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Daytime Overlapping Cloud Detection in MODIS Data

Bryan A. Baum, Shaima L. Nasiri, Peiqi Yang

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Abstract

Current efforts to derive a global cloud climatology from satellite data generally suffer in situations involving multilayered clouds. In fact, cloud properties are inferred for each imager pixel assuming only one cloud layer is present. Currently available satellite cloud climatologies provide a horizontal distribution of clouds, but need improvement in the description of the vertical distribution of clouds.

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What this paper is about

Current efforts to derive a global cloud climatology from satellite data generally suffer in situations involving multilayered clouds. In fact, cloud properties are inferred for each imager pixel assuming only one cloud layer is present. Currently available satellite cloud climatologies provide a horizontal distribution of clouds, but need improvement in the description of the vertical distribution of clouds.

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Available abstract

Current efforts to derive a global cloud climatology from satellite data generally suffer in situations involving multilayered clouds. In fact, cloud properties are inferred for each imager pixel assuming only one cloud layer is present. Currently available satellite cloud climatologies provide a horizontal distribution of clouds, but need improvement in the description of the vertical distribution of clouds.

Key concepts: Cloud computing, Cloud top, Cloud fraction, Meteorology, Environmental science, Cloud cover, Cloud height, Satellite

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