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首页> 外文期刊>IEEE Transactions on Geoscience and Remote Sensing >The potential of combining SSM/I and SSM/T2 measurements to improve the identification of snowcover and precipitation
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The potential of combining SSM/I and SSM/T2 measurements to improve the identification of snowcover and precipitation

机译:结合SSM / I和SSM / T2测量的潜力可改善对积雪和降水的识别

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摘要

The potential of passive microwave radiometry for classifying snowcover and precipitation using measurements from the Special Sensor Microwave/Imager (SSM/I) and the Special Sensor Microwave Water Vapor Profiler (SSM/T2) is investigated by modelling the radiative transfer for different surface types and atmospheric conditions. The model accounts for various land surfaces and vegetation covers, different snow types as well as wind roughened ocean water. The atmospheric part includes multiple scattering and depolarization by cloud droplets and precipitating water as well as ice spheres. It was found, that the combination of a window channel (91 GHz) and an atmospheric sounding channel (183/spl plusmn/7 GHz) can improve the separation of snowcover and precipitation which is difficult by using only SSM/I channels. The 183/spl plusmn/7 GHz channel is strongly influenced by the water vapor distribution which makes its use difficult for warm rain cases and low cloud tops. Then, the signature at this frequency is not unique and the above relation gives no further improvement of the classification. However, the identification of rainfall over cold land backgrounds can be significantly improved, which is illustrated by the application of a combined SSM/I-SSM/T2 algorithm to two satellite datasets when compared to the SSM/I algorithm and to operational surface weather maps.
机译:通过对不同表面类型和辐射表面的辐射传递进行建模,研究了使用特殊传感器微波/成像仪(SSM / I)和特殊传感器微波水蒸气廓线仪(SSM / T2)进行测量得出的无源微波辐射法对积雪和降水进行分类的潜力。大气条件。该模型考虑了各种陆地表面和植被覆盖,不同的雪类型以及风粗糙的海水。大气部分包括由云滴和水以及冰球沉淀引起的多次散射和去极化。已经发现,窗口通道(91 GHz)和大气探测通道(183 / spl plusmn / 7 GHz)的组合可以改善积雪和降水的分离,这仅通过使用SSM / I通道很难实现。 183 / spl plusmn / 7 GHz信道受到水蒸气分布的强烈影响,这使其难以在温暖的雨天和低云顶使用。这样,该频率下的签名不是唯一的,并且上述关系没有进一步改善分类。然而,与SSM / I算法和可操作的地表天气图相比,将组合的SSM / I-SSM / T2算法应用于两个卫星数据集可以明显地提高对寒冷土地背景上降雨的识别能力。 。

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