Abstract. The number of cloud droplets formed at the cloud base depends both on the properties of aerosol particles and the updraft velocity of an air parcel at the cloud base. As the spatial scale of updrafts is too small to be resolved in global atmospheric models, the updraft velocity is commonly parameterised based on the available turbulent kinetic energy. Here we present alternative methods through parameterising updraft velocity based on high-resolution large eddy simulation (LES) runs in the case of marine stratocumulus clouds. First we use our simulations to assess the accuracy of a simple linear parametrisation where the updraft velocity depends only on cloud top radiative cooling. In addition, we present two different machine learning methods (Gaussian process emulation and random forest) that account for different boundary layer conditions and cloud properties. We conclude that both machine learning parameterisations reproduce the LES-based updraft velocities at about the same accuracy, while the simple approach employing radiative cooling only produce on average lower coefficient of determination and higher root mean square error values. Finally, we apply these machine learning methods to find the key parameters affecting cloud base updraft velocities.

中文翻译：

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参数化海相层积云底上升气流速度

摘要。在云底形成的云滴数量取决于气溶胶粒子的性质和云底气团的上升气流速度。由于上升气流的空间尺度太小而无法在全球大气模型中解决，上升气流速度通常基于可用的湍流动能进行参数化。在这里，我们在海洋层积云的情况下，通过基于高分辨率大涡模拟 (LES) 运行的参数化上升气流速度，提出了替代方法。首先，我们使用我们的模拟来评估简单线性参数化的准确性，其中上升气流速度仅取决于云顶辐射冷却。此外，我们提出了两种不同的机器学习方法（高斯过程仿真和随机森林），它们解释了不同的边界层条件和云特性。我们得出结论，两种机器学习参数化都以大致相同的精度再现基于 LES 的上升气流速度，而采用辐射冷却的简单方法仅产生平均较低的确定系数和较高的均方根误差值。最后，我们应用这些机器学习方法来寻找影响云底上升气流速度的关键参数。而采用辐射冷却的简单方法平均只会产生较低的确定系数和较高的均方根误差值。最后，我们应用这些机器学习方法来寻找影响云底上升气流速度的关键参数。而采用辐射冷却的简单方法平均只会产生较低的确定系数和较高的均方根误差值。最后，我们应用这些机器学习方法来寻找影响云底上升气流速度的关键参数。