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首页> 外文期刊>Theoretical and applied climatology >Classifying convective and stratiform rain using multispectral infrared Meteosat Second Generation satellite data
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Classifying convective and stratiform rain using multispectral infrared Meteosat Second Generation satellite data

机译：利用多光谱红外Meteosat第二代卫星数据对对流和层状降雨进行分类

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This paper investigates the potential for developing schemes that classify convective and stratiform precipitation areas using the high infrared spectral resolution of the Meteosat Second Generation-Spinning Enhanced Visible and Infrared Imager (MSG-SEVIRI). Two different classification schemes were proposed that use the brightness temperature (BT) T_(10.8) along with the brightness temperature differences (BTDs) T_(10.8)-T_(12.1), T_(8.7)-T_(10.8), and T_(6.2)-T_(10.8) as spectral parameters, which provide information about cloud parameters. The first is a common multispectral thresholding scheme used to partition the space of the spectral cloud parameters and the second is an algorithm based on the probability of convective rain (PCR) for each pixel of the satellite data. Both schemes were calibrated using as a reference convectivestratiform rain classification fields derived from 87 stations in Greece for six rainy days with high convective activity. As a result, one single infrared technique (TB_(10) and two multidimensional techniques (BTD_(all) and PCR) were constructed and evaluated against an independent sample of rain gauge data for four daily convective precipitation events. It was found that the introduction of BTDs as additional information to a technique works in improving the discrimination of convective from stratiform rainy pixels compared to the single infrared technique BT_(10). During the training phase, BTD_(all)performed slightly better than BT_(10) while PCR technique outperformed both threshold techniques. All techniques clearly overestimate the convective rain occur- rences detected by the rain gauge network. When evaluating against the independent dataset, both threshold techniques exhibited the same performance with that of the dependent dataset whereas the PCR technique showed a notable skill degradation. As a result, BTD_(all) performed best followed at a short distance by PCR and BT_(10). These findings showed that it is possible to apply a convective/ stratiform rain classification algorithm based on the enhanced infrared spectral resolution of MSG-SEVIRI, for nowcasting or climate purposes, despite the highly variable nature of convective precipitation.
机译：本文研究了利用Meteosat第二代自旋增强型可见光和红外成像仪（MSG-SEVIRI）的高红外光谱分辨率来开发对流和层状降水区域分类方案的潜力。提出了两种不同的分类方案，它们使用亮度温度（BT）T_（10.8）以及亮度温度差（BTD）T_（10.8）-T_（12.1），T_（8.7）-T_（10.8）和T_（ 6.2）-T_（10.8）作为光谱参数，可提供有关云参数的信息。第一种是用于划分频谱云参数空间的通用多光谱阈值方案，第二种是基于对流卫星卫星每个像素的对流降雨（PCR）概率的算法。两种方案均以对流层状降雨分类场为参考进行校准，该场来自希腊的87个气象站，历时6个雨天，对流活动较高。结果，构造了一个单一的红外技术（TB_（10）和两个多维技术（BTD_（all）和PCR）），并针对一个独立的雨量计数据样本对四个日常对流降水事件进行了评估。与单红外技术BT_（10）相比，将BTDs作为一种技术的附加信息可改善对流与层状雨状像素的区分，在训练阶段，BTD_（all）的性能略好于BT_（10），而PCR技术所有技术均明显高估了雨量计网络检测到的对流降雨的发生率；在对独立数据集进行评估时，两种阈值技术均表现出与相关数据集相同的性能，而PCR技术则表现出显着的技巧结果，BTD_（all）表现最佳，其次是PCR和BT_（10）在短距离内。然而，尽管对流降水的性质变化很大，但仍可以将基于MSG-SEVIRI的增强红外光谱分辨率的对流/层状雨分类算法应用于临近预报或气候目的。

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  • 来源
    《Theoretical and applied climatology》 |2012年第4期|p.613-630|共18页
  • 作者

    Haralambos Feidas; Apostolos Giannakos;

  • 作者单位

    School of Geology, Department of Meteorology and Climatology,Aristotle University of Thessaloniki,54124 Thessaloniki, Greece;

    School of Geology, Department of Meteorology and Climatology,Aristotle University of Thessaloniki,54124 Thessaloniki, Greece;

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