论文标题
使用神经网络来稳定，准确且在物理上一致的亚网格大气过程，并以降低的精度表现良好
Use of neural networks for stable, accurate and physically consistent parameterization of subgrid atmospheric processes with good performance at reduced precision
论文作者
论文摘要
改善气候模拟模拟的一种有前途的方法是，通过数据驱动的机器学习算法，基于简化的物理模型来替换传统的子网格参数化。但是，当耦合到大气模型时，神经网络（NNS）通常会导致不稳定性和气候漂移。在这里，我们通过粗糙的模型方程和输出从理想化的域中的高分辨率大气模拟中学习NN参数化。 NN参数化具有确保物理限制的结构，并导致稳定的模拟，以与成功的随机孔子参数化相似的精度复制高分辨率模拟的气候，同时需要较少的内存。我们发现模拟对于各种NN架构和水平分辨率都是稳定的，并且具有大幅降低数值精度的NN可以降低计算成本而不会影响模拟质量。
A promising approach to improve climate-model simulations is to replace traditional subgrid parameterizations based on simplified physical models by machine learning algorithms that are data-driven. However, neural networks (NNs) often lead to instabilities and climate drift when coupled to an atmospheric model. Here we learn an NN parameterization from a high-resolution atmospheric simulation in an idealized domain by coarse graining the model equations and output. The NN parameterization has a structure that ensures physical constraints are respected, and it leads to stable simulations that replicate the climate of the high-resolution simulation with similar accuracy to a successful random-forest parameterization while needing far less memory. We find that the simulations are stable for a variety of NN architectures and horizontal resolutions, and that an NN with substantially reduced numerical precision could decrease computational costs without affecting the quality of simulations.
