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Near‐Cloud Aerosol Retrieval Using Machine Learning Techniques, and Implied Direct Radiative Effects
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2022
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Source: Geophysical Research Letters, 49(20)
[PDF-5.42 MB]
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Journal Title:Geophysical Research Letters
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Description:There is a lack of satellite-based aerosol retrievals in the vicinity of low-topped clouds, mainly because reflectance from aerosols is overwhelmed by three-dimensional cloud radiative effects. To account for cloud radiative effects on reflectance observations, we develop a Convolutional Neural Network and retrieve aerosol optical depth (AOD) with 100–500 m horizontal resolution for all cloud-free regions regardless of their distances to clouds. The retrieval uncertainty is 0.01 + 5%AOD, and the mean bias is approximately −2%. In an application to satellite observations, aerosol hygroscopic growth due to humidification near clouds enhances AOD by 100% in regions within 1 km of cloud edges. The humidification effect leads to an
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Source:Geophysical Research Letters, 49(20)
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DOI:
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ISSN:0094-8276;1944-8007;
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Rights Information:CC BY-NC
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Compliance:Library
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