Graph Neural Networks for Enhancing Ensemble Forecasts of Extreme Rainfall

• URL: http://arxiv.org/abs/2504.05471v1
• Date: Mon, 07 Apr 2025 20:01:55 GMT
• Title: Graph Neural Networks for Enhancing Ensemble Forecasts of Extreme Rainfall
• Authors: Christopher Bülte, Sohir Maskey, Philipp Scholl, Jonas von Berg, Gitta Kutyniok,
• Abstract summary: Climate change is increasing the occurrence of extreme precipitation events, threatening infrastructure, agriculture, and public safety.<n>Our framework leverages graph neural networks to post-process ensemble forecasts, specifically modeling the extremes of the underlying distribution.<n>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.
• Score: 12.89512966027616
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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.

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