Review article

Applied Water Science

pp 1-10

First online:

Open Access This content is freely available online to anyone, anywhere at any time.

Rainfall detection over northern Algeria by combining MSG and TRMM data

  • Fethi OualloucheAffiliated withLAMPA Laboratory, Mouloud Mammeri University Email author 
  • , Soltane AmeurAffiliated withLAMPA Laboratory, Mouloud Mammeri University


In this paper, a new method to delineate rain areas in northern Algeria is presented. The proposed approach is based on the blending of the geostationary meteosat second generation (MSG), infrared channel with the low-earth orbiting passive tropical rainfall measuring mission (TRMM). To model the system designed, we use an artificial neural network (ANN). We seek to define a relationships between three parameters calculated from TRMM microwave imager (TMI) associated with four parameters from infrared sensors of MSG satellite and two classes (rain, no-rain) from precipitation radar (PR) TRMM data. The seven spectral parameters issued from MSG and TMI are used as input data. Rain/no-rain classes from PR are used as the output data of this ANN. Two steps are necessary: training and validation. Results in the developed scheme are compared with the results of a reference method which is the scattering index (SI) method. The result shows that the developed model works very well and overcomes the shortcomings of the SI method.


Rainfall detection MSG TRMM ANN