粗粒度训练神经网络参数化的空间扩展测试
Spatially Extended Tests of a Neural Network Parametrization Trained by Coarse-graining
摘要：
大气环流模型的网格大小一般为25- 200公里。参数化被用来代表非绝热过程，如辐射传输和云微物理，并考虑次网格尺度的运动和变化。与传统方法不同，神经网络（NN）可以很容易地利用最近的观测数据集和全球云系统解析模型（CRM）模拟来学习子网格变化。本文描述了一个NN参数化训练的粗粒化的近全球CRM模拟与4公里的水平网格间距。神经网络预测了在（160 km）^2格点上平均的剩余加热和增湿，作为同一大气柱内粗分辨率场的函数。该神经网络耦合到具有相同的160公里分辨率的GCM的动力核心。最近的一项研究描述了如何训练这样一个神经网络，使其在单柱模式中与指定的随时间演变的平流强迫耦合时在数值上稳定，但神经网络和GCM组件之间的反馈会导致空间扩展的模拟在几天内崩溃。分析这种神经网络的线性化响应表明，它学会了利用降水和10公里以上的大气状态之间的强同步性。从NN的输入中删除这些变量可以稳定耦合模拟，这比没有任何亚网格尺度变化参数化的粗分辨率模拟更准确地预测未来状态，尽管平均状态会缓慢漂移。
英文摘要：
General circulation models (GCMs) typically have a grid size of 25--200 km. Parametrizations are used to represent diabatic processes such as radiative transfer and cloud microphysics and account for sub-grid-scale motions and variability. Unlike traditional approaches, neural networks (NNs) can readily exploit recent observational datasets and global cloud-system resolving model (CRM) simulations to learn subgrid variability. This article describes an NN parametrization trained by coarse-graining a near-global CRM simulation with a 4~km horizontal grid spacing. The NN predicts the residual heating and moistening averaged over (160 km)^2 grid boxes as a function of the coarse-resolution fields within the same atmospheric column. This NN is coupled to the dynamical core of a GCM with the same 160 km resolution. A recent study described how to train such an NN to be numerically stable when coupled to specified time-evolving advective forcings in a single column model, but feedbacks between NN and GCM components cause spatially-extended simulations to crash within a few days. Analyzing the linearized response of such an NN reveals that it learns to exploit a strong synchrony between precipitation and the atmospheric state above 10 km. Removing these variables from the NN's inputs stabilizes the coupled simulations, which predict the future state more accurately than a coarse-resolution simulation without any parametrizations of sub-grid-scale variability, although the mean state slowly drifts.
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