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- Email:
- Corey.Potvin@noaa.gov
- Phone:
- 405-325-6118
- Address:
- NSSL/FRDD Rm 4346, 120 David L. Boren Boulevard, Norman, OK 73072
Research Interests
AI emulation of convection-allowing models (CAMs)
CAM ensemble data assimilation, prediction, and verification
Post-processing CAM forecasts via machine learning
Thunderstorm predictability
Radar-based storm analysis techniques
Improving tornado climatology
Curriculum Vita
Corey K. Potvin (updated Jun 2025)
Publications
AI/ML applications to CAMs
Flora, M. L., P. Skinner, C. K. Potvin, B. Matilla, and A. Reinhart, 2025: Assessing the Impact of Biased Target Variables on Machine Learning Models of Severe Hail. Wea. Forecasting, 40, 1015–1028, DOI: 10.1175/WAF-D-24-0051.1.
Flora, M. L., and C. K. Potvin, 2025: WoFSCast: A machine learning model for predicting thunderstorms at watch-to-warning scales. Geophys. Res. Lett., 52, DOI: 10.1029/2024GL112383.
Lawson, J. R., J. E. Trujillo-Falcón, D. M. Schultz, M. L. Flora, K. H. Goebbert, S. N. Lyman, C. K. Potvin, and A. J. Stepanek, 2025: Pixels and Predictions: Potential of GPT-4V in Meteorological Imagery Analysis and Forecast Communication. Artif. Intell. Earth Syst., 4, 240029, DOI: 10.1175/AIES-D-24-0029.1.
Potvin, C. K., M. L. Flora, P. S. Skinner, A. E. Reinhart, and B. C. Matilla, 2024: Using machine learning to predict convection-allowing ensemble forecast skill: Evaluation with the NSSL Warn-on-Forecast System. Artif. Intell. Earth Syst., 3, e230106, DOI: 10.1175/AIES-D-23-0106.1.
Schmidt, T. G., and Coauthors, 2024: Gridded Severe Hail Nowcasting Using 3D U-Nets, Lightning Observations, and the Warn-on-Forecast System. Artif. Intell. Earth Syst., 3, 240026, 10.1175/AIES-D-24-0026.1.
Flora, M. L., B. Gallo, C. K. Potvin, A. J. Clark, and K. Wilson, 2024: Exploring the Usefulness of Machine Learning Severe Weather Guidance in the Warn-on-Forecast System: Results from the 2022 NOAA Hazardous Weather Testbed Spring Forecasting Experiment. Wea. Forecasting, 39, 1023–1044, DOI: https://doi.org/10.1175/WAF-D-24-0038.1.
Chase, R. J., A. McGovern, C. R. Homeyer, P. J. Marinescu, and C. K. Potvin, 2024: Machine Learning Estimation of Maximum Vertical Velocity from Radar. Artif. Intell. Earth Syst., 3, 230095, DOI: 10.1175/AIES-D-23-0095.1.
Flora, M. L., C. K. Potvin, A. McGovern, and S. Handler, 2023: A Machine Learning Explainability Tutorial for Atmospheric Sciences. Artif. Intell. Earth Syst., 3, e230018, DOI: 10.1175/AIES-D-23-0018.1.
McGovern, A., R. J. Chase, M. Flora, D. J. Gagne, R. Lagerquist, C. K. Potvin, N. Snook, and E. Loken, 2023: A Review of Machine Learning for Convective Weather. Artif. Intell. Earth Syst., 2, e220077. DOI: 10.1175/AIES-D-22-0077.1.
Flora, M. L., C. K. Potvin, P. S. Skinner, S. Handler, and A. McGovern, 2021: Using machine learning to generate storm-scale probabilistic guidance of severe weather hazards in the Warn-on-Forecast System. Mon. Wea. Rev., 149, 1535–1557. DOI: 10.1175/MWR-D-20-0194.1.
McGovern, A., C. K. Potvin, and R. A. Brown, 2017: Using large-scale machine learning to improve our understanding of the formation of tornadoes. Large-Scale Machine Learning in the Earth Sciences, CRC Press, 95-112. URL: https://books.google.com/books?id=KQMvDwAAQBAJ.
Thunderstorm predictability
Miller, W., C. K. Potvin, M. L. Flora, B. Gallo, L. Wicker, T. Jones, P. Skinner, B. Matilla, and K. Knopfmeier, 2022: Exploring the usefulness of downscaling free forecasts from the Warn-on-Forecast System. Wea. Forecasting, 37, 181-203. DOI: 10.1175/WAF-D-21-0079.1.
Lawson, J. R., C. K. Potvin, P. S. Skinner, and A. E. Reinhart, 2021: The vice and virtue of increased horizontal resolution in ensemble forecasts of tornadic thunderstorms in low-CAPE, high-shear environments. Mon. Wea. Rev., 149, 921-944. DOI: 10.1175/MWR-D-20-0281.1.
Lawson, J. R., Gallus, W. A., and C. K. Potvin, 2020: Sensitivity of a bowing mesoscale convective system to horizontal grid spacing in a convection-allowing ensemble. Atmosphere, 11. DOI: 10.3390/atmos11040384.
Flora, M. L., C. K. Potvin, and L. J. Wicker, 2018: Practical predictability of supercells: Exploring ensemble forecast sensitivity to initial condition spread. Mon. Wea. Rev., 146, 2361–2379. DOI: 10.1175/MWR-D-17-0374.1 .
Potvin, C. K., E. M. Murillo, M. L. Flora, and D. M. Wheatley, 2017: Sensitivity of supercell simulations to initial-condition resolution. J. Atmos. Sci., 74, 5-26. DOI: 10.1175/JAS-D-16-0098.1.
Potvin, C. K., and M. L. Flora, 2015: Sensitivity of idealized supercell simulations to horizontal grid spacing: Implications for Warn-on-Forecast. Mon. Wea. Rev., 143, 2998-3024. DOI: 10.1175/MWR-D-14-00416.1.
Potvin, C. K., and L. J. Wicker, 2013: Assessing ensemble forecasts of low-level supercell rotation within an OSSE framework. Wea. and Forecasting, 28, 940-960. DOI: 10.1175/WAF-D-12-00122.1.
Storm-scale ensemble data assimilation and forecasting
Stratman, D. R., C. K. Potvin, P. S. Skinner, and B. M. Lemke, 2025: Storm Displacement Errors in the NSSL Warn-on-Forecast System. Wea. Forecasting, 10.1175/WAF-D-24-0248.1, in press.
Clark, A. J., and Coauthors, 2024: Advancing Hazardous Weather Prediction in the 2024 NOAA Hazardous Weather Testbed Spring Forecasting Experiment. Bull. Amer. Meteor. Soc., 105, E2180–E2183, 10.1175/BAMS-D-24-0249.1.
Heinselman, P. L., and Coauthors, 2023: Warn-on-Forecast System: From Vision to Reality. Wea. Forecasting, 39, 75–95, DOI: 10.1175/WAF-D-23-0147.1.
Clark, A. J., and Coauthors, 2023: The third real-time, virtual Spring Forecasting Experiment to advance severe weather prediction capabilities. Bull. Amer. Meteor. Soc., 104, E456–E458. DOI: 10.1175/BAMS-D-22-0213.1.
Stratman, D., and C. K. Potvin, 2022: Testing the Feature Alignment Technique (FAT) in an ensemble-based data assimilation and forecast system with multiple-storm scenarios. Mon. Wea. Rev., 150, 2033-2054. DOI: 10.1175/MWR-D-21-0289.1.
Clark, A. J., and Coauthors, 2022: The second real-time, virtual Spring Forecasting Experiment to advance severe weather prediction. Bull. Amer. Meteor. Soc., 103, E1114-1116. DOI: 10.1175/BAMS-D-21-0239.1.
Stratman, D., C. K. Potvin, and L. J. Wicker, 2018: Correcting storm displacement errors in ensembles using the Feature Alignment Technique (FAT). Mon. Wea. Rev., 146, 2125–2145. DOI: 10.1175/MWR-D-17-0357.1.
Thompson, T. E., L. J. Wicker, X. Wang, and C. K. Potvin, 2015: A comparison between the local ensemble transform Kalman filter and the ensemble square root Kalman filter for the assimilation of radar data in convective-scale models. Quart. J. Roy. Meteor. Soc., 141, 1163–1176. DOI: 10.1002/qj.2423.
Potvin, C. K., and L. J. Wicker, 2013: Correcting fast-mode pressure errors in storm-scale ensemble Kalman filter analyses. Advances in Meteorology, 2013, 1-14. DOI: 10.1155/2013/624931.
Stensrud, D. J., and Co-authors, 2013: Progress and Challenges with Warn-on-Forecast. Atmos. Res., 123, 2-16. DOI: 0.1016/j.atmosres.2012.04.004.
Potvin, C. K., L. J. Wicker, M. I. Biggerstaff, D. Betten, and A. Shapiro, 2013: Comparison between dual-Doppler and EnKF storm-scale wind analyses: The 29-30 May 2004 Geary, Oklahoma, supercell thunderstorm. Mon. Wea. Rev., 141, 1612-1628. DOI: 10.1175/MWR-D-12-00308.1.
Potvin, C. K., and L. J. Wicker, 2012: Comparison between dual-Doppler and EnKF storm-scale wind analyses: Observing system experiments with a simulated supercell thunderstorm. Mon. Wea. Rev.., 140, 3972-3991. DOI: 10.1175/MWR-D-12-00044.1.
Convection-allowing model (CAM) Verification
Lawson, J. R., C. K. Potvin, and K. Nelson, 2024: Decoding the Atmosphere: Optimising Probabilistic Forecasts with Information Gain. MDPI Meteorology, 3, 212-231, DOI: 10.3390/meteorology3020010.
Britt, K. C., P. S. Skinner, P. L. Heinselman, C. K. Potvin, M. L. Flora, B. Matilla, K. H. Knopfmeier, and A. E. Reinhart, 2023: Verification of Quasi-Linear Convective Systems Predicted by the Warn-on-Forecast System (WoFS). Wea. Forecasting, 39, 155–176, DOI: 10.1175/WAF-D-23-0106.1.
Potvin, C. K., P. S. Skinner, K. A. Hoogewind, M. C. Coniglio, J. A. Gibbs, A. J. Clark, M. L. Flora, A. E. Reinhart, J. R. Carley, and E. N. Smith, 2020: Assessing systematic impacts of PBL schemes on storm evolution in the NOAA Warn-on-Forecast System. Mon. Wea. Rev., 148, 2567-2590. DOI: 10.1175/MWR-D-19-0389.1.
Flora, M. L., P. S. Skinner, C. K. Potvin, A. E. Reinhart, T. A. Jones, N. Yussouf, and K. H. Knopfmeier, 2019: Object-based verification of short-term, storm-scale probabilistic mesocyclone guidance from an experimental Warn-on-Forecast System. Wea. and Forecasting, 34, 1721-1739. DOI: 10.1175/WAF-D-19-0094.1.
Potvin, C.K., J.R. Carley, A. Clark, L.J. Wicker, P.S. Skinner, A.E. Reinhart, B.T. Gallo, J.S. Kain, G. Romine, E. Aligo, K.A. Brewster, D.C. Dowell, L.M. Harris, I.L. Jirak, F. Kong, T.A. Supinie, K.W. Thomas, X. Wang, Y. Wang, and M. Xue, 2019: Systematic comparison of convection-allowing models during the 2017 NOAA HWT Spring Forecasting Experiment. Wea. and Forecasting, 34, 1395-1416. DOI: 10.1175/WAF-D-19-0056.1.
Storm analysis technique development
Potvin, C. K., and Coauthors, 2022: An iterative storm identification and classification algorithm for convection-allowing models and gridded radar analyses. J. Atmos. Oceanic Technol., 39, 999-1013. DOI: 10.1175/JTECH-D-21-0141.1.
Shapiro, A., J. G. Gebauer, N. A. Dahl, D. J. Bodine, A. Mahre, and C. K. Potvin, 2021: Spatially variable advection correction of Doppler radial velocity data. J. Atmos. Sci., 78, 167-188. DOI: 10.1175/JAS-D-20-0048.1.
Homeyer, C. R., T. N. Sandmael, C. K. Potvin, and A. Murphy, 2020: Distinguishing characteristics of tornadic and nontornadic supercell storms from composite mean analyses of radar observations. Mon. Wea. Rev., 148, 5015-5040. DOI: 10.1175/MWR-D-20-0136.1.
Weinhoff, Z. B., H. B. Bluestein, L. J. Wicker, J. C. Snyder, A. Shapiro, C. K. Potvin, J. B. Houser, and D. W. Reif, 2018: Applications of a spatially variable advection correction technique for temporal correction of dual-Doppler analyses of tornadic supercells. Mon. Wea. Rev., 146, 2949–2971. DOI: 10.1175/MWR-D-17-0360.1.
Shapiro, A., S. Rahimi, C. K. Potvin, and L. Orf, 2015: On the use of advection correction in trajectory calculations. J. Atmos. Sci., 72, 4261-4280. DOI: 10.1175/JAS-D-15-0095.1.
Lakshmanan, V., K. Hondl, C. K. Potvin, and D. Preignitz, 2013: An improved method to compute radar echo top heights. Wea. and Forecasting, 28, 481-488. DOI: 10.1175/WAF-D-12-00084.1.
Shapiro, A., K. M. Willingham, and C. K. Potvin, 2010: Spatially variable advection correction of radar data. Part I: Theoretical considerations. J. Atmos. Sci., 67, 3445-3456. DOI: 10.1175/2010JAS3465.1.
Shapiro, A., K. M. Willingham, and C. K. Potvin, 2010: Spatially variable advection correction of radar data. Part II: Test results. J. Atmos. Sci., 67, 3457-3470. DOI: 10.1175/2010JAS3466.1 .
Variational dual-Doppler wind retrieval
Brook, J. P., A. Protat, C. K. Potvin, J. S. Soderholm, and H. McGowan, 2023: The Effects of Spatial Interpolation on a Novel, Dual-Doppler 3D Wind Retrieval Technique. J. Atmos. Oceanic Technol., 40, 1325–1347, DOI: 10.1175/JTECH-D-23-0004.
Gebauer, J. G., A. Shapiro, C. K. Potvin, N. A. Dahl, M. I. Biggerstaff, and A. Alford, 2022: Evaluating vertical velocity retrievals from vertical vorticity constrained dual-Doppler analysis of real, rapid-scan radar data. J. Atmos. Oceanic Technol., 39, 1591-1610. DOI: 10.1175/JTECH-D-21-0136.1 .
Jackson, R., S. Collis, T. Lang, C. K. Potvin, and T. Munson, 2020: PyDDA: A Pythonic direct data assimilation framework for wind retrievals. Journal of the Operational Research Society, 8. DOI: 10.5334/jors.264.
Dahl, N. A., A. Shapiro, C. K. Potvin, A. Theisen, J. G. Gebauer, A. D. Schenkman, and M. Xue, 2019: High-Resolution, Rapid-Scan Dual-Doppler Retrievals of Vertical Velocity in a Simulated Supercell. J. Atmos. Oceanic Technol., 36, 1477–1500. DOI: 10.1175/JTECH-D-18-0211.1 .
North, K. W., M. Oue, P. Kollias, S. E. Giangrande, S. M. Collis, and C. K. Potvin, 2017: Vertical air motion retrievals in deep convective clouds using the ARM scanning radar network in Oklahoma during MC3E. Atmos. Meas. Tech., 10, 2785-2806. DOI: 10.5194/amt-10-2785-2017.
Potvin, C. K., D. Betten, L. J. Wicker, K. L. Elmore, and M. I. Biggerstaff, 2012: 3DVAR vs. traditional dual-Doppler wind retrievals of a simulated supercell thunderstorm. Mon. Wea. Rev.., 140, 3487-3494. DOI: 10.1175/MWR-D-12-00063.1.
Potvin, C. K., L. J. Wicker, and A. Shapiro, 2012: Assessing dual-Doppler wind synthesis errors in supercell thunderstorms using OSS experiments. J. Atmos. Oceanic Technol., 29, 1009-1025. DOI: 10.1175/JTECH-D-11-00177.1.
Potvin, C. K., A. Shapiro, and M. Xue, 2012: Impact of a vertical vorticity constraint in variational dual-Doppler wind analysis: Tests with real and simulated supercell data. J. Atmos. Oceanic Technol., 29, 32-49. DOI: 10.1175/JTECH-D-11-00019.1.
Shapiro, A., C. K. Potvin, and J. Gao, 2009: Use of a vertical vorticity equation in variational dual-Doppler wind analysis. J. Atmos. Oceanic Technol., 26, 2089-2106. DOI: 10.1175/2010JAS3466.1.
Tornado climatology
Potvin, C. K., C. Broyles, P. S. Skinner, and H. E. Brooks, 2022: Improving estimates of U.S. tornado frequency by accounting for unreported and underrated tornadoes. J. Appl. Meteor. Climatol., 61, 909-930. DOI: 10.1175/JAMC-D-21-0225.1.
Potvin, C. K., C. Broyles, P. S. Skinner, H. E. Brooks, and E. Rasmussen, 2019: A Bayesian Hierarchical Modeling Framework for Correcting Reporting Bias in the U.S. Tornado Database. Wea. and Forecasting., 34, 15-30. DOI: 10.1175/WAF-D-18-0137.1.
Potvin, C. K., K. L. Elmore, and S. J. Weiss, 2010: Assessing the impacts of proximity sounding criteria on the climatology of significant tornado environments. Wea. and Forecasting, 25, 921-930. DOI: 10.1175/2010WAF2222368.1.
Supercell and tornado processes
Belik, P., B. Dahl, D. Dokken, C. K. Potvin, K. Scholz, and M. Shvartsman, 2018: Possible implications of self-similarity for tornadogenesis and maintenance. AIMS Mathematics, 3, 365-390. DOI: 10.3934/Math.2018.3.365.
Belik, P., D. Dokken, C. K. Potvin, K. Scholz, and M. Shvartsman, 2017: Applications of vortex gas models to tornadogenesis and maintenance. Open Journal of Fluid Dynamics, 7, 596-622. DOI: 10.4236/ojfd.2017.74040.
DiGangi, E. A., D. R. MacGorman, C. L. Ziegler, D. Betten, M. Biggerstaff, M. Bowlan, and C. K. Potvin, 2017: An overview of the 29 May 2012 Kingfisher supercell during DC3: Observations of the 29 May 2012 DC3 case. J. Geophys. Res.., 121, 14,316–14,343. DOI: 10.1002/2016JD025690.
Skinner, P. S., C. C. Weiss, L. J. Wicker, C. K. Potvin, and D. C. Dowell, 2015: Forcing mechanisms for an internal rear-flank downdraft momentum surge in the 18 May 2010 Dumas, Texas supercell. Mon. Wea. Rev., 143, 4305-4330. DOI: 10.1175/MWR-D-15-0164.1.
Vortex detection and characterization
Potvin, C. K., 2013: A variational method for detecting and characterizing intense vortices in Cartesian wind fields. Wea. and Forecasting, 141, 3102-3115. DOI: 10.1175/MWR-D-13-00015.1.
Potvin, C. K., A. Shapiro, M. I. Biggerstaff, and Joshua M. Wurman, 2011: The VDAC technique: A variational method for detecting and characterizing convective vortices in multiple-Doppler radar data. Mon. Wea. Rev., 139, 2593-2613. DOI: 10.1175/2011MWR3638.1.
Potvin, C. K., A. Shapiro, T.-Y. Yu, J. Gao, and M. Xue, 2009: Using a low-order model to detect and characterize tornadoes in multiple-Doppler radar data. Mon. Wea. Rev., 137, 1230-1249. DOI: 10.1175/2008MWR2446.1.