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Travis Smith

Travis Smith

Travis Smith

Senior Research
Associate

tms@ou.edu
travis.smith@noaa.gov
National Weather Center 3939

Education

  • MS, Meteorology, University of Oklahoma
  • BS, Meteorology, University of Oklahoma

Affiliated CIWRO Team

  • WRDD

About

Travis Smith’s primary focus is on severe weather warning technologies to improve decision-making for warnings of tornadoes, hail, and damaging convective windstorms. He was a founder of and has led multiple programs including Multi-Radar/Multi-Sensor (MRMS) severe weather applications, the Hazardous Weather Testbed’s (HWT) Experimental Warning Program (EWP), and the Forecasting A Continuum of Environmental Threats (FACETs) Probabilistic Hazard Information (PHI). 

Research Focus

  • Severe and Convective Weather
  • Radar Meteorology
  • Warning Decision Support
  • Meteorological Software Development
  • Thunderstorm Probability and Impacts
  • Nowcasting and Warnings
  • Testbeds and Experimental Design

CIWRO Research Themes

  • Weather Radar and Observations
  • Forecast Applications Improvements R&D
  • Social and Socioeconomic Impacts of High Impact Weather
Honors and awards
  • National Weather Association - Larry R. Johnson Special Award, 2016 (MRMS)
  • National Weather Association - Larry R. Johnson Special Award, 2015 (HWT)
  • University of Oklahoma - Innovator Award, 2012 (MRMS/WDSS-II)
  • World Meteorological Organization - Dr. Vilho Vaisala Award for an Outstanding Research Paper, 2010 (“Rapid Sampling of Severe Storms by the National Weather Radar Testbed Phased Array Radar.” Weather and Forecasting, 2008.)
  • NOAA/OAR - Outstanding Paper Award, 2009 (“Rapid Sampling of Severe Storms by the National Weather Radar Testbed Phased Array Radar.” Weather and Forecasting, 2008.)
  • AMS, Best Poster Award, 2008 (“An on-demand user interface for requesting multi-radar, multi-sensor time accumulated products to support severe weather verification.” 23rd Conference on Interactive Information Processing Systems)
  • NOAA Technology Transfer Award, 2008
  • NOAA High Performance Computing and Communication NOAA Tech Award, 2004
  • NOAA Bronze Medal Award (team award), 2003 (“for rapid and innovative actions in collecting, archiving, and analyzing weather radar data to assist the Shuttle Columbia accident investigation.”)
  • FAA Excellence in Aviation Award, 2002, (“for contributions to the FAA's Aviation Weather Research Program, which was organized to generate more accurate and accessible aviation weather observations, warnings and forecasts.”)
Publications
  • Satrio, C. N., K. M. Calhoun, P. A. Campbell, R. Steeves, T. M. Smith, 2022: An Objective Scoring Method for Evaluating the Comparative Performance of Automated Storm Identification and Tracking Algorithms. Weather and Forecasting, EOR, doi:10.1175/WAF-D-22-0047.1
  • Williams, S. S., K. L. Ortega, T. M. Smith, and A. E. Reinhart, 2022: Comprehensive Radar Data for the Contiguous United States: Multi-Year Reanalysis of Remotely Sensed Storms. Bull. Amer. Meteor. Soc., 103, E838–E854, https://doi.org/10.1175/BAMS-D-20-0316.1.
  • Calhoun, K. M., K. L. Berry, D. M. Kingfield, T. Meyer, M. J. Krocak, T. M. Smith, G. Stumpf, A. Gerard, 2021: The Experimental Warning Program of NOAA’s Hazardous Weather Testbed. Bulletin of the American Meteorological Society, 102, E2229–E2246, doi:10.1175/BAMS-D-21-0017.1
  • Jergensen, G. E., McGovern, A., Lagerquist, R., & Smith, T. (2020). Classifying Convective Storms Using Machine Learning, Weather and Forecasting, 35(2), 537-559
  • 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. Mon. Wea. Rev., 148, 2837–2861, https://doi.org/10.1175/MWR-D-19-0372.1.
  • McGovern, A., Lagerquist, R., John Gagne, D., II, Jergensen, G. E., Elmore, K. L., Homeyer, C. R., & Smith, T. (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., Karstens, C. D., Smith, T., & Lagerquist, R. (2019). Quasi-Operational Testing of Real-Time Storm-Longevity Prediction via Machine Learning, Weather and Forecasting, 34(5), 1437-1451.
  • Mahalik, M. C., Smith, B. R., Elmore, K. L., Kingfield, D. M., Ortega, K. L., & Smith, T. M. (2019). Estimates of Gradients in Radar Moments Using a Linear Least Squares Derivative Technique, Weather and Forecasting, 34(2), 415-434.
  • Rothfusz, L. P., Schneider, R., Novak, D., Klockow-McClain, K., Gerard, A. E., Karstens, C., Stumpf, G. J., & Smith, T. M. (2018). FACETs: A Proposed Next-Generation Paradigm for High-Impact Weather Forecasting, Bulletin of the American Meteorological Society, 99(10), 2025-2043.
  • Karstens, C. D., Correia, J., Jr., LaDue, D. S., Wolfe, J., Meyer, T. C., Harrison, D. R., Cintineo, J. L., Calhoun, K. M., Smith, T. M., Gerard, A. E., & Rothfusz, L. P. (2018). Development of a Human–Machine Mix for Forecasting Severe Convective Events, Weather and Forecasting, 33(3), 715-737.
  • McGovern, A., K. L. Elmore, D. J. Gagne, S. E. Haupt, C. D. Karstens, R. Lagerquist, T. Smith, and J. K. Williams, 2017: Using Artificial Intelligence to Improve Real-Time Decision-Making for High-Impact Weather. Bull. Amer. Meteor. Soc., 98, 2073–2090, https://doi.org/10.1175/BAMS-D-16-0123.1.
  • 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.
  • Smith, T.M., V. Lakshmanan, G.J. Stumpf, K.L. Ortega, K. Hondl, K. Cooper, K.M. Calhoun, D.M. Kingfield, K.L. Manross, R. Toomey, and J. Brogden, 2016:  Multi-Radar Multi-Sensor (MRMS) Severe Weather and Aviation Products: Initial Operating Capabilities, Bull. Amer. Meteor. Soc., 97, 1617–1630, doi:10.1175/BAMS-D-14-00173.1.
  • Karstens, C. D., G. Stumpf, C. Ling, L. Hua, D. Kingfield, T. M. Smith,, J. Correia, K. Calhoun,, K. Ortega, C.Melick and L. P. Rothfusz, 2015: Evaluation of a Probabilistic Forecasting Methodology for Severe Convective Weather in the 2014 Hazardous Weather Testbed. Wea. Forecasting, 30, 1551–1570, https://doi.org/10.1175/WAF-D-14-00163.1.
  • Calhoun, K. M., T. M. Smith, D. M. Kingfield, J. Gao, and D. J. Stensrud, 2014: Forecaster Use and Evaluation of Real-Time 3DVAR Analyses during Severe Thunderstorm and Tornado Warning Operations in the Hazardous Weather Testbed. Wea. Forecasting, 29, 601–613, https://doi.org/10.1175/WAF-D-13-00107.1.
  • Smith, T.M., J. Gao, K.M. Calhoun, D.J. Stensrud, K.L. Manross, K.L. Ortega, C. Fu, D.M. Kingfield, K.L. Elmore, V. Lakshmanan, and C. Riedel, 2013: Examination of a real-time 3DVAR analysis system in the Hazardous Weather Testbed.  Weather and Forecasting, early online release http://dx.doi.org/10.1175/WAF-D-13-00044.1
  • Clark, A. J., J. Gao, P. T. Marsh, T. Smith, J. S. Kain, J. Correia , M. Xue, and F. Kong, 2013: Tornado Pathlength Forecasts from 2010 to 2011 Using Ensemble Updraft Helicity. Wea. Forecasting, 28, 387–407, https://doi.org/10.1175/WAF-D-12-00038.1.
  • Miller, M. L., V. Lakshmanan, and T. M. Smith, 2013: An Automated Method for Depicting Mesocyclone Paths and Intensities. Wea. Forecasting, 28, 570–585, https://doi.org/10.1175/WAF-D-12-00065.1.
  • Gao, J., and Coauthors, 2013: A Real-Time Weather-Adaptive 3DVAR Analysis System for Severe Weather Detections and Warnings. Wea. Forecasting, 28, 727–745, https://doi.org/10.1175/WAF-D-12-00093.1.
  • Newman, J. F., V. Lakshmanan, P. L. Heinselman, M. B. Richman, and T. M. Smith, 2013: Range-Correcting Azimuthal Shear in Doppler Radar Data. Wea. Forecasting, 28, 194–211, https://doi.org/10.1175/WAF-D-11-00154.1.
  • Ralph, F. M., J. Intrieri, D. Andra, R. Atlas, S. Boukabara, D. Bright, P. Davidson, B. Entwistle, J. Gaynor, S. Goodman, J. Jiing, A. Harless, J. Huang, G. Jedlovec, J. Kain, S. Koch, B. Kuo, J. Levit, S. Murillo, L. P. Riishojgaar, T. Schneider, R.Schneider, T. Smith, and S. Weiss, 2013: The Emergence of Weather-Related Test Beds Linking Research and Forecasting Operations. Bull. Amer. Meteor. Soc., 94, 1187–1211, https://doi.org/10.1175/BAMS-D-12-00080.1.
  • Cintineo, J. L., T. M. Smith, V. Lakshmanan, H. E. Brooks, and K. L. Ortega, 2012: An Objective High-Resolution Hail Climatology of the Contiguous United States. Wea. Forecasting, 27, 1235–1248, https://doi.org/10.1175/WAF-D-11-00151.1.
  • Gallo, K., T. Smith, K. Jungbluth, and P. Schumacher, 2012: Hail Swaths Observed from Satellite Data and Their Relation to Radar and Surface-Based Observations: A Case Study from Iowa in 2009. Wea. Forecasting, 27, 796–802, https://doi.org/10.1175/WAF-D-11-00118.1.
  • Lakshmanan, V. and T. Smith, 2010: An Objective Method of Evaluating and Devising Storm-Tracking Algorithms. Wea. Forecasting, 25, 701–709. doi: 10.1175/2009WAF2222330.1
  • Smith, T.M. and V. Lakshmanan, 2010: Real-time, rapidly updating severe weather products for virtual globes. Computers and Geosciences, In Press. doi: 10.1016/j.cageo.2010.03.023 (Special Issue on Virtual Globes)
  • Gourley, J.J., J.M. Erlingis, T.M. Smith, K.L. Ortega, and Y. Hong, 2010: Remote collection and analysis of witness reports of flash floods. J. Hydrology, 394, 53-62. doi:10.1016/j.jhydrol.2010.05.042 (Special Issue on Flash Floods)
  • Lakshmanan, V.and T. M. Smith, 2009: Data Mining Storm Attributes from Spatial Grids. J. Atmos. Oceanic Technol., 26, 2353–2365.
  • Ortega, K.L., T.M. Smith, K.L. Manross, K.A. Scharfenberg, A. Witt, A.G. Kolodziej, and J.J. Gourley, 2009: The Severe Hazards Analysis and Verification Experiment. Bull. Amer. Meteor. Soc., 90, 1519–1530. doi: 10.1175/2009BAMS2815.1
  • Heinselman, P.L., D.L. Priegnitz, K.L. Manross, T.M. Smith, and R.W. Adams, 2008: Rapid Sampling of Severe Storms by the National Weather Radar Testbed Phased Array Radar. Wea. Forecasting, 23, 808–824. doi: 10.1175/2008WAF2007071.1. (Winner: NOAA/OAR Outstanding Scientific Paper, 2008 and WMO Professor Dr. Vilho Vaisala Award for an Outstanding Research Paper, 2010)
  • Lakshmanan, V., A. Fritz, T. Smith, K. Hondl, and G. J. Stumpf, 2007: An automated technique to quality control radar reflectivity data. J. Applied Meteorology 46, 288-305.
  • Lakshmanan, V., T. Smith, G. J. Stumpf, and K. Hondl, 2007: The warning decision support system - integrated information (WDSS-II). Weather and Forecasting 22, 592-608.
  • Lakshmanan, V., T. Smith, K. Hondl, G. J. Stumpf, and A. Witt, 2006: A real-time, three dimensional, rapidly updating, heterogeneous radar merger technique for reflectivity, velocity and derived products. Weather and Forecasting 21, 802-823.
  • Smith, T. M., K. L. Elmore, and S. A. Dulin, 2004: A damaging downburst prediction and detection algorithm for the WSR-88D. Wea. and Forecasting. 19: 240-250, Amer. Met. Soc.
  • Stumpf, G. J., T. M. Smith, and C. Thomas, 2003: The National Severe Storms Laboratory's contribution to severe weather improvement: multi-sensor severe weather applications. Atmospheric Research 67-68: 657-669. doi:10.1016/S0169-8095(03)00079-6
Professional organizations
  • American Meteorological Society
  • National Weather Association
Social Media Contact
  • Threads: @wxtrav
Cooperative Institute for Severe and High-Impact Weather Research and Operations (CIWRO)
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Updated 10/30/2025 by Cooperative Institute for Severe and High-Impact Weather Research and Operations (CIWRO): ciwro@ou.edu