Descrizione del progetto
L’intelligenza artificiale contribuisce a prevedere gli eventi meteorologici estremi
I cicloni tropicali, le ondate di calore e gli eventi di siccità estrema sono esempi di fenomeni climatici difficili da prevedere. I cambiamenti climatici hanno incrementato la probabilità e la gravità di tali fenomeni e prevederne la manifestazione, anche se è difficile, risulta essenziale. Il progetto CLINT, finanziato dall’UE, attingerà ai dati raccolti dal servizio relativo ai cambiamenti climatici di Copernicus e ai recenti progressi compiuti nel campo dell’intelligenza artificiale (IA). Applicando un quadro di IA costituito da tecniche e algoritmi di apprendimento automatico, CLINT elaborerà set di dati climatici di grandi dimensioni per migliorare la climatologia in termini di rilevamento, causalità e attribuzione degli eventi estremi. Il progetto si occuperà inoltre delle ripercussioni della quantificazione degli eventi estremi su vari settori socioeconomici su scala paneuropea e locale in diversi tipi di punti caldi dei cambiamenti climatici.
Obiettivo
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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Vedi altri progetti per questo bandoBando secondario
H2020-LC-CLA-2020-2
Meccanismo di finanziamento
RIA - Research and Innovation actionCoordinatore
20133 Milano
Italia