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Thea Sandmael

Thea Sandmael

Thea Sandmael

Research
Associate II

thea@ou.edu
thea.sandmael@noaa.gov
National Weather Center 3920

Education

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

Affiliated CIWRO Team

  • Storm Process Analysis, Research and Knowledge

About

Thea is a research associate with CIWRO/NSSL in the Warning Research and Development Division. Her research interests include radar data associated with severe storms, tornado prediction and detection, and machine learning. She also collaborates with the Probabilistic Hazards Information and the Warn-on-Forecast System teams to provide probabilistic guidance for tornadoes, and occasionally joins the NOXP crew for field projects. Additionally, Thea is involved with several NOAA Hazardous Weather Testbed Experimental Warning Program experiments and works towards research-to-operations goals by collaborating with National Weather Service forecasters. Other areas of interest include work with TC tornadoes, software development in WDSS-II, generating big datasets relating to tornadic storms, tornado warnings, severe storm reports, as well as mentoring and working with students.

Research Focus

  • Radar meteorology
  • Severe storms 
  • Tornadoes 
  • Machine learning
  • R2O
  • Fieldwork

CIWRO Research Themes

  • Weather Radar and Observations
Publications
  • Alford, A. A., B. Schenkel, S. Hernandez, J. A. Zhang, M. I. Biggerstaff, E. Blumenauer, T. N. Sandmæl, and S. M. Waugh, 2024: Examining Outer Band Supercell Environments in Landfalling Tropical Cyclones using Ground-Based Radar Analyses. Mon. Wea. Rev., https://doi.org/10.1175/MWR-D-23-0287.1, in press. 
  • Schenkel, B. A., K. M. Calhoun, T. N. Sandmæl, Z. R. Fruits, I. Schick, M. C. Ake, and B. F. Kassel, 2023: Lightning and radar characteristics of tornadic cells in landfalling tropical cyclones. J. Geophys. Res. Atmos., 128, e2023JD038685, https://doi.org/10.1029/2023JD038685. 
  • Sandmæl, T. N., B. R. Smith, A. E. Reinhart, I. M. Schick, M. C. Ake, S. S. Williams, J. G. Madden, R. B., Steeves, K. L. Elmore, and T. C. Meyer, 2023: The Tornado Probability Algorithm: A Probabilistic Machine Learning Tornadic Circulation Detection Algorithm. Wea. Forecasting, 38, 445–466, https://doi.org/10.1175/WAF-D-22-0123.1 
  • Sandmæl, T. N., B. R. Smith, J. G. Madden, J. W. Monroe, P. T. Hyland, B. A. Schenkel, and T. C. Meyer, 2023: The 2021 Hazardous Weather Testbed Experimental Warning Program Radar Convective Applications Experiment: A Forecaster Evaluation of the Tornado Probability Algorithm and the New Mesocyclone Detection Algorithm. Wea. Forecasting, 38, 1125–1142, https://doi.org/10.1175/WAF-D-23-0042.1 
  • Schenkel, B. A., K. Calhoun, T. N. Sandmæl, Z. R. Fruits, I. M. Schick, M. C. Ake, and B. F. Kassel, 2023: Lightning and radar characteristics of tornadic cells in landfalling tropical cyclones. J. Geophys. Res. Atmos., 128, e2023JD038685. https://doi.org/10.1029/2023JD038685. 
  • Alford, A. A., A. Messersmith, B. Pollock, Q. Thomas, T. N. Sandmæl, B. A. Schenkel, 2023: Tropical Cyclone Supercell Response to the Coast using a Climatology of Radar-Derived Azimuthal Shear. Geophys. Res. Lett., 50, e2023GL105977. https://doi.org/10.1029/2023GL105977. 
  • Smith, B. R., T. Sandmæl, M. C. Mahalik, K. L. Elmore, D. M. Kingfield, K.L. Ortega, and T. M. Smith, 2021: Corrigendum. Wea. Forecasting, 36(3), 1131-1133. DOI: https://doi.org/10.1175/WAF-D-20-0125.1 
  • J. R. Mecikalski, T. N. Sandmæl, E.M. Murillo, C. R. Homeyer, K. M. Bedka, J. M. Apke, and C. P. Jewett, 2021: A Random-Forest Model to Assess Predictor Importance and Nowcast Severe Storms Using High-Resolution Radar–GOES Satellite–Lightning Observations, Mon. Wea. Rev., 149(6), 1725-1746. DOI: https://doi.org/10.1175/MWR-D-19-0274.1 
  • Homeyer, C. R., T. N. Sandmæl, C. K. Potvin, and A. M. Murphy, 2020: Distinguishing Characteristics of Tornadic and Nontornadic Supercell Storms from Composite Mean Analyses of Radar Observations. Mon. Wea. Rev., 148(12), 5015-5040. DOI: https://doi.org/10.1175/MWR-D-20-0136.1 
  • Sandmæl, T. N., C. R. Homeyer, K. M. Bedka, J. M. Apke, J. R. Mecikalski, and K. Khlopenkov, 2019: Using Remotely Sensed Physical and Kinematic Characteristics to Discriminate Between Tornadic and Non-Tornadic Storms. J. Appl. Meteorol. Climatol., 58(12), 2569-2590. DOI: https://doi.org/10.1175/JAMC-D-18-0241.1
Conference presentations
  • Sandmael, T. N., R. B. Steeves, Z. Fruits, I. Schick, M. Ake, Z. A. Cooper, J. Widanski, Q. Thomas, and R. Galang, 2023: The Development of a Single-Radar Tornado Prediction Algorithm Using Machine Learning, 40th Conf. on Radar Meteorology, Minneapolis, MN, Amer. Meteor. Soc., 13B.2, https://ams.confex.com/ams/40RADAR/meetingapp.cgi/Paper/426150. 
  • Sandmæl, T. N., B. R. Smith, A. E. Reinhart, I. M. Schick, M. C. Ake, S. S. Williams, J. G. Madden, R. B., Steeves, K. L. Elmore, and T. C. Meyer, 2022: An Overview of the Machine Learning-Based Tornado Probability Algorithm for Real-Time Probabilistic Guidance, 30th Conf. on Severe Local Storms, Santa Fe, AZ, Amer. Meteor. Soc., 4.4B, https://ams.confex.com/ams/30SLS/meetingapp.cgi/Paper/407546. 
  • Sandmæl, T. N., R. B., Steeves, P. A. Campbell, B. R. Smith, A. E. Reinhart, I. M. Schick, M. C. Ake, S. S. Williams, J. G. Madden, K. L. Elmore, and T. C. Meyer, 2022: An Overview of the Tornado Potential Algorithm (TORP) for Real-Time Probabilistic Tornado Guidance, 47th Annual Meeting, Virtual, National Weather Association, https://nwas.org/annual-meeting-events/annual-meeting/annual-meeting-events-annual-meeting-2022-showcase-abstracts/. 
  • Sandmæl, T. N., B. R. Smith, J. W. Monroe, J. G. Madden, P. T. Hyland, B. A. Schenkel, 2022: The 2021 Hazardous Weather Testbed Experimental Warning Program Radar Convective Applications Experiment: Evaluating the Tornado Potential Algorithm and the AzShear Rotation Detection Algorithm, 31st Conf. on Weather Analysis and Forecasting (WAF)/27th Conf. on Numerical Weather Prediction (NWP), Virtual, Amer. Meteor. Soc., J15B.4, https://ams.confex.com/ams/102ANNUAL/meetingapp.cgi/Paper/393261. 
  • Sandmæl, T. N., C. N. Satrio, R. B. Steeves, K. M. Calhoun, P. A. Campbell, and P. T. Hyland, 2022: Using Tornado Probability Guidance from a Machine Learning Model in the 2021 Hazardous Weather Testbed Experimental Warning Program Probabilistic Hazards Information Prototype Tool Experiment, 31st Conf. on Weather Analysis and Forecasting (WAF)/27th Conf. on Numerical Weather Prediction (NWP), Virtual, Amer. Meteor. Soc., J7.3, https://ams.confex.com/ams/102ANNUAL/meetingapp.cgi/Paper/393277. 
  • Sandmæl, T. N., and A. E. Reinhart, 2022: Using Linear Least-Square Shear Product Signatures from Single-Radar to Evaluate Tornado Potential for Quasi-Linear Convective System Circulations, Symp. on Radar Science in the Service of Earth System Predictability, Virtual, Amer. Meteor. Soc., 14.3, https://ams.confex.com/ams/102ANNUAL/meetingapp.cgi/Paper/393253.
  • Sandmael, T., and A. E. Reinhart, 2021: Determining Quasi-Linear Convective System Tornadic Potential using Linear Least-Square Velocity Shear Products. Severe Local Storms Conf. for Students and Early Career Scientists, Virtual, Amer. Meteor. Soc. 
  • Sandmael, T., R. B. Steeves, P. A. Campbell, K. M. Calhoun, and Z. A. Cooper, 2021: The Probabilistic Hazard Information Tornado (PHItor) Product: Using Machine Learning to Provide Probability Guidance for Tornado Nowcasting. 11th Conf. on Transition of Research to Operations, Virtual, Amer. Meteor. Soc., 850, https://ams.confex.com/ams/101ANNUAL/meetingapp.cgi/Paper/380062.
  • Sandmael, T., K. L. Elmore, and B. R. Smith, 2020: A New Machine Learning–Based Tornado Detection Algorithm for the WSR-88D Network. 19th Conf. on Artificial Intelligence for Environmental Science, Boston, MA, Amer. Meteor. Soc., 364, https://ams.confex.com/ams/2020Annual/webprogram/Paper363586.html. 
  • Sandmael, T., and B. R. Smith, 2020: Linear Least Squares Derivative Gradients of Single-Radar Products and Their Applications for Severe Weather. 30th Conf. on Weather Analysis and Forecasting/26th Conf. on Numerical Weather Prediction, Boston, MA, Amer. Meteor. Soc., 1230, https://ams.confex.com/ams/2020Annual/webprogram/Paper365929.html. 
  • Sandmael, T., Kimberly Elmore1,2, and B. R. Smith, 2019: A New Machine Learning-Based Tornado Detection Algorithm for the WSR-88D Network. 39th International Conf. on Radar Meteorology, Iraka, Nara, Japan, Amer. Meteor. Soc., 2-71, https://cscenter.co.jp/icrm2019/program/data/abstracts/Poster2-71.pdf 
  • Sandmael, T., and C. R. Homeyer, 2018: Comparison of Tornadic and Severe Non-Tornadic Storms Using Probability Matched Means of Radar Observations. 29th Conf. on Severe Local Storms, Stowe, VT, Amer. Meteor. Soc., 2B82, https://ams.confex.com/ams/29SLS/webprogram/Paper348255.html. 
  • Sandmael, T., and C. R. Homeyer, 2017: Examining Tornadic and Non-Tornadic Storms Using High-Resolution Satellite Imagery and Dual-Polarization Radar. 38th Conf. on Radar Meteorology, Chicago, IL, Amer. Meteor. Soc., 225, https://ams.confex.com/ams/38RADAR/webprogram/Paper320897.html.
Products
  • Tornado Probability Algorithm (TORP)
  • AzShear and DivShear
Social media contact
  • X: @TheaSandmael


 

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Updated 11/3/2025 by Cooperative Institute for Severe and High-Impact Weather Research and Operations (CIWRO): ciwro@ou.edu