| Title: |
Direct Bayesian model reduction of smaller scale convective activity conditioned on large-scale dynamics. |
| Authors: |
Polzin, Robert; Müller, Annette; Rust, Henning; Névir, Peter; Koltai, Péter |
| Source: |
Nonlinear Processes in Geophysics; 2022, Vol. 29 Issue 1, p37-52, 16p |
| Subject Terms: |
VERTICAL motion; ATMOSPHERIC models; ATMOSPHERIC circulation; CONDITIONAL probability; POTENTIAL energy |
| 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 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. [ABSTRACT FROM AUTHOR] |
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| Database: |
Complementary Index |