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Institute of Stochastics
Benedikt Schulz M.Sc.
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.
Further information regarding our research can be found on the following websites:

  • Press release of the KIT on forecasting wind gusts with AI (15/03/22).
  • Interview in the "Campus-Report" series on forecasting wind gusts with AI (05/07/22; German only).
  • Article on the homepage of the IMK-TRO on the distinction of high-wind areas in winter storms (08/08/22).

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. The statistically postprocessed forecasts can be found here.

Current List of Courses
Semester Titel Typ
Winter Semester 2022/23 Forecasting: Theory and Praxis Lecture
Summer Semester 2022 Time Series Analysis Lecture
Winter Semester 2021/22 Einführung in die Stochastik Lecture
Summer Semester 2021 Nichtparametrische Statistik Lecture
Einblicke in die Stochastik Proseminar

Preprints

  • Schulz, B. and Lerch, S. (2022): Aggregating distribution forecasts from deep ensembles. Preprint, available at arXiv:2204.02291.

Publications

  • Gneiting, T., Lerch, S. and Schulz, B. (2023): Probabilistic solar forecasting: Benchmarks, post-processing, verification. Solar Energy, 252, 72-80. doi:10.1016/j.solener.2022.12.054.
  • Eisenstein, L., Schulz, B., Qadir, G. A., Pinto, J. G., and Knippertz, P. (2022): Identification of high-wind features within extratropical cyclones using a probabilistic random forest - Part 1: Method and case studies, Weather and Climate Dynamics, 3, 1157-1182. doi.org/10.5194/wcd-3-1157-2022.
  • 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.