Rainfall detection over northern Algeria by combining MSG and TRMM data

Abstract

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.

Two Different Integration Methods for Weather Radar-Based Quantitative Precipitation Estimation

“…Precipitation estimation has been improved using new methods such as a combination of satellite-based precipitation products [16][17][18][19], merging radar and satellite precipitation [20], machine learning [18,21], the fusion of multiple radar-based precipitation products method [22], probabilistic quantitative precipitation estimation (PQPE) [23,24], and the Climatological Vertical Profiles of Reflectivity Identification and Enhancement (CVPR-IE) method [25]. On the basis of Meteosat Second Generation (MSG) and Tropical Rainfall Measuring Mission (TRMM) data, Ouallouche and Ameur [18] used an artificial neural network (ANN) for modelling and presented a new method to delineate rain areas in Algeria. Their approach worked well and overcame the shortcomings of the scattering index (SI) method.…”