Projektbeschreibung
KI-Verfahren zur Vorhersage von Wirbelstürmen
Intensive Wirbelstürme sind gefährlich und bilden sich häufig in der Mittelmeerregion. Die Vorhersagbarkeit eines solchen Extremereignisses ist für das Management von Katastrophenrisiken von entscheidender Bedeutung. Während die derzeitigen saisonalen Vorhersagesysteme Anomalien atmosphärischer Variablen wie Temperatur und Niederschlag gut vorhersagen können, ist dies bei extremen Ereignissen, die durch kleinräumige Prozesse ausgelöst werden, nicht der Fall. Der Einsatz von künstlicher Intelligenz (KI) kann eine Lösung sein. Das im Rahmen der Marie-Skłodowska-Curie-Maßnahmen geförderte Projekt CYCLOPS zielt darauf ab, die Vorhersagbarkeit intensiver Wirbelstürme im Mittelmeerraum zu verbessern, indem fortschrittliche KI-Techniken mit einem modernen dynamischen saisonalen Vorhersagesystem kombiniert werden. Das Projekt wird neue Erkenntnisse über die Prozesse bei der Bildung von Wirbelstürmen im Mittelmeerraum liefern und den Weg zu einem besseren saisonalen Vorhersagesystem ebnen.
Ziel
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.
Wissenschaftliches Gebiet
Schlüsselbegriffe
Programm/Programme
- HORIZON.1.2 - Marie Skłodowska-Curie Actions (MSCA) Main Programme
Aufforderung zur Vorschlagseinreichung
Andere Projekte für diesen Aufruf anzeigenFinanzierungsplan
HORIZON-TMA-MSCA-PF-EF - HORIZON TMA MSCA Postdoctoral Fellowships - European FellowshipsKoordinator
73100 Lecce
Italien