论文标题
物理限制的生成对抗网络，用于改善地球系统模型的降水场
Physically Constrained Generative Adversarial Networks for Improving Precipitation Fields from Earth System Models
论文作者
论文摘要
降水是由许多尺度上的复杂过程产生的，这使其在地球系统模型（ESM）中的准确模拟具有挑战性。现有的后处理方法可以在本地改进ESM模拟，但无法纠正模型的空间模式中的错误。在这里，我们提出了一个基于物理上约束的生成对抗网络（GAN）的框架，以同时改善本地分布和空间结构。我们将方法应用于计算有效的ESM CM2MC-LPJML。我们的方法在纠正局部分布方面的表现优于现有方法，并且可以大大改善空间模式，尤其是在每日降水的间歇性方面。值得注意的是，删除了一个双峰式的受热带收敛区，这是ESMS中的一个常见问题。甘恩（GAN）执行物理限制以保留全球降水总和，可以概括培训期间看不见的未来气候场景。特征归因表明，GAN标识了ESM表现强偏见的区域。我们的方法构成了一个通用框架，用于校正ESM变量，并在计算成本的一小部分中实现逼真的模拟。
Precipitation results from complex processes across many scales, making its accurate simulation in Earth system models (ESMs) challenging. Existing post-processing methods can improve ESM simulations locally, but cannot correct errors in modelled spatial patterns. Here we propose a framework based on physically constrained generative adversarial networks (GANs) to improve local distributions and spatial structure simultaneously. We apply our approach to the computationally efficient ESM CM2Mc-LPJmL. Our method outperforms existing ones in correcting local distributions, and leads to strongly improved spatial patterns especially regarding the intermittency of daily precipitation. Notably, a double-peaked Intertropical Convergence Zone, a common problem in ESMs, is removed. Enforcing a physical constraint to preserve global precipitation sums, the GAN can generalize to future climate scenarios unseen during training. Feature attribution shows that the GAN identifies regions where the ESM exhibits strong biases. Our method constitutes a general framework for correcting ESM variables and enables realistic simulations at a fraction of the computational costs.
