Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 17, issue 17
Articles | Volume 17, issue 17
https://doi.org/10.5194/gmd-17-6657-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-17-6657-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 17, issue 17
Model description paper
 | 
10 Sep 2024
Model description paper |  | 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

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Short summary
This study introduces a novel approach that combines physics and artificial intelligence (AI) for improved cloud cover forecasting. This approach outperforms traditional deep learning (DL) methods in producing realistic and physically consistent results while requiring less training data. This 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.
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