Society would greatly benefit from reliable early warnings of weather-related risk. A good physical understanding is needed construct reliable statistical forecast models. In this project we aim to understand the dynamical mechanisms that lead to (persistent) summer extremes. By combining causal inference techniques, machine learning and expert knowledge, we try to improve both physical understanding
We provide a disciplinary and multidisciplinary research programme aimed at advanced understanding of environmental problems and advanced training of PhD candidates in this field.