Improvement of sub-seasonal probabilistic forecasts of European high-impact weather events using machine learning techniques (IMPRINT)

Each year Europe is subject to drought, heatwaves and periods of persistent rainfall that could lead to the flooding of rivers. Although short-range weather forecasts have improved substantially over the last decennia, long-range weather forecasts have improved less. The goal of this project is to improve these long-term probabilistic forecasts of extreme weather. Warnings can then be given earlier and more reliably. Long meteorological datasets and newly developed statistical post-processing and machine learning methods enable us to better integrate the relevant information, and correct shortcomings of operational ensemble prediction systems.