Description
Chair: Julia Kempe
In recent years, there have been significant advancements in data-driven approaches to global weather forecasting that have demonstrated accuracy competitive with modern operational systems. While current state-of-the-art learned models achieve lower errors at medium-range lead-times, physics-based models like IFS feature superior physical consistency. In this talk I’ll describe our ongoing research effort where we are developing a hybrid atmospheric model based on a differentiable dynamical core augmented with learned physics parameterizations, trained end-to-end. Specifically, I’ll discuss the rationale behind our model formulation and show preliminary results on accuracy, physical consistency and emergent long-term atmospheric phenomena.
