Abstract. We pursue a simplified stochastic representation of smaller scale convective activity conditioned on large scale dynamics in the atmosphere. For identifying a Bayesian model describing the relation of different scales we use a probabilistic approach (Gerber and Horenko, 2017) called Direct Bayesian Model Reduction (DBMR). The convective available potential energy (CAPE) is applied as large scale flow variable combined with a subgrid smaller scale time series for the vertical velocity. We found a probabilistic relation of CAPE and vertical up- and downdraft for day and night. The categorization is based on the conservation of total probability. This strategy is part of a development process for parametrizations in models of atmospheric dynamics representing the effective influence of unresolved vertical motion on the large scale flows. The direct probabilistic approach provides a basis for further research of smaller scale convective activity conditioned on other possible large scale drivers.

中文翻译：

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以大规模动态为条件的小规模对流活动的直接贝叶斯模型减少

摘要。我们追求以大气中大尺度动力学为条件的小尺度对流活动的简化随机表示。为了识别描述不同尺度关系的贝叶斯模型，我们使用概率方法（Gerber 和 Horenko，2017 年），称为直接贝叶斯模型缩减(DBMR)。对流可用势能 (CAPE) 作为大尺度流量变量与垂直速度的子网格小尺度时间序列相结合。我们发现了白天和黑夜的 CAPE 和垂直上升和下降气流的概率关系。分类基于总概率守恒。该策略是大气动力学模型参数化开发过程的一部分，代表未解决的垂直运动对大规模流动的有效影响。直接概率方法为进一步研究以其他可能的大规模驱动因素为条件的小规模对流活动提供了基础。