Project description
Artificial intelligence helps predict extreme weather events
Tropical cyclones, heatwaves and extreme droughts are examples of extreme climate events that are difficult to predict. Climate change has increased the likelihood and severity of such events, and predicting their occurrence is essential but difficult. The EU-funded CLINT project will draw from data collected by the Copernicus Climate Change Service and from recent advances in artificial intelligence (AI). By applying an AI framework composed of machine learning techniques and algorithms, it will process big climate datasets for improving climate science in terms of detection, causation, and attribution of extreme events. CLINT will also cover extreme events' quantification impacts on various socio-economic sectors at the pan-European scale and at the local scale in different types of climate change hotspots.
Objective
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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Programme(s)
Funding Scheme
RIA - Research and Innovation actionCoordinator
20133 Milano
Italy