SOURCE-LINKED INTELLIGENCE

TOPOCLIM-EWS: Topological Insights for Hydroclimatic Extremes and Early Warnings Signals

CORDIS · observation · Publication date unknown

Raw climate observations will be transformed into robust topological descriptors, with a strong emphasis on interoperability: these features will be designed to integrate seamlessly with established machine learning frameworks, ensuring enhanced predictive pipelines and extended forecast lead times. The research is structured around a synergistic transatlantic collaboration. The outgoing phase at Michigan State University (USA) provides expertise in applied topology and methodological innovation, while the return phase at Middle East Technical University (Türkiye, Horizon Europe Associated Country) offers a strong application environment in climate science, hydroclimatic datasets, and stakeholder engagement. By bridging mathematical theory and applied climatology, TOPOCLIM-EWS will deliver novel, open, and transferable early-warning indicators that can strengthen operational systems, supporting Horizon Europe objectives on climate resilience, disaster risk reduction, and science-based solutions for societal challenges. topological data analysis, dynamical systems, non-linear time s

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recordType

award

status

SIGNED

region

EU

value

398612.04

unit

EUR