The IMK-ASF

   

Prediction of extreme precipitation events (EPEs) is a scientific challenge and of key importance to society. EPEs result from interactions of different physical processes on a wide range of spatial and temporal scales and this complexity poses challenges for their skillful forecast. Large-scale slowly evolving flow can be predictable over many days, but fast processes, like convection,  can reduce predictability. A deeper understanding of how the large-scale atmospheric flow interacts with local dynamical and precipitation processes is fundamental to make significant progress in extreme precipitation and flood forecasting. With this in mind, at ECWMF we started wondering, since early 2000, how we could practically disentangle different predictability components linked at the different scales to achieve to gain insight into the EPE prediction. In this presentation, we will review some studies concerning the role of Rossby wave trains as precursors of EPEs over northern Italy, up to the most recent works that attempt to systematize the knowledge acquired for use in a machine learning hybrid forecasting system.