Increasing the information flow into glacier system models using an Ensemble Kalman Filter

We are introducing a novel technique for calibrating the unknown parameters of a glacier model by integrating remote sensing data. Our approach involves the fusion of the Instructed Glacier Model (IGM) with an Ensemble Kalman Filter designed explicitly for transient data assimilation. Our primary objective is to assimilate remotely sensed observations at the respective time of acquisition during forward simulations. During the presentation, we explore the Ensemble Kalman Filter's core concept and showcase our approach's effectiveness through twin experiments, fine-tuning model parameters related to ice dynamics and surface mass balance. Utilizing observations from prominent glaciers in the European Alps, our methodology concurrently minimizes uncertainty estimates of crucial parameters such as equilibrium line altitude and ablation/accumulation gradients. The resulting uncertainty estimate is then integrated into future projections.