A minimal model of the deep-convection lifecycle and its verification in remote-sensing observations

TL;DR

This paper introduces a simple, physically consistent minimal model of the deep convection lifecycle, validated against remote-sensing observations, which could improve weather prediction and convection parametrizations.

Contribution

The authors develop and validate a minimal analytic model of the deep convection lifecycle that aligns with empirical remote-sensing data, filling a gap in existing quantitative models.

Findings

Model reproduces key lifecycle characteristics within one standard deviation.

Conditional sampling reveals a recurrent temporal signature of convection.

Model has potential applications in nowcasting and convection parametrizations.

Abstract

Deep convection is one of the most important atmospheric transport mechanisms and associated with various severe weather phenomena. Manifestations of deep convection in the atmosphere are composed of a recurring fundamental building block, called cell, which evolves through a characteristic lifecycle. Despite its importance, no simple, physically consistent quantitative lifecycle model exists that correctly reproduces remote-sensing observations. Based on the standard conceptual model, we develop an analytic minimal model of the convection lifecycle in the form of coupled reaction rules. This reaction scheme is equivalent to a system of coupled nonlinear differential equations that qualitatively agree with the empirically known dynamics. In order to demonstrate quantitative agreement of our convection-lifecycle model, we construct a representative random sample of satellite and radar observations of deep-convection events over Germany. In particular, we introduce a conditional sampling strategy based on an objective timescale criterion to remove events not covered by our model from the random sample. We show that by this procedure, a definite and recurrent temporal signature of the convection lifecycle can be identified. Our model closely reproduces the principal characteristics while essentially staying within one standard deviation of the mean signature. Next to the definite relevance of our model for nowcasting, the approach may be useful for convection parametrisations.