Stratus Cloud Structure from mm-Radar Transects and Satellite Images: Scaling Properties and Artifact Detection with Semi-Discrete Wavelet Analyses

Spatial and/or temporal variabilities of clouds is of paramount importance for at least two intensely researched sub-problems in global and regional climate modeling: • cloud-radiation interaction where correlations can trigger three-dimensional (3D) radiative transfer effects; and • dynamical cloud modeling where the goal is to realistically reproduce the said correlations. We propose wavelets as a simple yet powerful way of quantifying cloud variability. More precisely, we use “semi-discrete” wavelet transforms that, at least in the present statistical applications, have advantages over both its continuous and discrete counterparts found in the bulk of the wavelet literature. With the particular choice of normalization we adopt, the scale-dependence of the variance of the wavelet coefficients (i.e., the wavelet energy spectrum) is always a better discriminator of transition