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Near‐cloud aerosol retrieval using machine learning techniques, and implied direct radiative effects

Yang, C. K. ORCID:, Chiu, J. C. ORCID:, Marshak, A. ORCID:, Feingold, G. ORCID:, Várnai, T. ORCID:, Wen, G. ORCID:, Yamaguchi, T. ORCID: and Van Leeuwen, P. J. ORCID: (2022) Near‐cloud aerosol retrieval using machine learning techniques, and implied direct radiative effects. Geophysical Research Letters, 49 (20). e2022GL098274. ISSN 0094-8276

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To link to this item DOI: 10.1029/2022GL098274


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.

Item Type:Article
Divisions:Science > School of Mathematical, Physical and Computational Sciences > Department of Meteorology
ID Code:109109


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