Hill, A. J. and J. Radford, 2024: Postprocessing Data-Driven AI Forecasting Models for Hazardous Weather Prediction. 31st Conference on Severe Local Storms, Virginia Beach, VA, October 2024.
Schumacher, R. S. and A. J. Hill, 2024: Extreme-rain-producing mesoscale convective systems in the contiguous US in observations and a convection-permitting regional climate model. International Conference on Mesoscale Convective Systems, Gyeongju City, South Korea, October 2024.
Schumacher, R. S. and A. J. Hill, 2024: Assessment of the representation of extreme rainfall in CONUS404. 8th Convection Permitting Climate Modeling Workshop, Fort Collins, CO, September 2024.
Hill, A. J. and others, 2024: Panel: FIG Town Hall Part 2: Forecasting Trends: The Balance between NWP and AI and How It Will Evolve in the Near or Distant Future. American Meteorological Society Webinar, June 2024.
Hill, A. J. and R. S. Schumacher, 2024: Medium-range Forecasts of Excessive Rainfall with the CSU-MLP. Hydrometeorological Testbed Flash Flood and Intensive Rainfall Experiment, virtual.
[INVITED] Hill, A. J., 2024: AI and Applications to Hazardous Weather Forecasting. ESIG 2024 Forecasting and Markets Workshop, Salt Lake City, UT, June 2024.
Hill, A. J. and R. S. Schumacher, 2024: Medium-Range Excessive Rainfall Prediction with Machine Learning. EGU General Assembly 2024, Vienna, Austria.
[INVITED] Hill, A. J., 2024: Our New Forecasting Paradigm: Artificial Intelligence. 2024 Severe Storms and Doppler Radar Conference, Central Iowa NWA Chapter, March 2024.
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.
[INVITED] Hill, A. J., 2023: Understanding and Improving Predictability of High-Impact Weather Hazards Through the
Lens of Machine Learning. American Geophysical Union Fall Meeting, virtual,
December 2023.
[INVITED] Hill, A. J., 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.
[INVITED] Hill, A. J., 2023: Machine Learning for Operational Weather Forecasting. National Weather Service Eastern Region Science Sharing Webinar, virtual, October 2023.
[INVITED] Hill, A. J., 2023: Machine Learning for Operational Weather Forecasting. National Weather Service SOO/DOH Meeting, Denver, CO, August 2023.
Clark, A. J., K. A. Hoogewind, A. J. Hill, B. T. Gallo, A. Berrington, and E. D. Loken, 2023: Extended Range Machine-Learning Severe Weather Guidance Based on the Operational GEFS. 28th Conference on Numerical Weather Prediction, Madison, WI.
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.
^Mazurek, A. C., A. J. Hill, and R. S. Schumacher, 2023: Making Sense of Random Forest-Based Severe Weather Forecasts Using Tree Interpreter. 32nd Conference on Weather Analysis and Forecasting, Madison, WI.
Schumacher, R. S., A. J. Hill, and M. Klein, 2023: How Far Into the Medium Range Can Probabilistic Excessive Rainfall Forecasts be Extended? 32nd Conference on Weather Analysis and Forecasting, Madison, WI.
Schumacher, R. S. and A. J. Hill, 2023: Sources of Forecast Errors for Extreme-Rain-Producing Mesoscale Convective Systems. 20th Conference on Mesoscale Processes, Madison, WI.
Schumacher, R. S. and A. J. Hill, 2023: Progress Towards Medium Range Excessive Rainfall Forecasts with the CSU-MLP. Hydrometeorology Testbed Flash Flood and Intense Rainfall Experiment Seminar Series, virtual.
Schumacher, R. S. and A. J. Hill, 2023: Sources of Forecast Errors for Extreme-Rain-Producing Mesoscale Convective Systems. 15th International Conference on Mesoscale Convective Systems, Fort Collins, CO, 10.3.
Hill, A. J. and R. S. Schumacher, 2023: Leveraging the Power of Machine Learning for Excessive Rainfall Forecasting. 15th International Conference on Mesoscale Convective Systems, Fort Collins, CO, 12.7.
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.
[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: A Forest of Forecasts? Leveraging Machine Learning for High-Impact Weather Forecasting. SUNY Albany Department of Atmospheric and Environmental Sciences, interview, March 2023.
[INVITED] Hill, A. J., 2023: A Forest of Forecasts? Leveraging Machine Learning for High-Impact Weather Forecasting. University of Oklahoma School of Meteorology, interview, March 2023.
[INVITED] Hill, A. J., 2023: Advancing High-Impact Weather Hazard Forecasting with Machine Learning. Florida State University Department of Earth, Ocean, and Atmospheric Sciences, interview, March 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.
Escobedo, J. A., R. S. Schumacher, and A. J. Hill, 2023: Investigating Colorado State University- Machine Learning Probabilities Day-1 Excessive Rainfall Forecasts in the Southwest United States During the Summer Monsoon. 37th Conference on Hydrology, Denver, CO, poster 767.
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.
James, E. P., R. S. Schumacher, and A. J. Hill, 2023: Random forests for prediction of excessive rainfall based on the High-Resolution Rapid Refresh (HRRR). 22nd Conference on Artificial Intelligence for Environmental Science, Denver, CO., 7B.1.
Mazurek, A., R. S. Schumacher, and A. J. Hill, 2023: Evaluating Random Forest-Based Predictions of Tornadoes, Wind, and Hail at Two- to Three-Day Lead Times. 22nd Conference on Artificial Intelligence for Environmental Science, Denver, CO., 11A.2.
^McDaniel, H., A. J. Hill, and R. S. Schumacher, 2023: Investigating Predictor Importance for a Next-Day Severe Weather Hazard Machine Learning Model. 22nd Conference on Artificial Intelligence for Environmental Science, Denver, CO, poster 893.
McDaniel, H., A. J. Hill, and R. S. Schumacher, 2023: Investigating Predictor Importance for a Next-Day Severe Weather Hazard Machine Learning Model. 22nd Annual Student Conference, Denver, CO, poster S9.
Schumacher, R. S., A. J. Hill, and M. Klein, 2023: How Far Into the Medium Range Can Probabilistic Excessive Rainfall Forecasts be Extended? 22nd Conference on Artificial Intelligence for Environmental Science, Denver, CO., 7B.5.
McDaniel, H., A. J. Hill, and R. S. Schumacher, 2022: Investigating Predictor Importance for a Next-Day Severe Weather Hazard Machine Learning Model. American Geophysical Union Fall Meeting, Chicago, IL, poster 392.
Hill, A. J., R. S. Schumacher, I. Jirak, 2022: Medium-Range Severe Weather Predictions with Random Forests. 30th Conference on Severe Local Storms, Santa Fe, NM, 4.1B.
Mazurek, A., R. S. Schumacher, and A. J. Hill, 2022: Evaluating Random Forest-Based Predictions of Tornadoes, Wind, and Hail at Two- and Three-day Lead Times. 30th Conference on Severe Local Storms, Santa Fe, NM, 7.1A.
Schumacher, R. S., A. J. Hill, and A. Mazurek, 2022: Probabilistic Forecast Guidance for Severe Convective Storms Using GEFS Reforecasts and Machine Learning. 30th Conference on Severe Local Storms, Santa Fe, NM, 1.3.
Schumacher R. S. and A. J. Hill, 2022: Advancing high-impact weather prediction with machine learning. DARPA FORWARD Conference, Fort Collins, CO, poster.
Schumacher, R. S. and A. J. Hill, 2022: Updates and Improvements to Colorado State University-Machine Learning Probabilities Excessive Rainfall Forecasts. Hydrometeorological Testbed Flash Flood and Intense Rainfall Experiment, virtual.
[INVITED] Hill, A. J., 2022: Generating probabilistic machine-learned forecasts for severe weather and excessive rainfall prediction. National Weather Service Central Region Headquarters, virtual, July 2022.
Hill, A. J., 2022: Probabilistic Predictions of Severe Weather with Machine Learning. Columbia, SC Weather Forecast Office Severe Weather Workshop, virtual.
Cheeseman, M., B. Ford, Z. Rosen, E. Wendt, A. J. DesRosiers, A. J. Hill, C. L’Orange, C. Quinn, M. Long, S. H. Jathar, J. Volckens, and J. R. Pierce, 2022: Neighborhood Scale Variability of Co-incident PM2.5 and AOD: Results from Citizen Enabled Aerosol Measurements for Satellites (CEAMS). 24th Conference on Atmospheric Chemistry, poster.
Escobedo, J. A., R. S. Schumacher, and A. J. Hill, 2022: Colorado State University Machine Learning Probabilities Day 1 Probabilistic Excessive Rainfall Forecasts: Synoptic Regimes of the Best- and Worst-Performing Forecasts. 21st Annual Student Conference, poster
Hill, A. J. and R. S. Schumacher, 2022: Medium-range Predictions of Severe Weather with Machine Learning. 31st Conference on Weather Analysis and Forecasting/27th Conference on Numerical Weather Prediction, J7.2.
James, E. P., A. J. Hill, and R. S. Schumacher, 2022: A first guess day-one Excessive Rainfall Outlook based on a skill-weighted blend of random forest prediction systems. 21st Conference on Artificial Intelligence for Environmental Science, poster.
Nielsen, E. R. and A. J. Hill, 2022: Exploring Multi-Hazard Joint Probability Forecasts Through the Lens of Tornadoes and Flash Floods. 19th Conference on Mesoscale Processes, J3.4.
Schumacher, R. S., A. J. Hill, and I. L. Jirak, 2022: Probabilistic forecast guidance for severe convective storms using GEFS reforecasts and machine learning. 31st Conference on Weather Analysis and Forecasting/27th Conference on Numerical Weather Prediction, J7.4.
Hill, A. J. and R. S. Schumacher, 2021: Medium-range forecasts of hazardous weather with machine learning. 3rd NOAA Workshop on Leverage AI in Environmental Sciences, virtual.
Hill, A. J., R. S. Schumacher, and J. Escobedo, 2021: Extending predictions of hazardous weather into the medium-range with machine learning. 2nd Knowledge-guided Machine Learning Workshop, virtual, poster.
Hill, A. J. and R. S. Schumacher, 2021: Advancing probabilistic prediction of high-impact weather using ensemble reforecasts and machine learning. National Weather Service AI Team, virtual.
Hill, A. J., E. James, R. S. Schumacher, M. Klein, J. Nelson, and M. J. Erickson, 2021: CSU CAM-based First Guess Excessive Rainfall Outlook Products. Hydrometeorological Testbed Flash Flood and Intense Rainfall Experiment, virtual.
Schumacher, R. S. and A. J. Hill, 2021: Advancing Probabilistic Prediction of High-Impact Weather Using Ensemble Reforecasts and Machine Learning. UFS Webinar Series, virtual.
[INVITED] Hill, A. J., 2021: Learning from machines: High-impact weather forecasting with Artificial Intelligence. University of Florida Department of Geography, virtual interview, March 2021
[INVITED] Hill, A. J., 2021: Learning from machines: improving high-impact weather forecasts with Artificial Intelligence. Northern Illinois University Department of Geographic and Atmospheric Sciences, virtual interview, March 2021
Hill, A. J. and R. S. Schumacher, 2021: Medium-range severe weather forecasts with random forests. 20th Conference on Artificial Intelligence for Environmental Science, 3.2.
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, joint 12.8.
Schumacher, R. S., A. J. Hill, M. Klein, J. Nelson, M. J. Erickson, and G. R. Herman, 2021: From Random Forests to Flood Forecasts: A Research to Operations Success Story. 11th Conference on Transition of Research to Operations, 14.9.
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. and R. S. Schumacher, 2020: Random-Forest Severe Guidance from the GEFS. Storm Prediction Center Fall Forecaster Training.
Hill, A. J., C. C. Weiss, and B. C. Ancell, 2020: Factors influencing ensemble sensitivity-based targeted observing predictions at convection-allowing resolutions. 24th Conference on Integrated Observing and Assimilation Systems for the Atmosphere, Boston, MA, 10.4.
Hill, A. J., C. C. Weiss, and D. C. Dowell, 2020: Assimilating near-surface observations from a portable mesoscale network of StickNet platforms during VORTEX-SE with the High Resolution Rapid Refresh Ensemble. Severe Local Storms Symposium, Boston, MA., 950
Hill, A. J., R. S. Schumacher, M. Klein, J. Nelson, and M. Erickson, 2020: First-guess excessive rainfall outlooks from machine learning models. Hydrometeorological Testbed Flash Flood and Intensive Rainfall Experiment.
[INVITED] Hill, A. J., 2020: Machine learning for convection hazard forecasts. NWS Southern Region Science and Technology Services Division Science Circle, virtual, 2020
[INVITED] Hill, A. J., 2020: Forecasting our future: machine learning and AI for high-impact weather. National Weather Association Annual Meeting, virtual, 2020
[INVITED] Hill, A. J., 2020: Statistical tools for high-impact weather. Naval Postgraduate School, Monterey, CA, interview, 2020
McDonald, J. M., C. C. Weiss, and A. J. Hill, 2020: Properties of cold pools observed during the VORTEX-SE: Meso18-19 field campaign. Severe Local Storms Symposium, Boston, MA., 946
Schumacher, R. S., A. J. Hill, G. R. Herman, M. Erickson, B. Albright, M. Klein, and J. A. Nelson Jr., 2020: If a flood falls in a (random) forest, does it get counted? Advances and challenges in predicting excessive precipitation using machine learning. 30th Conference on Weather and Forecasing / 26th Conference on Numerical Weather Prediction, Boston, MA., J71.3
Ancell, B. C., A. A. Coleman, and A. J. Hill, 2019: Ensemble sensitivity-based subsetting overview and evaluation activities at the 2018 NOAA HWT. European Geophysics Union General Assembly 2019, Vienna, Austria, EGU2019-2435.
Ancell, B. C., A. A. Coleman, and A. J. Hill, 2019: Ensemble sensitivity-based subsetting overview and evaluation activities at the 2018 NOAA HWT. 23rd Conference on Integrated Observing and Assimilation Systems for the Atmosphere, Oceans, and Land Surface, Phoenix, AZ, paper 2.3A.
Weiss, C. C., E. C. Bruning, J. Dahl, and A. J. Hill, 2019: Texas Tech VORTEX-SE Activities. VORTEX-SE Workshop, Huntsville, AL.
Weiss, C. C., D. C. Dowell, N. Yussouf, and A. J. Hill, 2019: Insights into mesoscale and storm-scale predictability gained through ensemble sensitivity analysis. 23rd Conference on Integrated Observing and Assimilation Systems for the Atmosphere, Oceans, and Land Surface, Phoenix, AZ, paper 20.1.
Ancell, B. C., A. A. Coleman, and A. J. Hill, 2018: Ensemble sensitivity-based subsetting overview and evaluation activities at the 2018 NOAA HWT. American Geophysical Union Fall Meeting, Washington, D.C.
Ancell, B. C., A. A. Coleman, A. J. Hill, and C. C. Weiss, 2018: Ensemble sensitivity-based subsetting overview and evaluation activities at the 2018 NOAA HWT. 29th Conference on Severe Local Storms, Stowe, VT, paper 3A.4.
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, 2018
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.
Weiss, C. C., D. C. Dowell, A. J. Hill, J. McDonald, E. C. Bruning, and J. Dahl, 2018: An update on VORTEX-SE activities at Texas Tech University. 29th Conference on Severe Local Storms, Stowe, VT, paper 3B.1.
Weiss, C. C., D. C. Dowell, A. J. Hill, and N. Yussouf, 2018: Ensemble sensitivity analysis of controls on storm-scale vertical vorticity for two southeastern U.S. tornado events. 22nd Conference on Integrated Observing and Assimilation Systems for the Atmosphere, Oceans, and Land Surface, Austin, TX, paper 610.
Hill, A. J., C. C. Weiss, and B.C. Ancell, 2017: Ensemble-sensitivity analysis based observation targeting for mesoscale convection forecasts and factors influencing observation-impact prediction. American Geophysical Union Fall Meeting, New Orleans, LA, paper NG31A-0157.
Weiss, C. C., E. C. Bruning, J. Dahl, D. C. Dowell, C. R. Alexander, A. J. Hill, and V. C. Chmielewski, 2017: Preliminary results from the 2016 and 2017 VORTEX-SE project. 9th European Conference on Severe Storms, Pula, Croatia, paper ECSS2017-155.
Kenyon, A. and A. J. Hill, 2017: Using Python to process and visualize real-time atmospheric data during VORTEX-SE. Scipy 2017: Scientific Computing with Python, Austin, TX.
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.
Weiss, C. C, E. C. Bruning, J. Dahl, D. C. Dowell, C. R. Alexander, A. J. Hill, and V. C. Chmielewski, 2017: An overview of Texas Tech operations during VORTEX-SE 2016. Special Symposium on Severe Local Storms: Observation Needs to Advance Research, Prediction, and Communication, Seattle, WA, paper 939.
Weiss, C. C., D. C. Dowell, A. J. Hill, and N. Yussouf, 2017: Ensemble sensitivity analysis of controls on updraft rotation for two southeastern US tornado events. 21st Conference on Integrated Observing and Assimilation Systems for the Atmosphere, Oceans, and Land Surface, Seattle, WA, paper 11.6.
Bruning, E. C., V. C. Chmielewski, C. C. Weiss, J. Dahl, A. J. Hill, C. J. Schultz, and J. Bailey, 2016: Flash size distributions characterized by mobile LMA deployments during VORTEX-SE. 28th Conference on Severe Local Storms, Portland, OR, paper 9.4.
Hill, A. J., C. C. Weiss, and B. C. Ancell, 2016: Ensemble sensitivity-based observation targeting experiments for Southern Plains dryline convection. 28th Conference on Severe Local Storms, Portland, OR, paper 7B.6.
Weiss, C. C, E. C. Bruning, J. Dahl, D. C. Dowell, C. R. Alexander, A. J. Hill, and V. C. Chmielewski, 2016: An overview of Texas Tech operations during VORTEX-SE 2016. 28th Conference on Severe Local Storms, Portland, OR, paper 3.5.
Weiss, C. C., D. C. Dowell, A. J. Hill, and N. Yossouf, 2016: Ensemble sensitivity analysis of controls on updraft rotation for the 27 April 2011 Tornado Outbreak. 28th Conference on Severe Local Storms, Portland, OR, paper 137.
Ancell, B. C., A. J. Hill, and B. Burghardt, 2016: The TTU WRF ensemble prediction system. 2nd Ensemble Design Workshop for Convection Allowing Models, College Park, Maryland. MD.
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.
Ancell, B. C., A. J. Hill, and B. Burghardt, 2015: The use of ensemble-based senstivity with observations to improve predictability of severe convective events. 27th Conference on Weather Analysis and Forecasting / 23rd Conference on Numerical Weather Prediction, Chicago, IL, paper 8B.5.
Ancell, B. C., A. J. Hill, and B. Burghardt, 2015: The use of ensemble-based sensitivity with observations to improve predictability of severe convective events. Preprints, 19th Conference on Integrated Observing and Assimilation Systems for the Atmosphere, Oceans, and Land Surface, Phoenix, AZ, paper 9.1.
Hill, A. J., C. C. Weiss, and B.C. Ancell, 2015: Mesoscale ensemble sensitivity and observation targeting of dryline convection. Preprints, 19th Conference on Integrated Observing and Assimilation Systems for the Atmosphere, Oceans, and Land Surface, Phoenix, AZ, paper 9.3.
Ancell, B. C., A. J. Hill, and B. Burghardt, 2014: The use of ensemble-based sensitivity and observations to improve predictability of severe convective events. American Geophysical Union Fall Meeting, San Francisco, CA, NG31B-3798.
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.
Houze, R. A., Jr., K. L. Rasmussen, and A. J. Hill, 2013: TRMM insights into recent floods in Pakistan. PMM Science Team Meeting, Annapolis, MD.
Houze, R. A., Jr., K. L. Rasmussen, A. J. Hill, and M. D. Zuluaga, 2013: Using TRMM Precipitation Radar to understand the Pakistan and India floods of 2010-2012. American Geophysical Union Fall Meeting, San Francisco, CA.