KIT | KIT-Bibliothek | Impressum | Datenschutz

Graph Neural Networks and Spatial Information Learning for Post-Processing Ensemble Weather Forecasts

Feik, Moritz 1; Lerch, Sebastian ORCID iD icon 1; Stühmer, Jan 2
1 Institut für Statistik (STAT), Karlsruher Institut für Technologie (KIT)
2 Institut für Anthropomatik und Robotik (IAR), Karlsruher Institut für Technologie (KIT)

Abstract:

Ensemble forecasts from numerical weather prediction models show systematic errors that require correction via post-processing. While there has been substantial progress in flexible neural network-based post-processing methods over the past years, most station-based approaches still treat every input data point separately which limits the capabilities for leveraging spatial structures in the forecast errors. In order to improve information sharing across locations, we propose a graph neural network architecture for ensemble post-processing, which represents the station locations as nodes on a graph and utilizes an attention mechanism to identify relevant predictive information from neighboring locations. In a case study on 2-m temperature forecasts over Europe, the graph neural network model shows substantial improvements over a highly competitive neural network-based post-processing method.


Volltext §
DOI: 10.5445/IR/1000172395
Veröffentlicht am 11.07.2024
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Anthropomatik und Robotik (IAR)
Institut für Statistik (STAT)
Publikationstyp Forschungsbericht/Preprint
Publikationsjahr 2024
Sprache Englisch
Identifikator KITopen-ID: 1000172395
Bemerkung zur Veröffentlichung Accepted at ICML 2024 - Machine Learning for Earth System Modeling Workshop
KIT – Die Forschungsuniversität in der Helmholtz-Gemeinschaft
KITopen Landing Page