Peer-Reviewed Publications and Preprints

2025

David Jobst, Annette Möller, and Jürgen Groß (2025). D-vine Generalized Additive Model copula-based quantile regression with application to ensemble postprocessing. Journal of the Royal Statistical Society Series C: Applied Statistics. accepted. [doi]

2024

David Jobst (2024). Gradient-Boosted Mixture Regression Models for Postprocessing Ensemble Weather Forecasts. submitted to Environmetrics. [doi]

David Jobst, Annette Möller, and Jürgen Groß (2024). Gradient-Boosted Generalized Linear Models for Conditional Vine Copulas. Environmetrics. accepted. [doi]

David Jobst, Annette Möller, and Jürgen Groß (2024). Time-series-based ensemble model output statistics for temperature forecasts postprocessing. Quarterly Journal of the Royal Meteorological Society. 150(765), 4838-4855. [doi]

2023

David Jobst, Annette Möller, and Jürgen Groß (2023). D-vine-copula-based postprocessing of wind speed ensemble forecasts. Quarterly Journal of the Royal Meteorological Society. 149(755), 2575-2597. [doi]

Jonathan Demaeyer, Jonas Bhend, Sebastian Lerch, Cristina Primo, Bert Van Schaeybroeck, Aitor Atencia, Zied Ben Bouallègue, Jieyu Chen, Markus Dabernig, Gavin Evans, Jana Faganeli Pucer, Ben Hooper, Nina Horat, David Jobst, Janko Merše, Peter Mlakar, Annette Möller, Olivier Mestre, Maxime Taillardat, and Stèphane Vannitsem (2023). The EUPPBench postprocessing benchmark dataset v1.0. Earth System Science Data. 15(6), 2635-2653. [doi]