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

Robert Polzin,Péter Koltai,Henning Rust,Peter Névir,Annette Müller

doi:10.5194/npg-29-37-2022

Robert Polzin, Péter Koltai + Show 3 more

Open Access

https://doi.org/10.5194/npg-29-37-2022

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Abstract

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.

Highlights

Complex dynamical processes involving scaling cascades are omnipresent in natural science
We develop a conceptual categorical description for smaller scale vertical velocity, which is linked to a large-scale flow variable
Since we focus on smaller scale convective events conditioned on large-scale dynamics in the atmosphere, we consider the summer months July and August in the years 1995 to 2015

Summary

Introduction

Complex dynamical processes involving scaling cascades are omnipresent in natural science. The smallest and largest scales are far apart, and much of the scale range is involved by scale interactions. Dynamics in the atmosphere take place across a large range of timescales and length scales, from micro-seconds to months and lengths from 10−5 to 106 m. For processes of a spatial scale above several kilometers, geostrophic and hydrostatic equilibria induce a spatial–. Medium-range weather forecasts are made up to 10 d in advance. Predictions of convection further in advance cannot be deterministic and are highly uncertain because errors of the variable on a small scale at the initial state are growing

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