Loading [MathJax]/jax/output/HTML-CSS/fonts/TeX/fontdata.js
Preprints
https://doi.org/10.5194/egusphere-2023-3078
https://doi.org/10.5194/egusphere-2023-3078
20 Feb 2024
 | 20 Feb 2024

HyPhAI v1.0: Hybrid Physics-AI architecture for cloud cover nowcasting

Rachid El Montassir, Olivier Pannekoucke, and Corentin Lapeyre

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 generalize to unseen data. The proposed approach enforces a physical behaviour. In the first model, denoted HyPhAI-1, a multi-level advection dynamics is considered as 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 the U-Net. Remarkably, these improvements are achieved while using only one-third of the data required by the other models. Another model, denoted HyPhAI-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.

Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this preprint. The responsibility to include appropriate place names lies with the authors.
Share

Journal article(s) based on this preprint

10 Sep 2024
HyPhAICC v1.0: a hybrid physics–AI approach for probability fields advection shown through an application to cloud cover nowcasting
Rachid El Montassir, Olivier Pannekoucke, and Corentin Lapeyre
Geosci. Model Dev., 17, 6657–6681, https://doi.org/10.5194/gmd-17-6657-2024,https://doi.org/10.5194/gmd-17-6657-2024, 2024
Short summary
Download

The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.

Short summary
This study introduces a novel approach, combining Physics and Artificial Intelligence (AI) for...
Share