Descripción del proyecto
La inteligencia artificial ayuda a predecir fenómenos meteorológicos extremos
Los ciclones tropicales, las olas de calor y las sequías extremas son ejemplos de fenómenos climáticos extremos difíciles de predecir. El cambio climático ha aumentado la probabilidad y la gravedad de estos fenómenos, y predecir su ocurrencia es esencial, pero también difícil. El proyecto CLINT, financiado con fondos europeos, se basará en los datos recogidos por el Servicio de Cambio Climático de Copernicus y en los recientes avances de la inteligencia artificial (IA). Se aplicará un marco de IA compuesto por técnicas y algoritmos de aprendizaje automático para procesar grandes conjuntos de datos climáticos y mejorar la ciencia del clima en términos de detección, causalidad y atribución de fenómenos extremos. CLINT también abordará los efectos de la cuantificación de los fenómenos extremos en varios sectores socioeconómicos a escala paneuropea y a escala local en diferentes tipos de puntos álgidos del cambio climático.
Objetivo
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.
Ámbito científico
Palabras clave
Programa(s)
Convocatoria de propuestas
Consulte otros proyectos de esta convocatoriaConvocatoria de subcontratación
H2020-LC-CLA-2020-2
Régimen de financiación
RIA - Research and Innovation actionCoordinador
20133 Milano
Italia