Online Test of a Neural Network Deep Convection Parameterization in ARP-GEM1

Autor:	Balogh, Blanka, Saint-Martin, David, Geoffroy, Olivier
Rok vydání:	2024
Předmět:	Physics - Atmospheric and Oceanic Physics Computer Science - Machine Learning
Druh dokumentu:	Working Paper
Popis:	In this study, we present the integration of a neural network-based parameterization into the global atmospheric model ARP-GEM1, leveraging the Python interface of the OASIS coupler. This approach facilitates the exchange of fields between the Fortran-based ARP-GEM1 model and a Python component responsible for neural network inference. As a proof-of-concept experiment, we trained a neural network to emulate the deep convection parameterization of ARP-GEM1. Using the flexible Fortran/Python interface, we have successfully replaced ARP-GEM1's deep convection scheme with a neural network emulator. To assess the performance of the neural network deep convection scheme, we have run a 5-years ARP-GEM1 simulation using the neural network emulator. The evaluation of averaged fields showed good agreement with output from an ARP-GEM1 simulation using the physics-based deep convection scheme. The Python component was deployed on a separate partition from the general circulation model, using GPUs to increase inference speed of the neural network. Comment: 10 pages, 5 figures, submitted to Artificial Intelligence for the Earth Systems
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2410.21920 Zobrazit plný text záznamu View this record from Arxiv