HyPhAICC v1.0: a hybrid physics–AI approach for probability fields advection shown through an application to cloud cover nowcasting

Abstract

This work proposes a hybrid approach that combines physics and artificial intelligence (AI) for cloud cover nowcasting. It addresses the limitations of traditional deep-learning methods in producing realistic and physically consistent results that can generalise to unseen data. The proposed approach, named HyPhAICC, enforces a physical behaviour. In the first model, denoted as HyPhAICC-1, a multi-level advection dynamics is considered a hard constraint for a trained U-Net model. Our experiments show that the hybrid formulation outperforms not only traditional deep-learning methods but also the EUMETSAT Extrapolated Imagery model (EXIM) in terms of both qualitative and quantitative results. In particular, we illustrate that the hybrid model preserves more details and achieves higher scores based on similarity metrics in comparison to U-Net. Remarkably, these improvements are achieved while using only one-third of the data required by the other models. Another model, denoted as HyPhAICC-2, adds a source term to the advection equation, it impaired the visual rendering but displayed the best performance in terms of accuracy. These results suggest that the proposed hybrid physics–AI architecture provides a promising solution to overcome the limitations of classical AI methods and contributes to open up new possibilities for combining physical knowledge with deep-learning models.