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Amy McGovern

Amy McGovern

Amy McGovern

Lloyd G. and Joyce Austin Presidential Professor

Office: National Weather Center/NWC 3435


Ph.D., Computer Science 
University of Massachusetts Amherst
M.S., Computer Science
University of Massachusetts Amherst
B.S., Computer Science 
Carnegie Mellon University (with honors)

Research Focus

  • Machine learning/data mining/data science for the physical sciences; Real-world applications with a special interest in high-impact weather. STEM education.

Experience and Awards

  • Professor, School of Computer Science, University of Oklahoma
    Professor, School of Meteorology, University of Oklahoma
    Associate Professor, School of Computer Science, University of Oklahoma
    Adjunct Associate Professor, School of Meteorology, University of Oklahoma
    Assistant Professor, School of Computer Science, University of Oklahoma
    Adjunct Assistant Professor, School of Meteorology, University of Oklahoma
  • Inaugural Member, AMS Culture and Inclusion Cabinet, 2020 - present
  • Editor, Weather and Forecasting, 2019-present
  • Associate Editor, Monthly Weather Review, 2019-2020
  • Co-Chair, Conference on Artificial and Computational Intelligence and its Applications to the Environmental Sciences, 2015-2016, 2019-2021
  • Vice-Chair, American Meteorological Society (AMS) committee on Artificial Intelligence and its Applications to Environmental Science, 2018-2021
  • American Meteorological Society Fellow, 2020
  • Lloyd G. and Joyce Austin Presidential Professorship, Spring 2020
  • University of Oklahoma Vice President for Research Award for Interdisciplinary Scholarship, Spring 2019
  • CIMMS faculty fellow, 2015
  • Teaching Scholars Initiative Award, Fall 2012
  • College of Engineering and Michael F. Price College of Business Alumni Teaching Award. Awarded to top teachers within the College of Engineering and Price School of Business. Spring 2008: Artificial Intelligence, Spring 2009: Data Mining
  • NSF CAREER Award, 2008-2015


The NSF AI Institute for Research on Trustworthy AI in Weather, Climate, and Coastal Oceanography (AI2ES) is a convergent center that will create trustworthy AI for environmental science, revolutionize prediction and understanding of high-impact weather and ocean hazards, and benefit society by protecting lives and property.  Leading experts from AI, atmospheric and ocean science, risk communication, and education, will work synergistically to develop and test trustworthy AI methods that will transform our understanding and prediction of the environment.  Trust is a social phenomenon, and our integration of risk communication research across AI2ES activities provides an empirical foundation for developing user-informed, trustworthy AI by engaging and partnering with key environmental decision makers from communities that will be using the techniques developed.  Our partnership of multiple academic institutions, NCAR, NOAA, and private industry spans the full cycle of fundamental research into trustworthy AI and enables rapid integration of trustworthy AI for increased societal impact.  Environmental science provides a perfect testbed to advance trustworthy AI given its grounding in nature’s physical laws and conservation principles as well as the broad range of stakeholder feedback and high societal impact.

AI2ES broadening participation and workforce development activities fully integrate with AI2ES research on trustworthy AI, environmental science and risk communication.  In coordination with two Hispanic (HSI) and Minority Serving (MSI) institutions, we will create and pilot test a novel community college certificate in AI for the environmental sciences and pilot.  This certificate will significantly enhance the diversity of the STEM workforce.  AI2ES will also develop AI/ES training for all levels of students, including K-12 outreach and modules, and online education modules that leverage our private industry’s existing educational resources and networks.  By leveraging private industry and existing successful internship programs for under-represented minority (URM) students, AI2ES will create a pipeline for URM students to gain valuable experience in AI/ES.

Dr. Amy McGovern is a professor in the School of Computer Science at the University of Oklahoma and in the School of Meteorology at the University of Oklahoma. Dr McGovern is also the director of the NSF AI Institute for Research on Trustworthy AI in Weather, Climate, and Coastal Oceanography.  Her research focuses on developing and applying trustworthy AI and machine learning methods primarily for severe weather phenomena. Dr. McGovern received her PhD in Computer Science from the University of Massachusetts Amherst in 2002 and was a senior postdoctoral research associate at the University of Massachusetts until joining the University of Oklahoma in January, 2005. She received her MS from the University of Massachusetts Amherst (1998) and her BS (honors) from Carnegie Mellon University (1996).

  • NSF, "NSF AI Institute for Research on Trustworthy AI in Weather, Climate, and Coastal Oceanography", $19,998,574, PI, 2020-2025
  • NOAA, "0-3 hour tornado prediction using the Warn on Forecast System and machine learning”, $517,038, co-PI, 2020-2022
  • NOAA, "Deep learning for operational identification and prediction of synoptic-scale fronts”, $333,952, PI, 2020-2022
  • NASA, "Automated Detection and Analysis of Severe, Tropopause-Penetrating Convective Storm Patterns Using Remote Sensing Data Fusion and Deep Learning", $324,907, co-PI, 2020-2023
  • S. A. Boukabara, V. M. Krasnopolsky, S. G. Penny, J. Q. Stewart, A. McGovern, D. Hall, J. E. Ten Hoeve, J. Hickey, H.-L. A. Huang, J. Williams, K. Ide, P. Tissot, S. E. Haupt, K. S. Casey, N. Oza, A. Geer, E. S. Maddy, and R. N. Hoffman. (2021) Outlook for exploiting artificial intelligence in the earth and environmental sciences. Bulletin of the American Meteorological Society. In press.
  • McGovern, Amy; Bostrom, Ann; Ebert-Uphoff, Imme; He, Ruoying; Thorncroft, Chris; Tissot, Philippe; Boukabara, Sid; Demuth, Julie; Gagne II, David John; Hickey, Jason; Williams, John K. (2020) Weathering Environmental Change Through Advances in AI. EOS, Volume 101, Published on 28 July 2020.
  • Lagerquist, Ryan; McGovern, Amy; Homeyer, Cameron R; Gagne II, David John; Smith, Travis. (2020) Deep Learning on Three-dimensional Multiscale Data for Next-hour Tornado Prediction. Monthly Weather Review. Volume 48, Number 7, pages 2837-2861. []
  • Lagerquist, Ryan; Allen, John T; McGovern, Amy. (2020) Climatology and Variability of Warm and Cold Fronts over North America from 1979-2019. Journal of Climate. Volume 33, Number 15, pages 6531–6554. []
  • McGovern, A., R. Lagerquist, and D. Gagne (2020) Using machine learning and model interpretation and visualization techniques to gain physical insights in atmospheric science. Proceedings of the International Conference on Learning Representations, [electronically published].
  • Handler, Shawn; Reeves, Heather; McGovern, Amy; (2020) Development of a Probabilistic Subfreezing Road Temperature Nowcast and Forecast Using Machine Learning. Weather and Forecasting. []
  • Jergensen, G. E, McGovern, A., Lagerquist, R., and Smith, Travis (2020). Classifying convective storms using machine learning, Weather and Forecasting. Volume 35, Number 2, Pages 537-559.
  • Burke, A., N. Snook, D.J. Gagne II, S. McCorkle, and A. McGovern (2020) Calibration of Machine Learning-Based Probabilistic Hail Predictions for Operational Forecasting. Weather and Forecasting, 35, 149-168,
  • McGovern, A., D.J. Gagne II, R. Lagerquist, K. Elmore, and G.E. Jergensen (2019) Making the black box more transparent: Understanding the physical implications of machine learning. Bulletin of the American Meteorological Society, Volume 100, Number 11, Pages 2175-2199. []
  • McGovern, A., C. Karstens, T. Smith, and R. Lagerquist, (2019) Quasi-Operational Testing of Real-time Storm-longevity Prediction via Machine Learning. Weather and Forecasting, Volume 34, Number 5, Pages 1437-1451. []
  • Lagerquist, R., A. McGovern, and D.J. Gagne II. (2019)  Deep learning for spatially explicit prediction of synoptic-scale fronts. Weather and Forecasting, Volume 34, Number 4, Pages 1137-1160. [
  • Loken, E. D., A. J. Clark, A. McGovern, M. Flora, and K. Knopfmeier. (2019) Postprocessing next-day ensemble probabilistic precipitation forecasts using random forests. Weather and Forecasting, 34, 2017-2044. []