A refined statistical cloud closure using double-Gaussian probability density functions

A refined statistical cloud closure

Introduction Conclusions References

Products and Convolutions of Gaussian Probability Density Functions

Probability density functions in the cloud-top mixing layer

The cloud-top mixing layer is an idealized configuration often employed in the literature to study local aspects (over length scales of the order of 10m) of the top of stratocumulus-topped mixed layers. Latent heat effects are further investigated here by means of direct numerical simulations, discussing the probability density functions of the horizontal and vertical velocities, as well as the...

Using Probability Density Functions to Derive Consistent Closure Relationships among Higher-Order Moments

Parameterizations of turbulence often predict several lower-order moments and make closure assumptions for higher-order moments. In principle, the lowand high-order moments share the same probability density function (PDF). One closure assumption, then, is the shape of this family of PDFs. When the higher-order moments involve both velocity and thermodynamic scalars, often the PDF shape has bee...

Continuation of probability density functions using a generalized Lyapunov approach

Techniques from numerical bifurcation theory are very useful to study transitions between steady fluid flow patterns and the instabilities involved. Here, we provide computational methodology to use parameter continuation in determining probability density functions of systems of stochastic partial differential equations near fixed points, under a small noise approximation. Key innovation is th...