Webrelaunch 2020
Photo of Benedikt Schulz

Dr. Benedikt Schulz

  • 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 IMKTRO on the distinction of high-wind areas in winter storms (08/08/22).

Statistical Postprocessing of Real-Time Forecasts

The IMKTRO (Institute of Meteorology and Climate Research, 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
Summer Semester 2023 Lecture
Summer Semester 2022 Lecture

Preprints

  • Arnold, S., Gavrilopoulos, G., Schulz, B. and Ziegel, J. (2024): Sequential model confidence sets. Preprint, available at arXiv:2404.18678.
  • Primo, C., Schulz, B., Lerch, S. and Hess, R. (2024): Comparison of Model Output Statistics and Neural Networks to Postprocess Wind Gusts. Preprint, available at arXiv:2401.11896.
  • Schulz, B. and Lerch, S. (2022): Aggregating distribution forecasts from deep ensembles. Preprint, available at arXiv:2204.02291.

Publications

  • Höhlein, K., Schulz, B., Westermann, R. and Lerch, S. (2024): Postprocessing of Ensemble Weather Forecasts Using Permutation-invariant Neural Networks, Artificial Intelligence for the Earth Systems, 3, e230070. doi.org/10.1175/AIES-D-23-0070.1.
  • Eisenstein, L., Schulz, B., Pinto, J. G., and Knippertz, P. (2023): Identification of high-wind features within extratropical cyclones using a probabilistic random forest - Part 2: Climatology over Europe, Weather and Climate Dynamics, 4, 981-999. doi.org/10.5194/wcd-4-981-2023.
  • Ageet, S., Fink, A., Maranan, M. and Schulz, B. (2023): Predictability of Rainfall over Equatorial East Africa in the ECMWF Ensemble Reforecasts on short to medium-range time scales. Weather and Forecasting, 38, 2613-2630. doi:10.1175/WAF-D-23-0093.1.
  • 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.

Dissertation


Conferences and workshops

  • VALPRED 4 (2023), Aussois (FR), mini-course: Statistical and Machine Learning Methods for Postprocessing Ensemble Weather Forecasts.
  • 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.