Precipitation nowcasting with generative diffusion models

Understand

In recent years traditional numerical methods for accurate weather prediction have been increasingly challenged by deep learning methods.

• Numerous historical datasets used for short and medium-range weather forecasts are typically organized into a regular spatial grid structure.
• This arrangement closely resembles images: each weather variable can be visualized as a map or, when considering the temporal axis, as a video.
• Several classes of generative models, comprising Generative Adversarial Networks, Variational Autoencoders, or the recent Denoising Diffusion Models have largely proved their applicability to the next-frame prediction problem, and is thus natural to test their performance on the weather prediction benchmarks.