64 citations · 111 across the 5 of their papers we have counts for
3 papers · 1 filter
Postprocessing of Ensemble Weather Forecasts Using Permutation-invariant Neural Networks
Kevin Höhlein, Benedikt Schulz, Rüdiger Westermann +1
Statistical postprocessing is used to translate ensembles of raw numerical weather forecasts into reliable probabilistic forecast distributions. In this study, we examine the use o…
Aggregating distribution forecasts from deep ensembles
Benedikt Schulz, Lutz Köhler, Sebastian Lerch
The importance of accurately quantifying forecast uncertainty has motivated much recent research on probabilistic forecasting. In particular, a variety of deep learning approaches…
Machine learning methods for postprocessing ensemble forecasts of wind gusts: A systematic comparison
Benedikt Schulz, Sebastian Lerch
Postprocessing ensemble weather predictions to correct systematic errors has become a standard practice in research and operations. However, only few recent studies have focused on…