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Filtering by: Climate Variability and Prediction Division

C3S2_413 – Enhanced Operational Windstorm Service

This contract presents a continuation, a temporal extension, and an enhancement of the current C3S Windstorm Service. Leveraging the current Service structure, contractorss will temporally extend the detection and tracking of Pan-European potentially harmful windstorms associated with extratropical cyclones along the whole available period provided by the ECMWF ERA5 reanalysis dataset (1940-present).

CLINT – 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 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 (EE), 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

MEDEWSA – Mediterranean and pan-European forecast and Early Warning System against natural hazards

Natural hazards, such as extreme weather events, are exacerbated by climate change. As a result, emergency responses are becoming more protracted, expensive, frequent, and stretching limited available resources. This is especially apparent in rapidly warming regions. MedEWSa addresses these challenges by providing novel solutions to ensure timely, precise, and actionable impact and finance forecasting, and early warning systems (EWS) that support the rapid deployment of first responders to vulnerable areas. 

ObsSea4Clim: Ocean observations and indicators for climate and assessments

ObsSea4Clim brings together key European actors within ocean observing science, climate assessment, Earth System modelling, data sharing and standards, with users of oceanographic products and services to deliver an improved observation framework based on Essential Ocean & Climate Variables (EOV/ECVs).

PIISA: Piloting Innovative Insurance Solutions for Adaptation

PIISA is a project funded by HORIZON Europe RIA (Research and Innovation Action) aiming to develop and deploy a range of insurance innovations that incite households and firms to adapt proactively and sufficiently for their own sake and their neighborhood’s sake. PIISA incites public authorities to set up adaptation and create adaptation promoting conditions. PIISA co-develops climate resilient insurance portfolios and develops solutions for sharing losses and climate risk data.

PNRR-HPC – “SPOKE 4 EARTH & CLIMATE”: National Centre for HPC, Big Data and Quantum Computing

Within Spoke 4, the scientific activity of CMCC, and of the Spoke affiliated partners, will be mainly aimed at developing a shared interdisciplinary framework for advanced Earth System Models and numerical experimentations. The framework will be focused on digital infrastructures and efficient workflows to streamline the production, facilitate the training, accelerate the understanding, and improve the quality of climate simulations and predictions.

SCEWERO: STRENGTHENING THE RESEARCH CAPACITIES FOR EXTREME WEATHER EVENTS IN ROMANIA

The SCEWERO project will be developed by a consortium of 5 organizations from 4 countries: Babeș-Bolyai University (UBB), a research institution located in Romania as a widening country and acting as coordinator, three top-class leading partners, Fondazione Centro Euro-Mediterraneo Sui Cambiamenti Climatici (IT), Universiteit Antwerpen (BE), and Justus-Liebig-Universität Giessen (DE), and a private partner (SME), Indeco Soft (RO) aiming to improve the excellence capacity in research, to raise the scientific reputation, research profile and attractiveness through networking, and strengthening research management capacity and administrative skills of the UBB team.

SD4SP: Stratospheric Dynamics for Seasonal Prediction

Seasonal forecasting is a field with enormous potential influence in different socio-economic sectors, such as water resources, agriculture, health, and energy. Yet, surface climate conditions in Europe still represent a hurdle to formulate skillful seasonal predictions. 

SILVANUS – Integrated Technological and Information Platform for wildfire Management

SILVANUS envisages to deliver an environmentally sustainable and climate resilient forest management platform through innovative capabilities to prevent and combat against the ignition and spread of forest fires. The platform will cater to the demands of efficient resource utilisation and provide protection against threats of wildfires encountered globally. The project will establish synergies between (i) environmental; (ii) technology and (iii) social science experts for enhancing the ability of regional and national authorities to monitor forest resources, evaluate biodiversity, generate more accurate fire risk indicators and promote safety regulations among citizens through awareness campaigns. The novelty of SILVANUS lies in the development and integration of advanced semantic technologies to systematically formalise the knowledge of forest administration and resource utilisation. Additionally, the platform will integrate a big-data processing framework capable of analysing heterogeneous data sources including earth observation resources, climate models and weather data, continuous on-board computation of multi-spectral video streams. Also, the project integrates a series of sensor and actuator technologies using innovative wireless communication infrastructure through the coordination of aerial vehicles and ground robots. The technological platform will be complemented with the integration of resilience models, and the results of environmental and ecological studies carried out for the assessment of fire risk indicators based on continuous surveys of forest regions. The surveys are designed to take into consideration the expertise and experience of frontline fire fighter organisations who collectively provide support for 47,504×104 sq. meters of forest area within Europe and across international communities. The project innovation will be validated

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