Project description DEENESFRITPL 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. Show the project objective Hide the project objective 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 natural sciencesearth and related environmental sciencesatmospheric sciencesclimatologyclimatic changesnatural sciencescomputer and information sciencesartificial intelligencemachine learning Keywords CYCLOPS Programme(s) HORIZON.1.2 - Marie Skłodowska-Curie Actions (MSCA) Main Programme Topic(s) HORIZON-MSCA-2021-PF-01-01 - MSCA Postdoctoral Fellowships 2021 Call for proposal HORIZON-MSCA-2021-PF-01 See other projects for this call Funding Scheme HORIZON-TMA-MSCA-PF-EF - HORIZON TMA MSCA Postdoctoral Fellowships - European Fellowships Coordinator FONDAZIONE CENTRO EURO-MEDITERRANEOSUI CAMBIAMENTI CLIMATICI Net EU contribution € 172 750,08 Address VIA MARCO BIAGI 5 73100 Lecce Italy See on map Region Sud Puglia Lecce Activity type Research Organisations Links Contact the organisation Opens in new window Website Opens in new window Participation in EU R&I programmes Opens in new window HORIZON collaboration network Opens in new window Total cost No data Partners (1) Sort alphabetically Sort by Net EU contribution Expand all Collapse all POLITECNICO DI MILANO Italy Net EU contribution € 0,00 Address PIAZZA LEONARDO DA VINCI 32 20133 Milano See on map Region Nord-Ovest Lombardia Milano Activity type Higher or Secondary Education Establishments Links Contact the organisation Opens in new window Website Opens in new window Participation in EU R&I programmes Opens in new window HORIZON collaboration network Opens in new window Total cost No data
