A Minimal Model of the Deep-Convection Life Cycle and Its Verification in Remote Sensing Observations

Abstract

Deep convection is one of the most important atmospheric transport mechanisms and is associated with various severe weather phenomena. Manifestations of deep convection in the atmosphere are composed of a recurring fundamental building block, called a cell, which evolves through a characteristic life cycle. Despite its importance, no simple, physically consistent quantitative life cycle model exists that correctly reproduces remote sensing observations. Based on the standard conceptual model, we develop an analytic minimal model of the convection life cycle 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. To demonstrate quantitative agreement of our convection life cycle model, we construct a representative random sample of satellite and radar observations of deep-convection events over Germany. To remove events not covered by our model from the random sample, we introduce a conditional sampling strategy based on an objective time-scale criterion. We show that by this procedure, a definite and recurrent temporal signature of the convection life cycle can be identified. Our model closely reproduces the principal characteristics while essentially staying within one standard deviation of the mean signature. For the first time, this study demonstrates the quantitative agreement between a minimal model of the deep-convection life cycle, derived in the framework of chemical reactions, and a statistical sample of remote sensing observations.