用神经网络替代气象模型中的对流参数化，提升计算效率且保持精度。
Online Test of a Neural Network Deep Convection Parameterization in ARP-GEM1
- 通过Python与Fortran接口联动，实现神经网络在气象模型中的在线推理。
- 5年模拟显示新方案与传统物理模型输出高度一致，均值误差小。
- 部署于独立GPU节点，推理速度显著提升，适合高性能气象模拟场景。
本研究将基于神经网络的对流参数化模块集成至全球大气模型ARP-GEM1，利用OASIS耦合器的Python接口实现Fortran编写的ARP-GEM1与负责神经网络推理的Python组件之间的场数据交换。作为概念验证实验，我们训练了一个神经网络来模拟ARP-GEM1原有的对流参数化方案，并成功在线替换原模型中的对流方案。为评估性能，我们运行了使用神经网络模拟器的5年ARP-GEM1模拟。平均场对比显示，其输出与采用物理基础对流方案的模拟结果具有良好一致性。神经网络组件部署在独立于气候模式的计算分区上，借助GPU加速推理过程，显著提升了计算效率。
原文摘要 · Abstract (English)
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.
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