AtmoRep: A stochastic model of atmosphere dynamics using large scale representation learning (Papers Track)
ilaria luise (CERN)
Abstract
AtmoRep is a novel, task-independent stochastic computer model of atmospheric dynamics inspired by the concept of foundation models in natural language processing, like the GPT line or PalmX, applied in the context of Earth system science. The main innovative aspect consists in the fact that the model can skillfully solve scientific tasks it was not specifically trained on, clearly exhibiting in-context learning capabilities. AtmoRep's skill has been tested on nowcasting, temporal interpolation, model correction, and counterfactuals, demonstrating that large-scale neural networks can provide skillful, task-independent models able to complement the existing numerical approaches in multiple applications. In addition, the authors also demonstrated the possibility to further increase the model accuracy by fine tuning it directly on observational data for tasks such as precipitation corrections or downscaling.