Minisymposium Presentation

AtmoRep is a first example of probabilistic multi-purpose model that extends the concept of representation learning to Earth system science, and in particular to atmospheric dynamics. Starting from a single pre-trained architecture used as a backbone, AtmoRep is able to achieve skilful results in multiple zero-shot applications such as nowcasting, temporal interpolation and scenario generation, compared to the state-of-the-art approaches. Thanks to a novel definition of the loss, the model is also probabilistic by design, as it outputs a set of ensemble members for each task, with well calibrated distributions as proven for weather forecasting. The first part of the talk will focus on the innovative aspects of the model architecture, with particular attention to the local space-time approach and the flexible plug-and-play design. The second part of the talk will show the current results for the most relevant applications, while the last part of the talk will focus on the future foreseen developments.