Two complementary multifractal analysis techniques for non-stationary atmospheric processes with an application to cloud liquid water content

The authors characterize the 3D structure of clouds (as sampled by 1D transects of liquid water content (LWC)) using extensions of simple, robust, and turbulence--proven multifractal statistical methods and models. The goal is to develop the simplest possible stochastic models with the minimum number of free parameters, yet one capable of capturing variability on the widest possible range of scales. Not only is this an important prelude to the theoretical and empirical study of cloud radiation problems, but also has applications to other ARM Programs such as comparison of model output with ARM observations at a much deeper level than traditional mean-variance, and interpolations and extrapolations in space and time scale