Articles | Volume 29, issue 1
Nonlin. Processes Geophys., 29, 37–52, 2022
https://doi.org/10.5194/npg-29-37-2022
Nonlin. Processes Geophys., 29, 37–52, 2022
https://doi.org/10.5194/npg-29-37-2022

Research article 16 Feb 2022

Research article | 16 Feb 2022

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

Robert Polzin et al.

Data sets

Data repository for Polzin et al. (2022) "Direct Bayesian model reduction of smaller scale convective activity conditioned on large-scale dynamics" R. M. Polzin https://doi.org/10.17169/refubium-33435

Model code and software

Bayesian-Model-Reduction-Toolkit S. Gerber https://github.com/SusanneGerber/Bayesian-Model-Reduction-Toolkit

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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 atmospheric flow.