School EPMI

L'Isle-sur-la-Sorgue, France

School EPMI

L'Isle-sur-la-Sorgue, France
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Lazri M.,Mouloud Mammeri University | Ameur Z.,Mouloud Mammeri University | Ameur S.,Mouloud Mammeri University | Mohia Y.,Mouloud Mammeri University | And 2 more authors.
Advances in Space Research | Year: 2013

The ultimate objective of this paper is the estimation of rainfall over an area in Algeria using data from the SEVIRI radiometer (Spinning Enhanced Visible and Infrared Imager). To achieve this aim, we use a new Convective/Stratiform Rain Area Delineation Technique (CS-RADT). The satellite rainfall retrieval technique is based on various spectral parameters of SEVIRI that express microphysical and optical cloud properties. It uses a multispectral thresholding technique to distinguish between stratiform and convective clouds. This technique (CS-RADT) is applied to the complex situation of the Mediterranean climate of this region. The tests have been conducted during the rainy seasons of 2006/2007 and 2010/2011 where stratiform and convective precipitation is recorded. The developed scheme (CS-RADT) is calibrated by instantaneous meteorological radar data to determine thresholds, and then rain rates are assigned to each cloud type by using radar and rain gauge data. These calibration data are collocated with SEVIRI data in time and space. © 2013 COSPAR. Published by Elsevier Ltd. All rights reserved.

Lazri M.,Mouloud Mammeri University | Ouallouche F.,Mouloud Mammeri University | Ameur S.,Mouloud Mammeri University | Brucker J.M.,School EPMI | Mohia Y.,Mouloud Mammeri University
Sensors and Transducers | Year: 2012

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. © 2012 IFSA.

Lazri M.,Mouloud Mammeri University | Brucker J.M.,School EPMI | Lahdir M.,Mouloud Mammeri University | Sehad M.,Mouloud Mammeri University
Journal of Earth System Science | Year: 2015

The present work studies the trends in drought in northern Algeria. This region was marked by a severe, wide-ranging and persistent drought due to its extraordinary rainfall deficit. In this study, drought classes are identified using SPI (standardized precipitation index) values. A Markovian approach is adopted to discern the probabilistic behaviour of the time series of the drought. Thus, a transition probability matrix is constructed from drought distribution maps. The trends in changes in drought types and the distribution area are analyzed. The results show that the probability of class severe/extreme drought increases considerably rising from the probability of 0.2650 in 2005 to a stable probability of 0.5756 in 2041. © Indian Academy of Sciences.

Lazri M.,Mouloud Mammeri University | Ameur S.,Mouloud Mammeri University | Brucker J.M.,School EPMI | Ouallouche F.,Mouloud Mammeri University
Atmospheric Research | Year: 2014

Convective precipitation events from Meteosat Second Generation (MSG) in northern Algeria during April 2006 to October 2006 are analyzed. This manuscript puts forward an improved method of precipitation estimation, named Cold Cloud Phase Duration (CCPD), which is based on different development phase duration (growth and decay phases) of convective cloud cluster to overcome the obvious deficiency of the present commonly used CCD (Cold Cloud Duration) method for satellite remote sensing estimation of convective precipitation. The life cycle phase of a given convective cloud (growth-decay) is evaluated through the different internal dynamics of cloud. The CCPD analyzes the evolution of three parameters; namely the average of cloud top temperature, the vertical extent of cloud and cloud water path to identify different phases of life cycle of convective clouds. Then, rain rates are assigned to each phase type by using rain gauge data. The evaluation of the CCPD method was performed by comparison with rain gauge data collected during April 2010 to October 2010. The results reveal that CCPD performs better compared to the original CCD method. © 2014 Elsevier B.V.

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