Abstract
In this study, we integrate a neural network-based parameterization into the global atmospheric model ARP-GEM1 using the Python interface of the OASIS coupler. This setup enables the exchange of fields between the Fortran-based ARP-GEM1 model and a Python component implementing the neural network inference. The Python component was deployed on a separate partition from the general circulation model, using GPUs. As a proof-of-concept, we trained a neural network to emulate ARP-GEM1’s deep convection parameterization. Leveraging the flexible Fortran/Python interface, we successfully replaced ARP-GEM1’s deep convection scheme with the neural network emulator. To evaluate its online performance, we realized a 30-year ARP-GEM1 simulation using the neural network for deep convection. The evaluation of the averaged fields showed good agreement with the output of an ARP-GEM1 simulation using the physics-based deep convection scheme.
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