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.
- Probabilistic weather forecasting
- Forecast verification
- Machine Learning
- 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, to appear.
- 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, under review.
- 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.