We explore the potential of feed‐forward deep neural networks (DNNs) for emulating cloud superparameterization in realistic geography, using offline fits to data from the Super Parameterized Community Atmospheric Model. To identify the network architecture of greatest skill, we formally optimize hyperparameters using ∼250 trials. Our DNN explains over 70 percent of the temporal variance at the 15‐minute sampling scale throughout the mid‐to‐upper troposphere. Autocorrelation timescale analysis compared against DNN skill suggests the less good fit in the tropical, marine boundary layer is driven by neural network difficulty emulating fast, stochastic signals in convection. However, spectral analysis in the temporal domain indicates skillful emulation of signals on diurnal to synoptic scales. A close look at the diurnal cycle reveals correct emulation of land‐sea contrasts and vertical structure in the heating and moistening fields, but some distortion of precipitation. Sensitivity tests targeting precipitation skill reveal complementary effects of adding positive constraints vs. hyperparameter tuning, motivating the use of both in the future. A first attempt to force an offline land model with DNN emulated atmospheric fields produces reassuring results further supporting neural network emulation viability in real‐geography settings. Overall, the fit skill is competitive with recent attempts by sophisticated Residual and Convolutional Neural Network architectures trained on added information, including memory of past states. Our results confirm the parameterizability of superparameterized convection with continents through machine learning and we highlight advantages of casting this problem locally in space and time for accurate emulation and hopefully quick implementation of hybrid climate models.

中文翻译：

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评估深度学习在具有真实地理边界条件的气候模型中模拟云超参数的潜力

我们探索前馈深度神经网络（DNN）在现实地理中模拟云超参数化的潜力，并使用对超级参数化社区大气模型中数据的离线拟合。为了确定最熟练的网络架构，我们使用约250次试验正式优化了超参数。我们的DNN在整个对流层中上层以15分钟的采样尺度解释了70％的时间变化。与DNN技巧相比，自相关时标分析表明，在热带海洋边界层中较差的拟合度是由神经网络难以模拟对流中的快速，随机信号而驱动的。但是，时域的频谱分析表明，信号在昼夜到天气尺度上都是熟练的仿真。仔细观察昼夜周期，就可以正确地模拟出加热和潮湿区域中的陆海对比和垂直结构，但降水却有些失真。针对降水技能的敏感性测试揭示了添加正约束与超参数调整的互补效果，从而在将来激发了两者的使用。首次尝试使用DNN模拟的大气场来强制建立离线土地模型会产生令人放心的结果，从而进一步支持神经网络在真实地理环境中的仿真可行性。总体而言，拟合技巧与经过复杂的残差和卷积神经网络体系结构的最新尝试相比具有竞争优势，这些体系经过训练，可以在增加的信息（包括对过去状态的记忆）方面进行训练。