Description du projet
Des techniques d’intelligence artificielle pour prédire les cyclones
Les cyclones intenses sont dangereux et se forment fréquemment dans la région méditerranéenne. La capacité de prévoir un tel événement extrême est vitale pour la gestion des risques de catastrophes. Si les systèmes actuels de prévision saisonnière sont efficaces pour prévoir les anomalies des variables atmosphériques telles que la température et les précipitations, ce n’est pas le cas des événements extrêmes induits par des processus à petite échelle. L’utilisation de l’intelligence artificielle (IA) peut être une solution. Le projet CYCLOPS des actions Marie Skłodowska-Curie vise à améliorer la prévisibilité des cyclones méditerranéens intenses en combinant des techniques d’IA avancées et un système de prévision saisonnière dynamique de pointe. Le projet générera de nouvelles connaissances sur les processus impliqués dans la formation des cyclones méditerranéens et ouvrira la voie à un meilleur système de prévision saisonnière.
Objectif
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.
Champ scientifique
Mots‑clés
Programme(s)
- HORIZON.1.2 - Marie Skłodowska-Curie Actions (MSCA) Main Programme
Régime de financement
HORIZON-TMA-MSCA-PF-EF - HORIZON TMA MSCA Postdoctoral Fellowships - European FellowshipsCoordinateur
73100 Lecce
Italie