Preprints
https://doi.org/10.5194/npg-2021-26
https://doi.org/10.5194/npg-2021-26

  11 Aug 2021

11 Aug 2021

Review status: this preprint is currently under review for the journal NPG.

Direct Bayesian model reduction of smaller scale convective activity conditioned on large scale dynamics

Robert Polzin1, Annette Müller2, Henning Rust2, Peter Névir2, and Péter Koltai1 Robert Polzin et al.
  • 1Institute of Mathematics, Free University Berlin, Germany
  • 2Institute of Meteorology, Free University Berlin, Germany

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.

Robert Polzin et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on npg-2021-26', Anonymous Referee #1, 22 Aug 2021
    • AC1: 'Reply on RC1', Robert Malte Polzin, 20 Sep 2021
  • RC2: 'Comment on npg-2021-26', Anonymous Referee #2, 14 Sep 2021

Robert Polzin et al.

Robert Polzin et al.

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Short summary
In this study, a recent algorithmic framework called Direct Bayesian Model Reduction (DBMR) is applied which provides a scalable probability-preserving identification of reduced models directly from data. The stochastic method is tested in a meteorological application towards a model reduction to latent states of smaller scale convective activity conditioned on large scale atmopsheric flow.