Michael Scheuerer

Image of Michael Scheuerer


Research Scientist


Attribution and Predictability Assessments




(303) 497-4281



I received my graduate degree and Ph.D. in mathematics with an emphasis on spatial statistics. Since then I have focused on statistical applications in meteorology.

Research Interests

  • Probabilistic weather forecasting
  • Forecast verification
  • Machine Learning


Selected Publications

  • Scheuerer, M., Switanek, M.B., Worsnop, R.P. and Hamill, T.M. (2020): Using artificial neural networks for generating probabilistic subseasonal precipitation forecasts over California, Monthly Weather Review, 148(8), 3489-3506.
  • Jacobson, J., Kleiber, W., Scheuerer, M. and Bellier, J. (2020): Beyond univariate calibration: Verifying spatial structure in ensembles of forecast fields. Nonlinear Processes in Geophysics, 27, 411-427.
  • Scheuerer, M., and Hamill, T.M. (2019): Probabilistic forecasting of snowfall amounts using a hybrid between a parametric and an analog approach. Monthly Weather Review, 147(3), 1047-1064.
  • Scheuerer, M., and Hamill, T.M. (2018): Generating calibrated ensembles of physically realistic, high-resolution precipitation forecast fields based on GEFS model output. Journal of Hydrometeorology, 19(10), 1651-1670.
  • Worsnop, R.P., Scheuerer, M., Hamill, T.M., and Lundquist J.K. (2018): Generating wind power scenarios for probabilistic ramp event prediction using multivariate statistical post-processing. Wind Energy Science, 3, 371-393.