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Cloud Classification and its Application in Reducing False Rain

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Improving Infrared-Based Precipitation Retrieval Algorithms Using Multi-Spectral Satellite Imagery

Part of the book series: Springer Theses ((Springer Theses))

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Abstract

Clouds are a key component in the weather and climate studies. However, their representations in climate models are associated with high uncertainty. For example, some studies show that compared to observations of real clouds, models significantly enhance solar radiation reflected by low clouds. This finding has major implications for the cloud-climate feedback problem in models . A cloud classification scheme would be a valuable tool for illuminating the uncertainty of our models and algorithms and improving the accuracy of weather, climate, and precipitation studies. After classifying clouds into different classes, the precipitation estimation can be improved by integrating the classification scheme into the precipitation algorithm.

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Authors and Affiliations

  1. University of California, Irvine, California, USA

    Nasrin Nasrollahi

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  1. Nasrin Nasrollahi
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Correspondence to Nasrin Nasrollahi .

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Nasrollahi, N. (2015). Cloud Classification and its Application in Reducing False Rain. In: Improving Infrared-Based Precipitation Retrieval Algorithms Using Multi-Spectral Satellite Imagery. Springer Theses. Springer, Cham. https://doi.org/10.1007/978-3-319-12081-2_6

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