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.
<p>The presented work contains an investigation of the stochastic aggregation&#160;of convective structures on different scales in the atmosphere. A<br>computational framework is applied that provides highly scalable&#160;identification of reduced Bayesian models. The deterministic large scale<br>flow variables are reduced into latent states, whereas the stochastic&#160;small scale convective structures are affiliated to these. The analysis of<br>the latent states in number and maximization reduction improves the&#160;understanding for the large scale forcing of convective processes. The<br>convective structures are determined by vertical velocities. Different&#160;variables of the large-scale flow, such as the convective available<br>potential energy, available moisture, vertical windshear and the Dynamic&#160;State Index (DSI), a diabaticity indicator, are investigated. Our approach<br>does not require a distributional assumption but works instead with a&#160;discretised and categorised state vector.</p>
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 by Gerber and Horenko (2017) called Direct Bayesian Model Reduction (DBMR). This is a Bayesian relation model between categorical processes (discrete states), formulated via the conditional probabilities. The convective available potential energy (CAPE) is applied as a 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. 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 on smaller scale convective activity conditioned on other possible large-scale drivers.
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