18 citations · 24 across the 8 of their papers we have counts for
10 papers
On the Calibration of Isotonic Distributional Regression
Tobias Biegert, Johannes Resin, Alexander Jordan +1
Isotonic distributional regression (IDR) has recently been proposed as a non-parametric technique for probabilistic forecasting via the estimation of conditional distributions unde…
Can We Trust Evaluations of AI Weather Forecasts for Extremes? How Evaluation Protocols Shape Model Rankings
Tobias Biegert, Sam Allen, Zhongwei Zhang +1
AI weather models perform competitively with traditional physics-based models at a fraction of the computational cost, and they are thus already used operationally. However, studie…
Towards Fair Comparisons of AI- and Physics-Based Weather Models for Extreme Events via the Weighted Potential CRPS
Tobias Biegert, Sam Allen, Annika Alber +1
We study whether deterministic AI weather prediction (AIWP) models issue more informative forecasts for extreme weather events than deterministic numerical weather prediction (NWP)…
Operational convection-permitting COSMO/ICON ensemble predictions at observation sites (CIENS)
Sebastian Lerch, Benedikt Schulz, Reinhold Hess +4
We present the CIENS dataset, which contains ensemble weather forecasts from the operational convection-permitting numerical weather prediction model of the German Weather Service.…
Probabilistic measures afford fair comparisons of AIWP and NWP model output
Tilmann Gneiting, Tobias Biegert, Kristof Kraus +3
We introduce a new measure for fair and meaningful comparisons of single-valued output from artificial intelligence based weather prediction (AIWP) and numerical weather prediction…
Probabilistic intraday electricity price forecasting using generative machine learning
Jieyu Chen, Sebastian Lerch, Melanie Schienle +2
The growing importance of intraday electricity trading in Europe calls for improved price forecasting and tailored decision-support tools. In this paper, we propose a novel generat…
Learning low-dimensional representations of ensemble forecast fields using autoencoder-based methods
Jieyu Chen, Kevin Höhlein, Sebastian Lerch
Large-scale numerical simulations often produce high-dimensional gridded data that is challenging to process for downstream applications. A prime example is numerical weather predi…
Graph Neural Networks and Spatial Information Learning for Post-Processing Ensemble Weather Forecasts
Moritz Feik, Sebastian Lerch, Jan Stühmer
Ensemble forecasts from numerical weather prediction models show systematic errors that require correction via post-processing. While there has been substantial progress in flexibl…
Comparison of Model Output Statistics and Neural Networks to Postprocess Wind Gusts
Cristina Primo, Benedikt Schulz, Sebastian Lerch +1
Wind gust prediction plays an important role in warning strategies of national meteorological services due to the high impact of its extreme values. However, forecasting wind gusts…
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…