The importance of data assimilation lies in its ability to enhance the accuracy and reliability of forecasts and simulations by continuously updating model inputs with observational data. This process reduces uncertainties, improves predictive skill, and enables better-informed decision-making in various fields such as weather forecasting, environmental monitoring, and climate research.
Our research focuses on ensemble-based data-assimilation, where we develop tailored methods for spatially sparse observations using (multi-level) ensemble Kalman filters and non-linear data assimilation methods such as particle filters.