论文标题
朝对流的数据驱动模型
Towards Physically-consistent, Data-driven Models of Convection
论文作者
论文摘要
数据驱动的算法，特别是神经网络，如果在高分辨率气候模拟中接受培训，则可以模仿粗分辨率气候模型中亚网格量表过程的影响。但是，他们可能会违反关键的身体限制，并且缺乏在训练集外概括的能力。在这里，我们表明，可以通过调整体系结构来调整损耗函数或在机器精度内进行大约通过调整架构来实施物理约束。由于这些物理上的约束不足以保证可推广性，因此我们还建议在物理上重新列出培训和验证数据，以提高神经网络概括到不见了的气候的能力。
Data-driven algorithms, in particular neural networks, can emulate the effect of sub-grid scale processes in coarse-resolution climate models if trained on high-resolution climate simulations. However, they may violate key physical constraints and lack the ability to generalize outside of their training set. Here, we show that physical constraints can be enforced in neural networks, either approximately by adapting the loss function or to within machine precision by adapting the architecture. As these physical constraints are insufficient to guarantee generalizability, we additionally propose to physically rescale the training and validation data to improve the ability of neural networks to generalize to unseen climates.
