Corey Potvin

I’m fascinated by the power and majesty of organized convective storms, and by our ability to observe and diagnose their complex yet intelligible structure and evolution. My overarching scientific objectives are to improve (1) our understanding of the development and evolution of storms and tornadoes, and (2) numerical forecasts and public warnings of thunderstorm hazards.

Email:
Corey.Potvin@noaa.gov
Phone:
405-325-6118
Address:
NSSL/FRDD Rm 4346, 120 David L. Boren Boulevard, Norman, OK 73072

Research Interests

Storm-scale predictability

Convection-allowing model (CAM) ensemble data assimilation & prediction

CAM verification technique development

Variational multiple-Doppler wind retrieval technique development

Improving tornado climatology

Curriculum Vita

Corey K. Potvin (last updated Aug 2019)

Recent News

Oct 2017: The variational dual-Doppler retrieval code developed by myself and Alan Shapiro has been incorporated into PyART as the MultiDop module: https://github.com/nasa/MultiDop

Jan 2017: Honored and thrilled to receive a Presidential Early Career Award for Scientists and Engineers (PECASE)! OU News White House

Oct 2016: TACC wrote an article on Amy McGovern's tornadogenesis data mining work that I am collaborating on: TACC News. The article has been picked up by science news sites (e.g., phys.org) and Facebook pages.

May 2016: My work to examine the significance of "mini tornado alleys" (Broyles and Crosbie 2004) in the U.S. tornado climatology is mentioned in this article on the recent spate of Moore tornadoes: 538.com

Publications (32)

Supercell predictability

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 EnKF data assimilation and forecasting

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.

Variational dual-Doppler wind retrieval

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.

Convection-allowing model (CAM) Verification

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 Program. Wea. and Forecasting, conditionally accepted.

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, in press. DOI: 10.1175/WAF-D-19-0056.1.

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.

Storm analysis technique development

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 .

Tornado climatology

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 dynamics

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.

Tornadogenesis

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.

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.