- P. Schaumann, M. Rempel, U. Blahak and V. Schmidt, Generating synthetic rainfall fields by R-vine copulas fitted to seamless probabilistic predictions. Working paper (under preparation)
- M. Rempel, P. Schaumann, R. Hess, V. Schmidt and U. Blahak, Adaptive blending of probabilistic precipitation forecasts with emphasis on calibration and temporal forecast consistency. (pdf). Artificial Intelligence for the Earth Systems 1 (2022), e20020.
- K.-M. Aigner, P. Schaumann, F. von Loeper, A. Martin, V. Schmidt and F. Liers, Robust DC optimal power flow with modeling of solar power supply uncertainty via R-vine copulas. (pdf). Optimization and Engineering (in print)
- A. Schinke-Nendza, F. von Loeper, P. Osinski, P. Schaumann, V. Schmidt and C. Weber, Probabilistic forecasting of photovoltaic power supply - A hybrid apporach using D-vine copulas to model spatial dependencies. (pdf). Applied Energy 304 (2021), 117599.
- P. Schaumann, R. Hess, M. Rempel, U. Blahak and V. Schmidt, A calibrated and consistent combination of probabilistic forecasts for the exceedance of several precipitation thresholds using neural networks. (pdf). Weather and Forecasting 36 (2021), 1079-1096.
- F. von Loeper, P. Schaumann, M. de Langlard, R. Hess, R. Bäsmann and V. Schmidt, Probabilistic prediction of solar power supply to distribution networks, using forecasts of global horizontal irradiation. (pdf). Solar Energy 203 (2020), 145-156.
- P. Schaumann, M. de Langlard, R. Hess, P. James and V. Schmidt, A calibrated combination of probabilistic precipitation forecasts to achieve a seamless transition from nowcasting to very-short-range forecasting. (pdf). Weather and Forecasting 35 (2020), 773-791.
- R. Hess, B. Kriesche, P. Schaumann, B.K. Reichert and V. Schmidt, Area precipitation probabilities derived from point forecasts for operational weather and warning service applications (pdf). Quarterly Journal of the Royal Meteorological Society 144 (2018), 2392-2403.