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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
Convection-allowing model (CAM) ensemble data assimilation & prediction
Post-processing CAM forecasts via machine learning (ML)
ML emulation of CAMs
Thunderstorm predictability
Radar-based storm analysis techniques
Improving tornado climatology
Current Research Foci
Developing ML models to predict the accuracy of Warn-on-Forecast System (WoFS) thunderstorm forecasts
Developing deep learning (DL) models to emulate WoFS forecasts (new)
Evaluating MPAS as a next-generation CAM dynamical core
Collaborating with Dr. Montgomery Flora on development of WoFS-based ML models and interpretability tools for probabilistic severe weather forecasting
Developing a Bayesian hierarchical modeling framework for improving the U.S. tornado climatology
Curriculum Vita
Corey K. Potvin (updated Dec 2023)
Publications
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 machine learning
Flora, M. L., C. K. Potvin, A. McGovern, and S. Handler, 2023: A Machine Learning Explainability Tutorial for Atmospheric Sciences. Artif. Intell. Earth Syst., in press, 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.
Storm-scale ensemble data assimilation and forecasting
Heinselman, P. L., and Coauthors, 2023: Warn-on-Forecast System: From Vision to Reality. Wea. Forecasting, in press, 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
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, in press, 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.