Graph Neural Networks for Enhancing Ensemble Forecasts of Extreme Rainfall
MCML Authors
Gitta Kutyniok
Prof. Dr.
Principal Investigator
Abstract
Gitta Kutyniok
Prof. Dr.
Principal Investigator
Abstract
Climate change is increasing the occurrence of extreme precipitation events, threatening infrastructure, agriculture, and public safety. Ensemble prediction systems provide probabilistic forecasts but exhibit biases and difficulties in capturing extreme weather. While post-processing techniques aim to enhance forecast accuracy, they rarely focus on precipitation, which exhibits complex spatial dependencies and tail behavior. Our novel framework leverages graph neural networks to post-process ensemble forecasts, specifically modeling the extremes of the underlying distribution. This allows to capture spatial dependencies and improves forecast accuracy for extreme events, thus leading to more reliable forecasts and mitigating risks of extreme precipitation and flooding.
inproceedings
Climate Change AI @ICLR 2025
Workshop on Tackling Climate Change with Machine Learning at the 13th International Conference on Learning Representations. Singapore, Apr 24-28, 2025.Authors
C. Bülte • S. Maskey • P. Scholl • J. von Berg • G. KutyniokLinks
URLResearch Area
BibTeXKey: BMS+25