Rainfall detection over northern Algeria by combining MSG and TRMM data
Rainfall detection over northern Algeria by combining MSG and TRMM data
复制标题
结合 MSG 和 TRMM 数据检测阿尔及利亚北部的降雨量
DOI：
10.1007/s13201-014-0204-8
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发表时间：
2016
影响因子：
5.5
通讯作者：
S. Ameur
中科院分区：
文献类型：
--
作者：
Fethi Ouallouche;S. Ameur
本文提出了一种新的方法来划定阿尔及利亚北方的雨区。所提出的方法是基于地球静止气象卫星第二代（MSG），红外通道与低地球轨道被动热带降雨测量使命（TRMM）的混合。为了模拟设计的系统，我们使用人工神经网络（ANN）。我们试图定义从TRMM微波成像仪（TMI）计算的三个参数与MSG卫星的红外传感器和两个类（雨，无雨）从降水雷达（PR）TRMM数据的四个参数之间的关系。由MSG和TMI发出的七个光谱参数被用作输入数据。人工神经网络的输出数据是PR中的雨/无雨类别，需要两个步骤：训练和验证。在开发计划的结果进行了比较与参考方法，这是散射指数（SI）的方法的结果。结果表明，该模型能较好地克服SI法的不足。
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.