Hybrid neural differential equation models for atmospheric dynamics

Combining process-based models in Earth system science with data-driven machine learning methods holds tremendous promise. Can we harness the best of both approaches? In our study, we integrate components of atmospheric models into artificial neural networks (ANN). The resulting hybrid atmospheric model can learn atmospheric dynamics from short trajectories while ensuring robust generalization and stability. We achieve this using the neural differential equations framework, combining ANNs with a differentiable, GPU-enabled version of the well-studied Marshall Molteni Quasigeostrophic Model (QG3). Similar to the approach of many atmospheric models, part of the model is computed in the spherical harmonics domain, and other parts in the grid domain. In our model, ANNs are used as parametrizations in both domains, and form together with the components of the QG3 model the right-hand side of our hybrid model. We showcase the capabilities of our model by demonstrating how it generalizes from the QG3 model to the significantly more complex primitive equation model of SpeedyWeather.jl.