Image Segmentation Method for Identifying Convective and Stratiform Rain using MSG SEVIRI Data

This paper provides a new method for the classification of rainfall areas in convective and stratiform rain using MSG/SEVIRI (Spinning Enhanced Visible and Infrared) data. The proposed approach is based on spectral and temporal properties of clouds. The spectral parameters used are: brightness temperature (BT) and brightness temperature differences (BTDs), and the temporal parameter (RCT10.8) is the rate of change of (BT) in the 10.8µm channel over two consecutive images. The developed rain area classification technique (RACT-DN) is based on two multilayer perceptron neural networks (MLP-D for daytime and MLP-N for nighttime) which relies on the correlation of satellite data with convective and stratiform rain. The two algorithms (MLP-D and MLP-N) are trained using as reference data from ground meteorological radar over northern Algeria. The results show that RACT-DN classifier gives accurate discrimination between convective and stratiform areas during daytime and nighttime.