An introduction to weather forecasting with deep learning
A few weeks ago, we showed how to forecast chaotic dynamical systems with deep learning, augmented by a custom constraint derived from domain-specific insight. Global weather is a chaotic system, but of much higher complexity than many tasks commonly addressed with machine and/or deep learning. In this post, we provide a practical introduction featuring a simple deep learning baseline for atmospheric forecasting. While far away from being competitive, it serves to illustrate how more sophisticated and compute-intensive models may approach that formidable task by means of methods situated on the “black-box end” of the continuum.
