Current moist physics parameterization schemes in general circulation models (GCMs) are the main source of biases in simulated precipitation and atmospheric circulation. Recent advances in machine learning make it possible to explore data‐driven approaches to developing parameterization for moist physics processes such as convection and clouds. This study aims to develop a new moist physics parameterization scheme based on deep learning. We use a residual convolutional neural network (ResNet) for this purpose. It is trained with 1‐year simulation from a superparameterized GCM, SPCAM. An independent year of SPCAM simulation is used for evaluation. In the design of the neural network, referred to as ResCu, the moist static energy conservation during moist processes is considered. In addition, the past history of the atmospheric states, convection, and clouds is also considered. The predicted variables from the neural network are GCM grid‐scale heating and drying rates by convection and clouds, and cloud liquid and ice water contents. Precipitation is derived from predicted moisture tendency. In the independent data test, ResCu can accurately reproduce the SPCAM simulation in both time mean and temporal variance. Comparison with other neural networks demonstrates the superior performance of ResNet architecture. ResCu is further tested in a single‐column model for both continental midlatitude warm season convection and tropical monsoonal convection. In both cases, it simulates the timing and intensity of convective events well. In the prognostic test of tropical convection case, the simulated temperature and moisture biases with ResCu are smaller than those using conventional convection and cloud parameterizations.

中文翻译：

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基于深度学习的潮湿物理参数化

通用循环模型（GCM）中当前的湿物理参数化方案是模拟降水和大气环流中偏差的主要来源。机器学习的最新进展使人们有可能探索数据驱动的方法来开发对流和云等湿物理过程的参数化。这项研究旨在开发一种基于深度学习的新的湿物理参数化方案。为此，我们使用了残差卷积神经网络（ResNet）。它由超参数化GCM SPCAM进行了为期1年的仿真训练。SPCAM模拟的独立年份用于评估。在称为ResCu的神经网络的设计中，考虑了潮湿过程中的潮湿静态能量守恒。此外，过去的大气状态，对流，云也被考虑在内。神经网络的预测变量是通过对流和云计算的GCM网格尺度加热和干燥速率，以及云中的液体和冰水含量。降水来自预测的水分趋势。在独立数据测试中，ResCu可以在时间均值和时间方差方面准确地再现SPCAM仿真。与其他神经网络的比较证明了ResNet体系结构的优越性能。在单列模型中对ResCu进行了进一步的测试，包括大陆中纬度暖季对流和热带季风对流。在这两种情况下，它都能很好地模拟对流事件的时间和强度。在热带对流病例的预后测试中，