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Reaching AI Projections Trustworthy for Unseen Rainfall Extremes

Cel

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

Dziedzina nauki (EuroSciVoc)

Projekt nie został jeszcze sklasyfikowany według klasyfikacji EuroSciVoc.
Wskaż dziedziny nauki, które twoim zdaniem są najbardziej istotne z punktu widzenia tego projektu i pomóż nam usprawnić naszą usługę klasyfikacji.

Słowa kluczowe

Program(-y)

Temat(-y)

System finansowania

HORIZON-ERC - HORIZON ERC Grants

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Zaproszenie do składania wniosków

(odnośnik otworzy się w nowym oknie) ERC-2026-STG

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Instytucja przyjmująca

UNIVERSITETET I BERGEN
Wkład UE netto
€ 1 500 000,00
Koszt całkowity
€ 1 500 000,00

Beneficjenci (1)