Process‐scale development, evaluation, and calibration of physically based parameterizations of clouds and radiation are powerful levers for improving weather and climate models. In a series of papers, we propose a strategy for process‐based calibration of climate models that uses machine learning techniques. It relies on systematic comparisons of single‐column versions of climate models with explicit simulations of boundary‐layer dynamics and clouds (Large‐Eddy Simulations [LES]). This paper focuses on the calibration of cloud geometry parameters (vertical overlap, horizontal heterogeneity, and cloud size) that appear in the parameterization of radiation. The solar component of a radiative transfer (RT) scheme that includes a parameterization for 3D radiative effects of clouds (SPARTACUS) is run in offline single‐column mode on an ensemble of input cloud profiles synthesized from LES outputs. The space of cloud geometry parameter values is efficiently explored by sampling a large number of parameter sets (configurations) from which radiative metrics are computed using fast surrogate models that emulate the SPARTACUS solver. The sampled configurations are evaluated by comparing these radiative metrics to reference values provided by a 3D RT Monte Carlo model. The best calibrated configurations yield better predictions of TOA and surface fluxes than the one that uses parameter values computed from the 3D cloud fields: The root‐mean‐square errors averaged over cumulus cloud fields and solar angles are reduced from ∼10 Wm⁻² with LES‐derived parameters to ∼5 Wm⁻² with adjusted parameters. However, the calibration of cloud geometry fails to reduce the errors on absorption, which remain around 2–4 Wm⁻².

中文翻译：

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利用机器学习进行基于过程的气候模型开发：III。积云几何的表示及其3D辐射效果

过程规模的开发，评估以及基于物理的云和辐射参数化的校准是改善天气和气候模型的强大杠杆。在一系列论文中，我们提出了一种使用机器学习技术对气候模型进行基于过程的校准的策略。它依赖于气候模型的单列版本与边界层动力学和云的显式模拟（大涡模拟[LES]）的系统比较。本文重点介绍出现在辐射参数化中的云几何参数（垂直重叠，水平非均质性和云大小）的校准。包含用于3D云辐射效应的参数化（SPARTACUS）的辐射传输（RT）方案的太阳能组件以脱机单列模式在从LES输出合成的输入云剖面集合中运行。通过对大量参数集（配置）进行采样，可以有效地探索云几何参数值的空间，这些参数集使用模拟SPARTACUS求解器的快速替代模型从中计算出辐射度量。通过将这些辐射度量与3D RT蒙特卡洛模型提供的参考值进行比较，可以评估采样的配置。与使用从3D云场计算出的参数值的预测值相比，最佳的校准结构可以更好地预测TOA和表面通量：^-2与LES-导出的参数来〜5了Wm ^-2与调整的参数。但是，云几何的校准无法减少吸收误差，该误差仍保持在2-4 Wm ^-2左右。