论文标题
使用生成对抗网络对列物理的随机参数化
Stochastic Parameterization of Column Physics using Generative Adversarial Networks
论文作者
论文摘要
我们证明了使用概率的机器学习技术来开发大气色谱柱的随机参数化。在对NASA的现代回顾性研究和应用进行了适当的预处理后，版本2（MERRA2）数据可最大程度地减少MERRA2垂直速度估计的高频，高波动的成分的影响，我们使用生成性逆流网络来学习垂直型号的垂直传播效率的垂直分布的垂直传播和均匀的垂直效率。这可能被视为比以前类似但确定性的方法的改进，这些方法试图减轻人类设计的物理参数化的缺点，以及气候模型中“物理学”步骤的计算需求。
We demonstrate the use of a probabilistic machine learning technique to develop stochastic parameterizations of atmospheric column-physics. After suitable preprocessing of NASA's Modern-Era Retrospective analysis for Research and Applications, version 2 (MERRA2) data to minimize the effects of high-frequency, high-wavenumber component of MERRA2 estimate of vertical velocity, we use generative adversarial networks to learn the probability distribution of vertical profiles of diabatic sources conditioned on vertical profiles of temperature and humidity. This may be viewed as an improvement over previous similar but deterministic approaches that seek to alleviate both, shortcomings of human-designed physics parameterizations, and the computational demand of the "physics" step in climate models.
