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Ryan Lagerquist

Research Scientist I: Atmospheric Scientist for Machine-learning Applications

About Me:

I’m a Research Scientist at CIRA and NOAA.  My main focus is on using machine learning (ML) to improve the prediction and understanding of various weather phenomena, especially high-impact weather.  I have built my career around this theme for 11 years and worked for organizations including Environment Canada, the University of Alberta, University of Oklahoma, Cooperative Institute for Mesoscale Meteorological Studies (CIMMS), Google, and the National Center for Atmospheric Research (NCAR).  I earned my B.Sc. Honours in Atmospheric Science in 2014 from the University of Alberta, M.Sc. in Meteorology in 2016 from the University of Oklahoma, and Ph.D. in Meteorology from the University of Oklahoma in 2020.

My overarching interests right now are explainable artificial intelligence (XAI), uncertainty quantification (UQ), physics-guided ML, trustworthy ML, and time series (both learning from and predicting time series), as well as teaching and mentorship (see links to my short courses below).  My work spans many scales of meteorology, from nowcasting to climate.  My current projects include applications to radiative transfer, predicting the intensity of tropical cyclones, and predicting extreme fire weather/behaviour.


You can find my dissertation here, my GitHub here, the CIRA short course on machine learning (myself & Imme Ebert-Uphoff) here, my short course on explainable machine learning here, and my papers below:

Lagerquist, R., D. Turner, I. Ebert-Uphoff, and J. Stewart, 2023: “Estimating full longwave and shortwave radiative transfer with neural networks of varying complexity”. Journal of Atmospheric and Oceanic Technology, conditionally accepted.

McGovern, A., R. Chase, M. Flora, D. Gagne, R. Lagerquist, C. Potvin, N. Snook, and E. Loken, 2023: “A review of machine learning for convective weather”. Bulletin of the American Meteorological Society, conditionally accepted.

McGovern, A., D.J. Gagne II, C.D. Wirz, I. Ebert-Uphoff, A. Bostrom, Y. Rao, A. Schumacher, M. Flora, R. Chase, A. Mamalakis, M. McGraw, R. Lagerquist, R.J. Redmon, and T. Peterson, 2023: “Trustworthy artificial intelligence for environmental science: An innovative approach for summer school”. Bulletin of the American Meteorological Society, early online release,

Haynes, K., R. Lagerquist, M. McGraw, K. Musgrave, and I. Ebert-Uphoff, 2023: “Creating and evaluating uncertainty estimates with neural networks for environmental-science applications”. Artificial Intelligence for the Earth Systems, 2 (2),

Lagerquist, R., and I. Ebert-Uphoff, 2022: “Can we integrate spatial verification methods into neural network loss functions for atmospheric science?”. Artificial Intelligence for the Earth Systems, 1 (4), e220021,

Lagerquist, R., J. Stewart, I. Ebert-Uphoff, and C. Kumler, 2021: “Using deep learning to nowcast the spatial coverage of convection from Himawari-8 satellite data”. Monthly Weather Review, 149 (12), 3897-3921,

Lagerquist, R., D. Turner, I. Ebert-Uphoff, J. Stewart, and V. Hagerty, 2021: “Using deep learning to emulate and accelerate a radiative-transfer model”. Journal of Atmospheric and Oceanic Technology, 38 (10), 1673-1696,

Lagerquist, R., A. McGovern, C.R. Homeyer, D.J. Gagne II, and T. Smith, 2020: “Deep learning on three-dimensional multiscale data for next-hour tornado prediction”. Monthly Weather Review, 148 (7), 2837-2861,

Lagerquist, R., J.T. Allen, and A. McGovern, 2020: “Climatology and variability of warm and cold fronts over North America from 1979-2018”. Journal of Climate, 33 (15), 6531-6554,

McGovern, A., R. Lagerquist, and D.J. Gagne II, 2020: “Using machine learning and model interpretation and visualization techniques to gain physical insights in atmospheric science”. Workshop on AI for Earth Sciences, International Conference on Learning Representations, Addis Ababa, Ethiopia (became virtual due to COVID-19).

Jergensen, G.E., A. McGovern, R. Lagerquist, and T. Smith, 2020: “Classifying convective storms using machine learning”. Weather and Forecasting, 35 (2), 537-559,

McGovern, A., R. Lagerquist, D.J. Gagne II, G.E. Jergensen, K. Elmore, C.R. Homeyer, and T. Smith, 2019: “Making the black box more transparent: Understanding the physical implications of machine learning”. Bulletin of the American Meteorological Society, 100 (11), 2175-2199,

McGovern, A., C.D. Karstens, T. Smith, and R. Lagerquist, 2019: “Quasi-operational testing of real-time storm-longevity prediction via machine learning”. Weather and Forecasting, 34 (5), 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, 34 (4), 1137-1160,

Lagerquist, R., A. McGovern, and T. Smith, 2017: “Machine learning for real-time prediction of damaging straight-line convective wind”. Weather and Forecasting, 32 (6), 2175-2193,

Lagerquist, R., M. Flannigan, X. Wang, and G. Marshall, 2017: “Automated prediction of extreme fire weather from synoptic patterns in northern Alberta”. Canadian Journal of Forest Research, 47 (9), 1175-1183,

McGovern, A. K. Elmore, D.J. Gagne II, S.E. Haupt, C. Karstens, R. Lagerquist, T. Smith, and J. Williams, 2017: “Using artificial intelligence to improve real-time decision-making for high-impact weather”. Bulletin of the American Meteorological Society, 98 (10), 2073-2090,