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Photo of Benedikt Schulz

Benedikt Schulz M.Sc.

  • Karlsruhe Institute of Technology (KIT)
    Institute of Stochastics
    Englerstraße 2
    76131 Karlsruhe

Within project C5 of the Transregional Collaborative Research Center Waves to Weather („Dynamical feature-based ensemble postprocessing of wind gusts within European winter storms“) I am working on methods for statistical postprocessing of weather forecasts. We focus on ensemble forecasts of wind gusts and apply modern machine learning methods like neural networks.
Click here for a press release of the KIT (March 22) regarding our research.

Statistical Postprocessing of Real-Time Forecasts

The IMK-TRO (Institute of Meteorology and Climate Research, Department Troposphere Research) displays various meteorological forecasts on the KIT-Weather portal, including operational (near) real-time forecasts of several meteorological variables in many European cities, for instance Karlsruhe. In collaboration with the Working Group “Atmospheric Dynamics“, we statistically postprocess the real-time forecasts for selected German cities.
Click here to get directly to the statistically postprocessed forecasts.

Current List of Courses
Semester Titel Typ
Summer Semester 2022 Lecture
Winter Semester 2021/22 Lecture
Summer Semester 2021 Lecture
Proseminar

Preprints

  • Schulz, B. and Lerch, S. (2022): Aggregating distribution forecasts from deep ensembles. Preprint, available at arXiv:2204.02291.
  • Eisenstein, L., Schulz, B., Qadir, G. A., Pinto, J. G., and Knippertz, P. (2022): Objective identification of high-wind features within extratropical cyclones using a probabilistic random forest (RAMEFI). Part I: Method and illustrative case studies, Weather Clim. Dynam. Discuss. (preprint), doi.org/10.5194/wcd-2022-29, in review.

Publications

  • Schulz, B. and Lerch, S. (2022): Machine learning methods for postprocessing ensemble forecasts of wind gusts: A systematic comparison. Monthly Weather Review, 150 (1), 235-257. doi:10.1175/MWR-D-21-0150.1.
  • Maier-Gerber, M., Fink, A., Riemer, M., Schoemer, E., Fischer, C. and Schulz, B. (2021): Statistical-Dynamical Forecasting of Sub-Seasonal North Atlantic Tropical Cyclone Occurrence. Weather and Forecasting, 36 (6), 2127-2142. doi:10.1175/WAF-D-21-0020.1.
  • Schulz, B., El Ayari, M., Lerch, S. and Baran, S. (2021): Post-processing numerical weather prediction ensembles for probabilistic solar irradiance forecasting. Solar Energy, 220, 1016-1031. doi:10.1016/j.solener.2021.03.023.

Conferences and workshops

  • ESA-ECMWF workshop 2021, online, poster: Machine Learning Methods for Postprocessing Ensemble Forecasts of Wind Gusts: A Systematic Comparison.
  • VALPRED 3 (2021), Aussois (FR), talk: Machine learning methods for postprocessing ensemble forecasts of wind gusts.
  • ICCARUS 2021, online, talk: Statistical and machine learning methods for postprocessing ensemble forecasts of wind gusts.
  • VALPRED 2 (2020), Aussois (FR), talk: Calibrating and combining probability forecasts.