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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Cited articles

Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., Devin, M., Ghemawat, S., Irving, G., Isard, M., Kudlur, M., Levenberg, J., Monga, R., Moore, S., Murray, D. G., Steiner, B., Tucker, P., Vasudevan, V., Warden, P., Wicke, M., Yu, Y., and Zheng, X.: TensorFlow: a system for large-scale machine learning, in: Proceedings of the 12th USENIX conference on Operating Systems Design and Implementation, OSDI'16, pp. 265–283, USENIX Association, USA, ISBN 978-1-931971-33-1, 2016. a
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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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