Abstract Autoconversion is the mass transfer from cloud to precipitation water in an early stage of cloud development, and is the dominant process in the formation of embryonic droplets that trigger precipitation formation. The accurate parameterization of this process is key, in order to improve the interaction between cloud microphysics and cloud dynamics for models from cloud scale to the global climate scale. For model based parameterizations of the auto-conversion process, the usual approach to develop an autoconversion parameterization is by curve fitting the autoconversion rates obtained from simulations or numerical solutions of the kinetic collection equation under a wide range of initial conditions. However, in this case, the autoconversion is modeled by a function that is a nonlinear product liquid water content and droplet concentration and depends on a small number of parameters. As a result, a large amount of scatter around the actual values can be obtained, indicating a weak relationship between actual and fitted autoconversion rates. The purpose of this paper is to analyze whether neural networks are better than traditional curve fitting or regression to obtain parameterizations of autoconversion. Then, a deep neural network was trained from an autconversion rates dataset generated by solving the kinetic collection equation for a wide range of droplet concentrations and liquid water contents. The obtained machine learned parameterization shows a very good match with actual rates calculated from the kinetic collection equation.

中文翻译：

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自动转换过程的两时刻机器学习参数化

摘要 自转换是云发展早期从云到降水水的质量转移，是形成引发降水形成的胚胎液滴的主要过程。该过程的准确参数化是关键，以改善从云尺度到全球气候尺度模型的云微物理和云动力学之间的相互作用。对于自动转换过程的基于模型的参数化，开发自动转换参数化的常用方法是通过曲线拟合从动力学收集方程在广泛的初始条件下的模拟或数值解获得的自动转换率。然而，在这种情况下，自动转化由一个函数建模，该函数是非线性产品液态水含量和液滴浓度，并取决于少量参数。因此，可以获得围绕实际值的大量散布，表明实际和拟合的自动转换率之间的关系很弱。本文的目的是分析神经网络是否优于传统的曲线拟合或回归以获得自动转换的参数化。然后，从通过求解动力学收集方程生成的自转化率数据集训练深度神经网络，该方程适用于各种液滴浓度和液态水含量。获得的机器学习参数化显示与根据动力学收集方程计算的实际速率非常匹配。