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Vol. 145, No. 10, September 2012, pp. 19-32




Identifying Convective and Stratiform Rain by Confronting SEVERI Sensor Multispectral Infrared to Radar Sensor Data
Using Neural Network


1 M. Lazri, 1 F. Ouallouche, 1 S. Ameur, 2 J. M. Brucker and 1 Y. Mohia

 1 Laboratoire LAMPA, University of Tizi Ouzou, Tizi Ouzou, Algeria

Tel.:+213554587577, fax:+21326218642

2 School EPMI, Paris, France,

EPMI - 13 Boulevard de l'Hautil 95092 CERGY PONTOISE Cedex

Tel.:, fax:

E-mail: m_lazri@yahoo.fr, ouafethi_04@yahoo.fr, ameursoltane@yahoo.com, jm.brucker@epmi.fr mohiayacine@yahoo.fr



Received: 14 August 2012   /Accepted: 22 October 2012   /Published: 31 October 2012

Digital Sensors and Sensor Sysstems


Abstract: This paper investigates the potential for developing schemes that classify convective and stratiform precipitation areas using the high infrared spectral resolution of the SEVIRI sensor (Spinning Enhanced Visible and Infrared Imager). It is a technique based on neural network (NN) using information about optical and microphysical cloud properties from SEVIRI. The nonparametric NN approach approximates the best nonlinear function between multispectral information about pixel derived from MSG satellite data and rain information from radar data to classify convective and stratiform rain. The neural network developed here accepts SEVIRI data as input and radar data as output data that are in spatiotemporal coincidence. The results show that the quality of information used from the SEVIRI sensor and the use of Multilayer architecture percepetron with two hidden layers were used to provide a good classification.


Keywords: SEVIRI sensor, Neural network, Radar sensor, Identifying raining cloud



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