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Automatic Classification of Clouds on METEOSAT Imagery: Application to High-Level Clouds

Michel DesboisLaboratoire de Météorologie Dynamique du CNRS, École Polytechnique-91128 Palaiseau Cedex, France

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Genevieve SezeLaboratoire de Météorologie Dynamique du CNRS, École Polytechnique-91128 Palaiseau Cedex, France

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Gerard SzejwachLaboratoire de Météorologie Dynamique du CNRS, École Polytechnique-91128 Palaiseau Cedex, France

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Abstract

A statistical classification method based on clustering on three-dimensional histograms is applied to the three channels of the METEOSAT imagery [Visible (VIS)–Infrared Window (IR)–Infrared Water Vapor (WV)]. The results of this classification are studied for different cloud cover cases over tropical regions. For high-level cloud classes, it is shown that the bidimensional histogram IR-WV allows one to deduce the cloud top temperature even for semi-transparent clouds.

Abstract

A statistical classification method based on clustering on three-dimensional histograms is applied to the three channels of the METEOSAT imagery [Visible (VIS)–Infrared Window (IR)–Infrared Water Vapor (WV)]. The results of this classification are studied for different cloud cover cases over tropical regions. For high-level cloud classes, it is shown that the bidimensional histogram IR-WV allows one to deduce the cloud top temperature even for semi-transparent clouds.

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