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David Harrison

David Harrison

David Harrison.

Research
Scientist II

dave1109@ou.edu
david.harrison@noaa.gov
National Weather Center 2330

Education

  • PhD, Meteorology, University of Oklahoma
  • MS, Meteorology, University of Oklahoma
  • BS, Meteorology, University of Oklahoma

Affiliated CIWRO Team

  • Storm Prediction Center

About

Dr. David Harrison is a research meteorologist with a strong background in artificial intelligence and machine learning. He works closely with forecasters at the National Weather Service's (NWS) Storm Prediction Center (SPC) to research, design, and implement new technologies and products in support of severe weather forecasting operations. Dr. Harrison has considerable experience in research-to-operations workflows and has successfully advanced multiple high-profile projects into NWS and SPC operations.

Research Focus

  • Artificial Intelligence/Machine Learning
  • Research to Operations
  • Severe convective storms
  • Lightning
  • Fire Weather

CIWRO Research Themes

  • Forecast Applications Improvements R&D
Honors and awards
  • 2015 NOAA Hollings Scholarship
  • 2022 CIWRO Outstanding Presentation Award
Publications
  • Harrison, D. R., M. S. Elliott, I. L. Jirak, and P. T. Marsh, 2022: Utilizing the High-Resolution Ensemble Forecast System to produce calibrated probabilistic thunderstorm guidance. Wea. Forecasting, 37, 1103–1115, https://doi.org/10.1175/WAF-D-22-0001.1.
  • Harrison, D. and C. D. Karstens, 2017: A Climatology of Operational Storm-Based Warnings: A Geospatial Analysis. Wea. Forecasting, 32, 47–60, https://doi.org/10.1175/WAF-D-15-0146.1. 
  • Clark, A. J. and coauthors, 2023: The First Hybrid NOAA Hazardous Weather Testbed Spring Forecasting Experiment for Advancing Severe Weather Prediction. Bull. Amer. Meteor. Soc., 104, E2305–E2307, https://doi.org/10.1175/BAMS-D-23-0275.1
  • Chase, R. J., D. R. Harrison, G. M. Lackmann, and A. McGovern, 2023: A Machine Learning Tutorial for Operational Meteorology, Part II: Neural Networks and Deep Learning. Wea. Forecasting, 38, 1271-1293, https://doi.org/10.1175/WAF-D-22-0187.1.
  • Clark, A. J., and coauthors, 2023: The 3rd Real-Time, Virtual Spring Forecasting Experiment to Advance Severe Weather Prediction Capabilities. Bull. Amer. Meteor. Soc., 104, E456-E458, https://doi.org/10.1175/BAMS-D-22-0213.1.
  • Chase, R. J., D. R. Harrison, A. Burke, G. M. Lackmann, and A. McGovern, 2022: A Machine Learning Tutorial for Operational Meteorology, Part I: Traditional Machine Learning. Wea. Forecasting, 37, 1509-1529, https://doi.org/10.1175/WAF-D-22-0070.1.
  • Clark, A. J., and coauthors, 2022: The 2nd Real-Time, Virtual Spring Forecasting Experiment to Advance Severe Weather Prediction. Bull. Amer. Meteor. Soc., 103, E1114–E1116, https://doi.org/10.1175/BAMS-D-21-0239.1.
  • Clark, A. J., and coauthors, 2021: A Real-Time, Virtual Spring Forecasting Experiment to Advance Severe Weather Prediction. Bull. Amer. Meteor. Soc., 102, E814–E816, https://doi.org/10.1175/BAMS-D-20-0268.1.
  • Karstens, C. D., J. Correia, Jr., D. S. LaDue, J. Wolfe, T. C. Meyer, D. R. Harrison, J. L. Cintineo, K. M. Calhoun, T. M. Smith, A. E. Gerard, and L. P. Rothfusz, 2017: Development of a human-machine mix for forecasting severe convective events. Wea. Forecasting, 33, 715-737, https://doi.org/10.1175/WAF-D-17-0188.1.
  • McGovern, A., A. Balfour, M. Beene, and D. Harrison, 2014: Storm Evader: Using an iPad to teach kids about meteorology and technology, Bull. Amer. Meteor. Soc. 96, 397–404, https://doi.org/10.1175/BAMS-D-13-00202.1.
Conference presentations
  • Harrison, D. R., I. L. Jirak, and P. T. Marsh, 2024: Developing and Evaluating First-Guess Watch Guidance within SPC Operations. 31st Conf. on Severe Local Storms, Virginia Beach, VA, Amer. Meteor. Soc., 4E.
  • Harrison, D., T. Supinie, I. Jirak, and A. Clark, 2024: An Evaluation of AI-Generated Global NWP Emulators in the NOAA HWT Spring Forecasting Experiment. Unifying Innovations in Forecasting Capabilities Workshop, Jackson, MS, NOAA.
  • Harrison, D. R., M. S. Elliott, I. L. Jirak, P. T. Marsh, 2024: Predicting Probabilistic Lightning Flash Density from the HREF Calibrated Thunder Guidance. Workshop on Fire Weather and Forecasting, Norman, OK, Cooperative Institute for Severe and High-Impact Weather Research and Operations.
  • Harrison, D., M. Elliott, I. Jirak, and P. Marsh, 2023: Predicting Probabilistic Lightning Flash Density from the HREF Calibrated Thunder Guidance. 22nd Conf. on Artificial Intelligence for Environmental Science, Denver, CO, Amer. Meteor. Soc., 6A.1. 
  • Harrison, D., A. McGovern, C. Karstens, I. Jirak, and P. Marsh, 2022: Evaluation of First-Guess Watch Guidance in the 2022 HWT Spring Forecasting Experiment. 30th Conf. on Severe Local Storms, Santa Fe, NM, Amer. Meteor. Soc., 7.3A. 
  • Harrison, D., A. McGovern, C. Karstens, I. Jirak, and P. Marsh, 2022: Winter Precipitation-Type Classification with a 1D Convolutional Neural Network. 31st Conf. on Wea. Analysis and Forecasting/27th Conf. on Num. Wea. Prediction, Houston, TX, Amer. Meteor. Soc., J11.4.
  • Harrison, D., A. McGovern, C. Karstens, I. Jirak, and P. Marsh, 2022: A Machine Learning Approach to Generating Guidance for SPC Watch Products. 31st Conf. on Wea. Analysis and Forecasting/27th Conf. on Num. Wea. Prediction, Houston, TX, Amer. Meteor. Soc., 480. 
  • Harrison, D., A. McGovern, C. Karstens, J. Demuth, A. Bostrom, I. Jirak, and P. Marsh, 2022: Challenges and Benefits of Machine Learning in an Operational Environment: Survey Results from the 2021 Hazardous Weather Testbed Spring Forecasting Experiment. 21st Conf. on Artificial Intelligence for Environmental Science, Houston, TX, Amer. Meteor. Soc., 1.4. 
  • Harrison, D., A. McGovern, C. Karstens, J. Demuth, A. Bostrom, I. Jirak, and P. Marsh, 2021: Challenges and Benefits of Machine Learning in an Operational Environment: Survey Results from the 2021 Hazardous Weather Testbed Spring Forecasting Experiment. 3rd NOAA Workshop on Leveraging AI in Environmental Sciences, NOAA, Boulder, CO, 2A.
  • Harrison, D., and A. McGovern, C. D. Karstens, I. L. Jirak, and P. Marsh, 2021: A Climatology of HREF Forecasts in Severe Convective Environments. 20th Conf. on Artificial Intelligence for Env. Sci., New Orleans, LA, Amer. Meteor. Soc., 3.9. 
  • Harrison, D., and N. Nauslar, N. Keene, M. Elliot, I. L. Jirak, P. Marsh, and J. Peters, 2021: A Comprehensive Climatology of NLDN CG Lightning Flashes in the CONUS. 10th Conf. on the Meteor. App. of Lightning Data, New Orleans, LA, Amer. Meteor. Soc., 5.8. 
  • Harrison, D., and I. L. Jirak, 2020: Utilizing the High-Resolution Ensemble Forecast (HREF) to Produce Calibrated Probabilistic Thunderstorm Guidance at the Storm Prediction Center. 30th Conf. on Num. Wea. Pred., Boston, MA, Amer. Meteor. Soc., 12B.3 
  • Harrison, D., A. McGovern, and C. D. Karstens, 2020: Predicting Storm Prediction Center Watch Likelihood Using Machine Learning. 19th Conf. on Artificial Intelligence for Env. Sci., Boston, Ma, Amer. Meteor. Soc., 8.1. 
  • Harrison, D., I. L. Jirak, and N. J. Nauslar, 2019: A Preliminary Investigation of the High-Resolution Ensemble Forecast (HREF) for Generating Calibrated Probabilistic Thunderstorm Forecasts. 9th Conf. on the Meteor. App. of Lightning Data, Phoenix, AZ, Amer. Meteor. Soc., 4.3. 
  • Harrison, D., C. D. Karstens, and A. McGovern, 2018: Using Machine Learning Techniques to Predict Near-Term Severe Weather Trends. 13th Symp. on Societal Applications: Policy, Research and Practice, Austin, TX, Amer. Meteor. Soc., 11.6. 
  • Harrison, D. and J. W. Rogers, 2018: Predicting 12-hour Storm Reports Using Random Forest Classification. 17th Conf. on Artificial Intelligence for Env. Sci., Austin, TX, Amer. Meteor. Soc., 2.4. 
  • Harrison, D., C. D. Karstens, and A. McGovern, 2017: Verification and Analysis of Probabilistic Hazards Information Guidance. 5th Symp. on Building a Weather-Ready Nation, Seattle, WA, Amer. Meteor. Soc., 7.2. 
  • Harrison, D., A. McGovern, C. D. Karstens, and R. A. Lagerquist, 2017: Best track: Object-based path identification and analysis. 97th Annual Meeting, Seattle, WA, Amer. Meteor. Soc., 319. 
  • Harrison, D., and C. D. Karstens, 2016: A statistical overview of operational storm-based warnings. 11th Symp. on Societal App., New Orleans, LA, Amer. Meteor. Soc., 4.3A. 
  • Harrison, D., Z. A. Roux, A. McGovern, and W. G. Blumberg, 2015: Promoting a Weather Ready Nation through serious games. 24th Symp. on Education, Phoenix, AZ, Amer. Meteor. Soc., 1.3. 
  • Harrison, D., A. Balfour, M. Beene, and A. McGovern, 2014: Teaching meteorology and technology through an iPad application.  23rd Symp. on Education, Atlanta, GA, Amer. Meteor. Soc., 4.2. 
  • Clark, A. J. and coauthors, 2024: An overview of the 2024 Hazardous Weather Testbed Spring Forecasting Experiment. 31st Conf. on Severe Local Storms, Virginia Beach, VA, Amer. Meteor. Soc., 3E.
  • Jirak, I., A. Clark, J. Vancil, T. Supinie, and D. Harrison, 2024: Evaluation of the Rapid Refresh Forecast System during the 2024 NOAA HWT Spring Forecasting Experiment. Unifying Innovations in Forecasting Capabilities Workshop, NOAA, Jackson, MS.
  • Jirak, I., A. Clark, J. Vancil, T. Supinie, and D. Harrison, 2024: Performance of the 3-km Rapid Refresh Forecast System with Deep Convective Parameterization during the 2024 NOAA HWT Spring Forecasting Experiment. 2nd Annual UFS Physics Workshop, NOAA NSSL, Norman, OK.
  • Jahn, D., D. Harrison, B. Earnest, E. Bentley, P. Marsh, I. Jirak, M. Elliott, A. McGovern, C. Karstens, 2024: CIWRO/SPC Activities in Fire Weather Research. Workshop on Fire Weather and Forecasting, Cooperative Institute for Severe and High-Impact Weather Research and Operations, Norman, OK.
  • Clark, A. J., and coauthors, 2024: Activities and Preliminary Results from the 1st Hybrid NOAA/Hazardous Weather Testbed Spring Forecasting Experiment. 14th Conf. on Transition of Research to Operations and 3rd Symp. on Community Modeling and Innovation, Baltimore, MD, Amer. Meteor. Soc., J15A.3.
  • Lin, P., M. S. Elliott, D. R. Harrison, E. S. Bentley, I. L. Jirak, J. Vancil, K. Halbert, and P. T. Marsh, 2023: Developing Dry Thunderstorm Verification Tools to Improve Fire Weather Forecasting at NOAA’s Storm Prediction Center. 14th Conf. on Transition of Research to Operations, Baltimore, MD, Amer. Meteor. Soc., 12B.6
  • Clark, A. J. and coauthors, 2023: An overview of the 1st hybrid NOAA Hazardous Weather Testbed Spring Forecasting Experiment in 2023. 32nd Conf. on Wea. Analysis and Forecasting, Madison, WI, Amer. Meteor. Soc., 10.1.
  • Chase, R. J., D. Harrison, A. Burke, G. Lackmann, A. McGovern, 2023: Machine Learning Tutorials for Operational Meteorology. 22nd Conf. on Artificial Intelligence for Environmental Science/32nd Conf. on Education, Denver, CO, Amer. Meteor. Soc., J12.4. 
  • Jirak, I. L., D. Harrison, and J. Vancil, 2022: HREF Climatology of Storm-Attribute Fields. 30th Conf. on Severe Local Storms, Santa Fe, NM, Amer. Meteor. Soc., 8.4B. 
  • Clark, A., and coauthors, 2022: The 3rd Virtual NOAA Hazardous Weather Testbed Spring Forecasting Experiment. 30th Conf. on Severe Local Storms, Santa Fe, NM, Amer. Meteor. Soc., 4F. 
  • Williamsberg, C. A., A. McGovern, J. Demuth, E. Roth, D. Harrison, D. J. Gagne, and J. K. Williams, 2022: Role of AI/ML and humans in the forecast loop. 2022 Summer Community Meeting, Boulder, CO, Amer. Meteor. Soc., Panel Discussion 13.
  • Clark, A., and coauthors, 2022: The Second Virtual NOAA Hazardous Weather Testbed Spring Forecasting Experiment. 12th Conf. on Transition of Research to Operations, Houston, TX, Amer. Meteor. Soc., 7B.1. 
  • Rogers, J., D. Harrison, P. Skinner, and P. Marsh, 2018: Large-Scale Trends in Upper-Air Sounding Data Across the United States.  43rd NWA Annual Meeting, St. Louis, MO, Nat. Wea. Assoc., I1.
  • McGovern, A., C. Karstens, D. Harrison, and T. Smith, 2018:  Using Machine Learning to Predict Storm Longevity in Real Time. 17th Conf. on Artificial Intelligence for Env. Sci., Austin, TX, Amer. Meteor. Soc., J44.1. 
  • Karstens, C. D., A. Gerard, D. LaDue, J. Correia, Jr., K. M. Calhoun, T. C. Meyer, J. P. Wolfe, C. Ling, J. L. Cintineo, A. McGovern, H. Obermeier, D. R. Harrison, R. Lagerquist, J. James and T. M. Smith, 2017: Overview of the 2017 PHI Prototype Experiment with NWS Forecasters. 42nd NWA Annual Meeting, Garden Grove, CA, National Wea. Association.
Products and patents
  • HREF-based calibrated lightning forecast guidance
  • First-Guess Convective Watch Guidance
Professional organizations
  • American Meteorological Society
  • NSF AI Institute for Research on Trustworthy AI in Weather, Climate, and Coastal Oceanography (AI2ES)
Social Media Contact
  • X: @dharrisonwx
Cooperative Institute for Severe and High-Impact Weather Research and Operations (CIWRO)
120 David L. Boren Blvd.
Suite 2100
Norman, OK 73072
Updated 1/30/2025 by Cooperative Institute for Severe and High-Impact Weather Research and Operations (CIWRO): ciwro@ou.edu