Deep Learning for Koopman Operator Estimation in Idealized Atmospheric Dynamics
This work addresses the problem of lack of transparency in data-driven weather models for atmospheric scientists, though it appears incremental as it refines existing methods.
The paper tackled the challenge of interpretability in deep learning weather forecasting models by proposing methodologies to estimate the Koopman operator for a linear representation of nonlinear dynamics, resulting in refined models and novel convolutional neural network architectures for simplified atmospheric dynamics.
Deep learning is revolutionizing weather forecasting, with new data-driven models achieving accuracy on par with operational physical models for medium-term predictions. However, these models often lack interpretability, making their underlying dynamics difficult to understand and explain. This paper proposes methodologies to estimate the Koopman operator, providing a linear representation of complex nonlinear dynamics to enhance the transparency of data-driven models. Despite its potential, applying the Koopman operator to large-scale problems, such as atmospheric modeling, remains challenging. This study aims to identify the limitations of existing methods, refine these models to overcome various bottlenecks, and introduce novel convolutional neural network architectures that capture simplified dynamics.