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Permanent URI for this collectionhttps://hdl.handle.net/10217/170617

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Browsing Research Data by Subject "Aerosol direct radiative effect"

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    Dataset associated with "Near-Cloud Aerosol Retrieval Using Machine Learning Techniques, and Implied Direct Radiative Effects"
    (Colorado State University. Libraries, 2022) Yang, C. Kevin; Chiu, Christine; Marshak, Alexander; Feingold, Graham; Várnai, Tamás; Wen, Guoyong; Yamaguchi, Takanobu; van Leeuwen, Peter Jan
    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 overall 55% increase in the clear-sky aerosol direct radiative effect. Although this increase is based on a case study, it highlights the importance of aerosol retrievals in near-cloud regions, and the need to incorporate the humidification effect in radiative forcing estimates.