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CLINT

Financiado

Cerrado

CLImate INTelligence Extreme events detection attribution and adaptation desig...

CLImate INTelligence Extreme events detection attribution and adaptation design using machine learning Weather and climate extremes pose challenges for adaptation and mitigation policies as well as disaster risk management, emphasizing the value of Climate Services in supporting strategic decision-making. Today Climate Services can... ver más

30/06/2025

POLIMI

6M€

Presupuesto del proyecto: 6M€

Líder del proyecto

POLITECNICO DI MILANO No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Fecha límite participación Sin fecha límite de participación.

Ver 16 Participantes

Financiación concedida El organismo H2020 notifico la concesión del proyecto el día 2021-05-10

LC-CLA-12-2020: Advancing climate services Specific Challenge:Under the Paris Agreement it was agreed to enhance adaptive capacity, strengtheni...

I+D

Cerrada hace 5 años

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Características del participante

Este proyecto no cuenta con búsquedas de partenariado abiertas en este momento.

Información adicional privada

No hay información privada compartida para este proyecto. Habla con el coordinador.

16 Participantes

POLIMI

1.10M€ | Lider

INMOIDEL

UAH

277.85K€ | Participante

ECMWF

328.64K€ | Participante

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Duración del proyecto: 49 meses Fecha Inicio: 2021-05-10
Fecha Fin: 2025-06-30

Líder del proyecto

POLITECNICO DI MILANO No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Presupuesto del proyecto 6M€

Fecha límite de participación Sin fecha límite de participación.

Descripción del proyecto Weather and climate extremes pose challenges for adaptation and mitigation policies as well as disaster risk management, emphasizing the value of Climate Services in supporting strategic decision-making. Today Climate Services can benefit from an unprecedented availability of data, in particular from the Copernicus Climate Change Service, and from recent advances in Artificial Intelligence (AI) to exploit the full potential of these data. The main objective of CLINT is the development of an AI framework composed of Machine Learning (ML) techniques and algorithms to process big climate datasets for improving Climate Science in the detection, causation and attribution of Extreme Events, including tropical cyclones, heatwaves and warm nights, and extreme droughts, along with compound events and concurrent extremes. Specifically, the framework will support (1) the detection of spatial and temporal patterns, and evolutions of climatological fields associated with Extreme Events, (2) the validation of the physically based nature of causality discovered by ML algorithms, and (3) the attribution of past and future Extreme Events to emissions of greenhouse gases and other anthropogenic forcing. The framework will also cover the quantification of the Extreme Events impacts on a variety of socio-economic sectors under historical, forecasted and projected climate conditions by developing innovative and sectorial AI-enhanced Climate Services. These will be demonstrated across different spatial scales, from the pan European scale to support EU policies addressing the Water-Energy-Food Nexus to the local scale in three types of Climate Change Hotspots. Finally, these services will be operationalized into Web Processing Services, according to most advanced open data and software standards by Climate Services Information Systems, and into a Demonstrator to facilitate the uptake of project results by public and private entities for research and Climate Services development.

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