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

El Montassir, Rachid; Pannekoucke, Olivier; Lapeyre, Corentin

doi:https://doi.org/10.5194/gmd-17-6657-2024

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

Data sets

A sample of the training data used in the paper "A Hybrid Physics-AI (HyPhAI) approach for probability fields advection: Application to cloud cover nowcasting" European Organisation for the Exploitation of Meteorological Satellites https://doi.org/10.5281/zenodo.10642094

Model code and software

relmonta/hyphai: Update paper information (v1.1.1) Rachid El Montassir https://doi.org/10.5281/zenodo.11518540

Pre-trained HyPhAICCast-1, HyPhAICCast-2 and U-Net's weights Rachid El Montassir et al. https://doi.org/10.5281/zenodo.10393415

Interactive computing environment

relmonta/hyphai: Update paper information (v1.1.1) Rachid El Montassir https://doi.org/10.5281/zenodo.11518540

Video supplement

HyPhAICCast-1 2-hour forecast on 01/01/2021 at 12:00 p.m. Rachid El Montassir et al. https://doi.org/10.5281/zenodo.10375284

Short summary

This study introduces a novel approach that combines physics and artificial intelligence (AI)...