Theses:
Harrison, D. R., 2022: Machine Learning Co-Production in Operational Meteorology. Doctoral Dissertation. University of Oklahoma, https://hdl.handle.net/11244/335971. [PDF]
Harrison, D. R., 2018: Correcting, Improving, and Verifying Automated Guidance in a New Warning Paradigm. Master’s Thesis. University of Oklahoma, https://hdl.handle.net/11244/299947. [PDF]
Lead Author Refereed Papers:
Harrison, D. R., A. McGovern, C. D. Karstens, J. L. Demuth, A. Bostrom, I. L. Jirak, and P. T. Marsh, 2024: Co-production for operational meteorology. In review.
Harrison, D. R., A. McGovern, C. D. Karstens, J. L. Demuth, A. Bostrom, I. L. Jirak, and P. T. Marsh, 2024: An assessment of how domain experts evaluate machine learning in operational meteorology. In review.
*Won 2022 CIRWO Outstanding Publication award
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. [PDF]
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. [PDF]
Co-Authored Refereed Papers:
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.
Invited Seminars and Presentations:
“Machine Learning Co-Production in Operational Meteorology,” 2022 Summer Community Meeting, Amer. Meteor. Soc. (July 2022).
“Developing Machine Learning for an Operational Environment”, Trustworthy Artificial Intelligence for Environmental Science Summer School, NCAR (July 2021). [Link]
Lead Author Informal Presentations:
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. [Link][PDF]
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. [Link][PDF]
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. [Link]
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. [Link][Poster]
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. [Link]
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. [Link]
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. [Link]
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 [Link][PDF]
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. [Link]
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. [Link]
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. [Link]
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. [Link]
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. [Link]
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. [Link]
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. [Link]
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. [Link]
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. [Link]
Coauthored Informal Presentations:
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. [Link]
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. [Link]
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. [Link]
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. [Link]
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. [Link]
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.
