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

TL;DR
This paper models mesoscale shallow convective cloud pattern formation using stochastic Ginzburg-Landau and Swift-Hohenberg equations, demonstrating improved simulation of real satellite cloud patterns through non-linear feedback and turbulence considerations.
Contribution
It introduces a novel application of stochastic Ginzburg-Landau and Swift-Hohenberg equations to describe cloud pattern formation and phase transitions, incorporating non-linear feedback and turbulence effects.
Findings
Patterns match satellite observations better than linear models
Non-linear terms are essential for realistic cloud dynamics
Turbulence inclusion leads to more organized patterns
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…
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