A. J., B. Burghardt, and B. C. Ancell, 2015: Advanced ensemble techniques for improved predictability of storm-scale features. 1st Ensemble Design Workshop for Storm-Scale Ensembles, Boulder, CO.

Hill, A. J., C. C. Weiss, and B. C. Ancell, 2014: Mesoscale ensemble sensitivity of dryline convective initiation. Preprints, 27th Conference on Severe Local Storms, Madison, WI, paper 8B.4.

Hill, A. J., C. C. Weiss, and B. C. Ancell, 2014: Applicaiton of mesoscale ensemble-based sensitivity analysis to observation targeting. 26th Conference on Weather Analysis and Forecasting / 22nd Conference on Numerical Weather Prediction, Atlanta, GA, paper 610.

Hill, A. J., C. C. Weiss, and B. C. Ancell, 2013: Utilizing Ensemble Sensitivity for Data Denial Experiments of the 4 April 2012 Dallas, Texas Dryline-Initiated Convective Outbreak Using West Texas Mesonet Observations and WRF-DART Data Assimilation. Preprints, 15th Conference on Mesoscale Processes, Portland, OR, paper 11.