A Machine-Learning-Assisted Stochastic Cloud Population Model as a Parameterization of Cumulus Convection
A Machine-Learning-Assisted Stochastic Cloud Population Model as a Parameterization of Cumulus Convection
复制标题
作为积云对流参数化的机器学习辅助随机云种群模型
DOI：
10.1029/2021ms002808
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发表时间：
2022
影响因子：
6.8
通讯作者：
Hagos S
中科院分区：
文献类型：
--
作者：
Hagos S
一个机器学习辅助的随机云群模型与高级研究天气研究和预报（WRF）模型相结合，以表示与积云对流单体之间的生命周期和相互作用相关的云基质量通量的波动。在这个云量模式中，对流单体的大小分布和相关的云基质量通量与它们先前的状态有关，并通过过渡函数与总对流面积的变化有关。对流面积的趋势又被假定取决于主机WRF模式所解决的云基质量通量。转换函数由单个隐藏层神经网络表示，该网络通过在澳大利亚季风区运行的1 km网格间距WRF模拟中对流单体大小分布的演变进行训练。在主机模式的每个网格点上，云群模式预测单元大小和云基质量通量分布，从中将单元的随机样本馈送到计算降水以及相关的液态水势温度和总湿度趋势的夹带包裹模式。这些趋势在细胞上平均并提供给宿主模型。在热带和中纬度区域进行了几次区域模拟，以测试这是一种潜在的尺度感知参数化方法。结果表明，这种方法可能是一种新的有前途的途径，以模拟现实的降水统计和传播的降水与马登-朱利安振荡，同时保持现实的昼夜周期在陆地和海洋。
A machine‐learning‐assisted stochastic cloud population model is coupled with the Advanced Research Weather Research and Forecasting (WRF) model to represent fluctuations in the cloud‐base mass flux associated with the life cycles and interactions among cumulus convection cells. In this cloud population model, the size distribution and the associated cloud‐base mass flux of the convective cells are related to their previous state and to the change in the total convective area via a transition function. The convective area tendency in turn is assumed to depend on the cloud‐base mass flux that is resolved by the host WRF model. The transition function is represented by a single hidden‐layer neural network trained by the evolution of convective cell size distributions in a 1‐km grid‐spacing WRF simulation run over the Australian Monsoon region. At every grid point of the host model, the cloud population model predicts the cell size and cloud‐base mass flux distributions from which a random sample of cells is fed to an entraining parcel model that calculates precipitation as well as the associated liquid water potential temperature and total moisture tendencies. These tendencies are averaged over the cells and provided to the host model. Several regional simulations are performed over tropical and midlatitude domains to test this as a potential approach to scale‐aware parameterization. It is shown that such an approach could be a new promising path to simulating realistic precipitation statistics and propagation of precipitation associated with the Madden‐Julian Oscillation while maintaining realistic depictions of the diurnal cycle over both land and ocean.