Articles | Volume 17, issue 17
https://doi.org/10.5194/gmd-17-6657-2024
https://doi.org/10.5194/gmd-17-6657-2024
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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Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload
AR by Rachid El Montassir on behalf of the Authors (07 Jun 2024)  Author's response   Author's tracked changes   Manuscript 
ED: Publish subject to minor revisions (review by editor) (01 Jul 2024) by Travis O'Brien
AR by Rachid El Montassir on behalf of the Authors (08 Jul 2024)  Author's response   Author's tracked changes   Manuscript 
ED: Publish as is (18 Jul 2024) by Travis O'Brien
AR by Rachid El Montassir on behalf of the Authors (27 Jul 2024)  Manuscript 
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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.