ICON-XPP in the CERISE project: towards an LDAS-including seasonal prediction System

The Copernicus Climate Change Service Evolution (CERISE) project, as an EU-HORIZON project, aims to enhance the quality of the C3S (Copernicus Climate Change Service) reanalysis and seasonal forecast portfolio, with a focus on land-atmosphere coupling. CERISE will develop new and innovative ensemble-based coupled land-atmosphere data assimilation approaches and land surface initialisation techniques to pave the way for the next generations of the C3S reanalysis and seasonal prediction systems. Deutscher Wetterdienst is developing its land data assimilation for the initialisation of seasonal forecasts with ICON-XPP. ICON-XPP (ICON eXtended Predictions and Projections) is not only the state-of-art climate modelling system in Germany but also an effort to unify knowledge and experiences from many institutes in one model system that can deliver seamless weather and climate simulations. The aim is to include snow analysis, soil moisture analysis and leaf area index assimilation into our climate forecast data assimilation system. Here we present results from the intermediate step of assimilating snow depth and the impact on multi-year historical forecasts for the period of 1993-2022. The system for generating initial conditions consists at this stage of an Ensemble Kalman Filter for the ocean data assimilation, nudging for the atmosphere and a 2DVAR scheme for the snow. The impact of the assimilation frequency of snow on the historical forecasts will be discussed. Further, we show our efforts in extending our land data assimilation system with soil moisture analysis and leaf area index assimilation based on an Extended Kalman Filter and we show the results of our first sensitivity experiments.