Nowadays research is triggered by the permanent need to better relate the measured radar reflectivity to the surface rainfall. The knowledge on flow structure within cloud formation systems and associated convective-stratiform separation may provide useful information in this respect. We will first discuss how the stochastic multifractals can handle the differences of scales and measurement densities of the rain gauge and radar data; and help to merge information from these data. We use the mosaics of the METEO-FRANCE ARAMIS radar network that correspond to horizontal projections of the radar rainfall estimates over a 1 km x 5 min grid over France. In particular, three storm events with different dynamics over the Paris region were selected to illustrate the efficiency of the multifractal framework. In spite of the difficulty that usually the same precipitation field comprises both, stratiform and convective formations, their respective scaling properties allow to decipher and to classify the radar data
