Description of mesoscale pattern formation in shallow convective cloud fields by using time-dependent Ginzburg-Landau and Swift-Hohenberg stochastic equations

Diana L. Monroy; Gerardo G. Naumis

doi:10.1103/physreve.103.032312

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arXiv · 2006.03981

Description of mesoscale pattern formation in shallow convective cloud fields by using time-dependent Ginzburg-Landau and Swift-Hohenberg stochastic equations

Diana L. Monroy·Gerardo G. Naumis

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Abstract

The time-dependent Ginzburg-Landau equation and the Swift-Hohenberg equation, both added with a stochastic term, are proposed to describe cloud pattern formation and cloud regime phase transitions of shallow convective clouds organized in mesoscale systems. The starting point is the Hottovy-Stechmann linear spatio-temporal stochastic model for tropical precipitation, used to describe the dynamics of water vapor and tropical convection. By taking into account that shallow stratiform clouds are close to a self-organized criticallity and that water vapor content is the order parameter, it is observed that sources must have non-linear terms in the equation to include the dynamical feedback due to precipitation and evaporation. The inclusion of this non-linearity leads to a kind of time-dependent Ginzburg-Landau stochastic equation, originally used to describe superconductivity phases. By performing numerical simulations, pattern formation is observed, specially for cellular convective phases. These patterns are much better compared with real satellite observations than the pure linear model. This is done by comparing the spatial Fourier transform of real and numerical cloud fields. Finally, by considering fluctuation terms for the turbulent eddy diffusion we arrive to a Hohenberg-Swift equation. The obtained patterns are much more organized that the patterns obtained from the Ginzburg-Landau equation in the case of closed cellular and roll convection.

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Diana L. Monroy, Gerardo G. Naumis. 2020-11-11. Description of mesoscale pattern formation in shallow convective cloud fields by using time-dependent Ginzburg-Landau and Swift-Hohenberg stochastic equations. https://doi.org/10.1103/physreve.103.032312

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