Our new preprint on fair comparisons of AI- and physics-based weather models for extreme events with Tobias Biegert, Sam Allen, and Annika Alber is available at arxiv.org/abs/2606.21170

Towards Fair Comparisons of AI- and Physics-Based Weather Models for Extreme Events via the Weighted Potential CRPS

We study whether deterministic AI weather prediction (AIWP) models issue more informative forecasts for extreme weather events than deterministic numerical weather prediction (NWP) models. The determi...

arxiv.org

If you are going to attend EGU 2026 and are interested in statistical ML-based forecasting / nowcasting / post-processing in the geosciences, consider sumitting an abstract to our session NP 5.1, www.egu26.eu/session/55791 ⏰ Deadline: January 15, 2026

In a new preprint available at arxiv.org/abs/2508.03845, we present the CIENS dataset, which contains contains more than 10 years of ensemble forecasts and observations from the convection-permitting ICON/COSMO system of the German meteorlogical service.

Operational convection-permitting COSMO/ICON ensemble predictions at observation sites (CIENS)

We present the CIENS dataset, which contains ensemble weather forecasts from the operational convection-permitting numerical weather prediction model of the German Weather Service. It comprises foreca...

arxiv.org

📣 New preprint "Probabilistic measures afford fair comparisons of AIWP and NWP model output" with Tilmann Gneiting, Tobias Biegert, Kristof Kraus, Eva Walz and Alexander Jordan available at arxiv.org/abs/2506.03744. Some details below 🧵

Probabilistic measures afford fair comparisons of AIWP and NWP model output

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 (NWP) models, calle...

arxiv.org

New preprint: "Probabilistic intraday electricity price forecasting using generative machine learning " available at arxiv.org/abs/2506.00044, led by Jieyu Chen

Probabilistic intraday electricity price forecasting using generative machine learning

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 generative neural network m...

arxiv.org

Personal update: After almost 10 years at KIT, I will move to the University of Marburg as a professor at the Department of Mathematics and Computer Science in April. I will of course miss the many great colleagues and students at KIT, but am very much looking forward to exciting new opportunities.

The AI Weather Quest is now open for participation! This @ecmwf.int led competition, endorsed by the @wmo-global.bsky.social, challenges participants to push the boundaries of sub-seasonal to seasonal forecasting using artificial intelligence (AI) and machine learning (ML). 🔗 bit.ly/4b1ulaB

New preprint: "Learning low-dimensional representations of ensemble forecast fields using autoencoder-based methods" with Jieyu Chen and Kevin Höhlein: arxiv.org/abs/2502.04409. We propose dimensionality reduction methods tailored to ensemble simulations of gridded fields.

Our new paper "Improving Model Chain Approaches for Probabilistic Solar Energy Forecasting through Post-processing and Machine Learning" (with Nina Horat and Sina Klerings) just appeared in Advances in Atmospheric Sciences, rdcu.be/d462L 🌤️🔌

I will be at the CMStatistics conference in London from Saturday to Monday, let me know if you'd like to meet, e.g. to talk about the available postdoc position (bwsyncandshare.kit.edu/s/HXJrexofCa...)

Open 3-year postdoc position at KIT on ML for probabilistic hydro-meteorological forecasting. The position is part of an interdisciplinary research project with partners from meteorology, hydrology and federal flood forecasting agencies. Contact me if you are interested or have any questions!

With all the GenCast news (and comments) of late, this is a great time to advertise all our wonderful sessions slated for the AMS AI Conference in January! See all the abstracts here including quite a few sessions on data-driven weather forecasting: ams.confex.com/ams/105ANNUA...

Interested in probabilistic forecasting, ensemble post-processing or verification? Submit an abstract to our EGU 2025 Session NP 5.2! ⏰Deadline: January 15, 2025

📣New preprint on "Uncertainty quantification for data-driven weather models" available at arxiv.org/abs/2403.13458. Joint work with Christopher Bülte, Nina Horat and Julian Quinting. 🧵