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Eric Loken

Eric Loken

Eric Loken.

Research
Scientist

eloken@ou.edu
eric.d.loken@noaa.gov
National Weather Center 4340A2

Education

  • PhD., Meteorology, University of Oklahoma
  • MS, Meteorology, University of Oklahoma
  • BS, Atmospheric and Oceanic Sciences, University of Wisconsin-Madison

Affiliated CIWRO Team

  • Forecast Applications and Social Science Team

About

Dr. Loken's research focuses on the use of artificial intelligence (AI) and machine learning techniques to improve the prediction of high-impact weather. This work involves both developing AI-based products for forecasters and evaluating these tools objectively and subjectively in pseudo-operational settings. Dr. Loken frequently serves as a facilitator of Hazardous Weather Testbed experiments and believes that incorporating end users into the product development cycle is of paramount importance.

Research Focus

  • Artificial Intelligence/Machine Learning 
  • Forecast Verification 
  • Numerical Weather Prediction 
  • Convection-Allowing Ensembles
  • User Engagement Studies

CIWRO Research Themes

  • Forecast Applications Improvements R&D
Honors and awards
  • Member of team receiving U.S. Department of Commerce Gold Medal, 2024 (for work on the Warn-On-Forecast System) 
  • Dr. Douglas K. Lilly Award (University of Oklahoma School of Meteorology), 2021 (for best Ph.D. student manuscript/publication) 
  • OAR Best Paper Award Nominee (Generating probabilistic next-day severe weather forecasts from convection-allowing ensembles using random forests), 2021. 
  • First Place Student Oral Presentation (18th Conference on Artificial and Computational Intelligence and its Applications to the Environmental Sciences, 2019 American Meteorological Society Annual Meeting, Phoenix, AZ), 2019 
  • Honors in the Atmospheric and Oceanic Science Major and Honors in the Liberal Arts (UW-Madison), May 2015 
  • Honorary Horn Award (UW-Madison Department of Atmospheric and Oceanic Sciences), 2014 (for excellence in overall performance as an undergraduate) 
  • Lettau-Wahl Award, (UW-Madison Department of Atmospheric and Oceanic Sciences), 2013 (for excellence in overall performance as an undergraduate)
Publications
  • McGovern, A., R. J. Chase, M. Flora, D. J. Gagne II, R. Lagerquist, C. K. Potvin, N. Snook, and E. Loken, 2023: A review of machine learning for convective weather. Artificial Intelligence for the Earth Systems, 2, 1-24, https://doi.org/10.1175/AIES-D-22-0077.1 
  • Clark, A. J., I. L. Jirak, B. T. Gallo, B. Roberts, K. H. Knopfmeier, J. Vancil, D. Jahn, M. Krocak, C. D. Karstens, E. D. Loken, N. A. Dahl, D. Harrison, D. Imy, A. R. Wade, J. M. Milne, K. A. Hoogewind, M. Flora, J. Martin, B. C. Matilla, J. C. Picca, C. K. Potvin, P. S. Skinner, and P. Burke, 2023: The third real-time, virtual Spring Forecasting Experiment to advance severe weather prediction capabilities. Bull. Amer. Meteor. Soc., 104, E456-E458, https://doi.org/10.1175/BAMS-D-22-0213.1. 
  • Loken, E. D., A. J. Clark, and A. McGovern, 2022: Comparing and interpreting differently designed random forests for next-day severe weather hazard prediction. Wea. Forecasting, 37, 871-899. 
  • Clark, A. J., and E. D. Loken, 2022: Machine-learning-derived severe weather probabilities from a Warn-on-Forecast System. Wea. Forecasting, 37, 1721-1740. 
  • Clark, A. J, and Coauthors, 2022: The 2nd real-time, virtual Spring Forecasting Experiment to Advance Severe Weather Prediction. Bull. Amer. Meteor. Soc., 103, E1114-E1116. 
  • Clark, A. J., and Coauthors, 2021: A real-time, virtual spring forecasting experiment to advance severe weather prediction. Bull. Amer. Meteor. Soc., 102, E814-E816. 
  • Loken, E. D., A. J. Clark, and C. D. Karstens, 2020: Generating probabilistic next-day severe weather forecasts from convection-allowing ensembles using random forests. Wea. Forecasting, 35, 1605-1631. 
  • Loken, E. D., A. J. Clark, A. McGovern, M. Flora, and K. Knopfmeier, 2019: Postprocessing next-day ensemble probabilistic precipitation forecasts using random forests. Wea. Forecasting, 34, 2017-2044. 
  • Loken, E. D., A. J. Clark, M. Xue, and F. Kong, 2019: Spread and skill in mixed- and single-physics convection-allowing ensembles. Wea. Forecasting, 34, 305-330. 
  • Loken, E. D., A. J. Clark, M. Xue, and F. Kong, 2017: Comparison of next-day probabilistic severe weather forecasts from coarse- and fine-resolution CAMs and a convection-allowing ensemble. Wea. Forecasting, 32, 1403–1421.
Products
  • Lead developer of WoFS-PHI short-term severe weather hazard probabilities (based on a combination of the Warn-On-Forecast System and ProbSevere)
  • Lead developer of Loken RF day 1 SPC-style severe weather hazard probabilities (based on the High-Resolution Ensemble Forecast System, Version 3).


 

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Updated 1/7/2025 by Cooperative Institute for Severe and High-Impact Weather Research and Operations (CIWRO): ciwro@ou.edu