Project description
AI techniques to predict cyclones
Intense cyclones are dangerous frequently forming in the Mediterranean region. The ability to predict such an extreme event is vital for disaster risk management. While current seasonal prediction systems are good at predicting anomalies of atmospheric variables such as temperature and precipitation, this is not the case with extreme events driven by small-scale processes. The use of artificial intelligence (AI) can be a solution. The Marie Skłodowska-Curie Actions project CYCLOPS aims to improve the predictability of intense Mediterranean cyclones by combining advanced AI techniques and a state-of-the-art dynamical seasonal prediction system. The project will generate new knowledge on the processes involved in Mediterranean cyclone formation and pave the way for a better seasonal prediction system.
Objective
Cyclones form frequently over the Mediterranean Sea. The most intense systems cause extensive damage in the region and beyond. The ability to make climate predictions several months in advance of such extreme events has a large number of crucial socio-economic applications for disaster risk reduction.
State-of the-art Seasonal Prediction Systems exhibit a good skill in predicting anomalies in the seasonal mean of meteorological fields such as temperature and precipitation. The models’ ability to reproduce variations in the occurrence of extreme events tends to be however much lower. This is particularly true in regions such as the Mediterranean, where extreme events are often driven by small scale processes that are not well reproduced at the resolution at which SPS typically run.
The use of artificial intelligence techniques such as machine learning in the study of climate has gained great traction in recent years. The power of those techniques lies in the ability to detect patterns in large datasets without having to make explicit statistical assumptions, allowing to build predictive model whose skill continuously improves as the volume of data on which the models are trained increases. One promising field of application is to use artificial intelligence to improve the prediction of extremes in climate models. In this setting a machine learning model is trained to find relationships between large-scale climate variables (for which dynamical models have a good predictive skill) and the occurrence of extremes.
The aim of this project is to improve the predictability of intense Mediterranean cyclones combining advanced artificial intelligence techniques and a state-of-the-art dynamical seasonal prediction system. The project will not only contribute to the knowledge of climate extremes predictability, but also lead to the implementation of a pre-operational dynamical-statistical prediction system with the potential to be extended to include different extremes.
Fields of science (EuroSciVoc)
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
- natural sciences earth and related environmental sciences atmospheric sciences climatology climatic changes
- natural sciences computer and information sciences artificial intelligence machine learning
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Programme(s)
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
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HORIZON.1.2 - Marie Skłodowska-Curie Actions (MSCA)
MAIN PROGRAMME
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Topic(s)
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Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Funding Scheme
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
HORIZON-TMA-MSCA-PF-EF - HORIZON TMA MSCA Postdoctoral Fellowships - European Fellowships
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Call for proposal
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
(opens in new window) HORIZON-MSCA-2021-PF-01
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Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.
73100 LECCE
Italy
The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.