Skip to main content
Weiter zur Homepage der Europäischen Kommission (öffnet in neuem Fenster)
Deutsch de
CORDIS - Forschungsergebnisse der EU
CORDIS

Reaching AI Projections Trustworthy for Unseen Rainfall Extremes

Ziel

RAPTURE addresses a critical and timely challenge: how can we simulate extreme rainfall on the kilometre-scale under conditions we have never seen, in a trustworthy way? Overcoming this challenge is vital for understanding the dynamics and impacts of localised extreme rainfall (LXR) in a future, warmer world. Two approaches now offer practical paths forward: regional models that can resolve convective rainfall and computationally-efficient machine-learning rainfall emulators.

Models we can trust for studying future LXR must realistically reproduce current LXR diversity across space and time, and its relationship to multi-scale dynamical processes. Yet this diversity is difficult to verify and how far current approaches meet this realism criteria is still poorly understood. Trustworthy emulators must also be able to generalise to weather conditions they have not been trained on, but when emulators can and cannot do this is still unknown. These measures of trustworthiness require domain-specific expertise to evaluate and (for emulators) computer science expertise to improve: a disconnect that has not yet been bridged.

RAPTURE will overcome these barriers to progress. We use an innovative weather systems approach to achieve unprecedented insight into European LXR in observations, regional simulations and emulators, then use that insight to improve models and create accessible diagnostics. We will ask and answer:
1) How realistic is LXR in physical models and emulators?
2) When can emulators generalise, when can’t they, and why?
3) Can we make emulators more trustworthy, and make this goal accessible and assessable for machine-learning researchers?

These answers will unlock the potential of cheap, trustworthy LXR emulation: local rainfall that can be simulated for centuries, opening new cross-timescale research frontiers. Our approach will extend directly to other regions and climate hazards, and our findings will have cascading impacts in core-AI and applied science

Wissenschaftliches Gebiet (EuroSciVoc)

Dieses Projekt wurde noch nicht bei EuroSciVoc klassifiziert.
Schlagen Sie die Wissenschaftsbereiche vor, die Ihrer Einschätzung nach besonders relevant sind, und helfen Sie uns, unseren Klassifizierungsdienst zu verbessern.

Schlüsselbegriffe

Programm/Programme

Thema/Themen

Finanzierungsplan

HORIZON-ERC - HORIZON ERC Grants

Alle im Rahmen dieses Finanzierungsinstruments finanzierten Projekte anzeigen

Aufforderung zur Vorschlagseinreichung

(öffnet in neuem Fenster) ERC-2026-STG

Alle im Rahmen dieser Aufforderung zur Einreichung von Vorschlägen finanzierten Projekte anzeigen

Gastgebende Einrichtung

UNIVERSITETET I BERGEN
Netto-EU-Beitrag
€ 1 500 000,00
Adresse
MUSEPLASSEN 1
5020 Bergen
Norwegen
Region
Norge Vestlandet Vestland
Aktivitätstyp
Higher or Secondary Education Establishments
Links
Gesamtkosten
€ 1 500 000,00

Begünstigte (1)