I’m coordinator of the data science cluster within the R&D Weather and climate modeling department. Part of that cluster is the statistical post-processing group. The focus of my group’s research is to improve probabilistic forecasts for (especially) severe weather, from the short range to (sub-)seasonal timescales and applications in hydrology, among others, using statistical and machine learning methods. Recently, we have started to investigate a so-called stretched grid approach in a data-driven weather model based on AI, namely AIFS of ECMWF.
I have worked together with IVM (VU Amsterdam, where I'm also affiliated with), ECMWF, Utrecht University, TU Delft, Radboud University Nijmegen, KIT, Deltares, UNESCO-IHE, Rijkswaterstaat and MET Norway. I have been principal investigator (PI) of the NWO-ALW project “Improvement of sub-seasonal probabilistic forecasts of European high-impact weather events using machine learning techniques” (https://www.nwo.nl/en/projects/alwop395), and I have cooperated with TU Delft in the NWO-TTW project "Probabilistic forecasts of extreme weather utilizing advanced methods from extreme value theory". I have also been involved in the JCPIII program in Indonesia (https://www.deltares.nl/en/news/indonesia-netherlands-work-even-closer-together-water-issues) and in the calibration of a short-term weather forecasting ensemble (HarmonEPS).